ipyrad is capable of incorporating a reference sequence to aid in the assembly. There are actually 4 different assembly methods, 3 of which use reference sequence in some way. Here we test reference assisted assembly for ipyrad and stacks, as ddocent and aftrrad do not allow for . Though aftrRAD performs nicely on empirical data and does allow for reference assisted assembly, we consider runtimes to be prohibitive and so exclude it from analysis here.
Ideas for datasets (all have data in SRA):
Selection and sex-biased dispersal in a coastal shark: the influence of philopatry on adaptive variation
- 134 individuals (paper assembled w/ ddocent)
Genome-wide data reveal cryptic diversity and genetic introgression in an Oriental cynopterine fruit bat radiation
- < 45 samples, 2 reference genomes in the same family
Beyond the Coral Triangle: high genetic diversity and near panmixia in Singapore's populations of the broadcast spawning sea star Protoreaster nodosus
- 77 samples, it's a passerine, so there must be something reasonably close
PSMC (pairwise sequentially Markovian coalescent) analysis of RAD (restriction site associated DNA) sequencing data
- 17 sticklebacks, they used stacks
For this analysis we chose the paired-end ddRAD dataset from Lah et al 2016 (http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0162792#sec002): Spatially Explicit Analysis of Genome-Wide SNPs Detects Subtle Population Structure in a Mobile Marine Mammal, the Harbor Porpoise - 49 samples from 3 populations of European Harbor Porpoise
import subprocess
import ipyrad as ip
import shutil
import glob
import sys
import os
## Set the default directories for exec and data.
WORK_DIR="/home/iovercast/manuscript-analysis/"
REFMAP_EMPIRICAL_DIR=os.path.join(WORK_DIR, "Phocoena_empirical/")
REFMAP_FASTQS=os.path.join(REFMAP_EMPIRICAL_DIR, "Final_Files_forDryad/Bbif_ddRADseq/fastq/")
IPYRAD_DIR=os.path.join(WORK_DIR, "ipyrad/")
STACKS_DIR=os.path.join(WORK_DIR, "stacks/")
for dir in [WORK_DIR, REFMAP_EMPIRICAL_DIR, IPYRAD_DIR, STACKS_DIR]:
if not os.path.exists(dir):
os.makedirs(dir)
To get some idea of how ipyrad and stacks perform with reference sequence mapping we'll first assemble a simulated dataset.
Right now i'm just grabbing the simulated data from the ipyrad ipsimdata directory cuz it's quick and dirty but if you want to get crazy you can simulate new seqs by ripping code from here: https://github.com/dereneaton/ipyrad/blob/master/tests/cookbook-making-sim-data.ipynb
The toy simulated data that lives in the ipyrad git repo consists of 1000 simulated loci, 500 of which are present in the simulated reference sequence.
REFMAP_SIM_DIR = os.path.join(WORK_DIR, "REFMAP_SIM/")
REFMAP_DAT_DIR = os.path.join(REFMAP_SIM_DIR, "ipsimdata/")
IPYRAD_SIM_DIR = os.path.join(REFMAP_SIM_DIR, "ipyrad/")
STACKS_SIM_DIR = os.path.join(REFMAP_SIM_DIR, "stacks/")
DDOCENT_SIM_DIR = os.path.join(REFMAP_SIM_DIR, "ddocent/")
## REFMAP_DAT_DIR will be created when we untar ipsimdata.tar.gz
for d in [REFMAP_SIM_DIR, IPYRAD_SIM_DIR, STACKS_SIM_DIR, DDOCENT_SIM_DIR]:
if not os.path.exists(d):
os.makedirs(d)
os.chdir(REFMAP_SIM_DIR)
!wget https://github.com/dereneaton/ipyrad/raw/master/tests/ipsimdata.tar.gz
!tar -xzf ipsimdata.tar.gz
--2016-12-31 14:12:22-- https://github.com/dereneaton/ipyrad/raw/master/tests/ipsimdata.tar.gz Resolving github.com (github.com)... 192.30.253.113, 192.30.253.112 Connecting to github.com (github.com)|192.30.253.113|:443... connected. HTTP request sent, awaiting response... 302 Found Location: https://raw.githubusercontent.com/dereneaton/ipyrad/master/tests/ipsimdata.tar.gz [following] --2016-12-31 14:12:23-- https://raw.githubusercontent.com/dereneaton/ipyrad/master/tests/ipsimdata.tar.gz Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.20.133 Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.20.133|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 12433112 (12M) [application/octet-stream] Saving to: ‘ipsimdata.tar.gz.1’ 100%[======================================>] 12,433,112 17.8MB/s in 0.7s 2016-12-31 14:12:24 (17.8 MB/s) - ‘ipsimdata.tar.gz.1’ saved [12433112/12433112]
For the first analysis we'll use the reference
assembly method which will just toss out
all reads that don't map the the reference sequence. We should expect to recover 500 reads per
sample.
The toy data runs in ~2 minutes.
os.chdir(IPYRAD_SIM_DIR)
## Make a new assembly and set some assembly parameters
data = ip.Assembly("refmap-sim")
data.set_params("raw_fastq_path", REFMAP_DAT_DIR + "pairddrad_wmerge_example_R*_.fastq.gz")
data.set_params("barcodes_path", REFMAP_DAT_DIR + "pairddrad_wmerge_example_barcodes.txt")
data.set_params("project_dir", "reference-assembly")
data.set_params("assembly_method", "reference")
data.set_params("reference_sequence", REFMAP_DAT_DIR + "pairddrad_wmerge_example_genome.fa")
data.set_params("datatype", "pairddrad")
data.set_params("restriction_overhang", ("TGCAG", "CGG"))
data.write_params(force=True)
cmd = "ipyrad -p params-refmap-sim.txt -s 1234567 -c 40".format(dir)
print(cmd)
!time $cmd
New Assembly: refmap-sim ipyrad -p params-refmap-sim.txt -s 1234567 -c 40 ------------------------------------------------------------- ipyrad [v.0.5.15] Interactive assembly and analysis of RAD-seq data ------------------------------------------------------------- loading Assembly: refmap-sim from saved path: ~/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim.json local compute node: [40 cores] on node001 Step 1: Demultiplexing fastq data to Samples Skipping: 12 Samples already found in Assembly refmap-sim. (can overwrite with force argument) Step 2: Filtering reads Skipping: All 12 selected Samples already edited. (can overwrite with force argument) Step 3: Clustering/Mapping reads Skipping: All 12 selected Samples already clustered. (can overwrite with force argument) Step 4: Joint estimation of error rate and heterozygosity Skipping: All 12 selected Samples already joint estimated (can overwrite with force argument) Step 5: Consensus base calling Skipping: All 12 selected Samples already consensus called (can overwrite with force argument) Step 6: Clustering at 0.85 similarity across 12 samples Skipping: All 12 selected Samples already clustered. (can overwrite with force argument) Step 7: Filter and write output files for 12 Samples ERROR:ipyrad.core.assembly:IPyradWarningExit: Output files already created for this Assembly in: /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim_outfiles To overwrite, rerun using the force argument. Encountered an error, see ./ipyrad_log.txt. Output files already created for this Assembly in: /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim_outfiles To overwrite, rerun using the force argument. real 0m18.623s user 0m1.899s sys 0m0.552s
Create a new branch and set the assembly method to denovo+reference
. Now we will expect to recover
1000 loci per sample (500 from the reference mapping and 500 from de novo).
Again, the toy data runs in slighly less than 3 minutes.
data2 = data.branch("denovo_plus_reference-sim")
data2.set_params("assembly_method", "denovo+reference")
data2.write_params(force=True)
cmd = "ipyrad -p params-denovo_plus_reference-sim.txt -s 1234567 -c 40".format(dir)
print(cmd)
!time $cmd
ipyrad -p params-denovo_plus_reference-sim.txt -s 1234567 -c 40 ------------------------------------------------------------- ipyrad [v.0.5.15] Interactive assembly and analysis of RAD-seq data ------------------------------------------------------------- loading Assembly: denovo_plus_reference-sim from saved path: ~/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/denovo_plus_reference-sim.json New Assembly: denovo_plus_reference-sim local compute node: [40 cores] on node001 Step 1: Demultiplexing fastq data to Samples [####################] 100% chunking large files | 0:00:00 [####################] 100% sorting reads | 0:00:09 [####################] 100% writing/compressing | 0:00:01 Step 2: Filtering reads [####################] 100% processing reads | 0:00:02 Step 3: Clustering/Mapping reads [####################] 100% dereplicating | 0:00:00 [####################] 100% mapping | 0:00:00 [####################] 100% clustering | 0:00:01 [####################] 100% building clusters | 0:00:00 [####################] 100% finalize mapping | 0:01:07 [####################] 100% chunking | 0:00:00 [####################] 100% aligning | 0:00:14 [####################] 100% concatenating | 0:00:00 Step 4: Joint estimation of error rate and heterozygosity [####################] 100% inferring [H, E] | 0:00:15 Step 5: Consensus base calling Mean error [0.00074 sd=0.00001] Mean hetero [0.00195 sd=0.00015] [####################] 100% calculating depths | 0:00:00 [####################] 100% chunking clusters | 0:00:00 [####################] 100% consens calling | 0:00:12 Step 6: Clustering at 0.85 similarity across 12 samples [####################] 100% concat/shuffle input | 0:00:00 [####################] 100% clustering across | 0:00:01 [####################] 100% building clusters | 0:00:00 [####################] 100% aligning clusters | 0:00:02 [####################] 100% database indels | 0:00:00 [####################] 100% indexing clusters | 0:00:02 [####################] 100% building database | 0:00:00 Step 7: Filter and write output files for 12 Samples [####################] 100% filtering loci | 0:00:06 [####################] 100% building loci/stats | 0:00:00 [####################] 100% building vcf file | 0:00:01 [####################] 100% writing vcf file | 0:00:00 [####################] 100% building arrays | 0:00:02 [####################] 100% writing outfiles | 0:00:01 Outfiles written to: ~/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/denovo_plus_reference-sim_outfiles real 2m50.283s user 0m22.885s sys 0m1.653s
Create a new branch and set the assembly method to denovo+reference
. Now we will expect to recover
1000 loci per sample (500 from the reference mapping and 500 from de novo).
Again, the toy data runs in slighly less than 2 minutes.
data2 = data.branch("denovo_minus_reference-sim")
data2.set_params("assembly_method", "denovo-reference")
data2.write_params(force=True)
cmd = "ipyrad -p params-denovo_minus_reference-sim.txt -s 1234567 -c 40".format(dir)
print(cmd)
!time $cmd
ipyrad -p params-denovo_minus_reference-sim.txt -s 1234567 -c 40 ------------------------------------------------------------- ipyrad [v.0.5.15] Interactive assembly and analysis of RAD-seq data ------------------------------------------------------------- loading Assembly: denovo_minus_reference-sim from saved path: ~/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/denovo_minus_reference-sim.json New Assembly: denovo_minus_reference-sim local compute node: [40 cores] on node001 Step 1: Demultiplexing fastq data to Samples [####################] 100% chunking large files | 0:00:00 [####################] 100% sorting reads | 0:00:07 [####################] 100% writing/compressing | 0:00:01 Step 2: Filtering reads [####################] 100% processing reads | 0:00:02 Step 3: Clustering/Mapping reads [####################] 100% dereplicating | 0:00:00 [####################] 100% mapping | 0:00:01 [####################] 100% clustering | 0:00:00 [####################] 100% building clusters | 0:00:01 [####################] 100% chunking | 0:00:00 [####################] 100% aligning | 0:00:07 [####################] 100% concatenating | 0:00:00 Step 4: Joint estimation of error rate and heterozygosity [####################] 100% inferring [H, E] | 0:00:12 Step 5: Consensus base calling Mean error [0.00074 sd=0.00002] Mean hetero [0.00192 sd=0.00023] [####################] 100% calculating depths | 0:00:00 [####################] 100% chunking clusters | 0:00:00 [####################] 100% consens calling | 0:00:05 Step 6: Clustering at 0.85 similarity across 12 samples [####################] 100% concat/shuffle input | 0:00:00 [####################] 100% clustering across | 0:00:01 [####################] 100% building clusters | 0:00:00 [####################] 100% aligning clusters | 0:00:01 [####################] 100% database indels | 0:00:00 [####################] 100% indexing clusters | 0:00:01 [####################] 100% building database | 0:00:00 Step 7: Filter and write output files for 12 Samples [####################] 100% filtering loci | 0:00:06 [####################] 100% building loci/stats | 0:00:00 [####################] 100% building vcf file | 0:00:01 [####################] 100% writing vcf file | 0:00:00 [####################] 100% building arrays | 0:00:02 [####################] 100% writing outfiles | 0:00:00 Outfiles written to: ~/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/denovo_minus_reference-sim_outfiles real 1m17.382s user 0m14.547s sys 0m1.512s
Stacks needs reference mapped sequences in .bam or .sam format. Since we already did the mapping
in ipyrad we'll just pluck the Nope! That would certainly be nice, but we *-mapped-sorted.bam
files out of the ipyrad _refmapping directory.dereplicate
reads before we map. This is obviously important information
(which ipyrad records and uses downstream), but stacks doesn't know about this, so you get bad quality
mapping. I learned this the hard way. We need to map each sample by hand from the original ipyrad _edits files.
On the toy simulated data stacks runs in an impressive 5 seconds.
We are going to poach bwa and samtools from the dDocent install, since we already installed them there. ugh.
This proceeds in 3 steps. First bwa maps to the reference (-t is # of cores, -v shuts down verbosity). Then samtools pulls out mapped and properly paired reads (-b outputs .bam format, -F 0x804 filters on mapping/pairing). Then it sorts the bam file, and cleans up the dangling sam file. Mapping is quick for the simulated data (<2 minutes).
IPYRAD_SIMEDITS_DIR = IPYRAD_SIM_DIR + "reference-assembly/refmap-sim_edits/"
REF_SEQ = REFMAP_DAT_DIR + "pairddrad_wmerge_example_genome.fa"
## Sim sample names
pop1 = ["1A_0", "1B_0", "1C_0", "1D_0"]
pop2 = ["2E_0", "2F_0", "2G_0", "2H_0"]
pop3 = ["3I_0", "3J_0", "3K_0", "3L_0"]
sim_sample_names = pop1 + pop2 + pop3
for samp in sim_sample_names:
R1 = IPYRAD_SIMEDITS_DIR + samp + ".trimmed_R1_.fastq.gz"
R2 = IPYRAD_SIMEDITS_DIR + samp + ".trimmed_R2_.fastq.gz"
samout = STACKS_SIM_DIR + samp + ".sam"
bamout = STACKS_SIM_DIR + samp + ".bam"
export_cmd = "export PATH=~/manuscript-analysis/dDocent:$PATH"
bwa_cmd = "bwa mem -t 40 -v 1 " + REF_SEQ\
+ " " + R1\
+ " " + R2\
+ " > " + samout
samtools_cmd = "samtools view -b -F 0x804 " + samout\
+ " | samtools sort -T /tmp/{}.sam -O bam -o {}".format(samp, bamout)
cleanup_cmd = "rm {}".format(samout)
cmd = ";".join([export_cmd, bwa_cmd, samtools_cmd, cleanup_cmd])
!$cmd
[M::bwa_idx_load_from_disk] read 0 ALT contigs [M::process] read 40080 sequences (3827640 bp)... [M::mem_pestat] # candidate unique pairs for (FF, FR, RF, RR): (0, 10025, 0, 0) [M::mem_pestat] skip orientation FF as there are not enough pairs [M::mem_pestat] analyzing insert size distribution for orientation FR... [M::mem_pestat] (25, 50, 75) percentile: (452, 464, 477) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (402, 527) [M::mem_pestat] mean and std.dev: (464.20, 14.47) [M::mem_pestat] low and high boundaries for proper pairs: (377, 552) [M::mem_pestat] skip orientation RF as there are not enough pairs [M::mem_pestat] skip orientation RR as there are not enough pairs [M::mem_process_seqs] Processed 40080 reads in 2.988 CPU sec, 0.112 real sec [main] Version: 0.7.15-r1142-dirty [main] CMD: bwa mem -t 40 /home/iovercast/manuscript-analysis/REFMAP_SIM/ipsimdata/pairddrad_wmerge_example_genome.fa /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim_edits/1A_0.trimmed_R1_.fastq.gz /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim_edits/1A_0.trimmed_R2_.fastq.gz [main] Real time: 0.434 sec; CPU: 3.219 sec [M::bwa_idx_load_from_disk] read 0 ALT contigs [M::process] read 39964 sequences (3816562 bp)... [M::mem_pestat] # candidate unique pairs for (FF, FR, RF, RR): (0, 10017, 0, 0) [M::mem_pestat] skip orientation FF as there are not enough pairs [M::mem_pestat] analyzing insert size distribution for orientation FR... [M::mem_pestat] (25, 50, 75) percentile: (452, 464, 477) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (402, 527) [M::mem_pestat] mean and std.dev: (464.28, 14.44) [M::mem_pestat] low and high boundaries for proper pairs: (377, 552) [M::mem_pestat] skip orientation RF as there are not enough pairs [M::mem_pestat] skip orientation RR as there are not enough pairs [M::mem_process_seqs] Processed 39964 reads in 2.968 CPU sec, 0.108 real sec [main] Version: 0.7.15-r1142-dirty [main] CMD: bwa mem -t 40 /home/iovercast/manuscript-analysis/REFMAP_SIM/ipsimdata/pairddrad_wmerge_example_genome.fa /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim_edits/1B_0.trimmed_R1_.fastq.gz /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim_edits/1B_0.trimmed_R2_.fastq.gz [main] Real time: 0.262 sec; CPU: 3.031 sec [M::bwa_idx_load_from_disk] read 0 ALT contigs [M::process] read 40210 sequences (3840055 bp)... [M::mem_pestat] # candidate unique pairs for (FF, FR, RF, RR): (0, 10125, 0, 0) [M::mem_pestat] skip orientation FF as there are not enough pairs [M::mem_pestat] analyzing insert size distribution for orientation FR... [M::mem_pestat] (25, 50, 75) percentile: (452, 464, 477) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (402, 527) [M::mem_pestat] mean and std.dev: (464.14, 14.41) [M::mem_pestat] low and high boundaries for proper pairs: (377, 552) [M::mem_pestat] skip orientation RF as there are not enough pairs [M::mem_pestat] skip orientation RR as there are not enough pairs [M::mem_process_seqs] Processed 40210 reads in 3.001 CPU sec, 0.112 real sec [main] Version: 0.7.15-r1142-dirty [main] CMD: bwa mem -t 40 /home/iovercast/manuscript-analysis/REFMAP_SIM/ipsimdata/pairddrad_wmerge_example_genome.fa /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim_edits/1C_0.trimmed_R1_.fastq.gz /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim_edits/1C_0.trimmed_R2_.fastq.gz [main] Real time: 0.271 sec; CPU: 3.068 sec [M::bwa_idx_load_from_disk] read 0 ALT contigs [M::process] read 40344 sequences (3852852 bp)... [M::mem_pestat] # candidate unique pairs for (FF, FR, RF, RR): (0, 10084, 0, 0) [M::mem_pestat] skip orientation FF as there are not enough pairs [M::mem_pestat] analyzing insert size distribution for orientation FR... [M::mem_pestat] (25, 50, 75) percentile: (452, 464, 477) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (402, 527) [M::mem_pestat] mean and std.dev: (464.31, 14.37) [M::mem_pestat] low and high boundaries for proper pairs: (377, 552) [M::mem_pestat] skip orientation RF as there are not enough pairs [M::mem_pestat] skip orientation RR as there are not enough pairs [M::mem_process_seqs] Processed 40344 reads in 3.024 CPU sec, 0.113 real sec [main] Version: 0.7.15-r1142-dirty [main] CMD: bwa mem -t 40 /home/iovercast/manuscript-analysis/REFMAP_SIM/ipsimdata/pairddrad_wmerge_example_genome.fa /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim_edits/1D_0.trimmed_R1_.fastq.gz /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim_edits/1D_0.trimmed_R2_.fastq.gz [main] Real time: 0.273 sec; CPU: 3.092 sec [M::bwa_idx_load_from_disk] read 0 ALT contigs [M::process] read 40164 sequences (3835662 bp)... [M::mem_pestat] # candidate unique pairs for (FF, FR, RF, RR): (0, 10021, 0, 0) [M::mem_pestat] skip orientation FF as there are not enough pairs [M::mem_pestat] analyzing insert size distribution for orientation FR... [M::mem_pestat] (25, 50, 75) percentile: (452, 464, 476) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (404, 524) [M::mem_pestat] mean and std.dev: (464.12, 14.42) [M::mem_pestat] low and high boundaries for proper pairs: (380, 548) [M::mem_pestat] skip orientation RF as there are not enough pairs [M::mem_pestat] skip orientation RR as there are not enough pairs [M::mem_process_seqs] Processed 40164 reads in 2.835 CPU sec, 0.114 real sec [main] Version: 0.7.15-r1142-dirty [main] CMD: bwa mem -t 40 /home/iovercast/manuscript-analysis/REFMAP_SIM/ipsimdata/pairddrad_wmerge_example_genome.fa /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim_edits/2E_0.trimmed_R1_.fastq.gz /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim_edits/2E_0.trimmed_R2_.fastq.gz [main] Real time: 0.269 sec; CPU: 2.900 sec [M::bwa_idx_load_from_disk] read 0 ALT contigs [M::process] read 40164 sequences (3835662 bp)... [M::mem_pestat] # candidate unique pairs for (FF, FR, RF, RR): (0, 10003, 0, 0) [M::mem_pestat] skip orientation FF as there are not enough pairs [M::mem_pestat] analyzing insert size distribution for orientation FR... [M::mem_pestat] (25, 50, 75) percentile: (452, 464, 477) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (402, 527) [M::mem_pestat] mean and std.dev: (464.13, 14.39) [M::mem_pestat] low and high boundaries for proper pairs: (377, 552) [M::mem_pestat] skip orientation RF as there are not enough pairs [M::mem_pestat] skip orientation RR as there are not enough pairs [M::mem_process_seqs] Processed 40164 reads in 2.821 CPU sec, 0.111 real sec [main] Version: 0.7.15-r1142-dirty [main] CMD: bwa mem -t 40 /home/iovercast/manuscript-analysis/REFMAP_SIM/ipsimdata/pairddrad_wmerge_example_genome.fa /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim_edits/2F_0.trimmed_R1_.fastq.gz /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim_edits/2F_0.trimmed_R2_.fastq.gz [main] Real time: 0.265 sec; CPU: 2.884 sec [M::bwa_idx_load_from_disk] read 0 ALT contigs [M::process] read 40190 sequences (3838145 bp)... [M::mem_pestat] # candidate unique pairs for (FF, FR, RF, RR): (0, 10068, 0, 0) [M::mem_pestat] skip orientation FF as there are not enough pairs [M::mem_pestat] analyzing insert size distribution for orientation FR... [M::mem_pestat] (25, 50, 75) percentile: (452, 464, 477) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (402, 527) [M::mem_pestat] mean and std.dev: (464.24, 14.37) [M::mem_pestat] low and high boundaries for proper pairs: (377, 552) [M::mem_pestat] skip orientation RF as there are not enough pairs [M::mem_pestat] skip orientation RR as there are not enough pairs [M::mem_process_seqs] Processed 40190 reads in 2.563 CPU sec, 0.106 real sec [main] Version: 0.7.15-r1142-dirty [main] CMD: bwa mem -t 40 /home/iovercast/manuscript-analysis/REFMAP_SIM/ipsimdata/pairddrad_wmerge_example_genome.fa /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim_edits/2G_0.trimmed_R1_.fastq.gz /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim_edits/2G_0.trimmed_R2_.fastq.gz [main] Real time: 0.260 sec; CPU: 2.626 sec [M::bwa_idx_load_from_disk] read 0 ALT contigs [M::process] read 40010 sequences (3820955 bp)... [M::mem_pestat] # candidate unique pairs for (FF, FR, RF, RR): (0, 10056, 0, 0) [M::mem_pestat] skip orientation FF as there are not enough pairs [M::mem_pestat] analyzing insert size distribution for orientation FR... [M::mem_pestat] (25, 50, 75) percentile: (452, 464, 477) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (402, 527) [M::mem_pestat] mean and std.dev: (464.25, 14.46) [M::mem_pestat] low and high boundaries for proper pairs: (377, 552) [M::mem_pestat] skip orientation RF as there are not enough pairs [M::mem_pestat] skip orientation RR as there are not enough pairs [M::mem_process_seqs] Processed 40010 reads in 2.893 CPU sec, 0.109 real sec [main] Version: 0.7.15-r1142-dirty [main] CMD: bwa mem -t 40 /home/iovercast/manuscript-analysis/REFMAP_SIM/ipsimdata/pairddrad_wmerge_example_genome.fa /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim_edits/2H_0.trimmed_R1_.fastq.gz /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim_edits/2H_0.trimmed_R2_.fastq.gz [main] Real time: 0.264 sec; CPU: 2.958 sec [M::bwa_idx_load_from_disk] read 0 ALT contigs [M::process] read 39648 sequences (3786384 bp)... [M::mem_pestat] # candidate unique pairs for (FF, FR, RF, RR): (0, 10000, 0, 0) [M::mem_pestat] skip orientation FF as there are not enough pairs [M::mem_pestat] analyzing insert size distribution for orientation FR... [M::mem_pestat] (25, 50, 75) percentile: (452, 464, 477) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (402, 527) [M::mem_pestat] mean and std.dev: (464.29, 14.39) [M::mem_pestat] low and high boundaries for proper pairs: (377, 552) [M::mem_pestat] skip orientation RF as there are not enough pairs [M::mem_pestat] skip orientation RR as there are not enough pairs [M::mem_process_seqs] Processed 39648 reads in 3.018 CPU sec, 0.108 real sec [main] Version: 0.7.15-r1142-dirty [main] CMD: bwa mem -t 40 /home/iovercast/manuscript-analysis/REFMAP_SIM/ipsimdata/pairddrad_wmerge_example_genome.fa /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim_edits/3I_0.trimmed_R1_.fastq.gz /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim_edits/3I_0.trimmed_R2_.fastq.gz [main] Real time: 0.264 sec; CPU: 3.084 sec [M::bwa_idx_load_from_disk] read 0 ALT contigs [M::process] read 40200 sequences (3839100 bp)... [M::mem_pestat] # candidate unique pairs for (FF, FR, RF, RR): (0, 10060, 0, 0) [M::mem_pestat] skip orientation FF as there are not enough pairs [M::mem_pestat] analyzing insert size distribution for orientation FR... [M::mem_pestat] (25, 50, 75) percentile: (452, 464, 477) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (402, 527) [M::mem_pestat] mean and std.dev: (464.19, 14.41) [M::mem_pestat] low and high boundaries for proper pairs: (377, 552) [M::mem_pestat] skip orientation RF as there are not enough pairs [M::mem_pestat] skip orientation RR as there are not enough pairs [M::mem_process_seqs] Processed 40200 reads in 3.027 CPU sec, 0.133 real sec [main] Version: 0.7.15-r1142-dirty [main] CMD: bwa mem -t 40 /home/iovercast/manuscript-analysis/REFMAP_SIM/ipsimdata/pairddrad_wmerge_example_genome.fa /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim_edits/3J_0.trimmed_R1_.fastq.gz /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim_edits/3J_0.trimmed_R2_.fastq.gz [main] Real time: 0.287 sec; CPU: 3.090 sec [M::bwa_idx_load_from_disk] read 0 ALT contigs [M::process] read 40152 sequences (3834516 bp)... [M::mem_pestat] # candidate unique pairs for (FF, FR, RF, RR): (0, 10123, 0, 0) [M::mem_pestat] skip orientation FF as there are not enough pairs [M::mem_pestat] analyzing insert size distribution for orientation FR... [M::mem_pestat] (25, 50, 75) percentile: (452, 464, 477) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (402, 527) [M::mem_pestat] mean and std.dev: (464.27, 14.41) [M::mem_pestat] low and high boundaries for proper pairs: (377, 552) [M::mem_pestat] skip orientation RF as there are not enough pairs [M::mem_pestat] skip orientation RR as there are not enough pairs [M::mem_process_seqs] Processed 40152 reads in 3.084 CPU sec, 0.120 real sec [main] Version: 0.7.15-r1142-dirty [main] CMD: bwa mem -t 40 /home/iovercast/manuscript-analysis/REFMAP_SIM/ipsimdata/pairddrad_wmerge_example_genome.fa /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim_edits/3K_0.trimmed_R1_.fastq.gz /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim_edits/3K_0.trimmed_R2_.fastq.gz [main] Real time: 0.282 sec; CPU: 3.155 sec [M::bwa_idx_load_from_disk] read 0 ALT contigs [M::process] read 39864 sequences (3807012 bp)... [M::mem_pestat] # candidate unique pairs for (FF, FR, RF, RR): (0, 9964, 0, 0) [M::mem_pestat] skip orientation FF as there are not enough pairs [M::mem_pestat] analyzing insert size distribution for orientation FR... [M::mem_pestat] (25, 50, 75) percentile: (452, 464, 477) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (402, 527) [M::mem_pestat] mean and std.dev: (464.23, 14.40) [M::mem_pestat] low and high boundaries for proper pairs: (377, 552) [M::mem_pestat] skip orientation RF as there are not enough pairs [M::mem_pestat] skip orientation RR as there are not enough pairs [M::mem_process_seqs] Processed 39864 reads in 3.026 CPU sec, 0.122 real sec [main] Version: 0.7.15-r1142-dirty [main] CMD: bwa mem -t 40 /home/iovercast/manuscript-analysis/REFMAP_SIM/ipsimdata/pairddrad_wmerge_example_genome.fa /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim_edits/3L_0.trimmed_R1_.fastq.gz /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim_edits/3L_0.trimmed_R2_.fastq.gz [main] Real time: 0.286 sec; CPU: 3.100 sec
## This is how we'd do it since we weren't using a popmap file
infiles = ["-s "+ff+" " for ff in glob.glob(STACKS_SIM_DIR + "*.bam")]
## Toggle the dryrun flag for testing
DRYRUN=""
DRYRUN="-d"
## Options
## -T The number of threads to use
## -O The popmap file specifying individuals and populations
## -S Disable database business
## -o Output directory. Just write to the empirical stacks directory
## -X Tell populations to create the output formats specified
## -X and use `-m 6` which sets min depth per locus
OUTPUT_FORMATS = "--vcf --genepop --structure --phylip "
cmd = "ref_map.pl -T 40 -b 1 -S " + DRYRUN\
+ " -X \'populations:" + OUTPUT_FORMATS + "\'"\
+ " -X \'populations:-m 6\'"\
+ " -o " + STACKS_SIM_DIR + " "\
+ " ".join(infiles)
print("\nCommand to run - {}".format(cmd))
Command to run - ref_map.pl -T 40 -b 1 -S -d -X 'populations:--vcf --genepop --structure --phylip ' -X 'populations:-m 6' -o /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/ -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/1A_0.bam -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/1B_0.bam -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/1C_0.bam -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/1D_0.bam -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/2E_0.bam -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/2F_0.bam -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/2G_0.bam -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/2H_0.bam -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/3I_0.bam -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/3J_0.bam -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/3K_0.bam -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/3L_0.bam
%%bash -s "$WORK_DIR" "$STACKS_SIM_DIR" "$cmd"
export PATH="$1/miniconda/bin:$PATH"; export "STACKS_SIM_DIR=$2"; export "cmd=$3"
## We have to play a little cat and mouse game here because of quoting in some of the args
## and how weird bash is we have to write the cmd to a file and then exec it.
## If you try to just run $cmd it truncates the command at the first single tic. Hassle.
cd $STACKS_SIM_DIR
echo $cmd > stacks.sh; chmod 777 stacks.sh
time ./stacks.sh
Identifying unique stacks; file 1 of 12 [1A_0] Identifying unique stacks; file 2 of 12 [1B_0] Identifying unique stacks; file 3 of 12 [1C_0] Identifying unique stacks; file 4 of 12 [1D_0] Identifying unique stacks; file 5 of 12 [2E_0] Identifying unique stacks; file 6 of 12 [2F_0] Identifying unique stacks; file 7 of 12 [2G_0] Identifying unique stacks; file 8 of 12 [2H_0] Identifying unique stacks; file 9 of 12 [3I_0] Identifying unique stacks; file 10 of 12 [3J_0] Identifying unique stacks; file 11 of 12 [3K_0] Identifying unique stacks; file 12 of 12 [3L_0]
Found 12 sample file(s). /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/1A_0.bam -o /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks -i 1 -p 40 -m 1 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/1B_0.bam -o /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks -i 2 -p 40 -m 1 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/1C_0.bam -o /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks -i 3 -p 40 -m 1 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/1D_0.bam -o /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks -i 4 -p 40 -m 1 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/2E_0.bam -o /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks -i 5 -p 40 -m 1 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/2F_0.bam -o /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks -i 6 -p 40 -m 1 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/2G_0.bam -o /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks -i 7 -p 40 -m 1 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/2H_0.bam -o /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks -i 8 -p 40 -m 1 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/3I_0.bam -o /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks -i 9 -p 40 -m 1 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/3J_0.bam -o /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks -i 10 -p 40 -m 1 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/3K_0.bam -o /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks -i 11 -p 40 -m 1 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/3L_0.bam -o /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks -i 12 -p 40 -m 1 2>&1 Depths of Coverage for Processed Samples: Generating catalog... /home/iovercast/manuscript-analysis/miniconda/bin/cstacks -g -b 1 -o /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/1A_0 -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/1B_0 -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/1C_0 -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/1D_0 -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/2E_0 -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/2F_0 -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/2G_0 -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/2H_0 -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/3I_0 -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/3J_0 -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/3K_0 -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/3L_0 -p 40 2>&1 Matching samples to the catalog... /home/iovercast/manuscript-analysis/miniconda/bin/sstacks -g -b 1 -c /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/batch_1 -o /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/1A_0 -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/1B_0 -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/1C_0 -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/1D_0 -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/2E_0 -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/2F_0 -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/2G_0 -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/2H_0 -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/3I_0 -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/3J_0 -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/3K_0 -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/3L_0 -p 40 2>&1 Calculating population-level summary statistics /home/iovercast/manuscript-analysis/miniconda/bin/populations -b 1 -P /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks -s -t 40 --vcf --genepop --structure --phylip -m 6 2>&1 real 0m0.040s user 0m0.030s sys 0m0.007s
dDocent reference sequence mapping is not clearly documented. It turns out you can skip the initial assembly and go right to mapping/snp calling, but it requires you to copy the reference sequence to the dDocent workind directory as "reference.fasta". This uses the trimmed and QC'd fastq files from the ipyrad simulated run, so it assumes you have already done that and the fq files are still in place.
IPYRAD_SIMEDITS_DIR = IPYRAD_SIM_DIR + "reference-assembly/refmap-sim_edits/"
REF_SEQ = REFMAP_DAT_DIR + "pairddrad_wmerge_example_genome.fa"
DDOCENT_DIR = "/home/iovercast/manuscript-analysis/dDocent/"
os.chdir(DDOCENT_SIM_DIR)
## Create a simlink to the reference sequence in the current directory
cmd = "ln -sf {} reference.fasta".format(REF_SEQ)
!$cmd
## Sim sample names
pop1 = ["1A_0", "1B_0", "1C_0", "1D_0"]
pop2 = ["2E_0", "2F_0", "2G_0", "2H_0"]
pop3 = ["3I_0", "3J_0", "3K_0", "3L_0"]
sim_sample_names = pop1 + pop2 + pop3
sim_mapping_dict = {}
for pop_num, samps in enumerate([pop1, pop2, pop3]):
for samp_num, samp_name in enumerate(samps):
sim_mapping_dict[samp_name] = "Pop{}_{:03d}".format(pop_num+1, samp_num+1)
## Now we have to rename all the files in the way dDocent expects them:
## 1A_0_R1_.fastq.gz -> Pop1_001.F.fq.gz
for k, v in sim_mapping_dict.items():
## Symlink R1 and R2
for i in ["1", "2"]:
source = os.path.join(IPYRAD_SIMEDITS_DIR, k + ".trimmed_R{}_.fastq.gz".format(i))
## This is the way the current documentation says to name imported trimmed
## files, but it doesn't work.
## dest = os.path.join(DDOCENT_SIM_DIR, v + ".R{}.fq.gz".format(i))
if i == "1":
dest = os.path.join(DDOCENT_SIM_DIR, v + ".F.fq.gz".format(i))
else:
dest = os.path.join(DDOCENT_SIM_DIR, v + ".R.fq.gz".format(i))
cmd = "ln -sf {} {}".format(source, dest)
!$cmd
config_file = "{}/sim-config.txt".format(DDOCENT_SIM_DIR)
with open(config_file, 'w') as outfile:
outfile.write('Number of Processors\n40\nMaximum Memory\n0\nTrimming\nno\nAssembly?\nno\nType_of_Assembly\nPE\nClustering_Similarity%\n0.85\nMapping_Reads?\nyes\nMapping_Match_Value\n1\nMapping_MisMatch_Value\n3\nMapping_GapOpen_Penalty\n5\nCalling_SNPs?\nyes\nEmail\nwatdo@mailinator.com\n')
cmd = "export LD_LIBRARY_PATH={}/freebayes-src/vcflib/tabixpp/htslib/:$LD_LIBRARY_PATH; ".format(DDOCENT_DIR)
cmd += "export PATH={}:$PATH; time dDocent {}".format(DDOCENT_DIR, config_file)
print(cmd)
with open("ddocent.sh", 'w') as outfile:
outfile.write("#!/bin/bash\n")
outfile.write(cmd)
!chmod 777 ddocent.sh
export LD_LIBRARY_PATH=/home/iovercast/manuscript-analysis/dDocent//freebayes-src/vcflib/tabixpp/htslib/:$LD_LIBRARY_PATH; export PATH=/home/iovercast/manuscript-analysis/dDocent/:$PATH; time dDocent /home/iovercast/manuscript-analysis/REFMAP_SIM/ddocent//sim-config.txt
## You have to post-process the vcf files to decompose complex genotypes and remove indels
os.chdir(DDOCENT_SIM_DIR)
exports = "export LD_LIBRARY_PATH=/home/iovercast/manuscript-analysis/dDocent//freebayes-src/vcflib/tabixpp/htslib/:$LD_LIBRARY_PATH; export PATH=/home/iovercast/manuscript-analysis/dDocent/:$PATH"
fullvcf = os.path.join(DDOCENT_SIM_DIR, "TotalRawSNPs.vcf")
filtvcf = os.path.join(DDOCENT_SIM_DIR, "Final.recode.vcf")
for f in [fullvcf, filtvcf]:
print("Finalizing - {}".format(f))
## Rename the samples to make them agree with the ipyrad/stacks names so
## the results analysis will work.
vcffile = os.path.join(DDOCENT_SIM_DIR, f)
infile = open(vcffile,'r')
filedata = infile.readlines()
infile.close()
outfile = open(vcffile,'w')
for line in filedata:
if "CHROM" in line:
for ipname, ddname in sim_mapping_dict.items():
line = line.replace(ddname, ipname)
outfile.write(line)
outfile.close()
## Naming the new outfiles as <curname>.snps.vcf
## Decompose complex genotypes and remove indels
outfile = os.path.join(DDOCENT_SIM_DIR, f.split("/")[-1].split(".vcf")[0] + ".snps.vcf")
cmd = "{}; vcfallelicprimitives {} > ddoc-tmp.vcf".format(exports, f)
print(cmd)
!$cmd
cmd = "{}; vcftools --vcf ddoc-tmp.vcf --remove-indels --recode --recode-INFO-all --out {}".format(exports, outfile)
print(cmd)
!$cmd
!rm ddoc-tmp.vcf
Finalizing - /home/iovercast/manuscript-analysis/REFMAP_SIM/ddocent/TotalRawSNPs.vcf export LD_LIBRARY_PATH=/home/iovercast/manuscript-analysis/dDocent//freebayes-src/vcflib/tabixpp/htslib/:$LD_LIBRARY_PATH; export PATH=/home/iovercast/manuscript-analysis/dDocent/:$PATH; vcfallelicprimitives /home/iovercast/manuscript-analysis/REFMAP_SIM/ddocent/TotalRawSNPs.vcf > ddoc-tmp.vcf export LD_LIBRARY_PATH=/home/iovercast/manuscript-analysis/dDocent//freebayes-src/vcflib/tabixpp/htslib/:$LD_LIBRARY_PATH; export PATH=/home/iovercast/manuscript-analysis/dDocent/:$PATH; vcftools --vcf ddoc-tmp.vcf --remove-indels --recode --recode-INFO-all --out /home/iovercast/manuscript-analysis/REFMAP_SIM/ddocent/TotalRawSNPs.snps.vcf VCFtools - v0.1.11 (C) Adam Auton 2009 Parameters as interpreted: --vcf ddoc-tmp.vcf --recode-INFO-all --out /home/iovercast/manuscript-analysis/REFMAP_SIM/ddocent/TotalRawSNPs.snps.vcf --recode --remove-indels Index file is older than variant file. Will regenerate. Building new index file. Scanning Chromosome: MT Writing Index file. File contains 4842 entries and 12 individuals. Applying Required Filters. Filtering sites by allele type After filtering, kept 12 out of 12 Individuals After filtering, kept 4840 out of a possible 4842 Sites Outputting VCF file... Done Run Time = 0.00 seconds Finalizing - /home/iovercast/manuscript-analysis/REFMAP_SIM/ddocent/Final.recode.vcf export LD_LIBRARY_PATH=/home/iovercast/manuscript-analysis/dDocent//freebayes-src/vcflib/tabixpp/htslib/:$LD_LIBRARY_PATH; export PATH=/home/iovercast/manuscript-analysis/dDocent/:$PATH; vcfallelicprimitives /home/iovercast/manuscript-analysis/REFMAP_SIM/ddocent/Final.recode.vcf > ddoc-tmp.vcf export LD_LIBRARY_PATH=/home/iovercast/manuscript-analysis/dDocent//freebayes-src/vcflib/tabixpp/htslib/:$LD_LIBRARY_PATH; export PATH=/home/iovercast/manuscript-analysis/dDocent/:$PATH; vcftools --vcf ddoc-tmp.vcf --remove-indels --recode --recode-INFO-all --out /home/iovercast/manuscript-analysis/REFMAP_SIM/ddocent/Final.recode.snps.vcf VCFtools - v0.1.11 (C) Adam Auton 2009 Parameters as interpreted: --vcf ddoc-tmp.vcf --recode-INFO-all --out /home/iovercast/manuscript-analysis/REFMAP_SIM/ddocent/Final.recode.snps.vcf --recode --remove-indels Index file is older than variant file. Will regenerate. Building new index file. Scanning Chromosome: MT Writing Index file. File contains 4723 entries and 12 individuals. Applying Required Filters. Filtering sites by allele type After filtering, kept 12 out of 12 Individuals After filtering, kept 4722 out of a possible 4723 Sites Outputting VCF file... Done Run Time = 1.00 seconds
We will use the sra-toolkit command fastq-dump
to pull the PE reads out of SRA.
This maybe isn't the best way, or the quickest, but it'll get the job done. Takes
~30 minutes and requires ~70GB of space. After I downloaded the fq I looked at a
couple random samples in FastQC to get an idea where to trim in step2.
os.chdir(REFMAP_EMPIRICAL_DIR)
!mkdir raws
!cd raws
## Grab the sra-toolkit pre-built binaries to download from SRA
## This works, but commented for now so it doesn't keep redownloading
!wget http://ftp-trace.ncbi.nlm.nih.gov/sra/sdk/2.8.0/sratoolkit.2.8.0-ubuntu64.tar.gz
!tar -xvzf sratoolkit*
FQ_DUMP = os.path.join(REFMAP_EMPIRICAL_DIR, "sratoolkit.2.8.0-ubuntu64/bin/fastq-dump")
res = subprocess.check_output(FQ_DUMP + " -version", shell=True)
## The SRR numbers for the samples from this bioproject range from SRR4291662 to SRR4291705
## so go fetch them one by one
for samp in range(662, 706):
print("Doing {}\t".format(samp)),
res = subprocess.check_output(FQ_DUMP + " --split-files SRR4291" + str(samp), shell=True)
Doing 662 Doing 663 Doing 664 Doing 665 Doing 666 Doing 667 Doing 668 Doing 669 Doing 670 Doing 671 Doing 672 Doing 673 Doing 674 Doing 675 Doing 676 Doing 677 Doing 678 Doing 679 Doing 680 Doing 681 Doing 682 Doing 683 Doing 684 Doing 685 Doing 686 Doing 687 Doing 688 Doing 689 Doing 690 Doing 691 Doing 692 Doing 693 Doing 694 Doing 695 Doing 696 Doing 697 Doing 698 Doing 699 Doing 700 Doing 701 Doing 702
## The SRA download files have wonky names, like SRR1234_R1.fastq.gz, but ipyrad expects SRR1234_R1_.fastq.gz,
## so we have to fix the filenames. Filename hax...
import glob
for f in glob.glob(REFMAP_EMPIRICAL_DIR + "raws/*.fastq.gz"):
splits = f.split("/")[-1].split("_")
newf = REFMAP_EMPIRICAL_DIR + "raws/" + splits[0] + "_R" + splits[1].split(".")[0] + "_.fastq.gz"
os.rename(f, newf)
Tursiops truncatus reference genome. Divergence time between dolphin and porpoise is approximately 15Mya, which is on the order of divergence between humans and orang. There is also a genome for the Minke whale, which is much more deeply diverged (~30Mya), could be interesting to try both to see how it works.
Minke whale - http://www.nature.com/ng/journal/v46/n1/full/ng.2835.html#accessions
SRA Data table for converting fq files to sample name as used in the paper: file:///home/chronos/u-b20882bda0f801c7265d4462f127d0cb4376d46d/Downloads/Tune/SraRunTable.txt
os.chdir(REFMAP_EMPIRICAL_DIR)
!mkdir TurtrunRef
!cd TurtrunRef
!wget ftp://ftp.ensembl.org/pub/release-87/fasta/tursiops_truncatus/dna/Tursiops_truncatus.turTru1.dna_rm.toplevel.fa.gz
## Ensembl distributes gzip'd reference sequence files, but samtools really wants it to be bgzipped or uncompressed
!gunzip Tursiops_truncatus.turTru1.dna_rm.toplevel.fa.gz
--2016-12-18 10:29:57-- ftp://ftp.ensembl.org/pub/release-87/fasta/tursiops_truncatus/dna/Tursiops_truncatus.turTru1.dna_rm.toplevel.fa.gz => ‘Tursiops_truncatus.turTru1.dna_rm.toplevel.fa.gz’ Resolving ftp.ensembl.org (ftp.ensembl.org)... 193.62.203.85 Connecting to ftp.ensembl.org (ftp.ensembl.org)|193.62.203.85|:21... connected. Logging in as anonymous ... Logged in! ==> SYST ... done. ==> PWD ... done. ==> TYPE I ... done. ==> CWD (1) /pub/release-87/fasta/tursiops_truncatus/dna ... done. ==> SIZE Tursiops_truncatus.turTru1.dna_rm.toplevel.fa.gz ... 453168562 ==> PASV ... done. ==> RETR Tursiops_truncatus.turTru1.dna_rm.toplevel.fa.gz ... done. Length: 453168562 (432M) (unauthoritative) 100%[======================================>] 453,168,562 2.88MB/s in 3m 33s 2016-12-18 10:33:32 (2.03 MB/s) - ‘Tursiops_truncatus.turTru1.dna_rm.toplevel.fa.gz’ saved [453168562]
To reduce any potential bias introduced by differences in trimming and filtering methods we will trim reads w/ cutadapt, and QC w/ ipyrad and use this dataset as the starting point for assembly. If you are wondering, you can run fastqc from the command line, like this
fastqc -o fastqc_out/ SRR4291662_1.fastq SRR4291662_2.fastq
We'll trim R1 and R2 to 85bp following Lah et al 2016. The -l
flag for cutadapt specifies the length to which each read will be trimmed.
%%bash -s "$REFMAP_EMPIRICAL_DIR"
cd $1
mkdir trimmed
for i in `ls raws`; do echo $i; cutadapt -l 85 raws/$i | gzip > trimmed/$i; done
## Housekeeping
rm -rf raws
mv trimmed raws
SRR4291662_R1_.fastq.gz SRR4291662_R2_.fastq.gz SRR4291663_R1_.fastq.gz SRR4291663_R2_.fastq.gz SRR4291664_R1_.fastq.gz SRR4291664_R2_.fastq.gz SRR4291665_R1_.fastq.gz SRR4291665_R2_.fastq.gz SRR4291666_R1_.fastq.gz SRR4291666_R2_.fastq.gz SRR4291667_R1_.fastq.gz SRR4291667_R2_.fastq.gz SRR4291668_R1_.fastq.gz SRR4291668_R2_.fastq.gz SRR4291669_R1_.fastq.gz SRR4291669_R2_.fastq.gz SRR4291670_R1_.fastq.gz SRR4291670_R2_.fastq.gz SRR4291671_R1_.fastq.gz SRR4291671_R2_.fastq.gz SRR4291672_R1_.fastq.gz SRR4291672_R2_.fastq.gz SRR4291673_R1_.fastq.gz SRR4291673_R2_.fastq.gz SRR4291674_R1_.fastq.gz SRR4291674_R2_.fastq.gz SRR4291675_R1_.fastq.gz SRR4291675_R2_.fastq.gz SRR4291676_R1_.fastq.gz SRR4291676_R2_.fastq.gz SRR4291677_R1_.fastq.gz SRR4291677_R2_.fastq.gz SRR4291678_R1_.fastq.gz SRR4291678_R2_.fastq.gz SRR4291679_R1_.fastq.gz SRR4291679_R2_.fastq.gz SRR4291680_R1_.fastq.gz SRR4291680_R2_.fastq.gz SRR4291681_R1_.fastq.gz SRR4291681_R2_.fastq.gz SRR4291682_R1_.fastq.gz SRR4291682_R2_.fastq.gz SRR4291683_R1_.fastq.gz SRR4291683_R2_.fastq.gz SRR4291684_R1_.fastq.gz SRR4291684_R2_.fastq.gz SRR4291685_R1_.fastq.gz SRR4291685_R2_.fastq.gz SRR4291686_R1_.fastq.gz SRR4291686_R2_.fastq.gz SRR4291687_R1_.fastq.gz SRR4291687_R2_.fastq.gz SRR4291688_R1_.fastq.gz SRR4291688_R2_.fastq.gz SRR4291689_R1_.fastq.gz SRR4291689_R2_.fastq.gz SRR4291690_R1_.fastq.gz SRR4291690_R2_.fastq.gz SRR4291691_R1_.fastq.gz SRR4291691_R2_.fastq.gz SRR4291692_R1_.fastq.gz SRR4291692_R2_.fastq.gz SRR4291693_R1_.fastq.gz SRR4291693_R2_.fastq.gz SRR4291694_R1_.fastq.gz SRR4291694_R2_.fastq.gz SRR4291695_R1_.fastq.gz SRR4291695_R2_.fastq.gz SRR4291696_R1_.fastq.gz SRR4291696_R2_.fastq.gz SRR4291697_R1_.fastq.gz SRR4291697_R2_.fastq.gz SRR4291698_R1_.fastq.gz SRR4291698_R2_.fastq.gz SRR4291699_R1_.fastq.gz SRR4291699_R2_.fastq.gz SRR4291700_R1_.fastq.gz SRR4291700_R2_.fastq.gz SRR4291701_R1_.fastq.gz SRR4291701_R2_.fastq.gz SRR4291702_R1_.fastq.gz SRR4291702_R2_.fastq.gz SRR4291703_R1_.fastq.gz SRR4291703_R2_.fastq.gz SRR4291704_R1_.fastq.gz SRR4291704_R2_.fastq.gz SRR4291705_R1_.fastq.gz SRR4291705_R2_.fastq.gz
mkdir: cannot create directory ‘trimmed’: File exists This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291662_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 18.66 s (6 us/read; 10.37 M reads/minute). === Summary === Total reads processed: 3,225,657 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,225,657 (100.0%) Total basepairs processed: 302,535,710 bp Total written (filtered): 273,671,316 bp (90.5%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291662_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 18.43 s (6 us/read; 10.50 M reads/minute). === Summary === Total reads processed: 3,225,657 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,225,657 (100.0%) Total basepairs processed: 321,680,483 bp Total written (filtered): 273,633,566 bp (85.1%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291663_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 14.78 s (6 us/read; 10.56 M reads/minute). === Summary === Total reads processed: 2,602,278 Reads with adapters: 0 (0.0%) Reads written (passing filters): 2,602,278 (100.0%) Total basepairs processed: 241,637,058 bp Total written (filtered): 220,902,242 bp (91.4%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291663_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 14.96 s (6 us/read; 10.44 M reads/minute). === Summary === Total reads processed: 2,602,278 Reads with adapters: 0 (0.0%) Reads written (passing filters): 2,602,278 (100.0%) Total basepairs processed: 259,728,172 bp Total written (filtered): 220,886,902 bp (85.0%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291664_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 7.47 s (6 us/read; 10.51 M reads/minute). === Summary === Total reads processed: 1,308,140 Reads with adapters: 0 (0.0%) Reads written (passing filters): 1,308,140 (100.0%) Total basepairs processed: 124,071,961 bp Total written (filtered): 111,051,112 bp (89.5%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291664_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 7.39 s (6 us/read; 10.62 M reads/minute). === Summary === Total reads processed: 1,308,140 Reads with adapters: 0 (0.0%) Reads written (passing filters): 1,308,140 (100.0%) Total basepairs processed: 130,566,117 bp Total written (filtered): 111,041,864 bp (85.0%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291665_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 8.12 s (6 us/read; 10.51 M reads/minute). === Summary === Total reads processed: 1,422,513 Reads with adapters: 0 (0.0%) Reads written (passing filters): 1,422,513 (100.0%) Total basepairs processed: 132,119,445 bp Total written (filtered): 120,780,161 bp (91.4%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291665_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 8.14 s (6 us/read; 10.49 M reads/minute). === Summary === Total reads processed: 1,422,513 Reads with adapters: 0 (0.0%) Reads written (passing filters): 1,422,513 (100.0%) Total basepairs processed: 142,019,450 bp Total written (filtered): 120,774,460 bp (85.0%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291666_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 18.56 s (6 us/read; 10.47 M reads/minute). === Summary === Total reads processed: 3,237,438 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,237,438 (100.0%) Total basepairs processed: 300,710,625 bp Total written (filtered): 274,900,243 bp (91.4%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291666_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 18.72 s (6 us/read; 10.38 M reads/minute). === Summary === Total reads processed: 3,237,438 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,237,438 (100.0%) Total basepairs processed: 323,231,561 bp Total written (filtered): 274,880,218 bp (85.0%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291667_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 23.73 s (6 us/read; 10.54 M reads/minute). === Summary === Total reads processed: 4,170,153 Reads with adapters: 0 (0.0%) Reads written (passing filters): 4,170,153 (100.0%) Total basepairs processed: 390,976,823 bp Total written (filtered): 353,700,081 bp (90.5%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291667_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 23.96 s (6 us/read; 10.44 M reads/minute). === Summary === Total reads processed: 4,170,153 Reads with adapters: 0 (0.0%) Reads written (passing filters): 4,170,153 (100.0%) Total basepairs processed: 415,710,634 bp Total written (filtered): 353,654,174 bp (85.1%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291668_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 20.04 s (6 us/read; 10.33 M reads/minute). === Summary === Total reads processed: 3,450,882 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,450,882 (100.0%) Total basepairs processed: 310,304,790 bp Total written (filtered): 293,093,219 bp (94.5%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291668_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 19.70 s (6 us/read; 10.51 M reads/minute). === Summary === Total reads processed: 3,450,882 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,450,882 (100.0%) Total basepairs processed: 344,647,193 bp Total written (filtered): 293,069,094 bp (85.0%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291669_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 21.57 s (6 us/read; 10.18 M reads/minute). === Summary === Total reads processed: 3,658,457 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,658,457 (100.0%) Total basepairs processed: 332,569,307 bp Total written (filtered): 310,678,470 bp (93.4%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291669_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 21.07 s (6 us/read; 10.42 M reads/minute). === Summary === Total reads processed: 3,658,457 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,658,457 (100.0%) Total basepairs processed: 365,337,470 bp Total written (filtered): 310,656,388 bp (85.0%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291670_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 8.09 s (6 us/read; 10.59 M reads/minute). === Summary === Total reads processed: 1,428,040 Reads with adapters: 0 (0.0%) Reads written (passing filters): 1,428,040 (100.0%) Total basepairs processed: 135,277,065 bp Total written (filtered): 121,110,383 bp (89.5%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291670_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 8.11 s (6 us/read; 10.57 M reads/minute). === Summary === Total reads processed: 1,428,040 Reads with adapters: 0 (0.0%) Reads written (passing filters): 1,428,040 (100.0%) Total basepairs processed: 142,339,159 bp Total written (filtered): 121,090,656 bp (85.1%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291671_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 21.11 s (6 us/read; 10.59 M reads/minute). === Summary === Total reads processed: 3,726,102 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,726,102 (100.0%) Total basepairs processed: 346,070,011 bp Total written (filtered): 316,369,233 bp (91.4%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291671_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 20.70 s (6 us/read; 10.80 M reads/minute). === Summary === Total reads processed: 3,726,102 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,726,102 (100.0%) Total basepairs processed: 371,995,572 bp Total written (filtered): 316,342,060 bp (85.0%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291672_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 26.01 s (8 us/read; 7.51 M reads/minute). === Summary === Total reads processed: 3,256,943 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,256,943 (100.0%) Total basepairs processed: 305,656,861 bp Total written (filtered): 276,475,866 bp (90.5%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291672_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 20.99 s (6 us/read; 9.31 M reads/minute). === Summary === Total reads processed: 3,256,943 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,256,943 (100.0%) Total basepairs processed: 325,037,311 bp Total written (filtered): 276,448,443 bp (85.1%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291673_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 12.23 s (6 us/read; 10.44 M reads/minute). === Summary === Total reads processed: 2,128,431 Reads with adapters: 0 (0.0%) Reads written (passing filters): 2,128,431 (100.0%) Total basepairs processed: 199,673,694 bp Total written (filtered): 180,616,031 bp (90.5%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291673_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 11.72 s (6 us/read; 10.90 M reads/minute). === Summary === Total reads processed: 2,128,431 Reads with adapters: 0 (0.0%) Reads written (passing filters): 2,128,431 (100.0%) Total basepairs processed: 212,332,918 bp Total written (filtered): 180,599,472 bp (85.1%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291674_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 14.54 s (6 us/read; 10.76 M reads/minute). === Summary === Total reads processed: 2,606,704 Reads with adapters: 0 (0.0%) Reads written (passing filters): 2,606,704 (100.0%) Total basepairs processed: 234,287,729 bp Total written (filtered): 221,303,963 bp (94.5%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291674_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 14.52 s (6 us/read; 10.77 M reads/minute). === Summary === Total reads processed: 2,606,704 Reads with adapters: 0 (0.0%) Reads written (passing filters): 2,606,704 (100.0%) Total basepairs processed: 260,154,896 bp Total written (filtered): 221,272,346 bp (85.1%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291675_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 18.16 s (6 us/read; 10.71 M reads/minute). === Summary === Total reads processed: 3,242,962 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,242,962 (100.0%) Total basepairs processed: 297,982,996 bp Total written (filtered): 275,355,483 bp (92.4%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291675_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 18.60 s (6 us/read; 10.46 M reads/minute). === Summary === Total reads processed: 3,242,962 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,242,962 (100.0%) Total basepairs processed: 323,769,609 bp Total written (filtered): 275,327,109 bp (85.0%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291676_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 14.46 s (6 us/read; 10.23 M reads/minute). === Summary === Total reads processed: 2,466,111 Reads with adapters: 0 (0.0%) Reads written (passing filters): 2,466,111 (100.0%) Total basepairs processed: 221,799,201 bp Total written (filtered): 209,492,582 bp (94.5%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291676_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 14.33 s (6 us/read; 10.33 M reads/minute). === Summary === Total reads processed: 2,466,111 Reads with adapters: 0 (0.0%) Reads written (passing filters): 2,466,111 (100.0%) Total basepairs processed: 246,375,382 bp Total written (filtered): 209,484,079 bp (85.0%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291677_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 19.64 s (6 us/read; 10.38 M reads/minute). === Summary === Total reads processed: 3,397,721 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,397,721 (100.0%) Total basepairs processed: 308,581,562 bp Total written (filtered): 288,301,569 bp (93.4%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291677_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 19.30 s (6 us/read; 10.56 M reads/minute). === Summary === Total reads processed: 3,397,721 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,397,721 (100.0%) Total basepairs processed: 338,887,177 bp Total written (filtered): 288,267,890 bp (85.1%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291678_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 18.95 s (6 us/read; 9.65 M reads/minute). === Summary === Total reads processed: 3,048,039 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,048,039 (100.0%) Total basepairs processed: 276,868,465 bp Total written (filtered): 258,666,672 bp (93.4%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291678_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 17.05 s (6 us/read; 10.73 M reads/minute). === Summary === Total reads processed: 3,048,039 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,048,039 (100.0%) Total basepairs processed: 304,094,709 bp Total written (filtered): 258,647,431 bp (85.1%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291679_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 23.03 s (6 us/read; 10.75 M reads/minute). === Summary === Total reads processed: 4,127,787 Reads with adapters: 0 (0.0%) Reads written (passing filters): 4,127,787 (100.0%) Total basepairs processed: 378,825,733 bp Total written (filtered): 350,115,785 bp (92.4%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291679_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 23.27 s (6 us/read; 10.64 M reads/minute). === Summary === Total reads processed: 4,127,787 Reads with adapters: 0 (0.0%) Reads written (passing filters): 4,127,787 (100.0%) Total basepairs processed: 411,474,061 bp Total written (filtered): 350,061,395 bp (85.1%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291680_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 16.20 s (6 us/read; 10.81 M reads/minute). === Summary === Total reads processed: 2,917,555 Reads with adapters: 0 (0.0%) Reads written (passing filters): 2,917,555 (100.0%) Total basepairs processed: 265,159,848 bp Total written (filtered): 247,711,645 bp (93.4%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291680_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 21.15 s (7 us/read; 8.28 M reads/minute). === Summary === Total reads processed: 2,917,555 Reads with adapters: 0 (0.0%) Reads written (passing filters): 2,917,555 (100.0%) Total basepairs processed: 291,272,774 bp Total written (filtered): 247,692,864 bp (85.0%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291681_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 19.07 s (6 us/read; 10.91 M reads/minute). === Summary === Total reads processed: 3,466,670 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,466,670 (100.0%) Total basepairs processed: 325,229,207 bp Total written (filtered): 294,186,403 bp (90.5%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291681_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 19.76 s (6 us/read; 10.53 M reads/minute). === Summary === Total reads processed: 3,466,670 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,466,670 (100.0%) Total basepairs processed: 345,853,871 bp Total written (filtered): 294,161,220 bp (85.1%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291682_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 20.14 s (6 us/read; 10.49 M reads/minute). === Summary === Total reads processed: 3,519,888 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,519,888 (100.0%) Total basepairs processed: 326,570,566 bp Total written (filtered): 298,588,290 bp (91.4%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291682_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 19.88 s (6 us/read; 10.62 M reads/minute). === Summary === Total reads processed: 3,519,888 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,519,888 (100.0%) Total basepairs processed: 350,929,362 bp Total written (filtered): 298,543,164 bp (85.1%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291683_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 11.83 s (6 us/read; 10.47 M reads/minute). === Summary === Total reads processed: 2,064,559 Reads with adapters: 0 (0.0%) Reads written (passing filters): 2,064,559 (100.0%) Total basepairs processed: 187,046,314 bp Total written (filtered): 174,797,570 bp (93.5%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291683_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 11.70 s (6 us/read; 10.59 M reads/minute). === Summary === Total reads processed: 2,064,559 Reads with adapters: 0 (0.0%) Reads written (passing filters): 2,064,559 (100.0%) Total basepairs processed: 205,296,143 bp Total written (filtered): 174,759,321 bp (85.1%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291684_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 17.51 s (6 us/read; 10.58 M reads/minute). === Summary === Total reads processed: 3,086,468 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,086,468 (100.0%) Total basepairs processed: 289,461,086 bp Total written (filtered): 261,848,374 bp (90.5%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291684_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 17.64 s (6 us/read; 10.50 M reads/minute). === Summary === Total reads processed: 3,086,468 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,086,468 (100.0%) Total basepairs processed: 307,783,378 bp Total written (filtered): 261,819,338 bp (85.1%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291685_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 13.52 s (6 us/read; 10.86 M reads/minute). === Summary === Total reads processed: 2,446,083 Reads with adapters: 0 (0.0%) Reads written (passing filters): 2,446,083 (100.0%) Total basepairs processed: 229,320,135 bp Total written (filtered): 207,457,048 bp (90.5%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291685_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 13.74 s (6 us/read; 10.68 M reads/minute). === Summary === Total reads processed: 2,446,083 Reads with adapters: 0 (0.0%) Reads written (passing filters): 2,446,083 (100.0%) Total basepairs processed: 243,821,853 bp Total written (filtered): 207,429,219 bp (85.1%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291686_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 18.07 s (6 us/read; 10.63 M reads/minute). === Summary === Total reads processed: 3,201,430 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,201,430 (100.0%) Total basepairs processed: 287,489,343 bp Total written (filtered): 271,572,152 bp (94.5%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291686_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 24.01 s (7 us/read; 8.00 M reads/minute). === Summary === Total reads processed: 3,201,430 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,201,430 (100.0%) Total basepairs processed: 319,198,511 bp Total written (filtered): 271,529,648 bp (85.1%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291687_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 18.34 s (6 us/read; 10.62 M reads/minute). === Summary === Total reads processed: 3,245,019 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,245,019 (100.0%) Total basepairs processed: 291,561,672 bp Total written (filtered): 275,408,840 bp (94.5%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291687_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 17.94 s (6 us/read; 10.85 M reads/minute). === Summary === Total reads processed: 3,245,019 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,245,019 (100.0%) Total basepairs processed: 323,754,288 bp Total written (filtered): 275,370,853 bp (85.1%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291688_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 8.72 s (6 us/read; 10.76 M reads/minute). === Summary === Total reads processed: 1,564,117 Reads with adapters: 0 (0.0%) Reads written (passing filters): 1,564,117 (100.0%) Total basepairs processed: 146,438,492 bp Total written (filtered): 132,507,950 bp (90.5%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291688_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 8.69 s (6 us/read; 10.80 M reads/minute). === Summary === Total reads processed: 1,564,117 Reads with adapters: 0 (0.0%) Reads written (passing filters): 1,564,117 (100.0%) Total basepairs processed: 155,657,876 bp Total written (filtered): 132,487,616 bp (85.1%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291689_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 18.13 s (6 us/read; 10.53 M reads/minute). === Summary === Total reads processed: 3,181,938 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,181,938 (100.0%) Total basepairs processed: 298,646,806 bp Total written (filtered): 270,124,604 bp (90.4%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291689_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 17.88 s (6 us/read; 10.68 M reads/minute). === Summary === Total reads processed: 3,181,938 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,181,938 (100.0%) Total basepairs processed: 317,599,989 bp Total written (filtered): 270,100,955 bp (85.0%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291690_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 15.60 s (6 us/read; 10.29 M reads/minute). === Summary === Total reads processed: 2,674,772 Reads with adapters: 0 (0.0%) Reads written (passing filters): 2,674,772 (100.0%) Total basepairs processed: 253,734,600 bp Total written (filtered): 227,093,686 bp (89.5%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291690_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 15.38 s (6 us/read; 10.43 M reads/minute). === Summary === Total reads processed: 2,674,772 Reads with adapters: 0 (0.0%) Reads written (passing filters): 2,674,772 (100.0%) Total basepairs processed: 267,026,097 bp Total written (filtered): 227,081,425 bp (85.0%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291691_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 35.33 s (6 us/read; 10.02 M reads/minute). === Summary === Total reads processed: 5,903,027 Reads with adapters: 0 (0.0%) Reads written (passing filters): 5,903,027 (100.0%) Total basepairs processed: 548,419,385 bp Total written (filtered): 501,329,616 bp (91.4%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291691_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 34.24 s (6 us/read; 10.34 M reads/minute). === Summary === Total reads processed: 5,903,027 Reads with adapters: 0 (0.0%) Reads written (passing filters): 5,903,027 (100.0%) Total basepairs processed: 589,507,289 bp Total written (filtered): 501,290,530 bp (85.0%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291692_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 6.55 s (6 us/read; 10.74 M reads/minute). === Summary === Total reads processed: 1,172,868 Reads with adapters: 0 (0.0%) Reads written (passing filters): 1,172,868 (100.0%) Total basepairs processed: 108,479,568 bp Total written (filtered): 99,222,769 bp (91.5%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291692_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 6.43 s (5 us/read; 10.94 M reads/minute). === Summary === Total reads processed: 1,172,868 Reads with adapters: 0 (0.0%) Reads written (passing filters): 1,172,868 (100.0%) Total basepairs processed: 116,504,462 bp Total written (filtered): 99,193,009 bp (85.1%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291693_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 12.27 s (6 us/read; 10.63 M reads/minute). === Summary === Total reads processed: 2,174,717 Reads with adapters: 0 (0.0%) Reads written (passing filters): 2,174,717 (100.0%) Total basepairs processed: 199,675,649 bp Total written (filtered): 184,528,700 bp (92.4%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291693_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 12.04 s (6 us/read; 10.84 M reads/minute). === Summary === Total reads processed: 2,174,717 Reads with adapters: 0 (0.0%) Reads written (passing filters): 2,174,717 (100.0%) Total basepairs processed: 216,926,920 bp Total written (filtered): 184,508,611 bp (85.1%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291694_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 20.30 s (6 us/read; 10.74 M reads/minute). === Summary === Total reads processed: 3,632,242 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,632,242 (100.0%) Total basepairs processed: 340,683,039 bp Total written (filtered): 308,182,877 bp (90.5%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291694_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 20.48 s (6 us/read; 10.64 M reads/minute). === Summary === Total reads processed: 3,632,242 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,632,242 (100.0%) Total basepairs processed: 362,256,218 bp Total written (filtered): 308,150,487 bp (85.1%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291695_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 10.68 s (6 us/read; 10.43 M reads/minute). === Summary === Total reads processed: 1,856,718 Reads with adapters: 0 (0.0%) Reads written (passing filters): 1,856,718 (100.0%) Total basepairs processed: 173,941,660 bp Total written (filtered): 157,377,022 bp (90.5%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291695_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 10.78 s (6 us/read; 10.33 M reads/minute). === Summary === Total reads processed: 1,856,718 Reads with adapters: 0 (0.0%) Reads written (passing filters): 1,856,718 (100.0%) Total basepairs processed: 184,905,679 bp Total written (filtered): 157,341,499 bp (85.1%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291696_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 19.31 s (6 us/read; 10.39 M reads/minute). === Summary === Total reads processed: 3,345,439 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,345,439 (100.0%) Total basepairs processed: 307,309,204 bp Total written (filtered): 283,984,631 bp (92.4%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291696_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 18.43 s (6 us/read; 10.89 M reads/minute). === Summary === Total reads processed: 3,345,439 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,345,439 (100.0%) Total basepairs processed: 333,884,503 bp Total written (filtered): 283,956,832 bp (85.0%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291697_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 18.52 s (6 us/read; 10.40 M reads/minute). === Summary === Total reads processed: 3,210,823 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,210,823 (100.0%) Total basepairs processed: 294,875,650 bp Total written (filtered): 272,500,412 bp (92.4%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291697_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 18.37 s (6 us/read; 10.49 M reads/minute). === Summary === Total reads processed: 3,210,823 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,210,823 (100.0%) Total basepairs processed: 320,361,719 bp Total written (filtered): 272,471,463 bp (85.1%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291698_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 10.47 s (6 us/read; 10.26 M reads/minute). === Summary === Total reads processed: 1,790,978 Reads with adapters: 0 (0.0%) Reads written (passing filters): 1,790,978 (100.0%) Total basepairs processed: 167,594,570 bp Total written (filtered): 151,658,671 bp (90.5%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291698_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 10.25 s (6 us/read; 10.48 M reads/minute). === Summary === Total reads processed: 1,790,978 Reads with adapters: 0 (0.0%) Reads written (passing filters): 1,790,978 (100.0%) Total basepairs processed: 178,125,550 bp Total written (filtered): 151,625,435 bp (85.1%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291699_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 15.90 s (6 us/read; 10.63 M reads/minute). === Summary === Total reads processed: 2,816,420 Reads with adapters: 0 (0.0%) Reads written (passing filters): 2,816,420 (100.0%) Total basepairs processed: 261,543,984 bp Total written (filtered): 239,104,344 bp (91.4%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291699_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 16.04 s (6 us/read; 10.54 M reads/minute). === Summary === Total reads processed: 2,816,420 Reads with adapters: 0 (0.0%) Reads written (passing filters): 2,816,420 (100.0%) Total basepairs processed: 281,129,007 bp Total written (filtered): 239,083,585 bp (85.0%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291700_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 15.41 s (6 us/read; 10.39 M reads/minute). === Summary === Total reads processed: 2,668,256 Reads with adapters: 0 (0.0%) Reads written (passing filters): 2,668,256 (100.0%) Total basepairs processed: 249,952,844 bp Total written (filtered): 226,146,252 bp (90.5%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291700_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 15.27 s (6 us/read; 10.48 M reads/minute). === Summary === Total reads processed: 2,668,256 Reads with adapters: 0 (0.0%) Reads written (passing filters): 2,668,256 (100.0%) Total basepairs processed: 265,721,099 bp Total written (filtered): 226,097,178 bp (85.1%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291701_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 20.10 s (6 us/read; 10.37 M reads/minute). === Summary === Total reads processed: 3,474,962 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,474,962 (100.0%) Total basepairs processed: 315,839,017 bp Total written (filtered): 295,059,339 bp (93.4%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291701_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 19.76 s (6 us/read; 10.55 M reads/minute). === Summary === Total reads processed: 3,474,962 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,474,962 (100.0%) Total basepairs processed: 346,913,613 bp Total written (filtered): 295,029,825 bp (85.0%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291702_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 28.11 s (6 us/read; 10.50 M reads/minute). === Summary === Total reads processed: 4,918,786 Reads with adapters: 0 (0.0%) Reads written (passing filters): 4,918,786 (100.0%) Total basepairs processed: 460,455,562 bp Total written (filtered): 416,655,206 bp (90.5%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291702_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 28.02 s (6 us/read; 10.53 M reads/minute). === Summary === Total reads processed: 4,918,786 Reads with adapters: 0 (0.0%) Reads written (passing filters): 4,918,786 (100.0%) Total basepairs processed: 489,428,555 bp Total written (filtered): 416,577,729 bp (85.1%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291703_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 18.27 s (6 us/read; 10.46 M reads/minute). === Summary === Total reads processed: 3,186,044 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,186,044 (100.0%) Total basepairs processed: 295,683,146 bp Total written (filtered): 270,343,584 bp (91.4%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291703_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 18.17 s (6 us/read; 10.52 M reads/minute). === Summary === Total reads processed: 3,186,044 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,186,044 (100.0%) Total basepairs processed: 317,754,200 bp Total written (filtered): 270,312,770 bp (85.1%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291704_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 18.37 s (6 us/read; 10.44 M reads/minute). === Summary === Total reads processed: 3,197,026 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,197,026 (100.0%) Total basepairs processed: 299,931,145 bp Total written (filtered): 271,300,448 bp (90.5%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291704_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 18.23 s (6 us/read; 10.52 M reads/minute). === Summary === Total reads processed: 3,197,026 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,197,026 (100.0%) Total basepairs processed: 318,941,014 bp Total written (filtered): 271,271,223 bp (85.1%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291705_R1_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 19.39 s (6 us/read; 10.60 M reads/minute). === Summary === Total reads processed: 3,424,403 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,424,403 (100.0%) Total basepairs processed: 314,439,868 bp Total written (filtered): 290,585,202 bp (92.4%) This is cutadapt 1.12 with Python 2.7.12 Command line parameters: -l 85 raws/SRR4291705_R2_.fastq.gz Trimming 0 adapters with at most 10.0% errors in single-end mode ... Finished in 19.99 s (6 us/read; 10.28 M reads/minute). === Summary === Total reads processed: 3,424,403 Reads with adapters: 0 (0.0%) Reads written (passing filters): 3,424,403 (100.0%) Total basepairs processed: 341,609,530 bp Total written (filtered): 290,553,612 bp (85.1%)
Now take the trimmed reads and filter for adapters, minimum sequence length, and max low quality bases.
IPYRAD_REFMAP_DIR = os.path.join(REFMAP_EMPIRICAL_DIR, "ipyrad/")
if not os.path.exists(IPYRAD_REFMAP_DIR):
os.makedirs(IPYRAD_REFMAP_DIR)
os.chdir(IPYRAD_REFMAP_DIR)
## Make a new assembly and set some assembly parameters
data = ip.Assembly("refmap-empirical")
data.set_params("sorted_fastq_path", REFMAP_EMPIRICAL_DIR + "raws/*.fastq.gz")
data.set_params("project_dir", "reference-assembly")
data.set_params("assembly_method", "reference")
data.set_params("reference_sequence", REFMAP_EMPIRICAL_DIR + "TurtrunRef/Tursiops_truncatus.turTru1.dna_rm.toplevel.fa")
data.set_params("datatype", "pairddrad")
data.set_params("restriction_overhang", ("TGCAG", "CGG"))
data.set_params('max_low_qual_bases', 5)
data.set_params('filter_adapters', 2)
data.write_params(force=True)
cmd = "ipyrad -p params-refmap-empirical.txt -s 1 --force".format(dir)
print(cmd)
!time $cmd
cmd = "ipyrad -p params-refmap-empirical.txt -s 2 --force".format(dir)
print(cmd)
!time $cmd
New Assembly: refmap-empirical ipyrad -p params-refmap-empirical.txt -s 1 --force ------------------------------------------------------------- ipyrad [v.0.5.15] Interactive assembly and analysis of RAD-seq data ------------------------------------------------------------- New Assembly: refmap-empirical local compute node: [40 cores] on node001 Step 1: Loading sorted fastq data to Samples [####################] 100% loading reads | 0:00:21 88 fastq files loaded to 44 Samples. real 0m42.604s user 0m2.510s sys 0m0.791s ipyrad -p params-refmap-empirical.txt -s 2 --force ------------------------------------------------------------- ipyrad [v.0.5.15] Interactive assembly and analysis of RAD-seq data ------------------------------------------------------------- loading Assembly: refmap-empirical from saved path: ~/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/refmap-empirical.json local compute node: [40 cores] on node001 Step 2: Filtering reads [####################] 100% processing reads | 0:13:44 real 13m57.667s user 0m10.699s sys 0m0.653s
Here we will use the ipyrad 'reference' assembly method which will only use reads that successfully map to the reference sequence. This is similar and should be comparable to the way stacks incorporates reference sequences.
Steps 1 & 2 run in ~15 minutes.
## Oops. If you run some other cell while this is running it steals stdout, so you lose track of progress.
cmd = "ipyrad -p params-refmap-empirical.txt -s 34567".format(dir)
print(cmd)
!time $cmd
ipyrad -p params-refmap-empirical.txt -s 3 ------------------------------------------------------------- ipyrad [v.0.5.15] Interactive assembly and analysis of RAD-seq data ------------------------------------------------------------- loading Assembly: refmap-empirical from saved path: ~/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/refmap-empirical.json local compute node: [40 cores] on node001 Step 3: Clustering/Mapping reads [####################] 100% dereplicating | 0:10:16 [######### ] 45% mapping | 0:24:11
Create a new branch and set the assembly method to denovo+reference
.
data2 = data.branch("denovo_ref-empirical")
data2.set_params("assembly_method", "denovo+reference")
data2.write_params(force=True)
cmd = "ipyrad -p params-denovo_ref-empirical.txt -s 34567 -c 40".format(dir)
print(cmd)
!time $cmd
ipyrad -p params-denovo_ref-sim.txt -s 1234567 -c 40 ------------------------------------------------------------- ipyrad [v.0.5.15] Interactive assembly and analysis of RAD-seq data ------------------------------------------------------------- loading Assembly: denovo_ref-sim from saved path: ~/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/denovo_ref-sim.json New Assembly: denovo_ref-sim local compute node: [40 cores] on node001 Step 1: Demultiplexing fastq data to Samples [####################] 100% chunking large files | 0:00:00 [####################] 100% sorting reads | 0:00:07 [####################] 100% writing/compressing | 0:00:01 Step 2: Filtering reads [####################] 100% processing reads | 0:00:02 Step 3: Clustering/Mapping reads [####################] 100% dereplicating | 0:00:00 [####################] 100% mapping | 0:00:00 [####################] 100% clustering | 0:00:00 [####################] 100% building clusters | 0:00:01 [####################] 100% finalize mapping | 0:00:29 [####################] 100% chunking | 0:00:00 [####################] 100% aligning | 0:00:15 [####################] 100% concatenating | 0:00:00 Step 4: Joint estimation of error rate and heterozygosity [####################] 100% inferring [H, E] | 0:00:15 Step 5: Consensus base calling Mean error [0.00074 sd=0.00001] Mean hetero [0.00195 sd=0.00015] [####################] 100% calculating depths | 0:00:00 [####################] 100% chunking clusters | 0:00:00 [####################] 100% consens calling | 0:00:11 Step 6: Clustering at 0.85 similarity across 12 samples [####################] 100% concat/shuffle input | 0:00:00 [####################] 100% clustering across | 0:00:01 [####################] 100% building clusters | 0:00:00 [####################] 100% aligning clusters | 0:00:03 [####################] 100% database indels | 0:00:00 [####################] 100% indexing clusters | 0:00:01 [####################] 100% building database | 0:00:00 Step 7: Filter and write output files for 12 Samples [####################] 100% filtering loci | 0:00:06 [####################] 100% building loci/stats | 0:00:00 [####################] 100% building vcf file | 0:00:01 [####################] 100% writing vcf file | 0:00:00 [####################] 100% building arrays | 0:00:02 [####################] 100% writing outfiles | 0:00:01 Outfiles written to: ~/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/denovo_ref-sim_outfiles real 2m12.816s user 0m23.420s sys 0m1.675s
Lah et al trim R1 and R2 to 85bp and then concatenate them for stacks analysis. I thought this was weird, but the stacks manual says "For double-digest RAD data that has been paired-end sequenced, Stacks supports this type of data by treating the loci built from the single-end and paired-end as two independent loci", which I guess is effectively the same thing as concatenating.
For reference sequence assembly stacks doesn't actually handle the mapping, you need to Do It Yourself and then pull in .sam or .bam files. Since we already do the bwa mapping in ipyrad lets just use those files. The question is do we use the full .sam file or the *-mapped.bam (which excludes unmapped and unpaired reads). We'll use the *-mapped.bam files because the stacks manual says the mapped reads should be clean and tidy, so no junk.
NB: Stacks makes the strong assumption that the "left" edge of all reads within a stack line up. It looks like it just uses the StartPos to define a stack (assuming the cut site is the same). We will clean up soft-clipped sequences with ngsutils (https://github.com/ngsutils/ngsutils.git).
NB: Stacks treats R1 and R2 as independent.
NB: The other consequence of being forced to remove soft clipped sequence is that you are bound to be losing some real variation.
For the empirical data stacks runs in ~2 hours (~8 if you include the time it takes to map the sequences).
## Set directories and make the popmap file
STACKS_REFMAP_DIR = os.path.join(REFMAP_EMPIRICAL_DIR, "stacks/")
if not os.path.exists(STACKS_REFMAP_DIR):
os.makedirs(STACKS_REFMAP_DIR)
os.chdir(STACKS_REFMAP_DIR)
make_stacks_popmap(STACKS_REFMAP_DIR)
Writing popmap file to /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/popmap.txt
Based on the same logic as above with the simulated data, we need to remap the raw reads from the empirical ipyrad _edits directory to make stacks happy. This step consumes a ton of ram and takes a non-negligible amount of time. See docs for sim mapping above for the meaning of all the bwa/samtools flags.
Mapping the real data will take several hours (5-6 hours if you're lucky).
IPYRAD_EDITS_DIR = os.path.join(IPYRAD_REFMAP_DIR, "reference-assembly/refmap-empirical_edits/")
REF_SEQ = REFMAP_EMPIRICAL_DIR + "TurtrunRef/Tursiops_truncatus.turTru1.dna_rm.toplevel.fa"
## Just get the
sample_names = glob.glob(IPYRAD_EDITS_DIR + "*.trimmed_R1_.fastq.gz")
sample_names = [x.split(".")[0].split("/")[-1] for x in sample_names]
for samp in sample_names:
R1 = IPYRAD_EDITS_DIR + samp + ".trimmed_R1_.fastq.gz"
R2 = IPYRAD_EDITS_DIR + samp + ".trimmed_R2_.fastq.gz"
samout = STACKS_REFMAP_DIR + samp + ".sam"
bamout = STACKS_REFMAP_DIR + samp + ".bam"
export_cmd = "export PATH=~/manuscript-analysis/dDocent:$PATH"
bwa_cmd = "bwa mem -t 40 -v 0 " + REF_SEQ\
+ " " + R1\
+ " " + R2\
+ " > " + samout
samtools_cmd = "samtools view -b -F 0x804 " + samout\
+ " | samtools sort -T /tmp/{}.sam -O bam -o {}".format(samp, bamout)
cleanup_cmd = "rm {}".format(samout)
cmd = "; ".join([export_cmd, bwa_cmd, samtools_cmd, cleanup_cmd])
!time $cmd
real 0m0.000s user 0m0.000s sys 0m0.000s real 0m0.000s user 0m0.000s sys 0m0.000s [M::mem_pestat] analyzing insert size distribution for orientation FF... [M::mem_pestat] (25, 50, 75) percentile: (604, 1784, 2185) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 5347) [M::mem_pestat] mean and std.dev: (1788.75, 1421.66) [M::mem_pestat] low and high boundaries for proper pairs: (1, 7475) [M::mem_pestat] analyzing insert size distribution for orientation FR... [M::mem_pestat] (25, 50, 75) percentile: (193, 229, 272) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (35, 430) [M::mem_pestat] mean and std.dev: (234.03, 57.92) [M::mem_pestat] low and high boundaries for proper pairs: (1, 509) [M::mem_pestat] analyzing insert size distribution for orientation RF... [M::mem_pestat] (25, 50, 75) percentile: (1516, 2019, 6187) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 15529) [M::mem_pestat] mean and std.dev: (3734.25, 2720.10) [M::mem_pestat] low and high boundaries for proper pairs: (1, 20200) [M::mem_pestat] analyzing insert size distribution for orientation RR... [M::mem_pestat] (25, 50, 75) percentile: (685, 2931, 5098) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 13924) [M::mem_pestat] mean and std.dev: (2765.14, 2259.57) [M::mem_pestat] low and high boundaries for proper pairs: (1, 18337) [M::mem_pestat] skip orientation FF [M::mem_pestat] skip orientation RF [M::mem_pestat] skip orientation RR [M::mem_pestat] analyzing insert size distribution for orientation FF... [M::mem_pestat] (25, 50, 75) percentile: (740, 1784, 2182) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 5066) [M::mem_pestat] mean and std.dev: (1764.10, 1151.54) [M::mem_pestat] low and high boundaries for proper pairs: (1, 6508) [M::mem_pestat] analyzing insert size distribution for orientation FR... [M::mem_pestat] (25, 50, 75) percentile: (194, 230, 273) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (36, 431) [M::mem_pestat] mean and std.dev: (234.42, 57.91) [M::mem_pestat] low and high boundaries for proper pairs: (1, 510) [M::mem_pestat] analyzing insert size distribution for orientation RF... [M::mem_pestat] (25, 50, 75) percentile: (1536, 4455, 6187) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 15489) [M::mem_pestat] mean and std.dev: (4025.73, 2707.42) [M::mem_pestat] low and high boundaries for proper pairs: (1, 20140) [M::mem_pestat] skip orientation RR as there are not enough pairs [M::mem_pestat] skip orientation FF [M::mem_pestat] skip orientation RF [main] Version: 0.7.15-r1142-dirty [main] CMD: bwa mem -t 40 -v 1 /home/iovercast/manuscript-analysis/Phocoena_empirical/TurtrunRef/Tursiops_truncatus.turTru1.dna_rm.toplevel.fa /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/refmap-empirical_edits/SRR4291701.trimmed_R1_.fastq.gz /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/refmap-empirical_edits/SRR4291701.trimmed_R2_.fastq.gz [main] Real time: 303.408 sec; CPU: 4020.255 sec [bam_sort_core] merging from 2 files... real 0m0.000s user 0m0.000s sys 0m0.000s [M::mem_pestat] analyzing insert size distribution for orientation FF... [M::mem_pestat] (25, 50, 75) percentile: (604, 1850, 2472) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 6208) [M::mem_pestat] mean and std.dev: (1776.17, 1180.59) [M::mem_pestat] low and high boundaries for proper pairs: (1, 8076) [M::mem_pestat] analyzing insert size distribution for orientation FR... [M::mem_pestat] (25, 50, 75) percentile: (203, 242, 287) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (35, 455) [M::mem_pestat] mean and std.dev: (245.55, 60.92) [M::mem_pestat] low and high boundaries for proper pairs: (1, 539) [M::mem_pestat] analyzing insert size distribution for orientation RF... [M::mem_pestat] (25, 50, 75) percentile: (1531, 2530, 6183) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 15487) [M::mem_pestat] mean and std.dev: (3645.37, 2595.32) [M::mem_pestat] low and high boundaries for proper pairs: (1, 20139) [M::mem_pestat] analyzing insert size distribution for orientation RR... [M::mem_pestat] (25, 50, 75) percentile: (685, 2931, 5098) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 13924) [M::mem_pestat] mean and std.dev: (3050.90, 1983.19) [M::mem_pestat] low and high boundaries for proper pairs: (1, 18337) [M::mem_pestat] skip orientation FF [M::mem_pestat] skip orientation RF [M::mem_pestat] skip orientation RR [M::mem_pestat] analyzing insert size distribution for orientation FF... [M::mem_pestat] (25, 50, 75) percentile: (740, 2182, 2472) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 5936) [M::mem_pestat] mean and std.dev: (2053.10, 1324.06) [M::mem_pestat] low and high boundaries for proper pairs: (1, 7668) [M::mem_pestat] analyzing insert size distribution for orientation FR... [M::mem_pestat] (25, 50, 75) percentile: (203, 242, 287) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (35, 455) [M::mem_pestat] mean and std.dev: (245.70, 60.87) [M::mem_pestat] low and high boundaries for proper pairs: (1, 539) [M::mem_pestat] analyzing insert size distribution for orientation RF... [M::mem_pestat] (25, 50, 75) percentile: (1556, 4515, 6183) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 15437) [M::mem_pestat] mean and std.dev: (4229.70, 2491.04) [M::mem_pestat] low and high boundaries for proper pairs: (1, 20064) [M::mem_pestat] skip orientation RR as there are not enough pairs [M::mem_pestat] skip orientation FF [M::mem_pestat] skip orientation RF [main] Version: 0.7.15-r1142-dirty [main] CMD: bwa mem -t 40 -v 1 /home/iovercast/manuscript-analysis/Phocoena_empirical/TurtrunRef/Tursiops_truncatus.turTru1.dna_rm.toplevel.fa /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/refmap-empirical_edits/SRR4291681.trimmed_R1_.fastq.gz /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/refmap-empirical_edits/SRR4291681.trimmed_R2_.fastq.gz [main] Real time: 280.003 sec; CPU: 3767.582 sec [bam_sort_core] merging from 2 files... real 0m0.000s user 0m0.000s sys 0m0.000s [M::mem_pestat] analyzing insert size distribution for orientation FF... [M::mem_pestat] (25, 50, 75) percentile: (604, 1850, 2418) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 6046) [M::mem_pestat] mean and std.dev: (1658.53, 1230.99) [M::mem_pestat] low and high boundaries for proper pairs: (1, 7860) [M::mem_pestat] analyzing insert size distribution for orientation FR... [M::mem_pestat] (25, 50, 75) percentile: (190, 224, 265) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (40, 415) [M::mem_pestat] mean and std.dev: (228.78, 56.99) [M::mem_pestat] low and high boundaries for proper pairs: (1, 490) [M::mem_pestat] analyzing insert size distribution for orientation RF... [M::mem_pestat] (25, 50, 75) percentile: (1531, 2530, 6183) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 15487) [M::mem_pestat] mean and std.dev: (3686.63, 2574.13) [M::mem_pestat] low and high boundaries for proper pairs: (1, 20139) [M::mem_pestat] analyzing insert size distribution for orientation RR... [M::mem_pestat] (25, 50, 75) percentile: (685, 1465, 4752) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 12886) [M::mem_pestat] mean and std.dev: (2641.26, 2199.88) [M::mem_pestat] low and high boundaries for proper pairs: (1, 16953) [M::mem_pestat] skip orientation FF [M::mem_pestat] skip orientation RF [M::mem_pestat] skip orientation RR [M::mem_pestat] analyzing insert size distribution for orientation FF... [M::mem_pestat] (25, 50, 75) percentile: (740, 1850, 2472) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 5936) [M::mem_pestat] mean and std.dev: (1866.46, 1192.82) [M::mem_pestat] low and high boundaries for proper pairs: (1, 7668) [M::mem_pestat] analyzing insert size distribution for orientation FR... [M::mem_pestat] (25, 50, 75) percentile: (190, 224, 266) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (38, 418) [M::mem_pestat] mean and std.dev: (229.05, 56.99) [M::mem_pestat] low and high boundaries for proper pairs: (1, 494) [M::mem_pestat] analyzing insert size distribution for orientation RF... [M::mem_pestat] (25, 50, 75) percentile: (1516, 2581, 6176) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 15496) [M::mem_pestat] mean and std.dev: (3641.20, 2518.20) [M::mem_pestat] low and high boundaries for proper pairs: (1, 20156) [M::mem_pestat] skip orientation RR as there are not enough pairs [M::mem_pestat] skip orientation FF [M::mem_pestat] skip orientation RF [main] Version: 0.7.15-r1142-dirty [main] CMD: bwa mem -t 40 -v 1 /home/iovercast/manuscript-analysis/Phocoena_empirical/TurtrunRef/Tursiops_truncatus.turTru1.dna_rm.toplevel.fa /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/refmap-empirical_edits/SRR4291682.trimmed_R1_.fastq.gz /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/refmap-empirical_edits/SRR4291682.trimmed_R2_.fastq.gz [main] Real time: 278.177 sec; CPU: 3841.945 sec [bam_sort_core] merging from 2 files... real 0m0.000s user 0m0.000s sys 0m0.000s [M::mem_pestat] analyzing insert size distribution for orientation FF... [M::mem_pestat] (25, 50, 75) percentile: (391, 1784, 2182) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 5764) [M::mem_pestat] mean and std.dev: (1495.00, 1179.37) [M::mem_pestat] low and high boundaries for proper pairs: (1, 7555) [M::mem_pestat] analyzing insert size distribution for orientation FR... [M::mem_pestat] (25, 50, 75) percentile: (192, 228, 270) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (36, 426) [M::mem_pestat] mean and std.dev: (232.69, 58.10) [M::mem_pestat] low and high boundaries for proper pairs: (1, 504) [M::mem_pestat] analyzing insert size distribution for orientation RF... [M::mem_pestat] (25, 50, 75) percentile: (1516, 2581, 6183) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 15517) [M::mem_pestat] mean and std.dev: (3892.85, 2693.16) [M::mem_pestat] low and high boundaries for proper pairs: (1, 20184) [M::mem_pestat] analyzing insert size distribution for orientation RR... [M::mem_pestat] (25, 50, 75) percentile: (685, 4153, 4219) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 11287) [M::mem_pestat] mean and std.dev: (2734.53, 1833.27) [M::mem_pestat] low and high boundaries for proper pairs: (1, 14821) [M::mem_pestat] skip orientation FF [M::mem_pestat] skip orientation RF [M::mem_pestat] skip orientation RR [M::mem_pestat] skip orientation FF as there are not enough pairs [M::mem_pestat] analyzing insert size distribution for orientation FR... [M::mem_pestat] (25, 50, 75) percentile: (191, 226, 269) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (35, 425) [M::mem_pestat] mean and std.dev: (231.42, 57.61) [M::mem_pestat] low and high boundaries for proper pairs: (1, 503) [M::mem_pestat] analyzing insert size distribution for orientation RF... [M::mem_pestat] (25, 50, 75) percentile: (1708, 4468, 6247) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 15325) [M::mem_pestat] mean and std.dev: (4684.27, 2680.30) [M::mem_pestat] low and high boundaries for proper pairs: (1, 19864) [M::mem_pestat] skip orientation RR as there are not enough pairs [M::mem_pestat] skip orientation RF [main] Version: 0.7.15-r1142-dirty [main] CMD: bwa mem -t 40 -v 1 /home/iovercast/manuscript-analysis/Phocoena_empirical/TurtrunRef/Tursiops_truncatus.turTru1.dna_rm.toplevel.fa /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/refmap-empirical_edits/SRR4291678.trimmed_R1_.fastq.gz /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/refmap-empirical_edits/SRR4291678.trimmed_R2_.fastq.gz [main] Real time: 278.955 sec; CPU: 3769.486 sec [bam_sort_core] merging from 2 files... real 0m0.000s user 0m0.000s sys 0m0.000s [M::mem_pestat] analyzing insert size distribution for orientation FF... [M::mem_pestat] (25, 50, 75) percentile: (740, 1850, 2472) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 5936) [M::mem_pestat] mean and std.dev: (1827.28, 1175.99) [M::mem_pestat] low and high boundaries for proper pairs: (1, 7668) [M::mem_pestat] analyzing insert size distribution for orientation FR... [M::mem_pestat] (25, 50, 75) percentile: (195, 232, 276) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (33, 438) [M::mem_pestat] mean and std.dev: (236.03, 59.48) [M::mem_pestat] low and high boundaries for proper pairs: (1, 519) [M::mem_pestat] analyzing insert size distribution for orientation RF... [M::mem_pestat] (25, 50, 75) percentile: (1514, 2678, 6183) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 15521) [M::mem_pestat] mean and std.dev: (3759.94, 2525.84) [M::mem_pestat] low and high boundaries for proper pairs: (1, 20190) [M::mem_pestat] analyzing insert size distribution for orientation RR... [M::mem_pestat] (25, 50, 75) percentile: (966, 2318, 4219) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 10725) [M::mem_pestat] mean and std.dev: (2703.32, 1997.38) [M::mem_pestat] low and high boundaries for proper pairs: (1, 13978) [M::mem_pestat] skip orientation FF [M::mem_pestat] skip orientation RF [M::mem_pestat] skip orientation RR [M::mem_pestat] analyzing insert size distribution for orientation FF... [M::mem_pestat] (25, 50, 75) percentile: (969, 2182, 2182) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 4608) [M::mem_pestat] mean and std.dev: (2038.79, 1330.03) [M::mem_pestat] low and high boundaries for proper pairs: (1, 7359) [M::mem_pestat] analyzing insert size distribution for orientation FR... [M::mem_pestat] (25, 50, 75) percentile: (193, 231, 274) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (31, 436) [M::mem_pestat] mean and std.dev: (234.46, 59.58) [M::mem_pestat] low and high boundaries for proper pairs: (1, 517) [M::mem_pestat] analyzing insert size distribution for orientation RF... [M::mem_pestat] (25, 50, 75) percentile: (1516, 4468, 6187) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 15529) [M::mem_pestat] mean and std.dev: (4027.00, 2526.71) [M::mem_pestat] low and high boundaries for proper pairs: (1, 20200) [M::mem_pestat] skip orientation RR as there are not enough pairs [M::mem_pestat] skip orientation FF [M::mem_pestat] skip orientation RF [main] Version: 0.7.15-r1142-dirty [main] CMD: bwa mem -t 40 -v 1 /home/iovercast/manuscript-analysis/Phocoena_empirical/TurtrunRef/Tursiops_truncatus.turTru1.dna_rm.toplevel.fa /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/refmap-empirical_edits/SRR4291686.trimmed_R1_.fastq.gz /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/refmap-empirical_edits/SRR4291686.trimmed_R2_.fastq.gz [main] Real time: 263.595 sec; CPU: 3605.401 sec [bam_sort_core] merging from 2 files... real 0m0.000s user 0m0.000s sys 0m0.000s [M::mem_pestat] analyzing insert size distribution for orientation FF... [M::mem_pestat] (25, 50, 75) percentile: (670, 1850, 2418) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 5914) [M::mem_pestat] mean and std.dev: (1741.52, 1201.23) [M::mem_pestat] low and high boundaries for proper pairs: (1, 7662) [M::mem_pestat] analyzing insert size distribution for orientation FR... [M::mem_pestat] (25, 50, 75) percentile: (207, 250, 299) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (23, 483) [M::mem_pestat] mean and std.dev: (253.12, 63.72) [M::mem_pestat] low and high boundaries for proper pairs: (1, 575) [M::mem_pestat] analyzing insert size distribution for orientation RF... [M::mem_pestat] (25, 50, 75) percentile: (1513, 1777, 6173) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 15493) [M::mem_pestat] mean and std.dev: (3434.44, 2553.81) [M::mem_pestat] low and high boundaries for proper pairs: (1, 20153) [M::mem_pestat] analyzing insert size distribution for orientation RR... [M::mem_pestat] (25, 50, 75) percentile: (685, 2931, 4219) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 11287) [M::mem_pestat] mean and std.dev: (3135.72, 2399.62) [M::mem_pestat] low and high boundaries for proper pairs: (1, 14821) [M::mem_pestat] skip orientation FF [M::mem_pestat] skip orientation RF [M::mem_pestat] skip orientation RR [M::mem_pestat] analyzing insert size distribution for orientation FF... [M::mem_pestat] (25, 50, 75) percentile: (785, 1850, 2418) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 5684) [M::mem_pestat] mean and std.dev: (1647.73, 822.81) [M::mem_pestat] low and high boundaries for proper pairs: (1, 7317) [M::mem_pestat] analyzing insert size distribution for orientation FR... [M::mem_pestat] (25, 50, 75) percentile: (205, 247, 296) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (23, 478) [M::mem_pestat] mean and std.dev: (250.71, 63.42) [M::mem_pestat] low and high boundaries for proper pairs: (1, 569) [M::mem_pestat] analyzing insert size distribution for orientation RF... [M::mem_pestat] (25, 50, 75) percentile: (1536, 1777, 6219) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 15585) [M::mem_pestat] mean and std.dev: (3547.52, 2557.66) [M::mem_pestat] low and high boundaries for proper pairs: (1, 20268) [M::mem_pestat] skip orientation RR as there are not enough pairs [M::mem_pestat] skip orientation FF [M::mem_pestat] skip orientation RF [main] Version: 0.7.15-r1142-dirty [main] CMD: bwa mem -t 40 -v 1 /home/iovercast/manuscript-analysis/Phocoena_empirical/TurtrunRef/Tursiops_truncatus.turTru1.dna_rm.toplevel.fa /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/refmap-empirical_edits/SRR4291689.trimmed_R1_.fastq.gz /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/refmap-empirical_edits/SRR4291689.trimmed_R2_.fastq.gz [main] Real time: 270.172 sec; CPU: 3508.493 sec [bam_sort_core] merging from 2 files... real 0m0.000s user 0m0.000s sys 0m0.000s [M::mem_pestat] analyzing insert size distribution for orientation FF... [M::mem_pestat] (25, 50, 75) percentile: (604, 1850, 2418) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 6046) [M::mem_pestat] mean and std.dev: (1720.91, 1142.69) [M::mem_pestat] low and high boundaries for proper pairs: (1, 7860) [M::mem_pestat] analyzing insert size distribution for orientation FR... [M::mem_pestat] (25, 50, 75) percentile: (190, 228, 272) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (26, 436) [M::mem_pestat] mean and std.dev: (231.34, 61.11) [M::mem_pestat] low and high boundaries for proper pairs: (1, 518) [M::mem_pestat] analyzing insert size distribution for orientation RF... [M::mem_pestat] (25, 50, 75) percentile: (1539, 2581, 6176) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 15450) [M::mem_pestat] mean and std.dev: (3768.03, 2522.60) [M::mem_pestat] low and high boundaries for proper pairs: (1, 20087) [M::mem_pestat] analyzing insert size distribution for orientation RR... [M::mem_pestat] (25, 50, 75) percentile: (685, 2931, 4436) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 11938) [M::mem_pestat] mean and std.dev: (3005.27, 2207.49) [M::mem_pestat] low and high boundaries for proper pairs: (1, 15689) [M::mem_pestat] skip orientation FF [M::mem_pestat] skip orientation RF [M::mem_pestat] skip orientation RR [main] Version: 0.7.15-r1142-dirty [main] CMD: bwa mem -t 40 -v 1 /home/iovercast/manuscript-analysis/Phocoena_empirical/TurtrunRef/Tursiops_truncatus.turTru1.dna_rm.toplevel.fa /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/refmap-empirical_edits/SRR4291683.trimmed_R1_.fastq.gz /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/refmap-empirical_edits/SRR4291683.trimmed_R2_.fastq.gz [main] Real time: 199.643 sec; CPU: 2349.067 sec [bam_sort_core] merging from 2 files... real 0m0.000s user 0m0.000s sys 0m0.000s [M::mem_pestat] analyzing insert size distribution for orientation FF... [M::mem_pestat] (25, 50, 75) percentile: (604, 1784, 2297) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 5683) [M::mem_pestat] mean and std.dev: (1676.03, 1147.20) [M::mem_pestat] low and high boundaries for proper pairs: (1, 7376) [M::mem_pestat] analyzing insert size distribution for orientation FR... [M::mem_pestat] (25, 50, 75) percentile: (192, 227, 270) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (36, 426) [M::mem_pestat] mean and std.dev: (232.32, 57.67) [M::mem_pestat] low and high boundaries for proper pairs: (1, 504) [M::mem_pestat] analyzing insert size distribution for orientation RF... [M::mem_pestat] (25, 50, 75) percentile: (1531, 2530, 6183) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 15487) [M::mem_pestat] mean and std.dev: (3638.20, 2588.46) [M::mem_pestat] low and high boundaries for proper pairs: (1, 20139) [M::mem_pestat] analyzing insert size distribution for orientation RR... [M::mem_pestat] (25, 50, 75) percentile: (685, 2931, 5098) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 13924) [M::mem_pestat] mean and std.dev: (2756.81, 2182.51) [M::mem_pestat] low and high boundaries for proper pairs: (1, 18337) [M::mem_pestat] skip orientation FF [M::mem_pestat] skip orientation RF [M::mem_pestat] skip orientation RR [M::mem_pestat] analyzing insert size distribution for orientation FF... [M::mem_pestat] (25, 50, 75) percentile: (357, 969, 2182) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 5832) [M::mem_pestat] mean and std.dev: (1471.56, 1306.32) [M::mem_pestat] low and high boundaries for proper pairs: (1, 7657) [M::mem_pestat] analyzing insert size distribution for orientation FR... [M::mem_pestat] (25, 50, 75) percentile: (192, 227, 270) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (36, 426) [M::mem_pestat] mean and std.dev: (232.40, 57.61) [M::mem_pestat] low and high boundaries for proper pairs: (1, 504) [M::mem_pestat] analyzing insert size distribution for orientation RF... [M::mem_pestat] (25, 50, 75) percentile: (1495, 2581, 6173) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 15529) [M::mem_pestat] mean and std.dev: (3508.49, 2429.33) [M::mem_pestat] low and high boundaries for proper pairs: (1, 20207) [M::mem_pestat] analyzing insert size distribution for orientation RR... [M::mem_pestat] (25, 50, 75) percentile: (4219, 5098, 5098) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (2461, 6856) [M::mem_pestat] mean and std.dev: (4801.57, 460.97) [M::mem_pestat] low and high boundaries for proper pairs: (1582, 7735) [M::mem_pestat] skip orientation FF [M::mem_pestat] skip orientation RF [M::mem_pestat] skip orientation RR [main] Version: 0.7.15-r1142-dirty [main] CMD: bwa mem -t 40 -v 1 /home/iovercast/manuscript-analysis/Phocoena_empirical/TurtrunRef/Tursiops_truncatus.turTru1.dna_rm.toplevel.fa /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/refmap-empirical_edits/SRR4291705.trimmed_R1_.fastq.gz /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/refmap-empirical_edits/SRR4291705.trimmed_R2_.fastq.gz [main] Real time: 268.112 sec; CPU: 3609.621 sec [bam_sort_core] merging from 2 files... real 0m0.000s user 0m0.000s sys 0m0.000s [M::mem_pestat] analyzing insert size distribution for orientation FF... [M::mem_pestat] (25, 50, 75) percentile: (604, 1784, 2418) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 6046) [M::mem_pestat] mean and std.dev: (1640.13, 1243.11) [M::mem_pestat] low and high boundaries for proper pairs: (1, 7860) [M::mem_pestat] analyzing insert size distribution for orientation FR... [M::mem_pestat] (25, 50, 75) percentile: (181, 211, 247) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (49, 379) [M::mem_pestat] mean and std.dev: (214.95, 53.03) [M::mem_pestat] low and high boundaries for proper pairs: (1, 445) [M::mem_pestat] analyzing insert size distribution for orientation RF... [M::mem_pestat] (25, 50, 75) percentile: (1556, 4410, 6183) [M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 15437) [M::mem_pestat] mean and std.dev: (3951.14, 2572.36) [M::mem_pestat] low and high boundaries for proper pairs: (1, 20064) [M::mem_pestat] skip orientation RR as there are not enough pairs [M::mem_pestat] skip orientation FF [M::mem_pestat] skip orientation RF [main] Version: 0.7.15-r1142-dirty [main] CMD: bwa mem -t 40 -v 1 /home/iovercast/manuscript-analysis/Phocoena_empirical/TurtrunRef/Tursiops_truncatus.turTru1.dna_rm.toplevel.fa /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/refmap-empirical_edits/SRR4291688.trimmed_R1_.fastq.gz /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/refmap-empirical_edits/SRR4291688.trimmed_R2_.fastq.gz [main] Real time: 142.756 sec; CPU: 1975.479 sec real 0m0.000s user 0m0.000s sys 0m0.000s
Install ngsutils and use it to remove soft-clipped sequences from each read. We could do this
by rerunning bwa with the -H
flag, but that would be slightly more annoying. This is not
guaranteed to 'work' because i had a hell of a time getting ngsutils to install on my system.
%%bash -s "$REFMAP_EMPIRICAL_DIR"
cd $1/stacks
git clone https://github.com/ngsutils/ngsutils.git
cd ngsutils
make
This will apply the removeclipping
method of bamutils to each mapped bam file in the stacks refmap directory. Then it deletes the old bam file and moves the new bam file to have the name of the old one. On a nice system this takes ~2 minutes per sample (so ~2 hours total).
infiles = glob.glob(STACKS_REFMAP_DIR + "SRR*.bam")
for f in infiles:
outfile = f + ".tmp"
print(f, outfile)
subprocess.call("bamutils removeclipping {} {}".format(f, outfile), shell=True)
subprocess.call("rm {}".format(f), shell=True)
subprocess.call("mv {} {}".format(outfile, f), shell=True)
('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291679.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291679.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291702.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291702.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291701.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291701.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291681.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291681.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291682.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291682.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291678.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291678.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291686.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291686.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291705.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291705.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291688.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291688.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291674.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291674.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291673.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291673.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291684.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291684.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291677.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291677.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291675.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291675.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291703.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291703.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291672.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291672.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291687.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291687.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291696.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291696.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291699.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291699.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291668.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291668.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291698.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291698.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291690.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291690.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291692.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291692.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291666.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291666.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291664.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291664.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291689.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291689.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291683.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291683.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291680.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291680.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291685.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291685.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291700.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291700.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291671.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291671.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291693.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291693.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291704.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291704.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291676.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291676.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291695.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291695.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291697.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291697.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291691.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291691.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291662.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291662.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291663.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291663.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291669.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291669.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291670.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291670.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291694.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291694.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291667.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291667.bam.tmp') ('/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291665.bam', '/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291665.bam.tmp')
We build the stacks command w/ python because then we call the command inside a bash cell because denovo_map expects all the submodules to be in the PATH. The default command line created will include the -d
flag to do the dry run, so the bash cell won't actually do anything real. But it does create a stacks.sh
file in the stacks directory that includes the command to run.
## This is how we'd do it if we weren't using a popmap file
#infiles = ["-s "+ff+" " for ff in glob.glob(IPYRAD_REFMAP_DIR+"*-mapped-sorted.bam")]
## Toggle the dryrun flag for testing
DRYRUN=""
DRYRUN="-d"
## Options
## -T The number of threads to use
## -O The popmap file specifying individuals and populations
## -S Disable database business
## -o Output directory. Just write to the empirical stacks directory
## -X The first -X tells populations to create the output formats sepcified
## -X The second one passes `-m 6` which sets min depth per locus
OUTPUT_FORMATS = "--vcf --genepop --structure --phylip "
cmd = "ref_map.pl -T 40 -b 1 -S " + DRYRUN\
+ " -O {}/popmap.txt".format(STACKS_REFMAP_DIR)\
+ " --samples {}".format(STACKS_REFMAP_DIR)\
+ " -X \'populations:" + OUTPUT_FORMATS + "\'"\
+ " -X \'populations:-m 6\'"\
+ " -o " + STACKS_REFMAP_DIR
print("\nCommand to run - {}".format(cmd))
Command to run - ref_map.pl -T 40 -b 1 -S -d -O /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks//popmap.txt --samples /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/ -X 'populations:--vcf --genepop --structure --phylip ' -X 'populations:-m 6' -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/
%%bash -s "$WORK_DIR" "$STACKS_REFMAP_DIR" "$cmd"
export PATH="$1/miniconda/bin:$PATH"; export "STACKS_REFMAP_DIR=$2"; export "cmd=$3"
## We have to play a little cat and mouse game here because of quoting in some of the args
## and how weird bash is we have to write the cmd to a file and then exec it.
## If you try to just run $cmd it truncates the command at the first single tic. Hassle.
cd $STACKS_REFMAP_DIR
echo $cmd > stacks.sh; chmod 777 stacks.sh
time ./stacks.sh
Identifying unique stacks; file 1 of 44 [SRR4291704] Identifying unique stacks; file 2 of 44 [SRR4291705] Identifying unique stacks; file 3 of 44 [SRR4291700] Identifying unique stacks; file 4 of 44 [SRR4291701] Identifying unique stacks; file 5 of 44 [SRR4291702] Identifying unique stacks; file 6 of 44 [SRR4291703] Identifying unique stacks; file 7 of 44 [SRR4291685] Identifying unique stacks; file 8 of 44 [SRR4291684] Identifying unique stacks; file 9 of 44 [SRR4291687] Identifying unique stacks; file 10 of 44 [SRR4291686] Identifying unique stacks; file 11 of 44 [SRR4291681] Identifying unique stacks; file 12 of 44 [SRR4291680] Identifying unique stacks; file 13 of 44 [SRR4291683] Identifying unique stacks; file 14 of 44 [SRR4291682] Identifying unique stacks; file 15 of 44 [SRR4291689] Identifying unique stacks; file 16 of 44 [SRR4291688] Identifying unique stacks; file 17 of 44 [SRR4291674] Identifying unique stacks; file 18 of 44 [SRR4291675] Identifying unique stacks; file 19 of 44 [SRR4291676] Identifying unique stacks; file 20 of 44 [SRR4291677] Identifying unique stacks; file 21 of 44 [SRR4291670] Identifying unique stacks; file 22 of 44 [SRR4291671] Identifying unique stacks; file 23 of 44 [SRR4291672] Identifying unique stacks; file 24 of 44 [SRR4291673] Identifying unique stacks; file 25 of 44 [SRR4291678] Identifying unique stacks; file 26 of 44 [SRR4291679] Identifying unique stacks; file 27 of 44 [SRR4291696] Identifying unique stacks; file 28 of 44 [SRR4291697] Identifying unique stacks; file 29 of 44 [SRR4291694] Identifying unique stacks; file 30 of 44 [SRR4291695] Identifying unique stacks; file 31 of 44 [SRR4291692] Identifying unique stacks; file 32 of 44 [SRR4291693] Identifying unique stacks; file 33 of 44 [SRR4291690] Identifying unique stacks; file 34 of 44 [SRR4291691] Identifying unique stacks; file 35 of 44 [SRR4291698] Identifying unique stacks; file 36 of 44 [SRR4291699] Identifying unique stacks; file 37 of 44 [SRR4291669] Identifying unique stacks; file 38 of 44 [SRR4291668] Identifying unique stacks; file 39 of 44 [SRR4291663] Identifying unique stacks; file 40 of 44 [SRR4291662] Identifying unique stacks; file 41 of 44 [SRR4291667] Identifying unique stacks; file 42 of 44 [SRR4291666] Identifying unique stacks; file 43 of 44 [SRR4291665] Identifying unique stacks; file 44 of 44 [SRR4291664]
Parsed population map: 44 files in 7 populations and 1 group. Found 44 sample file(s). /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291704.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 1 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291705.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 2 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291700.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 3 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291701.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 4 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291702.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 5 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291703.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 6 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291685.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 7 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291684.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 8 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291687.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 9 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291686.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 10 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291681.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 11 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291680.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 12 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291683.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 13 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291682.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 14 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291689.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 15 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291688.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 16 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291674.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 17 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291675.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 18 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291676.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 19 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291677.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 20 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291670.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 21 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291671.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 22 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291672.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 23 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291673.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 24 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291678.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 25 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291679.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 26 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291696.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 27 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291697.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 28 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291694.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 29 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291695.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 30 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291692.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 31 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291693.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 32 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291690.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 33 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291691.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 34 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291698.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 35 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291699.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 36 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291669.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 37 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291668.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 38 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291663.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 39 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291662.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 40 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291667.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 41 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291666.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 42 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291665.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 43 -p 40 2>&1 /home/iovercast/manuscript-analysis/miniconda/bin/pstacks -t bam -f /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291664.bam -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -i 44 -p 40 2>&1 Depths of Coverage for Processed Samples: Generating catalog... /home/iovercast/manuscript-analysis/miniconda/bin/cstacks -g -b 1 -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291704 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291705 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291700 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291701 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291702 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291703 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291685 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291684 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291687 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291686 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291681 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291680 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291683 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291682 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291689 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291688 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291674 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291675 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291676 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291677 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291670 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291671 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291672 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291673 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291678 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291679 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291696 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291697 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291694 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291695 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291692 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291693 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291690 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291691 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291698 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291699 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291669 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291668 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291663 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291662 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291667 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291666 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291665 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291664 -p 40 2>&1 Matching samples to the catalog... /home/iovercast/manuscript-analysis/miniconda/bin/sstacks -g -b 1 -c /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/batch_1 -o /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291704 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291705 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291700 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291701 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291702 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291703 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291685 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291684 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291687 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291686 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291681 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291680 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291683 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291682 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291689 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291688 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291674 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291675 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291676 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291677 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291670 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291671 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291672 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291673 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291678 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291679 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291696 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291697 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291694 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291695 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291692 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291693 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291690 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291691 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291698 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291699 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291669 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291668 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291663 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291662 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291667 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291666 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291665 -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/SRR4291664 -p 40 2>&1 Calculating population-level summary statistics /home/iovercast/manuscript-analysis/miniconda/bin/populations -b 1 -P /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks -s -t 40 -M /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks//popmap.txt --vcf --genepop --structure --phylip -m 6 2>&1 real 0m0.385s user 0m0.031s sys 0m0.012s
dDocent requires a lot of pre-flight housekeeping. You can't use an arbitrary directory for raw files, you have to puth the raw reads in a specific directory. You also can't use arbitrary file names, you have to use specific filenames that dDocent requires. zomg. I wonder if we can trick it with symlinks.
This next cell will do all the housekeeping and then print the nicely formatted command to run. You have to copy this command and run it by hand in a terminal on the machine you want to run it on bcz it doesn't like running inside the notebook.
~1 hour to munge reads and index the reference sequence. ~3hrs to map the reads and build the vcf. Several hours getting the .fq.gz files to have a naming format that dDocent agreed with.
## A housekeeping function for getting a dictionary to map SRR* filenames in the ipyrad edits directory
## to ddocent style.
##
## Gotcha: Nice 1-based indexing for the dDocent format.
##
## For raw reads the format (for R1) is pop1_sample1.F.fq.gz format a la:
## 1A_0_R1_.fastq.gz -> Pop1_Sample1.F.fq.gz
##
## For trimmed reads the format is pop1_001.R1.fq.gz a la:
## 1A_0_R1_.fastq.gz -> Pop1_001.R1.fq.gz
## So annoying because we have to translate across a bunch of different mappings. ugh.
def get_ddocent_filename_mapping():
mapping_dict = {}
## Maps sample name to population
pop_dict = get_popdict()
pops = set(pop_dict.values())
## For each population go through and add items to the dict per sample
## So we have to map the sample name to the SRR and then make an entry
## mapping SRR file name to ddocent format
for i, pop in enumerate(pops):
## Get a list of all the samples in this population. This is probably a stupid way but it works.
samps = [item[0] for item in pop_dict.items() if item[1] == pop]
for j, samp in enumerate(samps):
mapping_dict[samp] = "Pop{}_{:03d}".format(i+1, j+1)
## For the untrimmed format, if you want dDocent to do the trimming
## mapping_dict[samp] = "Pop{}_Sample{}".format(i, j)
return mapping_dict
print(get_ddocent_filename_mapping())
{'SRR4291704': 'Pop7_001', 'SRR4291705': 'Pop7_002', 'SRR4291700': 'Pop6_001', 'SRR4291701': 'Pop4_001', 'SRR4291702': 'Pop4_002', 'SRR4291703': 'Pop7_003', 'SRR4291685': 'Pop3_001', 'SRR4291684': 'Pop5_001', 'SRR4291687': 'Pop5_002', 'SRR4291686': 'Pop5_003', 'SRR4291681': 'Pop5_004', 'SRR4291680': 'Pop5_005', 'SRR4291683': 'Pop5_006', 'SRR4291682': 'Pop5_007', 'SRR4291689': 'Pop5_008', 'SRR4291688': 'Pop5_009', 'SRR4291674': 'Pop3_002', 'SRR4291675': 'Pop1_001', 'SRR4291676': 'Pop1_002', 'SRR4291677': 'Pop1_003', 'SRR4291670': 'Pop2_001', 'SRR4291671': 'Pop2_002', 'SRR4291672': 'Pop1_004', 'SRR4291673': 'Pop1_005', 'SRR4291678': 'Pop1_006', 'SRR4291679': 'Pop5_010', 'SRR4291696': 'Pop4_003', 'SRR4291697': 'Pop6_002', 'SRR4291694': 'Pop6_003', 'SRR4291695': 'Pop6_004', 'SRR4291692': 'Pop6_005', 'SRR4291693': 'Pop6_006', 'SRR4291690': 'Pop6_007', 'SRR4291691': 'Pop6_008', 'SRR4291698': 'Pop6_009', 'SRR4291699': 'Pop6_010', 'SRR4291669': 'Pop2_003', 'SRR4291668': 'Pop2_004', 'SRR4291663': 'Pop3_003', 'SRR4291662': 'Pop3_004', 'SRR4291667': 'Pop2_005', 'SRR4291666': 'Pop7_004', 'SRR4291665': 'Pop7_005', 'SRR4291664': 'Pop7_006'}
## Set up directory structures. change the force flag if you want to
## blow everything away and restart
# force = True
force = ""
DDOCENT_DIR = "/home/iovercast/manuscript-analysis/dDocent/"
DDOCENT_REFMAP_DIR = os.path.join(REFMAP_EMPIRICAL_DIR, "ddocent/")
if force and os.path.exists(DDOCENT_REFMAP_DIR):
shutil.rmtree(DDOCENT_REFMAP_DIR)
if not os.path.exists(DDOCENT_REFMAP_DIR):
os.makedirs(DDOCENT_REFMAP_DIR)
os.chdir(DDOCENT_REFMAP_DIR)
## Create a simlink to the reference sequence in the current directory
REF_SEQ = REFMAP_EMPIRICAL_DIR + "TurtrunRef/Tursiops_truncatus.turTru1.dna_rm.toplevel.fa"
cmd = "ln -s {} reference.fasta".format(REF_SEQ)
!$cmd
## Now we have to rename all the files in the way dDocent expects them:
## 1A_0_R1_.fastq.gz -> Pop1_Sample1.F.fq.gz
## Make symlinks to the trimmed data files in the ipyrad directory. It _should_ work.
## Trimmed reads in the ipyrad directory are of the format: SRR4291681.trimmed_R1_.fastq.gz
IPYRAD_EDITS_DIR = os.path.join(IPYRAD_REFMAP_DIR, "reference-assembly/refmap-empirical_edits/")
name_mapping = get_ddocent_filename_mapping()
for k,v in name_mapping.items():
## Symlink R1 and R2
for i in ["1", "2"]:
source = os.path.join(IPYRAD_EDITS_DIR, k + ".trimmed_R{}_.fastq.gz".format(i))
##dest = os.path.join(DDOCENT_REFMAP_DIR, v + ".R{}.fq.gz".format(i))
if i == "1":
dest = os.path.join(DDOCENT_REFMAP_DIR, v + ".R1.fq.gz".format(i))
else:
dest = os.path.join(DDOCENT_REFMAP_DIR, v + ".R2.fq.gz".format(i))
cmd = "ln -sf {} {}".format(source, dest)
!$cmd
## Write out the config file for this run.
## Compacted the config file into one long line here to make it not take up so much room
## Trimming = no because we trimmed in ipyrad
## Assembly = no because we are providing a reverence sequence
## Type of Assembly = PE for paired-end
config_file = "{}/empirical-config.txt".format(DDOCENT_REFMAP_DIR)
with open(config_file, 'w') as outfile:
outfile.write('Number of Processors\n40\nMaximum Memory\n0\nTrimming\nno\nAssembly?\nno\nType_of_Assembly\nPE\nClustering_Similarity%\n0.85\nMapping_Reads?\nyes\nMapping_Match_Value\n1\nMapping_MisMatch_Value\n3\nMapping_GapOpen_Penalty\n5\nCalling_SNPs?\nyes\nEmail\nwatdo@mailinator.com\n')
cmd = "export LD_LIBRARY_PATH={}/freebayes-src/vcflib/tabixpp/htslib/:$LD_LIBRARY_PATH; ".format(DDOCENT_DIR)
cmd += "export PATH={}:$PATH; time dDocent {}".format(DDOCENT_DIR, config_file)
print(cmd)
with open("ddocent.sh", 'w') as outfile:
outfile.write("#!/bin/bash\n")
outfile.write(cmd)
!chmod 777 ddocent.sh
## Have to run the printed command by hand from the ddocent REALDATA dir bcz it doesn't like running in the notebook
#!$cmd
## NB: Must rename all the samples in the output vcf and then use vcf-shuffle-cols
## perl script in the vcf/perl directory to reorder the vcf file to match
## the output of stacks and ipyrad for pca/heatmaps to work.
ln: failed to create symbolic link ‘reference.fasta’: File exists export LD_LIBRARY_PATH=/home/iovercast/manuscript-analysis/dDocent//freebayes-src/vcflib/tabixpp/htslib/:$LD_LIBRARY_PATH; export PATH=/home/iovercast/manuscript-analysis/dDocent/:$PATH; time dDocent /home/iovercast/manuscript-analysis/Phocoena_empirical/ddocent//empirical-config.txt
## You have to post-process the vcf files to decompose complex genotypes and remove indels
os.chdir(DDOCENT_REFMAP_DIR)
exports = "export LD_LIBRARY_PATH=/home/iovercast/manuscript-analysis/dDocent//freebayes-src/vcflib/tabixpp/htslib/:$LD_LIBRARY_PATH; export PATH=/home/iovercast/manuscript-analysis/dDocent/:$PATH"
fullvcf = os.path.join(DDOCENT_REFMAP_DIR, "TotalRawSNPs.vcf")
filtvcf = os.path.join(DDOCENT_REFMAP_DIR, "Final.recode.vcf")
for f in [fullvcf, filtvcf]:
print("Finalizing - {}".format(f))
## Rename the samples to make them agree with the ipyrad/stacks names so
## the results analysis will work.
vcffile = f
infile = open(vcffile,'r')
filedata = infile.readlines()
infile.close()
outfile = open(vcffile,'w')
for line in filedata:
if "CHROM" in line:
for ipname, ddname in name_mapping.items():
line = line.replace(ddname, ipname)
outfile.write(line)
outfile.close()
## Rename columns to match ipyrad and then resort columns to be in same order
IPYRAD_VCF = os.path.join(IPYRAD_REFMAP_DIR, "refmap-empirical_outfiles/refmap-empirical.vcf")
os.chdir(os.path.join(DDOCENT_DIR, "vcftools_0.1.11/perl"))
tmpvcf = os.path.join(DDOCENT_REFMAP_DIR, "ddocent-tmp.vcf")
cmd = "perl vcf-shuffle-cols -t {} {} > {}".format(IPYRAD_VCF, vcffile, tmpvcf)
print(cmd)
#!$cmd
os.chdir(DDOCENT_REFMAP_DIR)
## Naming the new outfiles as <curname>.snps.vcf
## Decompose complex genotypes and remove indels
outfile = os.path.join(DDOCENT_REFMAP_DIR, f.split("/")[-1].split(".vcf")[0] + ".snps.vcf")
cmd = "{}; vcfallelicprimitives {} > ddoc-tmp.vcf".format(exports, f)
print(cmd)
!$cmd
cmd = "{}; vcftools --vcf ddoc-tmp.vcf --remove-indels --recode --recode-INFO-all --out {}".format(exports, outfile)
print(cmd)
!$cmd
!rm ddoc-tmp.vcf
Finalizing - /home/iovercast/manuscript-analysis/Phocoena_empirical/ddocent/TotalRawSNPs.vcf perl vcf-shuffle-cols -t /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/refmap-empirical_outfiles/refmap-empirical.vcf /home/iovercast/manuscript-analysis/Phocoena_empirical/ddocent/TotalRawSNPs.vcf > /home/iovercast/manuscript-analysis/Phocoena_empirical/ddocent/ddocent-tmp.vcf export LD_LIBRARY_PATH=/home/iovercast/manuscript-analysis/dDocent//freebayes-src/vcflib/tabixpp/htslib/:$LD_LIBRARY_PATH; export PATH=/home/iovercast/manuscript-analysis/dDocent/:$PATH; vcfallelicprimitives /home/iovercast/manuscript-analysis/Phocoena_empirical/ddocent/TotalRawSNPs.vcf > ddoc-tmp.vcf
This should be at the top, but i'm leaving it here so it's out of the way. Just some housekeeping for translating between SRA sequence file name and sample name from the paper. This is only for the benefit of stacks populations script.
The files that come down from SRA are not helpfully named. We have to create a mapping between sample names from the paper and SRR numbers.
Get the RunInfo table from here: https://trace.ncbi.nlm.nih.gov/Traces/study/?acc=SRP090334
def get_sampsdict():
info_header = "BioSample_s Experiment_s Library_Name_s MBases_l MBytes_l Run_s SRA_Sample_s Sample_Name_s dev_stage_s ecotype_s lat_lon_s sex_s tissue_s Assay_Type_s AssemblyName_s BioProject_s BioSampleModel_s Center_Name_s Consent_s InsertSize_l LibraryLayout_s LibrarySelection_s LibrarySource_s LoadDate_s Organism_s Platform_s ReleaseDate_s SRA_Study_s g1k_analysis_group_s g1k_pop_code_s source_s"
info = """SAMN05806468 SRX2187156 Pp01 595 395 SRR4291662 SRS1709994 Pp01 <not provided> relicta 44.09 N 29.81 E female muscle OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806469 SRX2187157 Pp02 478 318 SRR4291663 SRS1709995 Pp02 <not provided> relicta 41.42 N 28.92 E female muscle OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806478 SRX2187158 Pp11 242 162 SRR4291664 SRS1709996 Pp11 adult phocoena 54.96 N 8.32 E female skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806479 SRX2187159 Pp12 261 174 SRR4291665 SRS1709997 Pp12 adult phocoena 54.95 N 8.32 E female skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806480 SRX2187160 Pp13 595 397 SRR4291666 SRS1709998 Pp13 juvenile phocoena 54.16 N 8.82 E male skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806481 SRX2187161 Pp14 769 511 SRR4291667 SRS1709999 Pp14 <not provided> phocoena 57.00 N 11.00 E female skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806482 SRX2187162 Pp15 624 414 SRR4291668 SRS1710000 Pp15 <not provided> phocoena 56.89 N 12.50 E male skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806483 SRX2187163 Pp16 665 446 SRR4291669 SRS1710001 Pp16 <not provided> phocoena 57.37 N 9.68 E female skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806484 SRX2187164 Pp17 264 177 SRR4291670 SRS1710002 Pp17 <not provided> phocoena 57.59 N 10.10 E female skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806485 SRX2187165 Pp18 684 453 SRR4291671 SRS1710003 Pp18 <not provided> phocoena 58.93 N 11.15 E female skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806486 SRX2187166 Pp19 601 398 SRR4291672 SRS1710004 Pp19 <not provided> phocoena 55.43 N 107.0 E female skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806487 SRX2187167 Pp20 392 261 SRR4291673 SRS1710005 Pp20 <not provided> phocoena 55.97 N 11.18 E female skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806470 SRX2187168 Pp03 471 316 SRR4291674 SRS1710006 Pp03 <not provided> relicta 41.48 N 28.31 E female muscle OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806488 SRX2187169 Pp21 592 397 SRR4291675 SRS1710007 Pp21 <not provided> phocoena 55.43 N 10.70 E female skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806489 SRX2187170 Pp22 446 300 SRR4291676 SRS1710008 Pp22 <not provided> phocoena 56.25 N 12.82 E female skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806490 SRX2187171 Pp23 617 409 SRR4291677 SRS1710009 Pp23 <not provided> phocoena 56.65 N 12.85 E female skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806491 SRX2187172 Pp24 554 367 SRR4291678 SRS1710010 Pp24 <not provided> phocoena 56.00 N 12.00 E female skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806492 SRX2187173 Pp25 753 500 SRR4291679 SRS1710011 Pp25 juvenile phocoena 55.00 N 10.23 E female skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806493 SRX2187174 Pp26 530 353 SRR4291680 SRS1710012 Pp26 <not provided> phocoena 54.38 N 10.99 E female skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806494 SRX2187175 Pp27 639 426 SRR4291681 SRS1710013 Pp27 juvenile phocoena 54.83 N 9.62 E female skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806495 SRX2187176 Pp28 646 430 SRR4291682 SRS1710014 Pp28 juvenile phocoena 54.59 N 10.03 E female skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806496 SRX2187177 Pp29 374 247 SRR4291683 SRS1710015 Pp29 juvenile phocoena 54.42 N 11.55 E female skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806497 SRX2187178 Pp30 569 376 SRR4291684 SRS1710016 Pp30 juvenile phocoena 54.53 N 11.12 E female skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806471 SRX2187179 Pp04 451 303 SRR4291685 SRS1710017 Pp04 <not provided> relicta 41.65 N 28.27 E female muscle OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806498 SRX2187180 Pp31 578 384 SRR4291686 SRS1710018 Pp31 adult phocoena 54.53 N 11.11 E female skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806499 SRX2187181 Pp32 586 392 SRR4291687 SRS1710019 Pp32 juvenile phocoena 54.32 N 13.09 E female skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806500 SRX2187182 Pp33 288 189 SRR4291688 SRS1710020 Pp33 juvenile phocoena 54.46 N 12.54 E female skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806501 SRX2187183 Pp34 587 389 SRR4291689 SRS1710021 Pp34 <not provided> phocoena 54.32 N 13.09 E female skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806502 SRX2187184 Pp35 496 330 SRR4291690 SRS1710022 Pp35 <not provided> phocoena 55.00 N 14.00 E female skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806503 SRX2187185 Pp36 1085 720 SRR4291691 SRS1710023 Pp36 juvenile phocoena 56.00 N 15.00 E male skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806504 SRX2187186 Pp37 214 141 SRR4291692 SRS1710024 Pp37 <not provided> phocoena 55.56 N 17.63 E female skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806505 SRX2187187 Pp38 397 263 SRR4291693 SRS1710025 Pp38 <not provided> phocoena 55.50 N 17.00 E female muscle OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806506 SRX2187188 Pp39 670 447 SRR4291694 SRS1710026 Pp39 juvenile phocoena 56.00 N 16.00 E male muscle OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806507 SRX2187189 Pp40 342 226 SRR4291695 SRS1710027 Pp40 <not provided> phocoena 54.73 N 18.58 E female skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806472 SRX2187190 Pp05 611 406 SRR4291696 SRS1710028 Pp05 <not provided> phocoena 64.78 N 13.22 E male skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806508 SRX2187191 Pp41 586 389 SRR4291697 SRS1710029 Pp41 <not provided> phocoena 54.80 N 18.44 E female muscle OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806509 SRX2187192 Pp42 329 219 SRR4291698 SRS1710030 Pp42 <not provided> phocoena 54.67 N 18.59 E male muscle OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806510 SRX2187193 Pp43 517 343 SRR4291699 SRS1710031 Pp43 juvenile phocoena 57.00 N 20.00 E female skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806511 SRX2187194 Pp44 491 326 SRR4291700 SRS1710032 Pp44 adult phocoena 57.01 N 20.00 E male skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806473 SRX2187195 Pp06 632 423 SRR4291701 SRS1710033 Pp06 <not provided> phocoena 64.58 N 13.58 E male skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806474 SRX2187196 Pp07 905 602 SRR4291702 SRS1710034 Pp07 <not provided> phocoena 64.31 N 14.00 E male skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806475 SRX2187197 Pp08 585 390 SRR4291703 SRS1710035 Pp08 adult phocoena 54.70 N 8.33 E female skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806476 SRX2187198 Pp09 590 392 SRR4291704 SRS1710036 Pp09 <not provided> phocoena 54.30 N 8.93 E female skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>
SAMN05806477 SRX2187199 Pp10 625 414 SRR4291705 SRS1710037 Pp10 adult phocoena 55.47 N 8.38 E female skin OTHER <not provided> PRJNA343959 Model organism or animal <not provided> public 0 PAIRED Restriction Digest GENOMIC 2016-09-22 Phocoena phocoena ILLUMINA 2016-09-27 SRP090334 <not provided> <not provided> <not provided>""".split("\n")
samps_dict = {}
for i in info:
line = i.split("\t")
samps_dict[line[2]] = line[5]
return(samps_dict)
Supplementary table S1 (http://journals.plos.org/plosone/article/file?type=supplementary&id=info:doi/10.1371/journal.pone.0162792.s006) contains detailed sample information. We can just extract the sample names and populations they belong to.
def get_popdict():
samps_dict = get_sampsdict()
popmap = \
"""01 WBS
02 WBS
03 WBS
04 WBS
05 IS
06 IS
07 IS
08 NOS
09 NOS
10 NOS
11 NOS
12 NOS
13 NOS
14 SK1
15 SK1
16 SK1
17 SK1
18 SK1
19 KB1
20 KB1
21 KB1
22 KB1
23 KB1
24 KB1
25 BES2
26 BES2
27 BES2
28 BES2
29 BES2
30 BES2
31 BES2
32 BES2
33 BES2
34 BES2
35 IBS
36 IBS
37 IBS
38 IBS
39 IBS
40 IBS
41 IBS
42 IBS
43 IBS
44 IBS""".split("\n")
pop_dict = {}
for i in popmap:
line = i.split("\t")
pop_dict[samps_dict["Pp"+line[0]]] = line[1]
return(pop_dict)
## Adding "-mapped-sorted" to each individual name to avoid having to rename the .bam files created by ipyrad
def make_stacks_popmap(OUTDIR):
pop_dict = get_popdict()
out = os.path.join(OUTDIR, "popmap.txt")
print("Writing popmap file to {}".format(out))
with open(out, 'w') as outfile:
for k,v in pop_dict.items():
outfile.write(k + "\t" + v + "\n")
Oh man. I thought dDocent could use a reference sequence for assembly, but it totally can't. I did all this work
to get the housekeeping in order, but then finally figured out at the last minute while setting the config
that it just can't use an external reference sequence. zomg. Keeping this here cuz it might be useful some day
in the event that dDocent makes this possible. Nvm, it can do reference sequence mapping, it's just not well documented.