Data from "Single-nucleotide polymorphism discovery and panel characterization in the African forest elephant"
Ptychadena
Peromyscus (Munshi-South)
Chinese sea bass (SRP094869)
import ipyrad as ip
import ipyrad.analysis as ipa
import ipyparallel as ipp
import pandas as pd
import toyplot
import glob
import gzip
## Ptychadena (Stephane)
## * http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0190440#sec022
## Peromyscus (Munshi-South)
## Chinese sea bass (SRP094869)
## Population Genomics Reveals Genetic Divergence and Adaptive Differentiation of Chinese Sea Bass (Lateolabrax maculatus)
## * https://link.springer.com/article/10.1007/s10126-017-9786-0#Sec2
%%bash
conda install -c eaton-lab toytree
conda install -c bioconda sra-tools entrez-direct
%%bash
## Fetch the elephant reference genome
mkdir ref
wget ftp://ftp.broadinstitute.org/pub/assemblies/mammals/elephant/loxAfr3/assembly_supers.fasta.gz -o ref/assembly_supers.fasta.gz
%%bash
## Data from "Single-nucleotide polymorphism discovery and panel characterization in the African forest elephant"
## http://onlinelibrary.wiley.com/doi/10.1002/ece3.3854/full?wol1URL=/doi/10.1002/ece3.3854/full®ionCode=US-NY&identityKey=34b23ec9-4666-4c37-8470-8448e64d6167
ipyrad --download SRP126637 raws/
## R1 and R2 were concatenated in the SRA data files so we have to pull them apart.
## This is probably not the most efficient way to do this, but it works.
raws = glob.glob("raws/*")
print(raws)
for r in raws:
name = r.split("/")[1].split("_")[0]
lines = gzip.open(r).readlines()
nlines = len(lines)/2
print("{} {}".format(name, nlines))
with gzip.open("raws/{}_R1_.fastq.gz".format(name), 'wb') as r1:
r1.write("".join(lines[:nlines]))
with gzip.open("raws/{}_R2_.fastq.gz".format(name), 'wb') as r2:
r2.write("".join(lines[nlines:]))
['raws/LOC0310_SRR6371511.fastq.gz', 'raws/LOC0088_SRR6371521.fastq.gz', 'raws/LOC0311_SRR6371510.fastq.gz', 'raws/LOC0051_SRR6371516.fastq.gz', 'raws/LOC0037_SRR6371514.fastq.gz', 'raws/LOC0038_SRR6371513.fastq.gz', 'raws/LOC0040_SRR6371512.fastq.gz', 'raws/LOC0041_SRR6371519.fastq.gz', 'raws/LOC0050_SRR6371517.fastq.gz', 'raws/LOC0309_SRR6371508.fastq.gz', 'raws/LOC0151_SRR6371505.fastq.gz', 'raws/LOC0127_SRR6371502.fastq.gz', 'raws/LOC0279_SRR6371509.fastq.gz', 'raws/LOC0274_SRR6371506.fastq.gz', 'raws/LOC0049_SRR6371518.fastq.gz', 'raws/LOC0035_SRR6371515.fastq.gz', 'raws/LOC0121_SRR6371520.fastq.gz', 'raws/LOC0263_SRR6371507.fastq.gz', 'raws/LOC0122_SRR6371503.fastq.gz'] LOC0310 535328 LOC0088 2532764 LOC0311 2381720 LOC0051 2267296 LOC0037 4639748 LOC0038 4352988 LOC0040 1516120 LOC0041 2733056 LOC0050 3633896 LOC0309 2004280 LOC0151 2005558 LOC0127 482392 LOC0279 5789736 LOC0274 3165976 LOC0049 2942484 LOC0035 1812120 LOC0121 450136 LOC0263 5038456 LOC0122 2266816
Fetching project data...
Run spots mates ScientificName SampleName
0 SRR6371502 241196 0 Loxodonta cyclotis LOC0127
1 SRR6371503 1133408 0 Loxodonta cyclotis LOC0122
2 SRR6371505 1002779 0 Loxodonta cyclotis LOC0151
3 SRR6371506 1582988 0 Loxodonta cyclotis LOC0274
4 SRR6371507 2519228 0 Loxodonta cyclotis LOC0263
5 SRR6371508 1002140 0 Loxodonta cyclotis LOC0309
6 SRR6371509 2894868 0 Loxodonta cyclotis LOC0279
7 SRR6371510 1190860 0 Loxodonta cyclotis LOC0311
8 SRR6371511 267664 0 Loxodonta cyclotis LOC0310
9 SRR6371512 758060 0 Loxodonta cyclotis LOC0040
10 SRR6371513 2176494 0 Loxodonta cyclotis LOC0038
11 SRR6371514 2319874 0 Loxodonta cyclotis LOC0037
12 SRR6371515 906060 0 Loxodonta cyclotis LOC0035
13 SRR6371516 1133648 0 Loxodonta cyclotis LOC0051
14 SRR6371517 1816948 0 Loxodonta cyclotis LOC0050
15 SRR6371518 1471242 0 Loxodonta cyclotis LOC0049
16 SRR6371519 1366528 0 Loxodonta cyclotis LOC0041
17 SRR6371520 225068 0 Loxodonta cyclotis LOC0121
18 SRR6371521 1266382 0 Loxodonta cyclotis LOC0088
df = pd.DataFrame([x.split("\t") for x in """SRR6371521 SAMN08167496 LOC0088 WG011 178 95 SRX3466825 LOC0088_a Lope
SRR6371520 SAMN08167497 LOC0121 WG012 31 16 SRX3466826 LOC0121_a Minkebe
SRR6371519 SAMN08167492 LOC0041 WG005 192 102 SRX3466827 LOC0041_a Waka
SRR6371518 SAMN08167493 LOC0049 WG008 207 110 SRX3466828 LOC0049_a Ivindo
SRR6371517 SAMN08167494 LOC0050 WG009 256 135 SRX3466829 LOC0050_b Ivindo
SRR6371516 SAMN08167495 LOC0051 WG010 160 85 SRX3466830 LOC0051_a Ivindo
SRR6371515 SAMN08167488 LOC0035 WG001 127 69 SRX3466831 LOC0035_a Minkebe
SRR6371514 SAMN08167489 LOC0037 WG002 327 175 SRX3466832 LOC0037_a Lope
SRR6371513 SAMN08167490 LOC0038 WG003 307 164 SRX3466833 LOC0038_a Lope
SRR6371512 SAMN08167491 LOC0040 WG004 106 56 SRX3466834 LOC0040_a Wonga Wongue
SRR6371511 SAMN08167506 LOC0310 WG023 37 20 SRX3466835 LOC0310_a Moukalaba Doudou
SRR6371510 SAMN08167507 LOC0311 WG024 168 89 SRX3466836 LOC0311_a Monts de Cristal
SRR6371509 SAMN08167504 LOC0279 WG020 408 215 SRX3466837 LOC0279_b South Mulundu
SRR6371508 SAMN08167505 LOC0309 WG022 141 75 SRX3466838 LOC0309_a Mayumba
SRR6371507 SAMN08167502 LOC0263 WG018 355 188 SRX3466839 LOC0263_a Wonga Wongue
SRR6371506 SAMN08167503 LOC0274 WG019 223 117 SRX3466840 LOC0274_a Loango
SRR6371505 SAMN08167500 LOC0151 WG015 141 71 SRX3466841 LOC0151_a Moukalaba Doudou
SRR6371503 SAMN08167498 LOC0122 WG013 159 85 SRX3466843 LOC0122_a Minkebe
SRR6371502 SAMN08167499 LOC0127 WG014 34 18 SRX3466844 LOC0127_a Moukalaba Doudou""".split("\n")])
## Just get the sequence identifier, the sample name, and the sample location
df = df.iloc[:, [0,7,8]]
0 7 8 0 SRR6371521 LOC0088_a Lope 1 SRR6371520 LOC0121_a Minkebe 2 SRR6371519 LOC0041_a Waka 3 SRR6371518 LOC0049_a Ivindo 4 SRR6371517 LOC0050_b Ivindo 5 SRR6371516 LOC0051_a Ivindo 6 SRR6371515 LOC0035_a Minkebe 7 SRR6371514 LOC0037_a Lope 8 SRR6371513 LOC0038_a Lope 9 SRR6371512 LOC0040_a Wonga Wongue 10 SRR6371511 LOC0310_a Moukalaba Doudou 11 SRR6371510 LOC0311_a Monts de Cristal 12 SRR6371509 LOC0279_b South Mulundu 13 SRR6371508 LOC0309_a Mayumba 14 SRR6371507 LOC0263_a Wonga Wongue 15 SRR6371506 LOC0274_a Loango 16 SRR6371505 LOC0151_a Moukalaba Doudou 17 SRR6371503 LOC0122_a Minkebe 18 SRR6371502 LOC0127_a Moukalaba Doudou
Launch the cluster by running this command on the command line:
ipcluster start --n=19 --daemonize
## connect to cluster and check if all the engines are running
ipyclient = ipp.Client()
ipyclient.ids
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]
data = ip.Assembly("Loxodonta")
New Assembly: Loxodonta
## set parameters
data.set_params("project_dir", "ddrad-denovo")
data.set_params("sorted_fastq_path", "raws/*_.fastq")
data.set_params("datatype", "pairddrad")
data.set_params("restriction_overhang", ("TGCAG", "CATGC"))
data.set_params("clust_threshold", "0.90")
data.set_params("filter_adapters", "2")
data.set_params("max_Hs_consens", (5, 5))
data.set_params("trim_loci", (0, 0, 0, 0))
data.set_params("output_formats", "*")
## see/print all parameters
data.get_params()
data.write_params(force=True)
0 assembly_name Loxodonta 1 project_dir ./ddrad-denovo 2 raw_fastq_path 3 barcodes_path 4 sorted_fastq_path ./raws/*_.fastq 5 assembly_method denovo 6 reference_sequence 7 datatype pairddrad 8 restriction_overhang ('TGCAG', 'CATGC') 9 max_low_qual_bases 5 10 phred_Qscore_offset 33 11 mindepth_statistical 6 12 mindepth_majrule 6 13 maxdepth 10000 14 clust_threshold 0.9 15 max_barcode_mismatch 0 16 filter_adapters 2 17 filter_min_trim_len 35 18 max_alleles_consens 2 19 max_Ns_consens (5, 5) 20 max_Hs_consens (5, 5) 21 min_samples_locus 4 22 max_SNPs_locus (20, 20) 23 max_Indels_locus (8, 8) 24 max_shared_Hs_locus 0.5 25 trim_reads (0, 0, 0, 0) 26 trim_loci (0, 0, 0, 0) 27 output_formats ['G', 'a', 'g', 'k', 'm', 'l', 'n', 'p', 's', 'u', 't', 'v'] 28 pop_assign_file
data.run("12")
Assembly: Loxodonta Skipping: 19 Samples already found in Assembly Loxodonta. (can overwrite with force argument) [####################] 100% processing reads | 0:00:02 | s2 | found an error in step2; see ipyrad_log.txt found an error in step2; see ipyrad_log.txt found an error in step2; see ipyrad_log.txt found an error in step2; see ipyrad_log.txt found an error in step2; see ipyrad_log.txt found an error in step2; see ipyrad_log.txt found an error in step2; see ipyrad_log.txt found an error in step2; see ipyrad_log.txt found an error in step2; see ipyrad_log.txt found an error in step2; see ipyrad_log.txt found an error in step2; see ipyrad_log.txt found an error in step2; see ipyrad_log.txt found an error in step2; see ipyrad_log.txt found an error in step2; see ipyrad_log.txt found an error in step2; see ipyrad_log.txt found an error in step2; see ipyrad_log.txt found an error in step2; see ipyrad_log.txt found an error in step2; see ipyrad_log.txt found an error in step2; see ipyrad_log.txt
## access the stats of the assembly (so far) from the .stats attribute
data.stats
state | reads_raw | reads_passed_filter | |
---|---|---|---|
LOC0035_SRR6371515 | 2 | 906060 | 905955 |
LOC0037_SRR6371514 | 2 | 2319874 | 2319590 |
LOC0038_SRR6371513 | 2 | 2176494 | 2176182 |
LOC0040_SRR6371512 | 2 | 758060 | 757942 |
LOC0041_SRR6371519 | 2 | 1366528 | 1366341 |
LOC0049_SRR6371518 | 2 | 1471242 | 1471081 |
LOC0050_SRR6371517 | 2 | 1816948 | 1816737 |
LOC0051_SRR6371516 | 2 | 1133648 | 1133530 |
LOC0088_SRR6371521 | 2 | 1266382 | 1266181 |
LOC0121_SRR6371520 | 2 | 225068 | 225031 |
LOC0122_SRR6371503 | 2 | 1133408 | 1133305 |
LOC0127_SRR6371502 | 2 | 241196 | 241079 |
LOC0151_SRR6371505 | 2 | 1002779 | 1002680 |
LOC0263_SRR6371507 | 2 | 2519228 | 2518887 |
LOC0274_SRR6371506 | 2 | 1582988 | 1582731 |
LOC0279_SRR6371509 | 2 | 2894868 | 2894465 |
LOC0309_SRR6371508 | 2 | 1002140 | 1002002 |
LOC0310_SRR6371511 | 2 | 267664 | 267643 |
LOC0311_SRR6371510 | 2 | 1190860 | 1190747 |
## run steps 3-6 of the assembly
data.run("34567")
Assembly: Loxodonta [####################] 100% dereplicating | 0:00:22 | s3 | [####################] 100% clustering | 0:06:02 | s3 | [####################] 100% building clusters | 0:00:21 | s3 | [####################] 100% chunking | 0:00:03 | s3 | [####################] 100% aligning | 0:20:57 | s3 | [####################] 100% concatenating | 0:00:13 | s3 | [####################] 100% inferring [H, E] | 0:01:48 | s4 | [####################] 100% calculating depths | 0:00:04 | s5 | [####################] 100% chunking clusters | 0:00:04 | s5 | [####################] 100% consens calling | 0:03:03 | s5 | [####################] 100% concat/shuffle input | 0:00:04 | s6 | [####################] 100% clustering across | 0:02:13 | s6 | [####################] 100% building clusters | 0:00:03 | s6 | [####################] 100% aligning clusters | 0:01:07 | s6 | [####################] 100% database indels | 0:00:07 | s6 | [####################] 100% indexing clusters | 0:00:04 | s6 | [####################] 100% building database | 0:00:13 | s6 | [####################] 100% filtering loci | 0:00:10 | s7 | [####################] 100% building loci/stats | 0:00:03 | s7 | [####################] 100% building alleles | 0:00:04 | s7 | [####################] 100% building vcf file | 0:00:07 | s7 | [####################] 100% writing vcf file | 0:00:00 | s7 | [####################] 100% building arrays | 0:00:04 | s7 | [####################] 100% writing outfiles | 0:00:06 | s7 | Outfiles written to: ~/ipyrad/test-data/manu-ddrad-elephants/ddrad-denovo/Loxodonta_outfiles
kvalues = [2, 3, 4, 5, 6]
s = ipa.structure(
name="quick",
workdir="./analysis-structure",
data="./ddrad-denovo/Loxodonta_outfiles/Loxodonta.ustr",
)
## set main params (use much larger values in a real analysis)
s.mainparams.burnin = 1000
s.mainparams.numreps = 5000
## submit N replicates of each test to run on parallel client
for kpop in kvalues:
s.run(kpop=kpop, nreps=4, ipyclient=ipyclient)
## wait for parallel jobs to finish
ipyclient.wait()
submitted 4 structure jobs [quick-K-2] submitted 4 structure jobs [quick-K-3] submitted 4 structure jobs [quick-K-4] submitted 4 structure jobs [quick-K-5] submitted 4 structure jobs [quick-K-6]
True
## return the evanno table (deltaK) for best K
etable = s.get_evanno_table(kvalues)
etable
Nreps | deltaK | estLnProbMean | estLnProbStdev | lnPK | lnPPK | |
---|---|---|---|---|---|---|
2 | 4 | 0.000 | -5.503e+05 | 5.116e+05 | 0.000 | 0.000e+00 |
3 | 4 | 3.163 | -3.206e+05 | 3.185e+05 | 229741.725 | 1.007e+06 |
4 | 4 | 0.455 | -1.098e+06 | 6.932e+05 | -777577.375 | 3.152e+05 |
5 | 4 | 1.264 | -1.560e+06 | 6.283e+05 | -462336.825 | 7.943e+05 |
6 | 4 | 0.000 | -1.229e+06 | 1.059e+06 | 331935.450 | 0.000e+00 |
## set some clumpp params
s.clumppparams.m = 3 ## use largegreedy algorithm
s.clumppparams.greedy_option = 2 ## test nrepeat possible orders
s.clumppparams.repeats = 10000 ## number of repeats
s.clumppparams
## run clumpp for each value of K
#tables = s.get_clumpp_table(kvalues, quiet=True)
print(tables)
#table = tables[4].sort_values(by=[0, 1, 2, 3])
table = tables[2].sort_values(by=[0, 1])
toyplot.bars(
table,
width=500,
height=200,
title=[[i] for i in table.index.tolist()],
xshow=False,
);
print(table)
{2: 0 1 LOC0035_SRR6371515 2.510e-01 7.490e-01 LOC0037_SRR6371514 4.995e-01 5.005e-01 LOC0038_SRR6371513 4.972e-01 5.027e-01 LOC0040_SRR6371512 1.000e-03 9.990e-01 LOC0041_SRR6371519 5.835e-01 4.165e-01 LOC0049_SRR6371518 9.995e-01 5.000e-04 LOC0050_SRR6371517 1.000e+00 0.000e+00 LOC0051_SRR6371516 9.988e-01 1.300e-03 LOC0088_SRR6371521 3.583e-01 6.418e-01 LOC0121_SRR6371520 2.000e-03 9.980e-01 LOC0122_SRR6371503 2.700e-03 9.972e-01 LOC0127_SRR6371502 1.512e-01 8.487e-01 LOC0151_SRR6371505 2.515e-01 7.485e-01 LOC0263_SRR6371507 8.000e-04 9.992e-01 LOC0274_SRR6371506 2.507e-01 7.492e-01 LOC0279_SRR6371509 4.995e-01 5.005e-01 LOC0309_SRR6371508 3.872e-01 6.128e-01 LOC0310_SRR6371511 2.500e-01 7.500e-01 LOC0311_SRR6371510 4.992e-01 5.008e-01, 3: 0 1 2 LOC0035_SRR6371515 1.500e-03 9.800e-02 9.005e-01 LOC0037_SRR6371514 3.000e-04 9.995e-01 3.000e-04 LOC0038_SRR6371513 3.000e-04 9.995e-01 3.000e-04 LOC0040_SRR6371512 2.382e-01 2.500e-01 5.117e-01 LOC0041_SRR6371519 1.500e-03 4.993e-01 4.993e-01 LOC0049_SRR6371518 9.992e-01 5.000e-04 3.000e-04 LOC0050_SRR6371517 1.000e+00 0.000e+00 0.000e+00 LOC0051_SRR6371516 9.998e-01 0.000e+00 3.000e-04 LOC0088_SRR6371521 2.520e-01 7.248e-01 2.320e-02 LOC0121_SRR6371520 2.000e-03 2.510e-01 7.470e-01 LOC0122_SRR6371503 2.500e-01 2.502e-01 4.997e-01 LOC0127_SRR6371502 1.203e-01 3.405e-01 5.392e-01 LOC0151_SRR6371505 5.620e-02 4.430e-01 5.007e-01 LOC0263_SRR6371507 3.200e-03 2.300e-03 9.945e-01 LOC0274_SRR6371506 2.720e-01 2.513e-01 4.767e-01 LOC0279_SRR6371509 9.000e-03 2.617e-01 7.293e-01 LOC0309_SRR6371508 7.132e-01 3.800e-03 2.830e-01 LOC0310_SRR6371511 3.850e-02 2.833e-01 6.783e-01 LOC0311_SRR6371510 4.722e-01 2.515e-01 2.762e-01, 4: 0 1 2 3 LOC0035_SRR6371515 0.010 1.950e-02 0.726 2.447e-01 LOC0037_SRR6371514 0.001 9.900e-01 0.007 1.800e-03 LOC0038_SRR6371513 0.001 9.828e-01 0.013 3.000e-03 LOC0040_SRR6371512 0.002 6.850e-02 0.454 4.760e-01 LOC0041_SRR6371519 0.052 4.800e-03 0.446 4.980e-01 LOC0049_SRR6371518 0.994 2.000e-03 0.003 1.000e-03 LOC0050_SRR6371517 0.998 3.000e-04 0.002 3.000e-04 LOC0051_SRR6371516 0.995 8.000e-04 0.003 1.500e-03 LOC0088_SRR6371521 0.067 6.450e-01 0.242 4.650e-02 LOC0121_SRR6371520 0.028 4.700e-01 0.471 3.070e-02 LOC0122_SRR6371503 0.038 2.665e-01 0.254 4.415e-01 LOC0127_SRR6371502 0.015 2.355e-01 0.299 4.500e-01 LOC0151_SRR6371505 0.382 2.782e-01 0.265 7.570e-02 LOC0263_SRR6371507 0.011 2.750e-02 0.536 4.257e-01 LOC0274_SRR6371506 0.003 2.515e-01 0.717 2.850e-02 LOC0279_SRR6371509 0.004 2.525e-01 0.249 4.945e-01 LOC0309_SRR6371508 0.186 8.700e-03 0.731 7.500e-02 LOC0310_SRR6371511 0.044 1.450e-02 0.025 9.158e-01 LOC0311_SRR6371510 0.034 2.054e-01 0.703 5.760e-02, 5: 0 1 2 3 4 LOC0035_SRR6371515 0.071 0.043 0.854 0.008 0.025 LOC0037_SRR6371514 0.004 0.004 0.005 0.002 0.986 LOC0038_SRR6371513 0.009 0.004 0.006 0.005 0.976 LOC0040_SRR6371512 0.435 0.260 0.259 0.003 0.043 LOC0041_SRR6371519 0.020 0.239 0.466 0.029 0.245 LOC0049_SRR6371518 0.006 0.005 0.009 0.965 0.015 LOC0050_SRR6371517 0.007 0.003 0.013 0.974 0.003 LOC0051_SRR6371516 0.009 0.005 0.018 0.965 0.003 LOC0088_SRR6371521 0.035 0.630 0.043 0.014 0.276 LOC0121_SRR6371520 0.030 0.263 0.674 0.015 0.018 LOC0122_SRR6371503 0.732 0.181 0.019 0.020 0.049 LOC0127_SRR6371502 0.408 0.042 0.131 0.077 0.341 LOC0151_SRR6371505 0.478 0.017 0.478 0.019 0.009 LOC0263_SRR6371507 0.020 0.736 0.012 0.011 0.221 LOC0274_SRR6371506 0.561 0.029 0.336 0.012 0.061 LOC0279_SRR6371509 0.198 0.297 0.013 0.019 0.473 LOC0309_SRR6371508 0.300 0.063 0.377 0.058 0.203 LOC0310_SRR6371511 0.015 0.298 0.598 0.011 0.076 LOC0311_SRR6371510 0.430 0.473 0.016 0.009 0.073, 6: 0 1 2 3 4 5 LOC0035_SRR6371515 0.262 0.005 0.236 0.225 0.015 0.258 LOC0037_SRR6371514 0.007 0.001 0.002 0.005 0.982 0.003 LOC0038_SRR6371513 0.004 0.001 0.002 0.003 0.986 0.003 LOC0040_SRR6371512 0.875 0.017 0.012 0.068 0.023 0.005 LOC0041_SRR6371519 0.271 0.011 0.254 0.183 0.003 0.278 LOC0049_SRR6371518 0.006 0.942 0.013 0.017 0.015 0.006 LOC0050_SRR6371517 0.006 0.951 0.012 0.025 0.004 0.002 LOC0051_SRR6371516 0.010 0.909 0.021 0.042 0.003 0.016 LOC0088_SRR6371521 0.143 0.020 0.137 0.028 0.415 0.257 LOC0121_SRR6371520 0.242 0.017 0.010 0.662 0.058 0.012 LOC0122_SRR6371503 0.126 0.091 0.210 0.363 0.068 0.142 LOC0127_SRR6371502 0.192 0.028 0.078 0.242 0.035 0.426 LOC0151_SRR6371505 0.014 0.127 0.226 0.087 0.015 0.531 LOC0263_SRR6371507 0.646 0.013 0.252 0.041 0.008 0.040 LOC0274_SRR6371506 0.035 0.016 0.231 0.210 0.012 0.495 LOC0279_SRR6371509 0.028 0.013 0.471 0.169 0.025 0.294 LOC0309_SRR6371508 0.174 0.009 0.042 0.020 0.009 0.746 LOC0310_SRR6371511 0.009 0.028 0.085 0.192 0.027 0.659 LOC0311_SRR6371510 0.019 0.008 0.930 0.025 0.009 0.009} 0 1 LOC0263_SRR6371507 8.000e-04 9.992e-01 LOC0040_SRR6371512 1.000e-03 9.990e-01 LOC0121_SRR6371520 2.000e-03 9.980e-01 LOC0122_SRR6371503 2.700e-03 9.972e-01 LOC0127_SRR6371502 1.512e-01 8.487e-01 LOC0310_SRR6371511 2.500e-01 7.500e-01 LOC0274_SRR6371506 2.507e-01 7.492e-01 LOC0035_SRR6371515 2.510e-01 7.490e-01 LOC0151_SRR6371505 2.515e-01 7.485e-01 LOC0088_SRR6371521 3.583e-01 6.418e-01 LOC0309_SRR6371508 3.872e-01 6.128e-01 LOC0038_SRR6371513 4.972e-01 5.027e-01 LOC0311_SRR6371510 4.992e-01 5.008e-01 LOC0037_SRR6371514 4.995e-01 5.005e-01 LOC0279_SRR6371509 4.995e-01 5.005e-01 LOC0041_SRR6371519 5.835e-01 4.165e-01 LOC0051_SRR6371516 9.988e-01 1.300e-03 LOC0049_SRR6371518 9.995e-01 5.000e-04 LOC0050_SRR6371517 1.000e+00 0.000e+00