Eaton & Ree (2013) single-end RAD data set

Here we demonstrate a denovo assembly for an empirical RAD data set using the ipyrad Python API. This example was run on a workstation with 20 cores and takes about 10 minutes to assemble, but you should be able to run it on a 4-core laptop in ~30-60 minutes.

For our example data we will use the 13 taxa Pedicularis data set from Eaton and Ree (2013) (Open Access). This data set is composed of single-end 75bp reads from a RAD-seq library prepared with the PstI enzyme. The data set also serves as an example for several of our analysis cookbooks that demonstrate methods for analyzing RAD-seq data files. At the end of this notebook there are also several examples of how to use the ipyrad analysis tools to run downstream analyses in parallel.

The figure below shows the ingroup taxa from this study and their sampling locations. The study includes all species within a small monophyletic clade of Pedicularis, including multiple individuals from 5 species and several subspecies, as well as an outgroup species. The sampling essentially spans from population-level variation where species boundaries are unclear, to higher-level divergence where species boundaries are quite distinct. This is a common scale at which RAD-seq data are often very useful.

Setup (software and data files)

If you haven't done so yet, start by installing ipyrad using conda (see ipyrad installation instructions) as well as the packages in the cell below. This is easiest to do in a terminal. Then open a jupyter-notebook, like this one, and follow along with the tuturial by copying and executing the code in the cells, and adding your own documentation between them using markdown. Feel free to modify parameters to see their effects on the downstream results.

In [1]:
## conda install ipyrad -c ipyrad
## conda install toytree -c eaton-lab
## conda install sra-tools -c bioconda
## conda install entrez-direct -c bioconda
In [2]:
## imports
import ipyrad as ip
import ipyrad.analysis as ipa
import ipyparallel as ipp

In contrast to the ipyrad CLI, the ipyrad API gives users much more fine-scale control over the parallelization of their analysis, but this also requires learning a little bit about the library that we use to do this, called ipyparallel. This library is designed for use with jupyter-notebooks to allow massive-scale multi-processing while working interactively.

Understanding the nuts and bolts of it might take a little while, but it is fairly easy to get started using it, especially in the way it is integrated with ipyrad. To start a parallel client to you must run the command-line program 'ipcluster'. This will essentially start a number of independent Python processes (kernels) which we can then send bits of work to do. The cluster can be stopped and restarted independently of this notebook, which is convenient for working on a cluster where connecting to many cores is not always immediately available.

Open a terminal and type the following command to start an ipcluster instance with N engines.

In [3]:
## ipcluster start --n=20
In [4]:
## connect to cluster
ipyclient = ipp.Client()
ipyclient.ids
Out[4]:
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]

Download the data set (Pedicularis)

These data are archived on the NCBI sequence read archive (SRA) under accession id SRP021469. As part of the ipyrad analysis tools we have a wrapper around the SRAtools software that can be used to query NCBI and download sequence data based on accession IDs. Run the code below to download the fastq data files associated with this study. The data will be saved the specified directory which will be created if it does not already exist. The compressed file size of the data is a little over 1GB. If you pass your ipyclient to the .run() command below then the download will be parallelized.

In [5]:
## download the Pedicularis data set from NCBI
sra = ipa.sratools(accession="SRP021469", workdir="fastqs-Ped")
sra.run(force=True, ipyclient=ipyclient)
[####################] 100%  Downloading fastq files | 0:01:19 |  
13 fastq files downloaded to /home/deren/Documents/ipyrad/tests/fastqs-Ped

Create an Assembly object

This object stores the parameters of the assembly and the organization of data files.

In [6]:
## you must provide a name for the Assembly
data = ip.Assembly("pedicularis")
New Assembly: pedicularis

Set parameters for the Assembly. This will raise an error if any of the parameters are not allowed because they are the wrong type, or out of the allowed range.

In [10]:
## set parameters
data.set_params("project_dir", "analysis-ipyrad")
data.set_params("sorted_fastq_path", "fastqs-Ped/*.fastq.gz")
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, 5, 0, 0))
data.set_params("output_formats", "psvnkua")

## see/print all parameters
data.get_params()
0   assembly_name               pedicularis                                  
1   project_dir                 ./analysis-ipyrad                            
2   raw_fastq_path                                                           
3   barcodes_path                                                            
4   sorted_fastq_path           ./example_empirical_rad/*.fastq.gz           
5   assembly_method             denovo                                       
6   reference_sequence                                                       
7   datatype                    rad                                          
8   restriction_overhang        ('TGCAG', '')                                
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, 5, 0, 0)                                 
27  output_formats              ('p', 's', 'v', 'n', 'k', 'u')               
28  pop_assign_file                                                          

Assemble the data set

In [24]:
## run steps 1 & 2 of the assembly
data.run("12")
Assembly: pedicularis
[####################] 100%  loading reads         | 0:00:04 | s1 | 
[####################] 100%  processing reads      | 0:01:14 | s2 | 
In [25]:
## access the stats of the assembly (so far) from the .stats attribute
data.stats
Out[25]:
state reads_raw reads_passed_filter
29154_superba 2 696994 689996
30556_thamno 2 1452316 1440314
30686_cyathophylla 2 1253109 1206947
32082_przewalskii 2 964244 955480
33413_thamno 2 636625 626084
33588_przewalskii 2 1002923 993873
35236_rex 2 1803858 1787366
35855_rex 2 1409843 1397068
38362_rex 2 1391175 1379626
39618_rex 2 822263 813990
40578_rex 2 1707942 1695523
41478_cyathophylloides 2 2199740 2185364
41954_cyathophylloides 2 2199613 2176210
In [26]:
## run steps 3-6 of the assembly
data.run("3456")
Assembly: pedicularis
[####################] 100%  dereplicating         | 0:00:07 | s3 | 
[####################] 100%  clustering            | 0:05:08 | s3 | 
[####################] 100%  building clusters     | 0:00:26 | s3 | 
[####################] 100%  chunking              | 0:00:05 | s3 | 
[####################] 100%  aligning              | 0:03:06 | s3 | 
[####################] 100%  concatenating         | 0:00:15 | s3 | 
[####################] 100%  inferring [H, E]      | 0:01:10 | s4 | 
[####################] 100%  calculating depths    | 0:00:06 | s5 | 
[####################] 100%  chunking clusters     | 0:00:06 | s5 | 
[####################] 100%  consens calling       | 0:01:56 | s5 | 
[####################] 100%  concat/shuffle input  | 0:00:05 | s6 | 
[####################] 100%  clustering across     | 0:03:47 | s6 | 
[####################] 100%  building clusters     | 0:00:06 | s6 | 
[####################] 100%  aligning clusters     | 0:00:29 | s6 | 
[####################] 100%  database indels       | 0:00:14 | s6 | 
[####################] 100%  indexing clusters     | 0:00:03 | s6 | 
[####################] 100%  building database     | 0:00:29 | s6 | 

Branch to create several final data sets with different parameter settings

In [11]:
## create a branch for outputs with min_samples = 4 (lots of missing data)
min4 = data.branch("min4")
min4.set_params("min_samples_locus", 4)
min4.run("7")

## create a branch for outputs with min_samples = 13 (no missing data)
min13 = data.branch("min13")
min13.set_params("min_samples_locus", 13)
min13.run("7")

## create a branch with no missing data for ingroups, but allow
## missing data in the outgroups by setting population assignments.
## The population min-sample values overrule the min-samples-locus param
pops = data.branch("min11-pops")
pops.populations = {
    "ingroup": (11, [i for i in pops.samples if "prz" not in i]),
    "outgroup" : (0, [i for i in pops.samples if "prz" in i]),
    }
pops.run("7")

## create a branch with no missing data and with outgroups removed
nouts = data.branch("nouts_min11", subsamples=[i for i in pops.samples if "prz" not in i])
nouts.set_params("min_samples_locus", 11)
nouts.run("7")
Assembly: min4
[####################] 100%  filtering loci        | 0:00:08 | s7 | 
[####################] 100%  building loci/stats   | 0:00:01 | s7 | 
[####################] 100%  building vcf file     | 0:00:09 | s7 | 
[####################] 100%  writing vcf file      | 0:00:00 | s7 | 
[####################] 100%  building arrays       | 0:00:04 | s7 | 
[####################] 100%  writing outfiles      | 0:00:05 | s7 | 
Outfiles written to: ~/Documents/ipyrad/tests/analysis-ipyrad/min4_outfiles

Assembly: min13
[####################] 100%  filtering loci        | 0:00:02 | s7 | 
[####################] 100%  building loci/stats   | 0:00:01 | s7 | 
[####################] 100%  building vcf file     | 0:00:03 | s7 | 
[####################] 100%  writing vcf file      | 0:00:00 | s7 | 
[####################] 100%  building arrays       | 0:00:04 | s7 | 
[####################] 100%  writing outfiles      | 0:00:00 | s7 | 
Outfiles written to: ~/Documents/ipyrad/tests/analysis-ipyrad/min13_outfiles

Assembly: min11-pops
[####################] 100%  filtering loci        | 0:00:02 | s7 | 
[####################] 100%  building loci/stats   | 0:00:01 | s7 | 
[####################] 100%  building vcf file     | 0:00:03 | s7 | 
[####################] 100%  writing vcf file      | 0:00:00 | s7 | 
[####################] 100%  building arrays       | 0:00:04 | s7 | 
[####################] 100%  writing outfiles      | 0:00:00 | s7 | 
Outfiles written to: ~/Documents/ipyrad/tests/analysis-ipyrad/min11-pops_outfiles

Assembly: nouts_min11
[####################] 100%  filtering loci        | 0:00:03 | s7 | 
[####################] 100%  building loci/stats   | 0:00:02 | s7 | 
[####################] 100%  building vcf file     | 0:00:02 | s7 | 
[####################] 100%  writing vcf file      | 0:00:00 | s7 | 
[####################] 100%  building arrays       | 0:00:04 | s7 | 
[####################] 100%  writing outfiles      | 0:00:00 | s7 | 
Outfiles written to: ~/Documents/ipyrad/tests/analysis-ipyrad/nouts_min11_outfiles

View final stats

The .stats attribute shows a stats summary for each sample, and a number of stats dataframes can be accessed for each step from the .stats_dfs attribute of the Assembly.

In [9]:
## we can access the stats summary as a pandas dataframes. 
min4.stats
Out[9]:
state reads_raw reads_passed_filter clusters_total clusters_hidepth hetero_est error_est reads_consens
29154_superba 6 696994 689996 134897 35143 0.010 0.002 34628
30556_thamno 6 1452316 1440314 212960 52776 0.011 0.003 51701
30686_cyathophylla 6 1253109 1206947 240824 54282 0.010 0.002 53347
32082_przewalskii 6 964244 955480 151762 42487 0.013 0.002 41841
33413_thamno 6 636625 626084 172709 31072 0.012 0.002 30674
33588_przewalskii 6 1002923 993873 158933 46555 0.013 0.002 45892
35236_rex 6 1803858 1787366 417632 54709 0.010 0.001 54064
35855_rex 6 1409843 1397068 184661 56595 0.013 0.003 55371
38362_rex 6 1391175 1379626 133541 53156 0.008 0.002 52496
39618_rex 6 822263 813990 148107 44016 0.010 0.002 43409
40578_rex 6 1707942 1695523 222328 56653 0.011 0.002 55937
41478_cyathophylloides 6 2199740 2185364 173188 55208 0.008 0.001 54482
41954_cyathophylloides 6 2199613 2176210 301343 76537 0.008 0.003 75167
In [25]:
## or print the full stats file 
cat $min4.stats_files.s7
## The number of loci caught by each filter.
## ipyrad API location: [assembly].stats_dfs.s7_filters

                            total_filters  applied_order  retained_loci
total_prefiltered_loci              96562              0          96562
filtered_by_rm_duplicates            2939           2939          93623
filtered_by_max_indels                189            189          93434
filtered_by_max_snps                  679             18          93416
filtered_by_max_shared_het            865            708          92708
filtered_by_min_sample              47958          46646          46062
filtered_by_max_alleles             10694           4565          41497
total_filtered_loci                 41497              0          41497


## The number of loci recovered for each Sample.
## ipyrad API location: [assembly].stats_dfs.s7_samples

                        sample_coverage
29154_superba                     21054
30556_thamno                      31790
30686_cyathophylla                26648
32082_przewalskii                 13201
33413_thamno                      18747
33588_przewalskii                 15461
35236_rex                         33228
35855_rex                         33403
38362_rex                         33748
39618_rex                         28135
40578_rex                         34107
41478_cyathophylloides            30826
41954_cyathophylloides            28260


## The number of loci for which N taxa have data.
## ipyrad API location: [assembly].stats_dfs.s7_loci

    locus_coverage  sum_coverage
1                0             0
2                0             0
3                0             0
4             5764          5764
5             4002          9766
6             3650         13416
7             3139         16555
8             3220         19775
9             4153         23928
10            4904         28832
11            5321         34153
12            4511         38664
13            2833         41497


## The distribution of SNPs (var and pis) per locus.
## var = Number of loci with n variable sites (pis + autapomorphies)
## pis = Number of loci with n parsimony informative site (minor allele in >1 sample)
## ipyrad API location: [assembly].stats_dfs.s7_snps

     var  sum_var    pis  sum_pis
0   2348        0  11768        0
1   4482     4482  10822    10822
2   5870    16222   7699    26220
3   6314    35164   4914    40962
4   5708    57996   2967    52830
5   4857    82281   1679    61225
6   3885   105591    874    66469
7   2932   126115    478    69815
8   1955   141755    188    71319
9   1255   153050     72    71967
10   827   161320     19    72157
11   506   166886     10    72267
12   262   170030      4    72315
13   141   171863      3    72354
14    78   172955      0    72354
15    39   173540      0    72354
16    19   173844      0    72354
17    10   174014      0    72354
18     6   174122      0    72354
19     2   174160      0    72354
20     1   174180      0    72354


## Final Sample stats summary

                        state  reads_raw  reads_passed_filter  clusters_total  clusters_hidepth  hetero_est  error_est  reads_consens  loci_in_assembly
29154_superba               7     696994               689996          134897             35143       0.010      0.002          34628             21054
30556_thamno                7    1452316              1440314          212960             52776       0.011      0.003          51701             31790
30686_cyathophylla          7    1253109              1206947          240824             54282       0.010      0.002          53347             26648
32082_przewalskii           7     964244               955480          151762             42487       0.013      0.002          41841             13201
33413_thamno                7     636625               626084          172709             31072       0.012      0.002          30674             18747
33588_przewalskii           7    1002923               993873          158933             46555       0.013      0.002          45892             15461
35236_rex                   7    1803858              1787366          417632             54709       0.010      0.001          54064             33228
35855_rex                   7    1409843              1397068          184661             56595       0.013      0.003          55371             33403
38362_rex                   7    1391175              1379626          133541             53156       0.008      0.002          52496             33748
39618_rex                   7     822263               813990          148107             44016       0.010      0.002          43409             28135
40578_rex                   7    1707942              1695523          222328             56653       0.011      0.002          55937             34107
41478_cyathophylloides      7    2199740              2185364          173188             55208       0.008      0.001          54482             30826
41954_cyathophylloides      7    2199613              2176210          301343             76537       0.008      0.003          75167             28260
In [13]:
## and we can access parts of the full stats outputs as dataframes 
min4.stats_dfs.s7_samples
Out[13]:
sample_coverage
29154_superba 21054
30556_thamno 31790
30686_cyathophylla 26648
32082_przewalskii 13201
33413_thamno 18747
33588_przewalskii 15461
35236_rex 33228
35855_rex 33403
38362_rex 33748
39618_rex 28135
40578_rex 34107
41478_cyathophylloides 30826
41954_cyathophylloides 28260
In [14]:
## compare this to the one above, coverage is more equal
min13.stats_dfs.s7_samples
Out[14]:
sample_coverage
29154_superba 2833
30556_thamno 2833
30686_cyathophylla 2833
32082_przewalskii 2833
33413_thamno 2833
33588_przewalskii 2833
35236_rex 2833
35855_rex 2833
38362_rex 2833
39618_rex 2833
40578_rex 2833
41478_cyathophylloides 2833
41954_cyathophylloides 2833
In [15]:
## similarly, coverage is equal here among ingroups, but allows missing in outgroups
pops.stats_dfs.s7_samples
Out[15]:
sample_coverage
29154_superba 5796
30556_thamno 5796
30686_cyathophylla 5796
32082_przewalskii 3266
33413_thamno 5796
33588_przewalskii 3747
35236_rex 5796
35855_rex 5796
38362_rex 5796
39618_rex 5796
40578_rex 5796
41478_cyathophylloides 5796
41954_cyathophylloides 5796

Analysis tools

We have a lot more information about analysis tools in the ipyrad documentation. But here I'll show just a quick example of how you can easily access the data files for these assemblies and use them in downstream analysis software. The ipyrad analysis tools include convenient wrappers to make it easier to parallelize analyses of RAD-seq data. Please see the full documentation for the ipyrad.analysis tools in the ipyrad documentation for more details.

In [6]:
import ipyrad as ip
import ipyrad.analysis as ipa
In [7]:
## you can re-load assemblies at a later time from their JSON file
min4 = ip.load_json("analysis-ipyrad/min4.json")
min13 = ip.load_json("analysis-ipyrad/min13.json")
nouts = ip.load_json("analysis-ipyrad/nouts_min11.json")
loading Assembly: min4
from saved path: ~/Documents/ipyrad/tests/analysis-ipyrad/min4.json
loading Assembly: min13
from saved path: ~/Documents/ipyrad/tests/analysis-ipyrad/min13.json
loading Assembly: nouts_min11
from saved path: ~/Documents/ipyrad/tests/analysis-ipyrad/nouts_min11.json

RAxML -- ML concatenation tree inference

In [17]:
## conda install raxml -c bioconda
## conda install toytree -c eaton-lab
In [12]:
## create a raxml analysis object for the min13 data sets
rax = ipa.raxml(
    name=min13.name, 
    data=min13.outfiles.phy,
    workdir="analysis-raxml",
    T=20,
    N=100,    
    o=[i for i in min13.samples if "prz" in i],
    )
In [19]:
## print the raxml command and call it
print rax.command
rax.run(force=True)
raxmlHPC-PTHREADS-SSE3 -f a -T 20 -m GTRGAMMA -N 100 -x 12345 -p 54321 -n min13 -w /home/deren/Documents/ipyrad/tests/analysis-raxml -s /home/deren/Documents/ipyrad/tests/analysis-ipyrad/min13_outfiles/min13.phy -o 33588_przewalskii,32082_przewalskii
job min13 finished successfully
In [13]:
## access the resulting tree files
rax.trees
Out[13]:
bestTree                   ~/Documents/ipyrad/tests/analysis-raxml/RAxML_bestTree.min13
bipartitions               ~/Documents/ipyrad/tests/analysis-raxml/RAxML_bipartitions.min13
bipartitionsBranchLabels   ~/Documents/ipyrad/tests/analysis-raxml/RAxML_bipartitionsBranchLabels.min13
bootstrap                  ~/Documents/ipyrad/tests/analysis-raxml/RAxML_bootstrap.min13
info                       ~/Documents/ipyrad/tests/analysis-raxml/RAxML_info.min13
In [15]:
## plot a tree in the notebook with toytree
import toytree
tre = toytree.tree(rax.trees.bipartitions)
tre.draw(
    width=350,
    height=400,
    node_labels=tre.get_node_values("support"),
    );
32082_przewalskii33588_przewalskii29154_superba30686_cyathophylla41478_cyathophylloides41954_cyathophylloides33413_thamno30556_thamno35855_rex40578_rex35236_rex39618_rex38362_rexidx: 1 name: 1 dist: 0.0112591855114 support: 100100idx: 2 name: 2 dist: 0.0112591855114 support: 100100idx: 3 name: 3 dist: 0.00138635903129 support: 100100idx: 4 name: 4 dist: 0.00130362717349 support: 100100idx: 5 name: 5 dist: 0.00515136675257 support: 100100idx: 6 name: 6 dist: 0.0026758642527 support: 100100idx: 7 name: 7 dist: 0.000567990916626 support: 7777idx: 8 name: 8 dist: 0.000342308540455 support: 7373idx: 9 name: 9 dist: 0.00100552991932 support: 100100idx: 10 name: 10 dist: 0.000365735718032 support: 4848idx: 11 name: 11 dist: 0.0030022742397 support: 100100

tetrad -- quartet tree inference

In [16]:
## create a tetrad analysis object
tet = ipa.tetrad(
    name=min4.name, 
    seqfile=min4.outfiles.snpsphy,
    mapfile=min4.outfiles.snpsmap,
    nboots=100,
    )
loading seq array [13 taxa x 174180 bp]
max unlinked SNPs per quartet (nloci): 39149
In [18]:
## run tree inference
tet.run(ipyclient)
host compute node: [20 cores] on tinus
inferring 715 induced quartet trees
[####################] 100%  initial tree | 0:00:05 |  
[####################] 100%  boot 100     | 0:02:23 |  
In [19]:
## access tree files
tet.trees
Out[19]:
boots   ~/Documents/ipyrad/tests/analysis-tetrad/min4.boots
cons    ~/Documents/ipyrad/tests/analysis-tetrad/min4.cons
nhx     ~/Documents/ipyrad/tests/analysis-tetrad/min4.nhx
tree    ~/Documents/ipyrad/tests/analysis-tetrad/min4.tree
In [22]:
## plot results (just like above, but unrooted by default)
## the consensus tree here differs from the ML tree above.
import toytree
qtre = toytree.tree(tet.trees.nhx)
qtre.root(wildcard="prz")
qtre.draw(
    width=350,
    height=400,
    node_labels=qtre.get_node_values("support"),
    );
33588_przewalskii32082_przewalskii30686_cyathophylla29154_superba41954_cyathophylloides41478_cyathophylloides33413_thamno35236_rex30556_thamno35855_rex40578_rex38362_rex39618_rexidx: 1 name: 1 dist: 100 support: 100100idx: 2 name: added-node dist: 100 support: 100100idx: 3 name: 2 dist: 100 support: 100100idx: 4 name: 3 dist: 100 support: 100100idx: 5 name: 4 dist: 100 support: 100100idx: 6 name: 5 dist: 100 support: 100100idx: 7 name: 6 dist: 99 support: 9999idx: 8 name: 7 dist: 98 support: 9898idx: 9 name: 8 dist: 85 support: 8585idx: 10 name: 9 dist: 100 support: 100100idx: 11 name: 10 dist: 100 support: 100100

STRUCTURE -- population cluster inference

In [ ]:
## conda install structure clumpp -c ipyrad
In [25]:
## create a structure analysis object for the no-outgroup data set
struct = ipa.structure(
    name=nouts.name, 
    data=nouts.outfiles.str, 
    mapfile=nouts.outfiles.snpsmap,    
)

## set params for analysis (should be longer in real analyses)
struct.mainparams.burnin=1000
struct.mainparams.numreps=8000
In [96]:
## run structure across 10 random replicates of sampled unlinked SNPs
for kpop in [2, 3, 4, 5, 6]:
    struct.run(kpop=kpop, nreps=10, ipyclient=ipyclient)
submitted 10 structure jobs [nouts_min11-K-2]
submitted 10 structure jobs [nouts_min11-K-3]
submitted 10 structure jobs [nouts_min11-K-4]
submitted 10 structure jobs [nouts_min11-K-5]
submitted 10 structure jobs [nouts_min11-K-6]
In [115]:
## wait for all of these jobs to finish
ipyclient.wait()
Out[115]:
True
In [26]:
## collect results
tables = {}
for kpop in [2, 3, 4, 5, 6]:
    tables[kpop] = struct.get_clumpp_table(kpop)
mean scores across 10 replicates.
mean scores across 10 replicates.
mean scores across 10 replicates.
mean scores across 10 replicates.
mean scores across 10 replicates.
In [27]:
## custom sorting order
myorder = [
    "41478_cyathophylloides", 
    "41954_cyathophylloides", 
    "29154_superba",
    "30686_cyathophylla", 
    "33413_thamno", 
    "30556_thamno", 
    "35236_rex", 
    "40578_rex", 
    "35855_rex",
    "39618_rex", 
    "38362_rex",
]
In [33]:
## import toyplot (packaged with toytree) 
import toyplot

## plot bars for each K-value (mean of 10 reps)
for kpop in [2, 3, 4, 5, 6]:
    table = tables[kpop]
    table = table.ix[myorder]
    
    ## plot barplot w/ hover
    canvas, axes, mark = toyplot.bars(
                            table, 
                            title=[[i] for i in table.index.tolist()],
                            width=400, 
                            height=200, 
                            yshow=False,                            
                            style={"stroke": toyplot.color.near_black},
                            )
41478_cyathophylloides41954_cyathophylloides29154_superba30686_cyathophylla33413_thamno30556_thamno35236_rex40578_rex35855_rex39618_rex38362_rex41478_cyathophylloides41954_cyathophylloides29154_superba30686_cyathophylla33413_thamno30556_thamno35236_rex40578_rex35855_rex39618_rex38362_rex0510
41478_cyathophylloides41954_cyathophylloides29154_superba30686_cyathophylla33413_thamno30556_thamno35236_rex40578_rex35855_rex39618_rex38362_rex41478_cyathophylloides41954_cyathophylloides29154_superba30686_cyathophylla33413_thamno30556_thamno35236_rex40578_rex35855_rex39618_rex38362_rex41478_cyathophylloides41954_cyathophylloides29154_superba30686_cyathophylla33413_thamno30556_thamno35236_rex40578_rex35855_rex39618_rex38362_rex0510
41478_cyathophylloides41954_cyathophylloides29154_superba30686_cyathophylla33413_thamno30556_thamno35236_rex40578_rex35855_rex39618_rex38362_rex41478_cyathophylloides41954_cyathophylloides29154_superba30686_cyathophylla33413_thamno30556_thamno35236_rex40578_rex35855_rex39618_rex38362_rex41478_cyathophylloides41954_cyathophylloides29154_superba30686_cyathophylla33413_thamno30556_thamno35236_rex40578_rex35855_rex39618_rex38362_rex41478_cyathophylloides41954_cyathophylloides29154_superba30686_cyathophylla33413_thamno30556_thamno35236_rex40578_rex35855_rex39618_rex38362_rex0510
41478_cyathophylloides41954_cyathophylloides29154_superba30686_cyathophylla33413_thamno30556_thamno35236_rex40578_rex35855_rex39618_rex38362_rex41478_cyathophylloides41954_cyathophylloides29154_superba30686_cyathophylla33413_thamno30556_thamno35236_rex40578_rex35855_rex39618_rex38362_rex41478_cyathophylloides41954_cyathophylloides29154_superba30686_cyathophylla33413_thamno30556_thamno35236_rex40578_rex35855_rex39618_rex38362_rex41478_cyathophylloides41954_cyathophylloides29154_superba30686_cyathophylla33413_thamno30556_thamno35236_rex40578_rex35855_rex39618_rex38362_rex41478_cyathophylloides41954_cyathophylloides29154_superba30686_cyathophylla33413_thamno30556_thamno35236_rex40578_rex35855_rex39618_rex38362_rex0510
41478_cyathophylloides41954_cyathophylloides29154_superba30686_cyathophylla33413_thamno30556_thamno35236_rex40578_rex35855_rex39618_rex38362_rex41478_cyathophylloides41954_cyathophylloides29154_superba30686_cyathophylla33413_thamno30556_thamno35236_rex40578_rex35855_rex39618_rex38362_rex41478_cyathophylloides41954_cyathophylloides29154_superba30686_cyathophylla33413_thamno30556_thamno35236_rex40578_rex35855_rex39618_rex38362_rex41478_cyathophylloides41954_cyathophylloides29154_superba30686_cyathophylla33413_thamno30556_thamno35236_rex40578_rex35855_rex39618_rex38362_rex41478_cyathophylloides41954_cyathophylloides29154_superba30686_cyathophylla33413_thamno30556_thamno35236_rex40578_rex35855_rex39618_rex38362_rex41478_cyathophylloides41954_cyathophylloides29154_superba30686_cyathophylla33413_thamno30556_thamno35236_rex40578_rex35855_rex39618_rex38362_rex0510

TREEMIX -- ML tree & admixture co-inference

In [15]:
## conda install treemix -c ipyrad
In [39]:
## group taxa into 'populations'
imap = {
    "prz": ["32082_przewalskii", "33588_przewalskii"],
    "cys": ["41478_cyathophylloides", "41954_cyathophylloides"],
    "cya": ["30686_cyathophylla"],
    "sup": ["29154_superba"],
    "cup": ["33413_thamno"],
    "tha": ["30556_thamno"],
    "rck": ["35236_rex"],
    "rex": ["35855_rex", "40578_rex"],
    "lip": ["39618_rex", "38362_rex"],  
}

## optional: loci will be filtered if they do not have data for at
## least N samples in each species. Minimums cannot be <1.
minmap = {
    "prz": 2,
    "cys": 2,
    "cya": 1,
    "sup": 1,
    "cup": 1,
    "tha": 1, 
    "rck": 1,
    "rex": 2,
    "lip": 2,
    }

## sets a random number seed
import numpy
numpy.random.seed(12349876)

## create a treemix analysis object
tmix = ipa.treemix(
    name=min13.name, 
    data=min13.outfiles.snpsphy,
    mapfile=min13.outfiles.snpsmap,
    imap=imap,
    minmap=minmap,  
    )

## you can set additional parameter args here
tmix.params.root = "prz"
tmix.params.global_ = 1

## print the full params
tmix.params
Out[39]:
binary      treemix             
bootstrap   0                   
climb       0                   
cormig      0                   
g           (None, None)        
global_     1                   
k           0                   
m           0                   
noss        0                   
root        prz                 
se          0                   
seed        737548365           
In [40]:
## a dictionary for storing treemix objects
tdict = {}

## iterate over values of m
for rep in xrange(4):
    for mig in xrange(4):
        
        ## create new treemix object copy
        name = "mig-{}-rep-{}".format(mig, rep)
        tmp = tmix.copy(name)

        ## set params on new object
        tmp.params.m = mig
    
        ## run treemix analysis
        tmp.run()
        
        ## store the treemix object
        tdict[name] = tmp
In [43]:
import toyplot

## select a single result
tmp = tdict["mig-1-rep-1"]

## draw the tree similar to the Treemix plotting R code
## this code is rather new and will be expanded in the future.
canvas = toyplot.Canvas(width=350, height=350)
axes = canvas.cartesian(padding=25, margin=75)
axes = tmp.draw(axes)
przcyscyasuprckliprexthacupweight: 0.166766-0.3-0.2-0.10.0Drift parameter
In [44]:
import toyplot
import numpy as np

## plot many results
canvas = toyplot.Canvas(width=800, height=1200)
idx = 0
for mig in range(4):
    for rep in range(4):
        tmp = tdict["mig-{}-rep-{}".format(mig, rep)]
        ax = canvas.cartesian(grid=(4, 4, idx), padding=25, margin=(25, 50, 100, 25))
        ax = tmp.draw(ax)
        idx += 1
przcyscyasupcupthaliprckrex-0.2-0.10.0Drift parameterprzcyscyasupcupthaliprckrex-0.2-0.10.0Drift parameterprzcyscyasupcupthaliprckrex-0.2-0.10.0Drift parameterprzcyscyasupcupthaliprckrex-0.2-0.10.0Drift parameterprzcyssupcyacuptharexliprckweight: 0.0325483-0.2-0.10.0Drift parameterprzcyscyasuprckliprexthacupweight: 0.166766-0.3-0.2-0.10.0Drift parameterprzcyssupcyacupliptharexrckweight: 0.0958665-0.3-0.2-0.10.0Drift parameterprzcyscyasupcupthaliprckrexweight: 0.065454-0.3-0.2-0.10.0Drift parameterprzcyssupcyacuptharexliprckweight: 0.0345748weight: 0.178132-0.3-0.2-0.10.0Drift parameterprzcyscyasupcuptharexrcklipweight: 0.0418195weight: 0.00647291-0.2-0.10.0Drift parameterprzcyssupcyacuprextharcklipweight: 0.0663672weight: 0.3213-0.3-0.2-0.10.0Drift parameterprzcyssupcyacupthaliprexrckweight: 0.232693weight: 0.0401708-0.3-0.2-0.10.0Drift parameterprzcyssupcyacupthaliprckrexweight: 0.125805weight: 0.101155weight: 0.0318235-0.3-0.2-0.10.0Drift parameterprzcyscyasupthacuprexrcklipweight: 0.0739663weight: 0.36854weight: 0.139219-0.3-0.2-0.10.0Drift parameterprzcyssupcyarckrexlipthacupweight: 0.167868weight: 0.368291weight: 0.0163652-0.3-0.2-0.10.0Drift parameterprzcyssupcyacupthaliprexrckweight: 0.49431weight: 0.0674044weight: 0.316649-0.3-0.2-0.10.0Drift parameter

ABBA-BABA admixture inference

In [47]:
bb = ipa.baba(
    data=min4.outfiles.loci,
    newick="analysis-raxml/RAxML_bestTree.min13",
)
In [55]:
## check params
bb.params
Out[55]:
database   None                
mincov     1                   
nboots     1000                
quiet      False               
In [53]:
## generate all tests from the tree where 32082 is p4
bb.generate_tests_from_tree(
    constraint_dict={
        "p4": ["32082_przewalskii"],
        "p3": ["30556_thamno"],
        }
    )
36 tests generated from tree
In [54]:
## run the tests in parallel
bb.run(ipyclient=ipyclient)
[####################] 100%  calculating D-stats  | 0:00:37 |  
In [59]:
bb.results_table.sort_values(by="Z", ascending=False).head()
Out[59]:
dstat bootmean bootstd Z ABBA BABA nloci
12 0.198 0.199 0.027 7.291 593.156 396.844 10629
27 -0.208 -0.207 0.031 6.648 370.500 565.000 10365
20 0.208 0.208 0.031 6.625 565.000 370.500 10365
16 0.186 0.186 0.032 5.830 555.250 381.250 10243
26 -0.186 -0.186 0.033 5.708 381.250 555.250 10243
In [63]:
## most significant result (more ABBA than BABA)
bb.tests[12]
Out[63]:
{'p1': ['35236_rex'],
 'p2': ['35855_rex', '40578_rex'],
 'p3': ['30556_thamno'],
 'p4': ['32082_przewalskii']}
In [64]:
## the next most signif (more BABA than ABBA)
bb.tests[27]
Out[64]:
{'p1': ['40578_rex'],
 'p2': ['35236_rex'],
 'p3': ['30556_thamno'],
 'p4': ['32082_przewalskii']}

BPP -- species tree inference/delim

In [68]:
## a dictionary mapping sample names to 'species' names
imap = {
    "prz": ["32082_przewalskii", "33588_przewalskii"],
    "cys": ["41478_cyathophylloides", "41954_cyathophylloides"],
    "cya": ["30686_cyathophylla"],
    "sup": ["29154_superba"],
    "cup": ["33413_thamno"],
    "tha": ["30556_thamno"],
    "rck": ["35236_rex"],
    "rex": ["35855_rex", "40578_rex"],
    "lip": ["39618_rex", "38362_rex"],  
    }

## optional: loci will be filtered if they do not have data for at
## least N samples/individuals in each species.
minmap = {
    "prz": 2,
    "cys": 2,
    "cya": 1,
    "sup": 1,
    "cup": 1,
    "tha": 1, 
    "rck": 1,
    "rex": 2,
    "lip": 2,
    }

## a tree hypothesis (guidetree) (here based on tetrad results)
## for the 'species' we've collapsed samples into.
newick = "((((((rex, lip), rck), tha), cup), (cys, (cya, sup))), prz);"
In [69]:
## initiata a bpp object 
b = ipa.bpp(
    name=min4.name,
    locifile=min4.outfiles.alleles,
    imap=imap,
    minmap=minmap,
    guidetree=newick,
    )
In [70]:
## set some optional params, leaving others at their defaults
## you should definitely run these longer for real analyses
b.params.burnin = 1000
b.params.nsample = 2000
b.params.sampfreq = 20

## print params
b.params
Out[70]:
burnin          1000                
cleandata       0                   
delimit_alg     (0, 5)              
finetune        (0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01)
infer_delimit   0                   
infer_sptree    0                   
nsample         2000                
sampfreq        20                  
seed            12345               
tauprior        (2, 2000, 1)        
thetaprior      (2, 2000)           
usedata         1                   
In [71]:
## set some optional filters leaving others at their defaults
b.filters.maxloci=100
b.filters.minsnps=4

## print filters
b.filters
Out[71]:
maxloci   100                 
minmap    {'cys': 4, 'rex': 4, 'cup': 2, 'rck': 2, 'cya': 2, 'lip': 4, 'sup': 2, 'tha': 2, 'prz': 4}
minsnps   4                   

run BPP

You can either call 'write_bpp_files()' to write input files for this data set to be run in BPP, and then call BPP on those files, or you can use the '.run()' command to run the data files directly, and in parallel on the cluster. If you specify multiple reps then a different random sample of loci will be selected, and different random seeds applied to each replicate.

In [75]:
b.write_bpp_files()
input files created for job min4 (100 loci)
In [76]:
b.run()
submitted 2 bpp jobs [min4] (100 loci)
In [77]:
## wait for all ipyclient jobs to finish
ipyclient.wait()
Out[77]:
True
In [90]:
## check results
## parse the mcmc table with pandas library
import pandas as pd
btable = pd.read_csv(b.files.mcmcfiles[0], sep="\t", index_col=0)
btable.describe().T
Out[90]:
count mean std min 25% 50% 75% max
theta_1cup 2000.0 0.002 4.720e-04 1.110e-03 1.949e-03 0.002 0.003 0.005
theta_2cya 2000.0 0.001 4.587e-04 1.944e-04 9.056e-04 0.001 0.002 0.003
theta_3cys 2000.0 0.001 2.569e-04 7.718e-04 1.091e-03 0.001 0.001 0.002
theta_4lip 2000.0 0.002 3.075e-04 9.518e-04 1.619e-03 0.002 0.002 0.003
theta_5prz 2000.0 0.007 7.235e-04 4.556e-03 6.150e-03 0.007 0.007 0.009
theta_6rck 2000.0 0.002 4.692e-04 1.090e-03 1.860e-03 0.002 0.002 0.004
... ... ... ... ... ... ... ... ...
tau_13rexliprcktha 2000.0 0.002 2.376e-04 1.470e-03 1.854e-03 0.002 0.002 0.003
tau_14rexliprck 2000.0 0.002 2.367e-04 1.191e-03 1.781e-03 0.002 0.002 0.003
tau_15rexlip 2000.0 0.002 2.466e-04 9.497e-04 1.717e-03 0.002 0.002 0.003
tau_16cyscyasup 2000.0 0.005 4.718e-04 3.503e-03 4.881e-03 0.005 0.006 0.007
tau_17cyasup 2000.0 0.002 7.494e-04 3.230e-04 1.270e-03 0.002 0.002 0.005
lnL 2000.0 -13806.667 2.362e+01 -1.389e+04 -1.382e+04 -13806.152 -13790.543 -13733.031

26 rows × 8 columns

Success -- you're finished

Now that you have an idea of the myriad ways you can assembly your data, and some of the downstream analysis tools you are ready to explore RAD-seq data.