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 available and takes about <10 minutes to run completely, but can be run on even a laptop in about less than an hour.
We will use the Lagonosticta and Vidua data set from DaCosta & Sorenson 2016. This data set is composed of single-end 101bp reads from a ddRAD-seq library prepared with the SbfI and EcoRI enzymes and is available on NCBI by its study accession SRP059199. At the end of this notebook we also demonstrate the use of ipyrad.analysis tools to run downstream analyses on this data set.
The figure below from this paper shows the general workflow in which two fairly distinct clades were sequenced together but then analyzed separately.
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 tutorial 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 entrez-direct -c bioconda
## conda install sratools -c bioconda
In [8]:
## 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.
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## ipcluster start --n=20
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## connect to cluster
ipyclient = ipp.Client()
ipyclient.ids
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These data are archived on the NCBI sequence read archive (SRA) under accession id SRP059199. For convenience, the data are also hosted at a public Dropbox link which is a bit easier to access. Run the code below to download and decompress the fastq data files, which will save them into a directory called fastqs-Finches/.
In [3]:
## download the Pedicularis data set from NCBI
sra = ipa.sratools(accession="SRP059199", workdir="fastqs-Finches")
sra.run(force=True, ipyclient=ipyclient)
In [4]:
## you must provide a name for the Assembly
data = ip.Assembly("Finches")
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.
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## set parameters
data.set_params("project_dir", "analysis-ipyrad/Finches")
data.set_params("sorted_fastq_path", "fastqs-Finches/*.fastq.gz")
data.set_params("datatype", "ddrad")
data.set_params("restriction_overhang", ("CCTGCAGG", "AATTC"))
data.set_params("clust_threshold", "0.85")
data.set_params("filter_adapters", "2")
data.set_params("max_Hs_consens", (5, 5))
data.set_params("output_formats", "psvnkua")
## see/print all parameters
data.get_params()
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## run steps 1 & 2 of the assembly
data.run("12")
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## access the stats of the assembly (so far) from the .stats attribute
data.stats
Out[8]:
In [9]:
## run steps 3-5 (within-sample steps) of the assembly
data.run("345")
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## create data set with only Vidua samples + outgroup
subs = [i for i in data.samples if "Vidua" in i] +\
[i for i in data.samples if "Anomalo" in i]
vidua = data.branch("vidua", subsamples=subs)
## create data set with only Lagonostica sampes + outgroup
subs = [i for i in data.samples if "Lagon" in i] +\
[i for i in data.samples if "Clyto" in i]
lagon = data.branch("lagon", subsamples=subs)
In [15]:
vidua.run("6")
In [16]:
lagon.run("6")
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## iterate over data set and parameters
for assembly in [vidua, lagon]:
for min_sample in [4, 10]:
## create new assembly, apply new name and parameters
newname = "{}_min{}".format(assembly.name, min_sample)
newdata = assembly.branch(newname)
newdata.set_params("min_samples_locus", min_sample)
## run step 7
newdata.run("7")
In [3]:
vm4 = ip.load_json("analysis-ipyrad/Finches/vidua_min4.json")
vm4.stats
Out[3]:
In [4]:
lm4 = ip.load_json("analysis-ipyrad/Finches/lagon_min4.json")
lm4.stats
Out[4]:
In [5]:
## or read the full stats file as a bash command (cat)
!cat $vm4.stats_files.s7
In [6]:
## the same full stats for lagon
!cat $lm4.stats_files.s7
Thee is 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. You should still read the full tutorial of the software you are using to understand the full scope of the parameters involved and their impacts, but once you understand that, the ipyrad analysis tools provide an easy way to setup up scripts to sample different distributions of SNPs and to run many replicates in parallel.
In [13]:
import ipyrad.analysis as ipa
In [14]:
## you can re-load assemblies at a later time from their JSON file
min4 = ip.load_json("analysis-ipyrad/Finches/vidua_min4.json")
min10 = ip.load_json("analysis-ipyrad/Finches/vidua_min10.json")
In [29]:
## conda install raxml -c bioconda
## conda install toytree -c eaton-lab
In [48]:
## create a raxml analysis object for the min13 data sets
rax = ipa.raxml(
name=min10.name,
data=min10.outfiles.phy,
workdir="analysis-raxml",
T=20,
N=100,
o=[i for i in min10.samples if "Ano" in i],
)
In [49]:
## print the raxml command and call it
print rax.command
rax.run(force=True)
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## access the resulting tree files
rax.trees
Out[50]:
In [51]:
## plot a tree in the notebook with toytree
import toytree
tre = toytree.tree(rax.trees.bipartitions)
tre.root(wildcard="Ano")
tre.draw(
width=350,
height=400,
node_labels=tre.get_node_values("support"),
#use_edge_lengths=True,
);
In [39]:
## create a tetrad analysis object
tet = ipa.tetrad(
name=min4.name,
seqfile=min4.outfiles.snpsphy,
mapfile=min4.outfiles.snpsmap,
nboots=100,
)
In [40]:
## run tree inference
tet.run(ipyclient)
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## access tree files
tet.trees
Out[41]:
In [112]:
## plot results (just like above, but unrooted by default)
import toytree
qtre = toytree.tree(tet.trees.nhx)
qtre.root(wildcard="Ano")
qtre.draw(
width=350,
height=400,
node_labels=tre.get_node_values("support"),
);
In [54]:
## draw a cloud-tree to see variation among bootstrap trees
## note that the trees are UNROOTED here, but tips are in the
## same order in all trees.
boots = toytree.multitree(tet.trees.boots, fixed_order=tre.get_tip_labels())
boots.draw_cloudtree(orient='right', edge_style={"opacity": 0.05});
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## conda install structure clumpp -c ipyrad
In [56]:
## create a structure analysis object for the no-outgroup data set
struct = ipa.structure(
name=min10.name,
data=min10.outfiles.str,
mapfile=min10.outfiles.snpsmap,
)
## set params for analysis (should be longer in real analyses)
struct.mainparams.burnin=1000
struct.mainparams.numreps=8000
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## run structure across 10 random replicates of sampled unlinked SNPs
for kpop in [2, 4, 6, 8]:
struct.run(kpop=kpop, nreps=10, ipyclient=ipyclient)
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## wait for all of these jobs to finish
ipyclient.wait()
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In [64]:
## these options make it run faster
struct.clumppparams.m = 3 ## use largegreedy algorithm
struct.clumppparams.greedy_option = 2 ## test nrepeat possible orders
struct.clumppparams.repeats = 10000 ## number of repeats
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## collect results
tables = {}
for kpop in [2, 4, 6, 8]:
tables[kpop] = struct.get_clumpp_table(kpop)
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## order of bars will be taken from ladderized tree above
myorder = tre.get_tip_labels()
In [69]:
## import toyplot (packaged with toytree)
import toyplot
## plot bars for each K-value (mean of 10 reps)
for kpop in [2, 4, 6, 8]:
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},
)
In [70]:
## conda install treemix -c ipyrad
In [93]:
## group taxa into 'populations'
imap = {
'orient': ['Vidua_orientalis'],
'interj': ['Vidua_interjecta'],
'obtusa': ['Vidua_obtusa'],
'paradi': ['Vidua_paradisaea'],
'hypoch': ['Vidua_hypocherina'],
'macrou': ['Vidua_macroura_macroura', 'Vidua_macroura_arenosa'],
'fische': ['Vidua_fischeri'],
'regia' : ['Vidua_regia'],
'chalyb': ['Vidua_chalybeata_amauropteryx', 'Vidua_chalybeata_neumanni'],
'purpur': ['Vidua_purpurascens'],
'rarico': ['Vidua_raricola'],
#'outgro': ['Anomalospiza_imberbis'],
}
## optional: loci will be filtered if they do not have data for at
## least N samples in each species. Minimums cannot be <1.
minmap = {
'orient': 1,
'interj': 1,
'obtusa': 1,
'paradi': 1,
'hypoch': 1,
'macrou': 2,
'fische': 1,
'regia' : 1,
'chalyb': 2,
'purpur': 1,
'rarico': 1,
#'outgro': 1,
}
In [104]:
## create a treemix analysis object
tmix = ipa.treemix(
name=min10.name,
data=min10.outfiles.snpsphy,
imap=imap,
minmap=minmap,
)
## set params on treemix object
tmix.params.m = 1
tmix.params.root = "interj,orient,paradi,obtusa"
tmix.params.global_ = 1
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## you can simply write the input files and run them externally
## or, as we show below, use the .run() command to run them here.
tmix.write_output_file()
In [108]:
## 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 [110]:
import toyplot
import numpy as np
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
In [15]:
## create a baba analysis object
bb = ipa.baba(
data=min4.outfiles.loci,
newick="analysis-raxml/RAxML_bestTree.vidua_min10"
)
In [16]:
## this will generate tests from the tree, using constraints.
bb.generate_tests_from_tree(
constraint_exact=False,
constraint_dict={
"p4": ['Anomalospiza_imberbis'],
'p3': ['Vidua_macroura_macroura', 'Vidua_macroura_arenosa'],
})
In [17]:
## run inference and significance testing on tests
bb.run(ipyclient=ipyclient)
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## sorted results for the tests performed
bb.results_table.sort_values(by="Z", ascending=False)
Out[18]:
In [19]:
## the test that had the most significant result: (BABA)
bb.tests[25]
Out[19]:
In [20]:
## next best (ABBA)
bb.tests[22]
Out[20]: