Extract all sequence data within a genomic window, concatenate, and write to a phylip file. Useful for inferring the phylogeny near a specific gene/region of interest. Follow up with downstream phylogenetic analysis of the region.
Key features:
In [1]:
# conda install ipyrad -c bioconda
# conda install raxml -c bioconda
# conda install toytree -c eaton-lab
In [2]:
import ipyrad.analysis as ipa
import toytree
In [3]:
# the path to your HDF5 formatted seqs file
seqfile = "/home/deren/Downloads/ref_pop2.seqs.hdf5"
The window_extracter() tool takes the .seqs.hdf5 database file from ipyrad as its input file. You select scaffolds by their index (integer) which can be found in the .scaffold_table. We can see from the table below that this genome has 12 large scaffolds (chromosome-scale linkage blocks) and many other smaller unplaced scaffolds. If you are working with a high quality reference genome then it will likely look similar to this, whereas many other reference genomes will be composed of many more scaffolds that are mostly smaller in size. Here I will focus just on the large chromosomes.
In [4]:
# first load the data file with no other arguments to see scaffold table
ext = ipa.window_extracter(seqfile)
# the scaffold table shows scaffold names and lens in length-order
ext.scaffold_table.head(15)
Out[4]:
The scaffold_idxs designates the scaffold to extract sequence data from. This is the index (row) of the named scaffold from the scaffold table (e.g., above). The window_extracter tool will select all RAD data within this window and exclude any sites that have no data (e.g., the space between RAD markers, or the space between paired reads) to create a clean concise alignment.
The .stats attribute shows the information content of the selected window before and after filtering. The stats are returned as a dataframe, showing the size, information content, missingness, and number of samples in the alignment. You can see that the 55Mbp scaffold is reduced to a 450Kbp alignment that includes 13K snps and has 20% missing data across 30 samples (NB: this dataset already had some minimum sample filtering applied during assembly). The default filtering applied to sites only reduced the number of sites by a few thousand.
In [5]:
# select a scaffold idx, start, and end positions
ext = ipa.window_extracter(
data=seqfile,
scaffold_idxs=0,
)
# show stats of the window
ext.stats
Out[5]:
You can use the start and end arguments to select subsets of scaffolds as smaller window sizes to be extracted. As with the example above the selected window will be filtered to reduce missing data. If there is no data in the selected window the stats will show no sites, and a warning will be printed. An example with no data and with some data are both shown below.
In [6]:
# select a scaffold idx, start, and end positions
ext = ipa.window_extracter(
data=seqfile,
scaffold_idxs=0,
start=0,
end=10000,
)
# show stats of the window
ext.stats
Out[6]:
In [7]:
# select a scaffold idx, start, and end positions
ext = ipa.window_extracter(
data=seqfile,
scaffold_idxs=0,
start=500000,
end=800000,
)
# show stats of the window
ext.stats
Out[7]:
mincovYou can filter sites from the alignment by using mincov, which applies a filter to all sites in the alignment. For example, mincov=0.5 will require that 50% of samples contain a site that is not N or - for the site to be included in the alignment. This value can be a proportion like 0.5, or it can be a number, like 10.
In [8]:
# select a scaffold idx, start, and end positions
ext = ipa.window_extracter(
data=seqfile,
scaffold_idxs=0,
start=500000,
end=800000,
mincov=0.8,
rmincov=0.5,
)
# show stats of the window
ext.stats
Out[8]:
imap and minmapAn imap dictionary can be used to group samples into populations/species, as in the example below. It takes key,value pairs where the key is the name of the group, and the value is a list of sample names. One way to use an imap is to apply a minmap filter. This acts just like the global mincov filter, but applies to each group separately. Only if a site meets the minimum coverage argument for each group will it be retained in the data set. In this case the imap sampling selected 28/30 samples and required 75% of data in each group which reduced the number of SNPs from 92 to 86.
In [10]:
# assign samples to groups/taxa
imap = {
"reference": ["reference"],
"virg": ["TXWV2", "LALC2", "SCCU3", "FLSF33", "FLBA140"],
"mini": ["FLSF47", "FLMO62", "FLSA185", "FLCK216"],
"gemi": ["FLCK18", "FLSF54", "FLWO6", "FLAB109"],
"bran": ["BJSL25", "BJSB3", "BJVL19"],
"fusi": ["MXED8", "MXGT4", "TXGR3", "TXMD3"],
"sagr": ["CUVN10", "CUCA4", "CUSV6"],
"oleo": ["CRL0030", "HNDA09", "BZBB1", "MXSA3017"],
}
# set a simple minmap requiring 1 sample from each group
minmap = {name: 0.75 for name in imap}
In [11]:
# select a scaffold idx, start, and end positions
ext = ipa.window_extracter(
data=seqfile,
scaffold_idxs=0,
start=500000,
end=800000,
mincov=0.8,
imap=imap,
minmap=minmap,
)
# show stats of the window
ext.stats
Out[11]:
imapYou can also use an imap dictionary to select which samples to include/exclude from an analysis. This is an easy way to remove rogue taxa, hybrids, or technical replicates from phylogenetic analyses. Here I select a subset ot taxa to include in the analyses and keep only sites that have 80% coverage from scaffold 2 (Qrob_Chr03).
In [12]:
# select a scaffold idx, start, and end positions
ext = ipa.window_extracter(
data=seqfile,
scaffold_idxs=2,
mincov=0.8,
imap={
"include": [
"TXWV2", "LALC2", "SCCU3", "FLSF33", "FLBA140",
"FLSF47", "FLMO62", "FLSA185", "FLCK216",
"FLCK18", "FLSF54", "FLWO6", "FLAB109",
]
},
)
# show stats of the window
ext.stats
Out[12]:
You can also concatenate multiple scaffolds together using window_extracter. This can be useful for creating genome-wide alignments, or smaller subsets of the genome. For example, you may want to combine multiple scaffolds from the same chromosome together, or, if you are working with denovo data, you could even combine a random sample of anonymous loci together as a sort of pseudo bootstrapping procedure. To select multiple scaffolds you simply provide a list or range of scaffold idxs.
In [13]:
# select a scaffold idx, start, and end positions
ext = ipa.window_extracter(
data=seqfile,
scaffold_idxs=[0, 1, 2, 3, 4, 5],
mincov=0.5,
)
# show stats of the window
ext.stats
Out[13]:
imapYou can further reduce missing data by condensing data from multiple samples into a single "consensus" representative using the consensus_reduce=True option. This uses the imap dictionary to group samples into groups and sample the most frequent allele. This can be particularly useful for analyses in which you want dense species-level coverage with little missing data, but it is not particularly important which individual represents the sampled allele for a species at a given locus. For example, if you want to construct many gene trees with one representative per species to use as input to a two-step species tree inference program like ASTRAL.
In [22]:
# select a scaffold idx, start, and end positions
ext = ipa.window_extracter(
data=seqfile,
scaffold_idxs=0,
start=200000,
end=5000000,
mincov=0.8,
imap=imap,
minmap=minmap,
consensus_reduce=True,
)
# show stats of the window
ext.stats
Out[22]:
In [24]:
ext.run(force=True)
In [26]:
# path to the phylip file output
ext.outfile
Out[26]:
You can pass in the file path that was created above to a number of inference programs. The ipyrad tools for raxml and mrbayes both accept phylip format (ipyrad converts it to nexus under the hood for mrbayes).
In [19]:
# run raxml on the phylip file
rax = ipa.raxml(data=ext.outfile, name="test", N=50, T=4)
# show the raxml command
print(rax.command)
In [20]:
# run job and wait to finish
rax.run(force=True)
In [21]:
# plot the tree for this genome window
print(rax.trees.bipartitions)
tre = toytree.tree(rax.trees.bipartitions)
rtre = tre.root("reference").collapse_nodes(min_support=50)
rtre.draw(node_labels="support");
Now that you've seen how easy it is to extract a single window from the genome, and to apply a number of filters to it and then infer a tree, you can imagine how easy it is to apply this framework to many hundreds or thousands of windows along the genome. Head over to the tree_slider tutorial next to see this in action.