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PyBlast

PyBlast is a small library using mostly BioPython, designed to process easily local blast requests and global alignment on multiple cores.

Installation

The library was written using python 3.6 but should work on lower version of python 3, however I did not test that.

Install first biopython (using conda for example) :

conda install -c anaconda biopython

Using pip

pip install git+https://github.com/jsgounot/PyBlast.git

Or download / clone the github

git clone https://github.com/jsgounot/PyBlast.git
cd PyBlast
python setup.py install --user

Local alignment

A blast command line can be launched using BCLine. By default only one core is used, unless ncore is provided (see below for more informations about how multiprocessing is done). If multiple cores are used, the default chunksize (corresponding to the maximum number of sequences in the query file of each process) is set to 200.

from pyblast import BCLine

query = "path/to/your/query"
db = "path/to/your/blastdb"

bcl = BCLine("blastp", query=query, subject=db, word_size=3)
print (bcl.run(ncore=4, chunksize=20))

Most of the process is similar to usual biopython blastcommandline functions. Additionally, PyBlast includes BCLine6, which automatically process blast table output (outfmt 6) and returns a pandas dataframe.

from pyblast import BCLine6

query = "path/to/your/query"
db = "path/to/your/blastdb"

bcl = BCLine6("blastp", query=query, subject=db)
print (bcl.run(ncore=4, chunksize=20))

The dataframe includes several columns, such as identity, coverage and percentage of each of these columns compared to the query and subject size.

One can add supplemental outfmt columns :

bcl = BCLine6("blastn", query=query, subject=db, outfmt="evalue")
res = bcl.run(ncore=1, chunksize=100, quiet=False)

However, please be aware that wrong outfmt names in the outfmt mostly lead to treatment errors, since blast will simply ignore wrong outfmt names and that pyblast is not able to check which supplemental outfmt names are correct or not.

Global alignment

Global alignment is provided by the BioPython pairwise2 module. By default, the pairwise2.align.globalxx function is used. Before using this function, I highly encourage to read the biopython documentation about this module since most of the results while depend of the input function and parameters. Within PyBlast, local alignments can be launched using the GlobalAlignment object which took as parameter an iterable of SeqRecord pairs. In this example, we will just make local alignments of every pairs of a single fasta file.

from Bio import SeqIO
from itertools import combinations
from pyblast import GlobalAlignment

records = SeqIO.parse(query, "fasta")
pairs = combinations(records, 2)

gal = GlobalAlignment(pairs)
print (gal.run(ncore=4))

By default, results are only output alignments. You can also use the callback function run_pair_identity which will automatically determine query and subject similarity of each alignments.

records = SeqIO.parse(query, "fasta")
pairs = combinations(records, 2)

gal = GlobalAlignment(pairs)
callback = GlobalAlignment.run_pair_identity
print (gal.run(ncore=4, callback=callback))

Note that the biopython alignment function and paramaters can be changed using the gfun, args and kwargs attributes.

args = (5, -4, -2, -0.5)
gal = GlobalAlignment(pairs)
callback = GlobalAlignment.run_pair_identity
print (gal.run(* args, ncore=4, callback=callback, gfun="localms", one_alignment_only=True))

Combining local and global alignments

PyBlast provides a way to combine (blast) local and (biopython) global alignment into a single object : Global2Local. The idea is to select only the best local alignments for each query and produce a global alignments for the selected pairs. By default, the BCLine6.get_best function is used, which took the 10 best local alignments based on the query positive value (qpos). Both local and global functions can be modified.

from pyblast import Local2Global

l2g = Local2Global(query=query, subject=db)
res = l2g.run("blastp", ncore=4, chunksize=20, gkwargs={"one_alignment_only" : True, "gfun" : None})
print (res)

Merging HSP

In some case, merging HSPs from the same query / subject pair can provide a faster solution instead of global alignments. In this case, all HSPs from the same pair are mixed together and the identity and similarity values correspond to all positions which have matched in at least one HSP. Nevertheless, note that it could not provide similar results than global alignments and can leads to wrong similarity values. This method can be used with the BCLineHSPFuse object, which performs both the blast query and the result analysis in each core.

from pyblast import BCLineHSPFuse

bopt = {"evalue" : "2", "word_size" : "3"}
bc = BCLineHSPFuse("blastp", query=fname, subject=fname, ** bopt)
res = bc.run(ncore=4)
print (res)

Columns meaning

Below are explained the signification of columns added by pyblast. Note that some of them can be linked to either the query (q) or the subject (s). Example : qlen corresponding to the size of the query sequence.

Column name Meaning
len Size of the sequence
length Length of the part covered by the HSP
ide Percentage of the sequence size (len) covered by identical element (nident)
cov Percentage of the sequence size (len) covered by the HSP (length)
pos Percentage of the sequence size (len) covered by positive elements (positive)
gid Number of identical element from global alignment analysis
gLEN Size of the global alignment
gPID This division : gid / gLEN
gXPID Percentage of the gid divided by either the query or subject length
gSCORE Score of the global alignment
ide_prc Percentage of identical elements compared to the total length
pos_prc Percentage of positive elements compared to the total length

Some notes about multiprocessing

For multicore (ncore > 1) Blast analysis, the query fasta file is splitted into several sub fasta files with a predifined sequences number called chunksize and the results are afterward merged together. This option can be combined with the blast option num_threads. However, while an increasing number of threads allows a lower memory consomption and increase execution speed, splitting the fasta file and running multiple blast tasks is way faster.

Below is shown the time used to blast 3000 random S. cerevisiae protein sequences against the S. cerevisiae proteom (SGD), full data (50x50) can be found in the blast_time directory. Note that while num_thread does not speed up that much execution time, increasing only the number of ncore can lead to memory overflow.

blast_performance

Known issues

Multicore issue with BCLine6 and MacOS

The multicore library might have some issue depending on your operating system, especially on Mac OS. If you encounter errors when using ncore > 1, you can add macos_formating=True in the BCLine6 constructor:

from pyblast import BCLine6

query = "path/to/your/query"
db = "path/to/your/blastdb"

bcl = BCLine6("blastn", query=query, db=db, macos_formating=False)
print (bcl.run(ncore=4, chunksize=500, quiet=False))

RuntimeError:freeze_support()

Please see this or this. You have to enclose your codelines into a main function as such:

from pyblast import BCLine6

if __name__ == "__main__":   
	query = "path/to/your/query"
	db = "path/to/your/blastdb"

	bcl = BCLine6("blastn", query=query, db=db, macos_formating=False)
	print (bcl.run(ncore=4, chunksize=500, quiet=False))