view tools/protein_analysis/wolf_psort.py @ 20:a19b3ded8f33 draft

v0.2.11 Job splitting fast-fail; RXLR tools supports HMMER2 from BioConda; Capture more version information; misc internal changes

#!/usr/bin/env python
"""Wrapper for WoLF PSORT v0.2 for use in Galaxy.

This script takes exactly four command line arguments:
 * the organism type (animal, plant or fungi)
 * number of threads to use (integer)
 * an input protein FASTA filename
 * output tabular filename.

It then calls the standalone WoLF PSORT v0.2 program runWolfPsortSummary
(not the webservice), and coverts the output from something like this:

# k used for kNN is: 27
gi|301087619|ref|XP_002894699.1| extr 12, mito 4, E.R. 3, golg 3, mito_nucl 3
gi|301087623|ref|XP_002894700.1| extr 21, mito 2, cyto 2, cyto_mito 2

In order to make it easier to use in Galaxy, this wrapper script reformats
this to use tab separators, with one line per compartment prediction:

#ID	Compartment	Score	Rank
gi|301087619|ref|XP_002894699.1|	extr	12	1
gi|301087619|ref|XP_002894699.1|	mito	4	2
gi|301087619|ref|XP_002894699.1|	E.R.	3	3
gi|301087619|ref|XP_002894699.1|	golg	3	4
gi|301087619|ref|XP_002894699.1|	mito_nucl	3	5
gi|301087623|ref|XP_002894700.1|	extr	21	1
gi|301087623|ref|XP_002894700.1|	mito	2	2
gi|301087623|ref|XP_002894700.1|	cyto	2	3
gi|301087623|ref|XP_002894700.1|	cyto_mito	2	4

Additionally in order to take full advantage of multiple cores, by subdividing
the input FASTA file multiple copies of WoLF PSORT are run in parallel. I would
normally use Python's multiprocessing library in this situation but it requires
at least Python 2.6 and at the time of writing Galaxy still supports Python 2.4.
"""

from __future__ import print_function

import os
import sys

from seq_analysis_utils import run_jobs, split_fasta, thread_count

FASTA_CHUNK = 500
exe = "runWolfPsortSummary"

"""
Note: I had trouble getting runWolfPsortSummary on the path (via a link), other
than by including all of /opt/WoLFPSORT_package_v0.2/bin , so used a wrapper
python script called runWolfPsortSummary as follows:

#!/usr/bin/env python
#Wrapper script to call WoLF PSORT from its own directory.
import os
import sys
import subprocess
saved_dir = os.path.abspath(os.curdir)
os.chdir("/opt/WoLFPSORT_package_v0.2/bin")
args = ["./runWolfPsortSummary"] + sys.argv[1:]
return_code = subprocess.call(args)
os.chdir(saved_dir)
sys.exit(return_code)

For more details on this workaround, see:
https://lists.galaxyproject.org/pipermail/galaxy-dev/2015-December/023386.html
"""

if "-v" in sys.argv or "--version" in sys.argv:
    sys.exit("WoLF-PSORT wrapper version 0.0.11")

if len(sys.argv) != 5:
    sys.exit("Require four arguments, organism, threads, input protein FASTA file & output tabular file")

organism = sys.argv[1]
if organism not in ["animal", "plant", "fungi"]:
    sys.exit("Organism argument %s is not one of animal, plant, fungi" % organism)

num_threads = thread_count(sys.argv[2], default=4)
fasta_file = sys.argv[3]
tabular_file = sys.argv[4]


def clean_tabular(raw_handle, out_handle):
    """Clean up WoLF PSORT output to make it tabular."""
    for line in raw_handle:
        if not line or line.startswith("#"):
            continue
        name, data = line.rstrip("\r\n").split(None, 1)
        for rank, comp_data in enumerate(data.split(",")):
            comp, score = comp_data.split()
            out_handle.write("%s\t%s\t%s\t%i\n"
                             % (name, comp, score, rank + 1))


fasta_files = split_fasta(fasta_file, tabular_file, n=FASTA_CHUNK)
temp_files = [f + ".out" for f in fasta_files]
assert len(fasta_files) == len(temp_files)
jobs = ["%s %s < %s > %s" % (exe, organism, fasta, temp)
        for (fasta, temp) in zip(fasta_files, temp_files)]
assert len(fasta_files) == len(temp_files) == len(jobs)


def clean_up(file_list):
    for f in file_list:
        if os.path.isfile(f):
            os.remove(f)


if len(jobs) > 1 and num_threads > 1:
    # A small "info" message for Galaxy to show the user.
    print("Using %i threads for %i tasks" % (min(num_threads, len(jobs)), len(jobs)))
results = run_jobs(jobs, num_threads)
assert len(fasta_files) == len(temp_files) == len(jobs)
for fasta, temp, cmd in zip(fasta_files, temp_files, jobs):
    error_level = results[cmd]
    try:
        output = open(temp).readline()
    except IOError:
        output = ""
    if error_level or output.lower().startswith("error running"):
        clean_up(fasta_files)
        clean_up(temp_files)
        sys.exit("One or more tasks failed, e.g. %i from %r gave:\n%s" % (error_level, cmd, output),
                 error_level)
del results

out_handle = open(tabular_file, "w")
out_handle.write("#ID\tCompartment\tScore\tRank\n")
for temp in temp_files:
    data_handle = open(temp)
    clean_tabular(data_handle, out_handle)
    data_handle.close()
out_handle.close()

clean_up(fasta_files)
clean_up(temp_files)