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FeGenie.py
executable file
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FeGenie.py
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#!/usr/bin/env python3
from collections import defaultdict
import re
import os
import textwrap
import argparse
import sys
import time
# TODO: ADD CYTOCHROME 579 HMM
# TODO: ADD COLUMN WITH ORF STRAND
def main():
def SUM(ls):
count = 0
for i in ls:
count += float(i)
return count
def firstNum(string):
outputNum = []
for i in string:
try:
int(i)
outputNum.append(i)
except ValueError:
break
Num = "".join(outputNum)
return Num
def Strip(ls):
outList = []
for i in ls:
gene = i.split("|")[0]
outList.append(gene)
return outList
def unique(ls, ls2):
unqlist = []
for i in ls:
if i not in unqlist and i in ls2:
unqlist.append(i)
return len(unqlist)
def Unique(ls):
unqList = []
for i in ls:
if i not in unqList:
unqList.append(i)
return unqList
def Unique2(ls):
unqList = []
for i in ls:
hmm = i.split("|")[0]
if hmm not in unqList:
unqList.append(hmm)
return unqList
def checkFe(ls):
count = 0
uniqueLS = []
for i in ls:
hmm = i.split("|")[0]
if hmm not in uniqueLS:
uniqueLS.append(hmm)
if geneToCatDict[hmm] in ["iron_reduction", "iron_oxidation"]:
count += 1
return count
def checkDFE1(ls):
count = 0
uniqueLS = []
for i in ls:
hmm = i.split("|")[0]
if hmm not in uniqueLS:
uniqueLS.append(hmm)
if hmm in ["DFE_0461", "DFE_0462", "DFE_0463", "DFE_0464", "DFE_0465"]:
count += 1
return count
def checkDFE2(ls):
count = 0
uniqueLS = []
for i in ls:
hmm = i.split("|")[0]
if hmm not in uniqueLS:
uniqueLS.append(hmm)
if hmm in ["DFE_0448", "DFE_0449", "DFE_0450", "DFE_0451"]:
count += 1
return count
def checkGACE(ls):
count = 0
uniqueLS = []
for i in ls:
hmm = i.split("|")[0]
if hmm not in uniqueLS:
uniqueLS.append(hmm)
if hmm in ["GACE_1843", "GACE_1844", "GACE_1845", "GACE_1846", "GACE_1847"]:
count += 1
return count
def check1(ls):
count = 0
uniqueLS = []
for i in ls:
hmm = i.split("|")[0]
if hmm not in uniqueLS:
uniqueLS.append(hmm)
if geneToCatDict[hmm] in ["iron_aquisition-siderophore_transport_potential", "iron_aquisition-heme_transport", "iron_aquisition-siderophore_transport"]:
count += 1
return count
def check1_2(ls):
count = 0
uniqueLS = []
for i in ls:
hmm = i.split("|")[0]
if hmm not in uniqueLS:
uniqueLS.append(hmm)
if geneToCatDict[hmm] in ["iron_aquisition-siderophore_synthesis"]:
count += 1
return count
def check2(ls):
count = 0
uniqueLS = []
for i in ls:
hmm = i.split("|")[0]
if hmm not in uniqueLS:
uniqueLS.append(hmm)
if geneToCatDict[hmm] in ["iron_aquisition-iron_transport", "iron_aquisition-heme_oxygenase"]:
count += 1
return count
def check3(ls):
count = 0
uniqueLS = []
for i in ls:
hmm = i.split("|")[0]
if hmm not in uniqueLS:
uniqueLS.append(hmm)
if geneToCatDict[hmm] in ["iron_aquisition-siderophore_synthesis"]:
count += 1
return count
def checkReg(ls):
count = 0
for i in ls:
hmm = i.split("|")[0]
if re.findall(r'regulation', geneToCatDict[hmm]):
count += 1
return count
def checkMam(ls):
count = 0
uniqueLS = []
for i in ls:
hmm = i.split("|")[0]
if hmm not in uniqueLS:
uniqueLS.append(hmm)
if geneToCatDict[hmm] == "magnetosome_formation":
count += 1
return count
def derep(ls):
outLS = []
for i in ls:
if i not in outLS:
outLS.append(i)
return outLS
def cluster(data, maxgap):
'''Arrange data into groups where successive elements
differ by no more than *maxgap*
#->>> cluster([1, 6, 9, 100, 102, 105, 109, 134, 139], maxgap=10)
[[1, 6, 9], [100, 102, 105, 109], [134, 139]]
#->>> cluster([1, 6, 9, 99, 100, 102, 105, 134, 139, 141], maxgap=10)
[[1, 6, 9], [99, 100, 102, 105], [134, 139, 141]]
'''
# data = sorted(data)
data.sort(key=int)
groups = [[data[0]]]
for x in data[1:]:
if abs(x - groups[-1][-1]) <= maxgap:
groups[-1].append(x)
else:
groups.append([x])
return groups
def lastItem(ls):
x = ''
for i in ls:
x = i
return x
def RemoveDuplicates(ls):
empLS = []
for i in ls:
if i not in empLS:
empLS.append(i)
else:
pass
return empLS
def allButTheLast(iterable, delim):
x = ''
length = len(iterable.split(delim))
for i in range(0, length - 1):
x += iterable.split(delim)[i]
x += delim
return x[0:len(x) - 1]
def secondToLastItem(ls):
x = ''
for i in ls[0:len(ls) - 1]:
x = i
return x
def pull(item, one, two):
ls = []
counter = 0
for i in item:
if counter == 0:
if i != one:
pass
else:
counter += 1
ls.append(i)
else:
if i != two:
ls.append(i)
else:
ls.append(i)
counter = 0
outstr = "".join(ls)
return outstr
def stabilityCounter(int):
if len(str(int)) == 1:
string = (str(0) + str(0) + str(0) + str(0) + str(int))
return (string)
if len(str(int)) == 2:
string = (str(0) + str(0) + str(0) + str(int))
return (string)
if len(str(int)) == 3:
string = (str(0) + str(0) + str(int))
return (string)
if len(str(int)) == 4:
string = (str(0) + str(int))
return (string)
def replace(stringOrlist, list, item):
emptyList = []
for i in stringOrlist:
if i not in list:
emptyList.append(i)
else:
emptyList.append(item)
outString = "".join(emptyList)
return outString
def remove(stringOrlist, list):
emptyList = []
for i in stringOrlist:
if i not in list:
emptyList.append(i)
else:
pass
outString = "".join(emptyList)
return outString
def remove2(stringOrlist, list):
emptyList = []
for i in stringOrlist:
if i not in list:
emptyList.append(i)
else:
pass
# outString = "".join(emptyList)
return emptyList
def removeLS(stringOrlist, list):
emptyList = []
for i in stringOrlist:
if i not in list:
emptyList.append(i)
else:
pass
return emptyList
def fasta(fasta_file):
seq = ''
header = ''
Dict = defaultdict(lambda: defaultdict(lambda: 'EMPTY'))
for i in fasta_file:
i = i.rstrip()
if re.match(r'^>', i):
if len(seq) > 0:
Dict[header] = seq
header = i[1:]
seq = ''
else:
header = i[1:]
seq = ''
else:
seq += i
Dict[header] = seq
# print(count)
return Dict
def fastaRename(fasta_file):
counter = 0
seq = ''
header = ''
Dict = defaultdict(lambda: defaultdict(lambda: 'EMPTY'))
for i in fasta_file:
i = i.rstrip()
if re.match(r'^>', i):
if len(seq) > 0:
Dict[header] = seq
header = i[1:]
header = header.split(" ")[0]
counter += 1
header = header + "_" + str(counter)
seq = ''
else:
header = i[1:]
header = header.split(" ")[0]
counter += 1
header = header + "_" + str(counter)
seq = ''
else:
seq += i
Dict[header] = seq
# print(count)
return Dict
def filter(list, items):
outLS = []
for i in list:
if i not in items:
outLS.append(i)
return outLS
def delim(line):
ls = []
string = ''
for i in line:
if i != " ":
string += i
else:
ls.append(string)
string = ''
ls = filter(ls, [""])
return ls
parser = argparse.ArgumentParser(
prog="FeGenie.py",
formatter_class=argparse.RawDescriptionHelpFormatter,
description=textwrap.dedent('''
*******************************************************
Developed by Arkadiy Garber and Nancy Merino;
University of Southern California, Earth Sciences
Please send comments and inquiries to arkadiyg@usc.edu
)`-.--. )\.---. )\.-. )\.---. )\ )\ .'( )\.---.
) ,-._( ( ,-._( ,' ,-,_) ( ,-._( ( \, / \ ) ( ,-._(
\ `-._ \ '-, ( . __ \ '-, ) \ ( ) ( \ '-,
) ,_( ) ,-` ) '._\ _) ) ,-` ( ( \ \ \ ) ) ,-`
( \ ( ``-. ( , ( ( ``-. `.)/ ) ) \ ( ``-.
).' )..-.( )/'._.' )..-.( '.( )/ )..-.(
%(?/////////&//%
.,,. (%((&@@@#/*. .,,.
.,,. @(((/&@@@#///** ...
#&((///////////////*/@
#*@.
() * )//*
<^^> * (/* .
.-""-. *)
.---. ."-....-"-._ _...---''`/. '
( (`\ \ .' ``-'' _.-"'`
\ \ \ : :. .-'
`\`.\: `:. _.'
( .'`.` _.'
`` `-..______.-'
):. (
."-....-".
.':. `.
"-..______..-"
Image design: Nancy Merino (2018);
ASCII art: https://manytools.org/hacker-tools/convert-images-to-ascii-art/
https://ascii.co.uk/text
*******************************************************
'''))
parser.add_argument('-bin_dir', type=str, help="directory of bins", default="NA")
parser.add_argument('-bin_ext', type=str, help="extension for bins (do not include the period)", default="NA")
parser.add_argument('-d', type=int, help="maximum distance between genes to be considered in a genomic \'cluster\'."
"This number should be an integer and should reflect the maximum number of "
"genes in between putative iron-related genes identified by the HMM database "
"(default=5)", default=5)
parser.add_argument('-ref', type=str, help="path to a reference protein database, which must be in FASTA format",
default="NA")
parser.add_argument('-out', type=str, help="name output directory (default=fegenie_out)",
default="fegenie_out")
parser.add_argument('-inflation', type=int, help="inflation factor for final gene category counts (default=1000)",
default=1000)
parser.add_argument('-t', type=int, help="number of threads to use for DIAMOND BLAST and HMMSEARCH "
"(default=1, max=16)", default=1)
parser.add_argument('-bams', type=str, help="a tab-delimited file with two columns: first column has the genome or "
"metagenome file names; second column has the corresponding BAM file "
"(provide full path to the BAM file). Use this option if you have genomes "
"that each have different BAM files associated with them. If you have a set "
"of bins from a single metagenome sample and, thus, have only one BAM file, "
" then use the \'-bam\' option. BAM files are only required if you would like to create "
"a heatmap that summarizes the abundance of a certain gene that is based on "
"read coverage, rather than gene counts.", default="NA")
parser.add_argument('-which_bams', type=str, help="if you provided a tab-delimited file specifying multiple BAM files for "
"your metagenome assemblies or bins/genomes, FeGenie will, by default, "
"make the heatmap CSV and dotplot based on the average depth across all of BAM files. "
"However, with this argument, you can specify which bam in that file that you "
"want FeGenie to use for the generation of a heatmap/dotplot. "
"For example, if only coverage from the first BAM file is desired, "
"then you can specify \'-which_bams 1\'. "
"For the third BAM file in the provided tab-delimited file, \'-which_bams 3\ should be specified'", default="average")
parser.add_argument('-bam', type=str, help="BAM file. This option is only required if you would like to create "
"a heatmap that summarizes the abundance of a certain gene that is based on "
"read coverage, rather than gene counts. If you have more than one BAM file"
"corresponding to different genomes that you are providing, please use the \'-bams\' "
"argument to provide a tab-delimited file that denotes which BAM file (or files) belongs "
"with which genome", default="NA")
# parser.add_argument('-delim', type=str, help="delimiter that separates contig names from ORF names (provide this flag if you are "
# "providing your own ORFs. Default delimiter for Prodigal-predicted ORFs is \'_\'", default="_")
parser.add_argument('-contig_names', type=str, help="contig names in your provided FASTA files. Use this option"
"if you are providing gene calls in amino acid format (don't forget"
"to add the \'--orfs\' flag)", default="NA")
parser.add_argument('-cat', type=str, help="comma-separated list of iron gene categories you'd like FeGenie to look for (default = all categories)", default="NA")
parser.add_argument('--gbk', type=str, help="include this flag if your bins are in Genbank format", const=True,
nargs="?")
parser.add_argument('--orfs', type=str,
help="include this flag if you are providing bins as open-reading frames or genes in FASTA amino-acid format",
const=True,
nargs="?")
parser.add_argument('--skip', type=str,
help="skip the main part of the algorithm (ORF prediction and HMM searching) "
"and re-summarize previously produced results (for example, if you want to re-run using "
"the --norm flag, or providing a BAM file). All other flags/arguments need to "
"be provided (e.g. -bin_dir, -bin_ext, -out, etc.)", const=True, nargs="?")
parser.add_argument('--meta', type=str,
help="include this flag if the provided contigs are from metagenomic/metatranscriptomic assemblies",
const=True, nargs="?")
parser.add_argument('--norm', type=str,
help="include this flag if you would like the gene counts for each iron gene category to be normalized to "
"the number of predicted ORFs in each genome or metagenome. Without "
"normalization, FeGenie will create a heatmap-compatible "
"CSV output with raw gene counts. With normalization, FeGenie will create a "
"heatmap-compatible with \'normalized gene abundances\'", const=True, nargs="?")
parser.add_argument('--all_results', type=str,
help="report all results, regardless of clustering patterns and operon structure", const=True, nargs="?")
parser.add_argument('--heme', type=str,
help="find all genes with heme-binding motifs (CXXCH), and output them to a separate summary file", const=True, nargs="?")
parser.add_argument('--hematite', type=str,
help="find all genes with hematite-binding motifs, and output them to a separate summary file", const=True, nargs="?")
parser.add_argument('--makeplots', type=str,
help="include this flag if you would like FeGenie to make some figures from your data?. "
"To take advantage of this part of the pipeline, you will need to have Rscipt installed. It is a way for R to be called directly from the command line. "
"Please be sure to install all the required R packages as instrcuted in the FeGenie Wiki: "
"https://github.com/Arkadiy-Garber/FeGenie/wiki/Installation. "
"If you see error or warning messages associated with Rscript, you can still expect to "
"see the main output (CSV files) from FeGenie.", const=True, nargs="?")
parser.add_argument('--nohup', type=str, help="include this flag if you are running FeGenie under \'nohup\', and would like to re-write a currently existing directory.", const=True,
nargs="?")
# CHECKING FOR CONDA INSTALL
os.system("echo ${iron_hmms} > HMMlib.txt")
os.system("echo ${rscripts} > rscripts.txt")
file = open("HMMlib.txt")
HMMdir = ""
for i in file:
HMMdir = i.rstrip()
bits = HMMdir + "/" + "HMM-bitcutoffs.txt"
file = open("rscripts.txt")
rscriptDir = ""
for i in file:
rscriptDir = i.rstrip()
try:
test = open(bits)
except FileNotFoundError:
os.system("which FeGenie.py > mainDir.txt")
file = open("mainDir.txt")
for i in file:
location = i.rstrip()
location = allButTheLast(location, "/")
HMMdir = location + "/hmms/iron/"
bits = HMMdir + "/" + "HMM-bitcutoffs.txt"
rscriptDir = location + "/rscripts/"
try:
test = open(bits)
except FileNotFoundError:
print("FeGenie could not locate the required directories. Please run the setup.sh script if "
"you have Conda installed. Otherwise, please run the setupe-noconda.sh script and put FeGenie.py into your $PATH")
raise SystemExit
os.system("rm mainDir.txt")
os.system("rm -f HMMlib.txt rscripts.txt")
if len(sys.argv) == 1:
parser.print_help(sys.stderr)
sys.exit(0)
args = parser.parse_known_args()[0]
# ************** Checking for the required arguments ******************* #
cwd = os.getcwd()
print("checking arguments")
if args.bin_dir != "NA":
binDir = args.bin_dir + "/"
binDirLS = os.listdir(args.bin_dir)
print(".")
else:
print("Looks like you did not provide a directory of genomes/bins or assemblies.")
print("Exiting")
raise SystemExit
if args.bam != "NA" and args.bams != "NA":
print("Please provide only one of the following flags: \'-bam\' or \'-bams\'.")
raise SystemExit
if args.bin_ext != "NA":
print(".")
else:
print(
'Looks like you did not provide an extension for your genomes/bins or assemblies, so FeGenie does not know'
' which files in the provided directory are fasta files that you would like analyzed.')
print("Exiting")
raise SystemExit
try:
os.listdir(args.out)
print("Looks like you already have a directory with the name: " + args.out)
if args.nohup:
answer = "y"
else:
answer = input("Would you like FeGenie to proceed and potentially overwrite files in this directory? (y/n): ")
if answer == "y":
print("Ok, proceeding with analysis!")
try:
os.listdir(args.out + "/ORF_calls")
except FileNotFoundError:
os.system("mkdir %s/ORF_calls" % args.out)
else:
print("Exiting")
raise SystemExit
except FileNotFoundError:
print(".")
os.system("mkdir %s" % args.out)
os.system("mkdir %s/ORF_calls" % args.out)
if lastItem(args.out) == "/":
outDirectory = "%s" % args.out[0:len(args.out)-1]
outDirectoryLS = os.listdir(outDirectory)
else:
outDirectory = "%s" % args.out
outDirectoryLS = os.listdir("%s" % args.out)
print("All required arguments provided!")
print("")
prodigal = 0
# *************** MAKE NR A DIAMOND DB AND READ THE FILE INTO HASH MEMORY ************************ #
if args.ref != "NA":
try:
testFile = open(args.ref + ".dmnd")
except FileNotFoundError:
print("Making diamond database out of provided reference file")
os.system("diamond makedb --in %s -d %s" % (args.ref, args.ref))
# *************** CALL ORFS FROM BINS AND READ THE ORFS INTO HASH MEMORY ************************ #
BinDict = defaultdict(lambda: defaultdict(lambda: 'EMPTY'))
binCounter = 0
for i in binDirLS:
if lastItem(i.split(".")) == args.bin_ext and not re.match(r'\.', i):
cell = i
binCounter += 1
if not args.gbk:
if args.orfs:
testFile = open("%s/%s" % (binDir, i), "r")
for line in testFile:
if re.match(r'>', line):
if re.findall(r'\|]', line):
print("Looks like one of your fasta files has a header containing the character: \|")
print(
"Unfortunately, this is a problem for FeGenie because it uses that character as delimiter to store important information.")
print("Please rename your FASTA file headers")
raise SystemExit
else:
try:
testFile = open("%s/ORF_calls/%s-proteins.faa" % (outDirectory, i), "r")
print("ORFS for %s found. Skipping Prodigal, and going with %s-proteins.faa" % (i, i))
for line in testFile:
if re.match(r'>', line):
if re.findall(r'\|]', line):
print(
"Looks like one of your fasta files has a header containing the character: \|")
print(
"Unfortunately, this is a problem for FeGenie because it uses that character as delimiter to store important information.")
print("Please rename your FASTA file headers")
raise SystemExit
except FileNotFoundError:
binFile = open("%s/%s" % (binDir, i), "r")
for line in binFile:
if re.match(r'>', line):
if re.findall(r'\|]', line):
print("Looks like one of your fasta files has a header containing the character: \|")
print(
"Unfortunately, this is a problem for FeGenie because it uses that character as delimiter to store important information.")
print("Please rename your FASTA file headers")
raise SystemExit
prodigal = 1
print("Finding ORFs for " + cell)
if args.meta:
os.system("prodigal -i %s/%s -a %s/ORF_calls/%s-proteins.faa -o %s/ORF_calls/%s-prodigal.out -p meta -q" % (
binDir, i, outDirectory, i, outDirectory, i))
else:
os.system(
"prodigal -i %s/%s -a %s/ORF_calls/%s-proteins.faa -o %s/ORF_calls/%s-prodigal.out -q" % (
binDir, i, outDirectory, i, outDirectory, i))
else:
os.system('gtt-genbank-to-AA-seqs -i %s/%s -o %s/%s.faa' % (binDir, i, outDirectory, i))
faa = open("%s/%s.faa" % (outDirectory, i))
faa = fasta(faa)
gbkDict = defaultdict(list)
counter = 0
count = 0
gbk = open("%s/%s" % (binDir, i))
for gbkline in gbk:
ls = gbkline.rstrip()
if re.findall(r'/locus_tag', ls):
count += 1
if count > 0:
gbk = open("%s/%s" % (binDir, i))
for gbkline in gbk:
ls = gbkline.rstrip()
if re.findall(r'LOCUS', ls):
locus = (ls)
locus = (locus.split(" ")[1])
locus = locus.split(" ")[0]
if re.findall(r'gene ', ls):
gene = (ls)
gene = (gene.split(" ")[1])
start = (gene.split("..")[0])
end = (gene.split("..")[1])
start = remove(start, ["c", "o", "m", "p", "l", "e", "m", "e", "n", "t", "(", ")"])
end = remove(end, ["c", "o", "m", "p", "l", "e", "m", "e", "n", "t", "(", ")"])
altContigName = (locus + "_" + start + "_" + end)
if re.findall(r'/locus_tag', ls):
locusTag = (ls)
locusTag = (locusTag.split("=")[1])
locusTag = remove(locusTag, ["\""])
counter += 1
if counter > 0:
gbkDict[locus].append(locusTag)
counter = 0
else:
# print(i)
gbk = open("%s/%s" % (binDir, i))
for gbkline in gbk:
ls = gbkline.rstrip()
if re.findall(r'LOCUS', ls):
locus = (ls)
locus = (locus.split(" ")[1])
locus = locus.split(" ")[0]
if re.findall(r'gene ', ls):
gene = (ls)
gene = (gene.split(" ")[1])
start = (gene.split("..")[0])
end = (gene.split("..")[1])
start = remove(start, ["c", "o", "m", "p", "l", "e", "m", "e", "n", "t", "(", ")"])
end = remove(end, ["c", "o", "m", "p", "l", "e", "m", "e", "n", "t", "(", ")"])
altContigName = (locus + "_" + start + "_" + end)
counter += 1
if re.findall(r'/locus_tag', ls):
locusTag = (ls)
locusTag = (locusTag.split("=")[1])
locusTag = remove(locusTag, ["\""])
if counter > 0:
gbkDict[locus].append(altContigName)
counter = 0
idxOut = open("%s/ORF_calls/%s-proteins.idx" % (outDirectory, i), "w")
faaOut = open("%s/ORF_calls/%s-proteins.faa" % (outDirectory, i), "w")
for gbkkey1 in gbkDict.keys():
counter = 0
for gbkey2 in gbkDict[gbkkey1]:
counter += 1
if len(faa[gbkey2]) > 0:
newOrf = gbkkey1 + "_" + str(counter)
idxOut.write(gbkey2 + "," + newOrf + "\n")
faaOut.write(">" + newOrf + "\n")
faaOut.write(str(faa[gbkey2]) + "\n")
idxOut.close()
faaOut.close()
if args.orfs:
file = open("%s/%s" % (binDir, i))
else:
file = open("%s/ORF_calls/%s-proteins.faa" % (outDirectory, i))
file = fasta(file)
for j in file.keys():
orf = j.split(" ")[0]
BinDict[cell][orf] = file[j]
if binCounter == 0:
print("Did not detect any files in the provided directory (%s) matching the provided filename extension (%s). "
"Please double-check the filenames and your command" % (args.bin_dir, args.bin_ext))
raise SystemExit
# ******************** READ BITSCORE CUT-OFFS INTO HASH MEMORY ****************************** #
meta = open(bits, "r")
metaDict = defaultdict(lambda: defaultdict(lambda: 'EMPTY'))
for i in meta:
ls = i.rstrip().split("\t")
metaDict[ls[0]] = ls[1]
# ******************* BEGINNING MAIN ALGORITHM **********************************))))
if not args.skip:
if args.cat == "NA":
catList = []
HMMdirLS = os.listdir(HMMdir)
for FeCategory in HMMdirLS:
if not re.match(r'\.', FeCategory) and FeCategory not in ["HMM-bitcutoffs.txt", "FeGenie-map.txt"]:
catList.append(FeCategory)
else:
categories = args.cat
catList = categories.split(",")
print("starting main pipeline...")
HMMdirLS = os.listdir(HMMdir)
for FeCategory in HMMdirLS:
if not re.match(r'\.', FeCategory) and FeCategory not in ["HMM-bitcutoffs.txt", "FeGenie-map.txt"] and FeCategory in catList:
print("")
print("Looking for following iron-related functional category: " + FeCategory)
hmmDir = "%s/%s/" % (HMMdir, FeCategory)
hmmDirLS2 = os.listdir("%s/%s" % (HMMdir, FeCategory))
HMMdict = defaultdict(lambda: defaultdict(lambda: defaultdict(lambda: "EMPTY")))
for i in binDirLS: # ITERATION THROUGH EACH BIN IN A GIVEN DIRECTORY OF BINS
if lastItem(i.split(".")) == args.bin_ext: # FILTERING OUT ANY NON-BIN-RELATED FILES
os.system(
"mkdir -p " + outDirectory + "/" + i + "-HMM") # CREATING DIRECTORY, FOR EACH BIN, TO WHICH HMMSEARCH RESULTS WILL BE WRITTEN
count = 0
for hmm in hmmDirLS2: # ITERATING THROUGH ALL THE HMM FILES IN THE HMM DIRECTORY
count += 1
perc = (count / len(hmmDirLS2)) * 100
sys.stdout.write("analyzing " + i + ": %d%% \r" % (perc))
sys.stdout.flush()
if len(metaDict[hmm.split(".")[0]]) == 0:
bit = 0
else:
bit = metaDict[hmm.split(".")[0]]
if args.orfs:
os.system(
"hmmsearch --cpu %d -T %d --tblout %s/%s-HMM/%s.tblout -o %s/%s-HMM/%s.txt %s/%s %s/%s"
% (int(args.t), float(bit), outDirectory, i, hmm, outDirectory, i, hmm, hmmDir, hmm, binDir, i)
)
else:
os.system(
"hmmsearch --cpu %d -T %d --tblout %s/%s-HMM/%s.tblout -o %s/%s-HMM/%s.txt %s/%s %s/ORF_calls/%s-proteins.faa"
% (int(args.t), float(bit), outDirectory, i, hmm, outDirectory, i, hmm, hmmDir, hmm, outDirectory, i)
)
# REMOVING THE STANDARD OUTPUT FILE
os.system(
"rm " + outDirectory + "/" + i + "-HMM/" + hmm + ".txt"
)
# READING IN THE HMMSEARCH RESULTS (TBLOUT) OUT FILE
try:
hmmout = open(outDirectory + "/" + i + "-HMM/" + hmm + ".tblout", "r")
except FileNotFoundError:
print("FeGenie cannot find the correct hmmsearch output files. "
"If you provided gene or ORF-call sequences, "
"please be sure to specify this in the command using the \'--orfs\' flag")
# COLLECTING SIGNIFICANT HMM HITS IN THE FILE
for line in hmmout:
if not re.match(r'#', line):
ls = delim(line)
evalue = float(ls[4])
bit = float(ls[5])
orf = ls[0]
if evalue < float(1E-1): # FILTERING OUT BACKGROUND NOISE
# LOADING HMM HIT INTO DICTIONARY, BUT ONLY IF THE ORF DID NOT HAVE ANY OTHER HMM HITS
if orf not in HMMdict[i]:
HMMdict[i][orf]["hmm"] = hmm
HMMdict[i][orf]["evalue"] = evalue
HMMdict[i][orf]["bit"] = bit
else:
# COMPARING HITS FROM DIFFERENT HMM FILES TO THE SAME ORF
if bit > HMMdict[i][orf]["bit"]:
HMMdict[i][orf]["hmm"] = hmm
HMMdict[i][orf]["evalue"] = evalue
HMMdict[i][orf]["bit"] = bit
print("")
out = open(outDirectory + "/%s-summary.csv" % (FeCategory), "w")
out.write("cell" + "," + "ORF" + "," + "HMM" + "," + "evalue" + "," + "bitscore" + "\n")
for key in HMMdict.keys():
for j in HMMdict[key]:
out.write(key + "," + j + "," + HMMdict[key][j]["hmm"] + "," +
str(HMMdict[key][j]["evalue"]) + "," + str(HMMdict[key][j]["bit"]) + "\n")
out.close()
time.sleep(5)
print("\n")
print("Consolidating summary files into one master summary file")
out = open(outDirectory + "/FinalSummary.csv", "w")
out.write("category" + "," + "cell" + "," + "orf" + "," + "related_hmm" + "," + "HMM-bitscore" + "\n")
resultsDir = os.listdir(outDirectory)
for i in resultsDir:
if lastItem(i.split("-")) == "summary.csv":
result = open(outDirectory + "/" + i, "r")
for j in result:
ls = j.rstrip().split(",")
cell = ls[0]
orf = ls[1]
hmm = ls[2]
bit = ls[4]
if cell != "cell":
out.write(i.split("-summary")[0] + "," + cell + "," + orf + "," + hmm + "," + str(bit) + "\n")
out.close()
time.sleep(5)
# ****************************************** DEREPLICATION *********************************************************
summary = open(outDirectory + "/FinalSummary.csv", "r")
SummaryDict = defaultdict(lambda: defaultdict(lambda: defaultdict(lambda: 'EMPTY')))
for i in summary:
ls = i.rstrip().split(",")
if ls[0] != "category" and ls[0] != "FeGenie":
if len(ls) > 0:
category = ls[0]
cell = ls[1]
orf = ls[2]
hmm = ls[3]
hmmBit = ls[4]
if cell not in SummaryDict.keys():
SummaryDict[cell][orf]["hmm"] = hmm
SummaryDict[cell][orf]["hmmBit"] = hmmBit
SummaryDict[cell][orf]["category"] = category
else:
if orf not in SummaryDict[cell]:
SummaryDict[cell][orf]["hmm"] = hmm
SummaryDict[cell][orf]["hmmBit"] = hmmBit
SummaryDict[cell][orf]["category"] = category
else:
if float(hmmBit) > float(SummaryDict[cell][orf]["hmmBit"]):
SummaryDict[cell][orf]["hmm"] = hmm
SummaryDict[cell][orf]["hmmBit"] = hmmBit
SummaryDict[cell][orf]["category"] = category
# ************************** CLUSTERING OF ORFS BASED ON GENOMIC PROXIMITY *********************************
if not args.orfs:
print("Identifying genomic proximities and putative operons")
CoordDict = defaultdict(lambda: defaultdict(list))
orfNameDict = defaultdict(lambda: defaultdict(list))
for i in SummaryDict.keys():
if i != "category":
for j in SummaryDict[i]:
contig = allButTheLast(j, "_")
numOrf = lastItem(j.split("_"))
CoordDict[i][contig].append(int(numOrf))
counter = 0
print("Clustering ORFs...")
print("")
out = open(outDirectory + "/FinalSummary-dereplicated-clustered.csv", "w")
for i in CoordDict.keys():
print(".")
for j in CoordDict[i]:
LS = (CoordDict[i][j])
clusters = (cluster(LS, args.d))
unknown = [[759,762,763,764,765], [5079,5080,5081]]
for k in clusters:
if len(RemoveDuplicates(k)) == 1:
orf = j + "_" + str(k[0])
out.write(SummaryDict[i][orf]["category"] + "," + i + "," + orf + "," + SummaryDict[i][orf]["hmm"] +
"," + str(SummaryDict[i][orf]["hmmBit"]) + "," + str(counter) + "\n")
out.write("#" + "," + "#" + "," + "#" + "," + "#" + "," + "#" + "," + "#" + "," + "#" + "," + "#" + "\n")
counter += 1
else:
for l in RemoveDuplicates(k):
orf = j + "_" + str(l)
out.write(SummaryDict[i][orf]["category"] + "," + i + "," + orf + "," + SummaryDict[i][orf][
"hmm"] + "," + str(SummaryDict[i][orf]["hmmBit"]) + "," + str(counter) + "\n")
out.write(
"#" + "," + "#" + "," + "#" + "," + "#" + "," + "#" + "," + "#" + "," + "#" + "," + "#" + "\n")
counter += 1
out.close()
else:
if args.contig_names != "NA":
contigNameDict = defaultdict(lambda: defaultdict(lambda: 'EMPTY'))
contigNames = open(args.contig_names)
for orfname in contigNames:
orfnameLS = orfname.rstrip().split("\t")
contigNameDict[orfnameLS[0]]["contig"] = ls[1]
contigNameDict[orfnameLS[0]]["position"] = ls[2]
CoordDict = defaultdict(lambda: defaultdict(list))
orfNameDict = defaultdict(lambda: defaultdict(list))
for i in SummaryDict.keys():
if i != "category":
for j in SummaryDict[i]:
# contigLS = contig.split(args.contig_names + args.delim)
numOrf = contigNameDict[j]["position"]
contig = contigNameDict[j]["contig"]
CoordDict[i][contig].append(int(numOrf))
orfNameDict[contig + "_" + numOrf] = j
CoordDict[i][contig].append(int(numOrf))