def query_vsearch(inputs, outdir, simmilarity, processes, aln_user_string,
extraargstring, pool):
"""Runs a VSEARCH alignment on pairs of query/reference sequences.
:param inputs: A list of pairs of (filepaths to) query_fastas and the refrence fastas to compare them to.
:param outdir: Filepath to the directory where the alignment result should be written.
:param aln_user_string: An optional string of commandline parameters passed to the VSEARCH program.
:param simmilarity: The minimum simmilarity percentage (between reference and query sequences), \
as a decimal between 0 and 1), required for a positive match.
:param processes: The number of processes to use in the identification process.
:param extraargstring: Advanced program parameter string.
:param pool: A fully initalized multiprocessing.Pool object.
"""
printVerbose("Aligning against reference sequences...")
# # vsearch --usearch_global %s seeds.pick.fasta --db ../data/BiocodePASSED_SAP.txt --id 0.9 \
# --userfields query+target+id+alnlen+qcov --userout %sout --alnout %s alnout.txt
run_parallel([
ProgramRunner(ProgramRunnerCommands.ALIGN_VSEARCH, [
processes, query_fasta, ref_fasta, simmilarity,
"%s/%s.out" % (outdir, strip_ixes(query_fasta)),
"%s/%s.alnout" % (outdir, strip_ixes(query_fasta)), aln_user_string
], {
"exists": [query_fasta, ref_fasta],
"positive": [processes]
}, extraargstring) for query_fasta, ref_fasta in inputs
], pool)
printVerbose("Done aligning.")
return
def clean_quality_trimmomatic(self, input_f, outdir, window_size, quality, min_len, processes, extraargstring):
"""Uses a sliding window to identify and trim away areas of low quality.
:param input_f: Filepath to input file or folder.
:param outdir: Filepath to the output directory.
:param window_size: Width of the sliding window. (Number of consecutive base-pairs to average for quality \
analysis).
:param quality: Minimum quality allowed. Sections with lower average quality than this will be dropped.
:param min_len: Minimum allowed length for TRIMMED sequences. (i.e. if a sequence is too short after trimming,
its dropped.)
:param processes: Number of processes to use to clean the input fileset.
"""
# "trimomatic": "java -jar ~/ARMS/programs/Trimmomatic-0.33/trimmomatic-0.33.jar SE \
# -%phred %input %output SLIDINGWINDOW:%windowsize:%minAvgQuality MINLEN:%minLen"
inputs = getInputFiles(input_f)
debugPrintInputInfo(inputs, "clean")
pool = init_pool(min(len(inputs), processes))
printVerbose("Cleaning sequences with Trimmomatic...")
run_parallel([ProgramRunner(ProgramRunnerCommands.CLEAN_TRIMMOMATIC,
[input_, "%s/%s_cleaned.fastq" % (outdir, strip_ixes(input_)), window_size, quality,
min_len],
{"exists": [outdir, input_], "positive": [window_size, quality, min_len]},
extraargstring)
for input_ in inputs], pool)
printVerbose("Done cleaning sequences.")
cleanup_pool(pool)
def assemble_pear(self, input_f, input_r, outdir, name, processes, pearthreads, extraargstring):
"""Uses PEAR to assemble paired F/R read files in run_parallel.
:param input_f: File path to forward Fastq Reads file or folder.
:param input_r: File path to reverse Fastq Reads file or folder.
:param outdir: File path to the output directory.
:param name: File prefix for the assembled reads.
:param processes: The maximum number of processes to use.
:param extraargstring: Advanced program parameter string.
:param pearthreads: The number of threads per process to use.
"""
# "~/programs/pear-0.9.4-bin-64/pear-0.9.4-64 -f %s -r %s -o %s -j %s -m %d"
inputs = validate_paired_fastq_reads(input_f, input_r)
pool = init_pool(min(len(inputs), processes))
printVerbose("\tAssembling reads with pear")
debugPrintInputInfo(inputs, "assemble")
run_parallel([ProgramRunner(ProgramRunnerCommands.ASSEMBLE_PEAR,
[forwards, reverse, "%s/%s_%s" % ( outdir, name, getFileName(forwards)),
pearthreads],
{"exists": [forwards, reverse], "positive": [pearthreads]},
extraargstring)
for forwards, reverse in inputs], pool)
printVerbose("Done assembling sequences...")
# Grab all the auxillary files (everything not containing ".assembled."
aux_files = getInputFiles(outdir, "*", "*.assembled.*", ignore_empty_files=False)
# make aux dir for extraneous files and move them there
bulk_move_to_dir(aux_files, makeAuxDir(outdir))
cleanup_pool(pool)
def demux_by_name(self, input_f, barcodes, outdir, filetype, processes, extraargstring):
"""Demuxes using SeqIO.
:param input_f: File path to input file or folder of input files.
:param barcodes: File path to input barcodes file.
:param outdir: Filepath to output directory.
:param filetype: Either 'fasta' or 'fastq'.
:param processes: Number of processes to use to demux input fileset.
:param extraargstring: Advanced program parameter string.
"""
aux_dir = makeAuxDir(outdir)
# Get input files
files_to_split = getInputFiles(input_f)
# Assign the files shard numbers
file_id = range(len(files_to_split))
file_id_pairs = zip(files_to_split, file_id)
debugPrintInputInfo(files_to_split, "demux")
pool = init_pool(min(len(file_id_pairs), processes))
printVerbose("Demuxing sequences...")
run_parallel([PythonRunner(split_on_name,
[input_, barcodes, outdir, id_, filetype], {"exists": [input_]})
for input_, id_ in file_id_pairs], pool)
# Grab all the auxillary files
aux_files = getInputFiles(outdir, "unmatched_*", ignore_empty_files=False)
# make aux dir for extraneous files and move them there
bulk_move_to_dir(aux_files, aux_dir)
cleanup_pool(pool)
def clean_quality_trimmomatic(self, input_f, outdir, window_size, quality,
min_len, processes, extraargstring):
"""Uses a sliding window to identify and trim away areas of low quality.
:param input_f: Filepath to input file or folder.
:param outdir: Filepath to the output directory.
:param window_size: Width of the sliding window. (Number of consecutive base-pairs to average for quality \
analysis).
:param quality: Minimum quality allowed. Sections with lower average quality than this will be dropped.
:param min_len: Minimum allowed length for TRIMMED sequences. (i.e. if a sequence is too short after trimming,
its dropped.)
:param processes: Number of processes to use to clean the input fileset.
"""
# "trimomatic": "java -jar ~/ARMS/programs/Trimmomatic-0.33/trimmomatic-0.33.jar SE \
# -%phred %input %output SLIDINGWINDOW:%windowsize:%minAvgQuality MINLEN:%minLen"
inputs = getInputFiles(input_f)
debugPrintInputInfo(inputs, "clean")
pool = init_pool(min(len(inputs), processes))
printVerbose("Cleaning sequences with Trimmomatic...")
run_parallel([
ProgramRunner(
ProgramRunnerCommands.CLEAN_TRIMMOMATIC, [
input_,
"%s/%s_cleaned.fastq" %
(outdir, strip_ixes(input_)), window_size, quality, min_len
], {
"exists": [outdir, input_],
"positive": [window_size, quality, min_len]
}, extraargstring) for input_ in inputs
], pool)
printVerbose("Done cleaning sequences.")
cleanup_pool(pool)
def annotate_otu_chewbacca(self, input_f, outdir, annotation, processes):
"""Annotates an OTU table.
:param input_f: Filepath to a file or folder of files to annotate.
:param annotation: Filepath to a file or a folder of files to use as annotations.
:param outdir: Filepath to the output directory where annotated files will be written.
:param procs: The maximum number of procs to use.
"""
# matrixes = getInputFiles(input_f)
matricies = getInputFiles(input_f)
debugPrintInputInfo(matricies, "annotated.")
annotations = getInputFiles(annotation)
# if all the annotations files are empty, just copy over files.
if len(annotations) == 0 and len(getInputFiles(annotation, ignore_empty_files=False)) > 0:
pool = init_pool(min(len(matricies), processes))
print "**WARNING**: Annotation File is empty. Skipping annotation and copying old OTU tables to output \
directory.\n"
run_parallel([PythonRunner(copy_file, [matrix, outdir],
{"exists": [matrix]}) for matrix in matricies], pool)
else:
pool = init_pool(min(len(matricies) * len(annotations), processes))
debugPrintInputInfo(annotations, "parsed.")
inputs = product(matricies, annotations)
printVerbose("Annotating matrix...")
run_parallel([PythonRunner(annotateOTUtable, [matrix, annotation, "%s/%s.txt" % (outdir, "matrix")],
{"exists": [matrix, annotation]})
for matrix, annotation in inputs], pool)
printVerbose("Done Annotating.")
cleanup_pool(pool)
def rename_chewbacca(self, input_f, outdir, filetype, clip, processes):
"""Renames sequences in a fasta/fastq file as <filename>_ID0, <filename>_ID1, <filename>_ID2, etc., where
<filename> is the name of the fasta/fastq file without any extensions or chewbacca suffixes.
:param input_f: Filepath to an input file or folder to rename.
:param outdir: Filepath to the output directory.
:param filetype: Either 'fasta' or 'fastq'.
:param clip: If True, remove dereplication counts from sequence names before renaming.
:param processes: The maximum number of processes to use.
"""
# Gather input files
inputs = getInputFiles(input_f)
debugPrintInputInfo(inputs, "rename")
pool = init_pool(min(len(inputs), processes))
printVerbose("Renaming sequences...")
# Run serialRename in run_parallel
run_parallel([PythonRunner(serialRename,
[input_,
"%s/%s_renamed%s" % (outdir, strip_ixes(input_), os.path.splitext(input_)[1]),
filetype, clip], {"exists": [input_]})
for input_ in inputs], pool)
printVerbose("Done renaming sequences...")
samples_dir = makeDirOrdie("%s_samples" % outdir)
samples_files = getInputFiles(outdir, "*.samples", ignore_empty_files=False)
bulk_move_to_dir(samples_files, samples_dir)
aux_dir = makeAuxDir(outdir)
aux_files = getInputFiles(outdir, "*.mapping", ignore_empty_files=False)
bulk_move_to_dir(aux_files, aux_dir)
cleanup_pool(pool)
def demux_fastx(self, input_f, barcodes, outdir, processes, extraargstring):
"""Demuxes using FAST X BARCODE SPLITTER.
:param input_f: File path to input file or folder of input files.
:param barcodes: File path to input barcodes file.
:param outdir: Filepath to output directory.
:param processes: Number of processes to use to demux input fileset.
:param extraargstring: Advanced program parameter string.
"""
# Get input files
files_to_split = getInputFiles(input_f)
# Assign the files shard numbers
file_id = range(len(files_to_split))
file_id_pairs = zip(files_to_split, file_id)
debugPrintInputInfo(files_to_split, "demux")
pool = init_pool(min(len(file_id_pairs), processes))
printVerbose("Demuxing sequences...")
run_parallel([ProgramRunner(ProgramRunnerCommands.DEMUX_FASTX,
[input_, barcodes, "%s/" % outdir, "_%d_demux.fastq" % id_],
{"exists": [input_, barcodes]}, extraargstring)
for input_, id_ in file_id_pairs], pool)
printVerbose("Demuxed sequences.")
# Grab all the auxillary files
aux_files = getInputFiles(outdir, "unmatched_*", ignore_empty_files=False)
# make aux dir for extraneous files and move them there
bulk_move_to_dir(aux_files, makeAuxDir(outdir))
cleanup_pool(pool)
def preclean_bayeshammer(self, input_f, input_r, outdir, processes,
bayesthreads, extraargstring):
"""Assembles reads from two (left and right) fastq files/directories.
:param input_f: File path to file or folder of left reads to clean.
:param input_r: File path to file or folder of right reads to clean.
:param outdir: Filepath to output directory.
:param bayesthreads: The number of threads per process to use.
:param processes: The maximum number of processes to use.
:param kmerlen: The kmer length to use. Default: 16.
:param extraargstring: Advanced program parameter string.
"""
# Collect input files, and validate that they match
inputs = validate_paired_fastq_reads(input_f, input_r)
pool = init_pool(min(len(inputs), processes))
printVerbose("\tPrecleaning %s reads with Spades-Baye's Hammer..." %
len(inputs))
debugPrintInputInfo(inputs, "preclean/fix.")
run_parallel([
ProgramRunner(ProgramRunnerCommands.PRECLEAN_SPADES,
[forwards, reverse, outdir, bayesthreads], {
"exists": [forwards, reverse],
"positive": [bayesthreads]
}, extraargstring) for forwards, reverse in inputs
], pool)
printVerbose("Done cleaning reads.")
# Grab all the auxillary files (everything not containing ".assembled."
# aux_files = getInputFiles(outdir, "*", "*.assembled.*", ignore_empty_files=False)
# make aux dir for extraneous files and move them there
# bulk_move_to_dir(aux_files, makeAuxDir(outdir))
# Select output files
aux_files = getInputFiles(outdir, "*", ignore_empty_files=False)
corrected_dir = "%s/corrected" % outdir
bulk_move_to_dir(getInputFiles(corrected_dir, "*"), outdir)
aux_files += getInputFiles(outdir,
"*unpaired*",
ignore_empty_files=False)
aux_files += getInputFiles(outdir, "configs", ignore_empty_files=False)
# Gather aux files
aux_dir = makeAuxDir(outdir)
bulk_move_to_dir(aux_files, aux_dir)
# Rename output files
output_files = getInputFiles(outdir, "*", "corrected_*")
for out_file in output_files:
move(out_file,
"%s/%s_corrected.fastq" % (outdir, strip_ixes(out_file)))
# move the last minute log file
try:
move("%s/corrected_corrected.fastq" % outdir,
"%s/corrected_corrected.fastq" % aux_dir)
except:
pass
cleanup_pool(pool)
def query_fasta_vsearch(self, input_f, referencefasta, taxinfo, outdir, processes, simmilarity, coverage,
extraargstring):
"""Compare reference sequences to the fasta-formatted query sequences, using global pairwise alignment.
:param input_f: Filepath to a file or folder of files to identify.
:param outdir: Filepath to the output directory.
:param referencefasta: Filepath to a file or folder of files to use as a reference.
:param taxinfo: Filepath to a file containing taxonomic info correlated with the referencefasta.
:param simmilarity: The % simmilarity between a query and reference sequence required for positive
identification.
:param coverage: The % coverage of matching regions between a query and reference sequence required for positive
identification.
:param processes: The number of processes to use in the identification process.
:param extraargstring: Advanced program parameter string.
"""
# vsearch --usearch_global %s seeds.pick.fasta --db ../data/BiocodePASSED_SAP.txt --id 0.9 \
# --userfields query+target+id+alnlen+qcov --userout %sout --alnout %s alnout.txt
# expecting a fasta to annotate
query_fastas = getInputFiles(input_f)
debugPrintInputInfo(query_fastas, "queried for identification.")
ref_fastas = getInputFiles(referencefasta)
debugPrintInputInfo(ref_fastas, "referenced for sequence identification.")
tax_info_files = getInputFiles(taxinfo)
debugPrintInputInfo(tax_info_files, "referenced for taxanomic names.")
# make sure the number of reference fasta files is the same as the number of tax_info files
if len(tax_info_files) != len(ref_fastas):
print "Error: The number of reference fastas and taxonomic mapping files is not the same. There must be \
one taxonomic mapping file for each reference fasta."
return
ref_data_pairs = zip(ref_fastas, tax_info_files)
inputs = [x for x in product(query_fastas, ref_fastas)]
aln_user_string = ""
pool = init_pool(min(len(inputs), processes))
# VSEARCH ALIGNMENT
query_vsearch(inputs, outdir, simmilarity, processes, aln_user_string, extraargstring, pool)
printVerbose("Parsing output...")
# Parse the alignment results and put those that pass the criterion (97 similarity, 85 coverage) in
# parsed_BIOCODE.out. Parameters can be changed and this command can be rerun as many times as necessary
#
# parseVSearchOutputAgainstFasta(vsearch_outfile, taxInfo, output_file, min_simmilarity, min_coverage):
inputs = [x for x in product(query_fastas, ref_data_pairs)]
debugPrintInputInfo(inputs, "queryied against paired refereces.")
run_parallel([PythonRunner(parseVSearchOutputAgainstFasta,
["%s/%s.out" % (outdir, strip_ixes(query)), tax_info,
"%s/%s.tax" % (outdir, strip_ixes(query)), simmilarity, coverage],
{"exists": [query, ref_fasta, tax_info]})
for query, (ref_fasta, tax_info) in inputs], pool)
printVerbose("\nDone parsing...")
# Gather and move auxillary files
aux_files = getInputFiles(outdir, "*", "*.tax", ignore_empty_files=False)
bulk_move_to_dir(aux_files, makeAuxDir(outdir))
cleanup_pool(pool)
def query_fasta_db_vsearch(self, input_f, outdir, ref_fasta, ref_db,
simmilarity, coverage, processes,
extraargstring):
"""Compare reference sequences to the fasta-formatted query sequences, using global pairwise alignment.
:param input_f: Filepath to a file or folder of files to identify.
:param outdir: Filepath to the output directory.
:param ref_fasta: Filepath to the curated fasta file to use as a reference.
:param ref_db: Filepath to the curated fasta file to use as a reference.
:param simmilarity:"Minimum % simmilarity (decimal between 0 and 1) between query and reference sequences
required for positive identification.
:param coverage:Minimum % coverage (decimal between 0 and 1) required query and reference sequences required
for positive identification.
:param processes: The number of processes to use in the identification process.
:param extraargstring: Advanced program parameter string.
"""
# blast6 output format http://www.drive5.com/usearch/manual/blast6out.html
aln_user_string = "--userfields query+target+id+alnlen+qcov"
# coi_fasta = os.path.expanduser("~/ARMS/refs/COI.fasta")
# ncbi_db_string = os.path.expanduser("~/ARMS/refs/ncbi.db")
coi_fasta = ref_fasta
ncbi_db_string = ref_db
query_fastas = getInputFiles(input_f)
debugPrintInputInfo(query_fastas, "queried against the DB.")
inputs = [x for x in product(query_fastas, [coi_fasta])]
pool = init_pool(min(len(query_fastas), processes))
# VSEARCH ALIGNMENT
query_vsearch(inputs, outdir, simmilarity, processes, aln_user_string,
extraargstring, pool)
printVerbose("Parsing output...")
# Parse the alignment results and put those that pass the criterion (97 similarity, 85 coverage) in
# parsed_BIOCODE.out. Parameters can be changed and this command can be rerun as many times as necessary
#
# parseVSearchOutputAgainstNCBI(vsearch_out, ncbi_db, min_coverage, min_similarity)> parsed_nt.out
run_parallel([
PythonRunner(parseVSearchOutputAgainstNCBI, [
"%s/%s.out" % (outdir, strip_ixes(query)), ncbi_db_string,
"%s/%s.tax" %
(outdir, strip_ixes(query)), simmilarity, coverage
], {"exits": [query, ncbi_db_string]}) for query in query_fastas
], pool)
printVerbose("Done processing.")
# Gather and move auxillary files
aux_files = getInputFiles(outdir,
"*",
"*.tax",
ignore_empty_files=False)
bulk_move_to_dir(aux_files, makeAuxDir(outdir))
cleanup_pool(pool)
def convert_chewbacca(self, input_f, outdir, proceses):
inputs = getInputFiles(input_f)
debugPrintInputInfo(inputs, "convert to fasta.")
printVerbose("Converting to fasta...")
pool = init_pool(min(len(inputs), proceses))
run_parallel([PythonRunner(translateFastqToFasta,
[input_, "%s/%s.fasta" % (outdir, getFileName(input_))],
{"exists": input_})
for input_ in inputs], pool)
printVerbose("Done converting.")
cleanup_pool(pool)
def convert_chewbacca(self, input_f, outdir, proceses):
inputs = getInputFiles(input_f)
debugPrintInputInfo(inputs, "convert to fasta.")
printVerbose("Converting to fasta...")
pool = init_pool(min(len(inputs), proceses))
run_parallel([
PythonRunner(
translateFastqToFasta,
[input_, "%s/%s.fasta" %
(outdir, getFileName(input_))], {"exists": input_})
for input_ in inputs
], pool)
printVerbose("Done converting.")
cleanup_pool(pool)
def clean_trim_adapters_flexbar(self, input_f, adapters, adaptersrc,
outdir, allowedns, processes,
extraargstring):
"""Use flexbar to trim adapters and barcodes from sequences. By default, Flexbar does not allow any 'N' \
characters in SEQUENCE, and will toss any sequences that do contain 'N'. To avoid this, use the -u or \
--allowedns flags to specify the maximum number of 'N's to allow
:param input_f: Filepath to input file or folder.
:param adapters: Filepath to a list of adapters.
:param adaptersrc: Filepath to a list of reverse-complemented adapters.
:param outdir: Filepath to the output directory.
:param allowedns: Non-negative integer value indicating the maximum number of 'N's to tolerate in a sequence.
:param processes: The maximum number of processes to use.
:param extraargstring: Advanced program parameter string.
"""
inputs = getInputFiles(input_f)
pool = init_pool(min(len(inputs), processes))
debugPrintInputInfo(inputs, "trim adapters from")
# "flexbar": "flexbar -r \"%s\" -t \"%s\" -ae \"%s\" -a \"%s\"",
printVerbose("Trimming barcodes and adapters with flexbar")
temp_file_name_template = "%s/temp_%s"
debarcoded_file_name_template = "%s/%s_debarcoded"
# Trim adapters from the left
run_parallel([
ProgramRunner(ProgramRunnerCommands.TRIM_FLEXBAR, [
input_file, temp_file_name_template %
(outdir, strip_ixes(input_file)), "LEFT", adapters, allowedns
], {"exists": [input_file, adapters]}, extraargstring)
for input_file in inputs
], pool)
temp_files = getInputFiles(outdir, "temp_*")
debugPrintInputInfo(temp_files, "trim adapters from")
# Trim the reverse complemented adapters from the right
run_parallel([
ProgramRunner(ProgramRunnerCommands.TRIM_FLEXBAR, [
input_file, debarcoded_file_name_template %
(outdir, strip_ixes(input_file)[5:]), "RIGHT", adaptersrc,
allowedns
], {"exists": [input_file, adaptersrc]}, extraargstring)
for input_file in temp_files
], pool)
printVerbose("Done Trimming sequences.")
# Move temp files
aux_files = getInputFiles(outdir, "temp_*", ignore_empty_files=False)
bulk_move_to_dir(aux_files, makeAuxDir(outdir))
cleanup_pool(pool)
def align_clean_macse(self, input_f, ref, samplesdir, outdir, processes, extraargstring=""):
"""Removes non-nucleotide characters in MACSE aligned sequences for all fasta files in the samples directory
(the samplesDir argument).
:param input_f: File path to file or folder of files to clean.
:param samplesdir: Filepath to the original, unaligned input files (the inputs to the macse aligner).
:param ref: Filepath to the reference file used to align the input files.
:param outdir: Filepath to the directory to write outputs to.
:param processes: The maximum number of processes to use.
:param extraargstring: Advanced program parameter string.
"""
# "macse_format": "java -jar " + programPaths["MACSE"] + " -prog exportAlignment -align \"%s\" \
# -charForRemainingFS - -gc_def 5 -out_AA \"%s\" -out_NT \"%s\" -statFile \"%s\""
inputs = getInputFiles(input_f)
pool = init_pool(min(len(inputs), processes))
printVerbose("\t %s Processing MACSE alignments")
samples_list = getInputFiles(samplesdir)
run_parallel([ProgramRunner(ProgramRunnerCommands.MACSE_FORMAT,
["%s/%s_NT" % (input_f, getFileName(sample)),
"%s/%s_AA_macse.fasta" % (outdir, getFileName(sample)),
"%s/%s_NT_macse.fasta" % (outdir, getFileName(sample)),
"%s/%s_macse.csv" % (outdir, getFileName(sample))],
{"exists": ["%s/%s_NT" % (input_f, getFileName(sample))]}, extraargstring)
for sample in samples_list], pool)
printVerbose("\tCleaning MACSE alignments")
printVerbose("Processing %s samples..." % len(samples_list))
nt_macse_outs = ["%s/%s_NT_macse.fasta" % (outdir, strip_ixes(sample)) for sample in samples_list]
# Clean the alignments
from classes.PythonRunner import PythonRunner
run_parallel([PythonRunner(remove_refs_from_macse_out, [input_, ref,
"%s/%s" % (outdir, "%s_cleaned.fasta" % strip_ixes(input_))],
{"exists": [input_, ref]})
for input_ in nt_macse_outs], pool)
# Cat the cleaned alignments
cleaned_alignments = getInputFiles(outdir, "*_cleaned.fasta")
merge_files(cleaned_alignments, "%s/MACSE_OUT_MERGED.fasta" % outdir)
aux_dir = makeAuxDir(outdir)
aux_files = getInputFiles(outdir, "*", "MACSE_OUT_MERGED.fasta", ignore_empty_files=False)
bulk_move_to_dir(aux_files, aux_dir)
cleanup_pool(pool)
def clean_trim_adapters_flexbar(self, input_f, adapters, adaptersrc, outdir, allowedns, processes, extraargstring):
"""Use flexbar to trim adapters and barcodes from sequences. By default, Flexbar does not allow any 'N' \
characters in SEQUENCE, and will toss any sequences that do contain 'N'. To avoid this, use the -u or \
--allowedns flags to specify the maximum number of 'N's to allow
:param input_f: Filepath to input file or folder.
:param adapters: Filepath to a list of adapters.
:param adaptersrc: Filepath to a list of reverse-complemented adapters.
:param outdir: Filepath to the output directory.
:param allowedns: Non-negative integer value indicating the maximum number of 'N's to tolerate in a sequence.
:param processes: The maximum number of processes to use.
:param extraargstring: Advanced program parameter string.
"""
inputs = getInputFiles(input_f)
pool = init_pool(min(len(inputs), processes))
debugPrintInputInfo(inputs, "trim adapters from")
# "flexbar": "flexbar -r \"%s\" -t \"%s\" -ae \"%s\" -a \"%s\"",
printVerbose("Trimming barcodes and adapters with flexbar")
temp_file_name_template = "%s/temp_%s"
debarcoded_file_name_template = "%s/%s_debarcoded"
# Trim adapters from the left
run_parallel([ProgramRunner(ProgramRunnerCommands.TRIM_FLEXBAR,
[input_file, temp_file_name_template % (outdir, strip_ixes(input_file)),
"LEFT", adapters, allowedns],
{"exists": [input_file, adapters]}, extraargstring)
for input_file in inputs], pool)
temp_files = getInputFiles(outdir, "temp_*")
debugPrintInputInfo(temp_files, "trim adapters from")
# Trim the reverse complemented adapters from the right
run_parallel([ProgramRunner(ProgramRunnerCommands.TRIM_FLEXBAR,
[input_file, debarcoded_file_name_template % (outdir, strip_ixes(input_file)[5:]),
"RIGHT", adaptersrc, allowedns],
{"exists": [input_file, adaptersrc]}, extraargstring)
for input_file in temp_files], pool)
printVerbose("Done Trimming sequences.")
# Move temp files
aux_files = getInputFiles(outdir, "temp_*", ignore_empty_files=False)
bulk_move_to_dir(aux_files, makeAuxDir(outdir))
cleanup_pool(pool)
def annotate_otu_chewbacca(self, input_f, outdir, annotation, processes):
"""Annotates an OTU table.
:param input_f: Filepath to a file or folder of files to annotate.
:param annotation: Filepath to a file or a folder of files to use as annotations.
:param outdir: Filepath to the output directory where annotated files will be written.
:param procs: The maximum number of procs to use.
"""
# matrixes = getInputFiles(input_f)
matricies = getInputFiles(input_f)
debugPrintInputInfo(matricies, "annotated.")
annotations = getInputFiles(annotation)
# if all the annotations files are empty, just copy over files.
if len(annotations) == 0 and len(
getInputFiles(annotation, ignore_empty_files=False)) > 0:
pool = init_pool(min(len(matricies), processes))
print "**WARNING**: Annotation File is empty. Skipping annotation and copying old OTU tables to output \
directory.\n"
run_parallel([
PythonRunner(copy_file, [matrix, outdir], {"exists": [matrix]})
for matrix in matricies
], pool)
else:
pool = init_pool(min(len(matricies) * len(annotations), processes))
debugPrintInputInfo(annotations, "parsed.")
inputs = product(matricies, annotations)
printVerbose("Annotating matrix...")
run_parallel([
PythonRunner(
annotateOTUtable,
[matrix, annotation,
"%s/%s.txt" %
(outdir, "matrix")], {"exists": [matrix, annotation]})
for matrix, annotation in inputs
], pool)
printVerbose("Done Annotating.")
cleanup_pool(pool)
def partition_chewbacca(self, input_f, outdir, processes, chunksize, filetype):
"""Partition a fasta/fastq file into chunks of user-defined size.
:param input_f: Filepath to a file or folder of files to partition.
:param outdir: The directory to write split files to.
:param processes: The number of processes to use to partition the input fileset.
:param chunksize: The number of sequences per file.
:param filetype: Either 'fasta' or 'fastq'.
"""
# Gather input files
inputs = getInputFiles(input_f)
debugPrintInputInfo(inputs, "partitioned")
pool = init_pool(min(len(inputs), processes))
printVerbose("Partitioning Files...")
run_parallel([PythonRunner(splitK,
[input_, "%s/%s" % (outdir, strip_ixes(input_)), chunksize, filetype],
{"exists": [input_]})
for input_ in inputs], pool)
printVerbose("Done partitioning files.")
cleanup_pool(pool)
def ungap_chewbacca(self, input_f, outdir, gapchars, file_ext, processes):
"""Ungaps a character using Bio python.
:param input_f: Filepath to input file or folder to ungap.
:param outdir: Filepath to the output directory where ungapped files should be written.
:param gapchars: A string containing the gap characters to remove.
:param file_ext: Either 'fasta' or 'fastq'.
:param processes: The number of threads to use to ungap the input fileset.
"""
inputs = getInputFiles(input_f, "*.fasta")
debugPrintInputInfo(inputs, "ungap.")
pool = init_pool(min(len(inputs), processes))
printVerbose("Removing all '%s' from sequences..." % gapchars)
# ungap(file_to_clean, output_file_name, gap_char, file_type):
run_parallel([PythonRunner(remove_gap_chars,
[input_, "%s/%s_cleaned.%s" % (outdir, strip_ixes(input_), 'fasta'),
gapchars, file_ext],
{"exists": [input_]}) for input_ in inputs], pool)
printVerbose("Done removing.")
cleanup_pool(pool)
def rename_chewbacca(self, input_f, outdir, filetype, clip, processes):
"""Renames sequences in a fasta/fastq file as <filename>_ID0, <filename>_ID1, <filename>_ID2, etc., where
<filename> is the name of the fasta/fastq file without any extensions or chewbacca suffixes.
:param input_f: Filepath to an input file or folder to rename.
:param outdir: Filepath to the output directory.
:param filetype: Either 'fasta' or 'fastq'.
:param clip: If True, remove dereplication counts from sequence names before renaming.
:param processes: The maximum number of processes to use.
"""
# Gather input files
inputs = getInputFiles(input_f)
debugPrintInputInfo(inputs, "rename")
pool = init_pool(min(len(inputs), processes))
printVerbose("Renaming sequences...")
# Run serialRename in run_parallel
run_parallel([
PythonRunner(serialRename, [
input_,
"%s/%s_renamed%s" %
(outdir, strip_ixes(input_), os.path.splitext(input_)[1]),
filetype, clip
], {"exists": [input_]}) for input_ in inputs
], pool)
printVerbose("Done renaming sequences...")
samples_dir = makeDirOrdie("%s_samples" % outdir)
samples_files = getInputFiles(outdir,
"*.samples",
ignore_empty_files=False)
bulk_move_to_dir(samples_files, samples_dir)
aux_dir = makeAuxDir(outdir)
aux_files = getInputFiles(outdir,
"*.mapping",
ignore_empty_files=False)
bulk_move_to_dir(aux_files, aux_dir)
cleanup_pool(pool)
def partition_chewbacca(self, input_f, outdir, processes, chunksize,
filetype):
"""Partition a fasta/fastq file into chunks of user-defined size.
:param input_f: Filepath to a file or folder of files to partition.
:param outdir: The directory to write split files to.
:param processes: The number of processes to use to partition the input fileset.
:param chunksize: The number of sequences per file.
:param filetype: Either 'fasta' or 'fastq'.
"""
# Gather input files
inputs = getInputFiles(input_f)
debugPrintInputInfo(inputs, "partitioned")
pool = init_pool(min(len(inputs), processes))
printVerbose("Partitioning Files...")
run_parallel([
PythonRunner(splitK, [
input_,
"%s/%s" % (outdir, strip_ixes(input_)), chunksize, filetype
], {"exists": [input_]}) for input_ in inputs
], pool)
printVerbose("Done partitioning files.")
cleanup_pool(pool)
def ungap_chewbacca(self, input_f, outdir, gapchars, file_ext, processes):
"""Ungaps a character using Bio python.
:param input_f: Filepath to input file or folder to ungap.
:param outdir: Filepath to the output directory where ungapped files should be written.
:param gapchars: A string containing the gap characters to remove.
:param file_ext: Either 'fasta' or 'fastq'.
:param processes: The number of threads to use to ungap the input fileset.
"""
inputs = getInputFiles(input_f, "*.fasta")
debugPrintInputInfo(inputs, "ungap.")
pool = init_pool(min(len(inputs), processes))
printVerbose("Removing all '%s' from sequences..." % gapchars)
# ungap(file_to_clean, output_file_name, gap_char, file_type):
run_parallel([
PythonRunner(remove_gap_chars, [
input_,
"%s/%s_cleaned.%s" %
(outdir, strip_ixes(input_), 'fasta'), gapchars, file_ext
], {"exists": [input_]}) for input_ in inputs
], pool)
printVerbose("Done removing.")
cleanup_pool(pool)
def assemble_pear(self, input_f, input_r, outdir, name, processes,
pearthreads, extraargstring):
"""Uses PEAR to assemble paired F/R read files in run_parallel.
:param input_f: File path to forward Fastq Reads file or folder.
:param input_r: File path to reverse Fastq Reads file or folder.
:param outdir: File path to the output directory.
:param name: File prefix for the assembled reads.
:param processes: The maximum number of processes to use.
:param extraargstring: Advanced program parameter string.
:param pearthreads: The number of threads per process to use.
"""
# "~/programs/pear-0.9.4-bin-64/pear-0.9.4-64 -f %s -r %s -o %s -j %s -m %d"
inputs = validate_paired_fastq_reads(input_f, input_r)
pool = init_pool(min(len(inputs), processes))
printVerbose("\tAssembling reads with pear")
debugPrintInputInfo(inputs, "assemble")
run_parallel([
ProgramRunner(ProgramRunnerCommands.ASSEMBLE_PEAR, [
forwards, reverse,
"%s/%s_%s" % (outdir, name, getFileName(forwards)), pearthreads
], {
"exists": [forwards, reverse],
"positive": [pearthreads]
}, extraargstring) for forwards, reverse in inputs
], pool)
printVerbose("Done assembling sequences...")
# Grab all the auxillary files (everything not containing ".assembled."
aux_files = getInputFiles(outdir,
"*",
"*.assembled.*",
ignore_empty_files=False)
# make aux dir for extraneous files and move them there
bulk_move_to_dir(aux_files, makeAuxDir(outdir))
cleanup_pool(pool)
def align_macse(self, input_f, db, outdir, processes, extraargstring):
"""Aligns sequences by iteratively adding them to a known good alignment.
:param input_f: Filepath to an input file or folder to rename.
:param db: Filepath to a reference file or folder of reference files for alignment.
:param outdir: Filepath to the output directory.
:param processes: The maximum number of processes to use.
:param extraargstring: Advanced program parameter string.
"""
# "macse_align": "java -jar " + programPaths["MACSE"] + " -prog enrichAlignment -seq \"%s\" -align \
# \"%s\" -seq_lr \"%s\" -maxFS_inSeq 0 -maxSTOP_inSeq 0 -maxINS_inSeq 0 \
# -maxDEL_inSeq 3 -gc_def 5 -fs_lr -10 -stop_lr -10 -out_NT \"%s\"_NT \
# -out_AA \"%s\"_AA -seqToAdd_logFile \"%s\"_log.csv",
inputs = getInputFiles(input_f)
pool = init_pool(min(len(inputs), processes))
printVerbose("Aligning reads using MACSE")
inputs = getInputFiles(input_f)
run_parallel([ProgramRunner(ProgramRunnerCommands.MACSE_ALIGN,
[db, db, input_] + ["%s/%s" % (outdir, getFileName(input_))] * 3,
{"exists": [input_, db]}, extraargstring)
for input_ in inputs], pool)
printVerbose("Done with MACSE alignment.")
cleanup_pool(pool)
def cluster_vsearch(self, input_f, outdir, groupsfile, processes, idpct, extraargstring):
"""Clusters sequences using SWARM.
:param input_f: A file or folder containing fasta files to cluster.
:param outdir: The output directory results will be written to.
:param groupsfile: A groups file or folder containinggroups files that describe the input. Note: if no groups
file is supplied, then entries in the fasta file are assumed to be singleton sequences.
:param idpct: Real number in the range (0,1] that specifies the minimum simmilarity threshold for
clustering. e.g. .95 indicates that a candidate sequence 95% must be at least
95% simmilar to the seed sequence to be included in the cluster.
:param processes: The maximum number of processes to use.
:param extraargstring: Advanced program parameter string.
"""
# Grab the fasta file(s) to cluster
inputs = getInputFiles(input_f)
debugPrintInputInfo(inputs, "clustered")
pool = init_pool(min(len(inputs), processes))
# RUN CLUSTERING
# " --cluster_size %s -id %f --centroids %s --uc %s",
run_parallel([ProgramRunner(ProgramRunnerCommands.CLUSTER_VSEARCH,
[input_, float(idpct), "%s/%s_seeds.fasta" % (outdir, strip_ixes(input_)),
"%s/%s_clustered_uc" % (outdir, strip_ixes(input_))],
{"exists": [input_]}, extraargstring) for input_ in inputs], pool)
# PARSE UC FILE TO GROUPS FILE
printVerbose("Parsing the clustered uc files to groups files")
clustered_uc_files = getInputFiles(outdir, "*_clustered_uc")
debugPrintInputInfo(clustered_uc_files, "parsed to groups")
run_parallel([PythonRunner(parseUCtoGroups, [input_, "%s/%s.groups" % (outdir, strip_ixes(input_))],
{"exists": [input_]})
for input_ in clustered_uc_files], pool)
# REMOVE COUNTS FROM CLUSTERING GROUPS FILE
printVerbose("Cleaning the .groups file from clustering")
# Grab the current groups file and the new clustered groups file (which needs to be cleaned)
clustered_groups_files = getInputFiles(outdir, "*_clustered.groups")
# Remove counts from the clustering groups files
debugPrintInputInfo(clustered_groups_files, "cleaned")
run_parallel([PythonRunner(removeCountsFromGroupsFile,
[input_, "%s/%s_uncount.groups" % (outdir, strip_ixes(input_))],
{"exists": [input_]})
for input_ in clustered_groups_files], pool)
printVerbose("Done cleaning groups files.")
# Collect the groups file from clustering with counts removed
cleaned_clustered_groups_files = getInputFiles(outdir, "*_uncount.groups", ignore_empty_files=False)
# Resolve the user specified names file if necessary
final_groups_files = handle_groups_file_update(outdir, groupsfile, cleaned_clustered_groups_files)
# Move the final groups file(s) to the groups dir
groups_dir = makeDirOrdie("%s_groups_files" % outdir)
bulk_move_to_dir(final_groups_files, groups_dir)
# Move aux files to the aux dir
aux_files = getInputFiles(outdir, "*", "*_seeds.fasta", ignore_empty_files=False)
aux_dir = makeAuxDir(outdir)
bulk_move_to_dir(aux_files, aux_dir)
# Cleanup the pool
cleanup_pool(pool)
def dereplicate_vsearch(self, input_f, outdir, groupsfile, processes,
stripcounts, extraargstring):
"""Dereplicates with vsearch.
:param input_f: Filepath to the file or folder of files to dereplicate.
:param outdir: Filepath to the output directory.
:param groupsfile: A groups file to use as a reference for replicant counting. If no groups file is
provided, input sequences are conidered singletons (regardless of their name-annotated
dereplication count).
:param processes: The number of processes to use to dereplicate the fileset.
:param stripcounts: If True, strips the trailing dereplication counts from a file before dereplication.
:param extraargstring: Advanced program parameter string.
"""
inputs = getInputFiles(input_f)
pool = init_pool(min(len(inputs), processes))
# REMOVES COUNTS FROM SEQUENCE NAMES IN ORDER TO CLUSTER PROPERLY
# strip counts if we need to.
if stripcounts:
printVerbose("Removing counts from sequence names...")
debugPrintInputInfo(inputs, "renamed")
run_parallel([
PythonRunner(removeCountsFromFastFile, [
input_,
"%s/%s_uncount.fasta" %
(outdir, strip_ixes(input_)), 'fasta'
], {"exists": input_}) for input_ in inputs
], pool)
printVerbose("Done removing counts.")
# Grab the cleaned files as input for the next step
inputs = getInputFiles(outdir, "*_uncount.fasta")
# DEREPLICATE
debugPrintInputInfo(inputs, "dereplicated")
printVerbose("Dereplicating...")
run_parallel([
ProgramRunner(ProgramRunnerCommands.DEREP_VSEARCH, [
processes, input_,
"%s/%s_derep.fasta" % (outdir, strip_ixes(input_)),
"%s/%s_uc.out" % (outdir, strip_ixes(input_))
], {
"exists": [input_],
"positive": [processes]
}, extraargstring) for input_ in inputs
], pool)
printVerbose("Done dereplicating")
# LOG DEREPLICATED SEQUENCES INTO A .GROUPS FILE
# generates a .groups file named _uc_parsed.out
# python parseUCtoGroups.py uc.out uc_parsed.out
input_ucs = getInputFiles(outdir, "*_uc.out")
printVerbose("Generating a groups file from dereplication.")
debugPrintInputInfo(inputs, "parsed (into a .groups file)")
run_parallel([
PythonRunner(
parseUCtoGroups,
[input_,
"%s/%s_derep.groups" %
(outdir, strip_ixes(input_))], {"exists": [input_]})
for input_ in input_ucs
], pool)
most_recent_groups_files = getInputFiles(outdir,
"*_derep.groups",
ignore_empty_files=False)
# UPDATE THE MOST CURRENT GROUPS FILES WITH DEREPLICATION COUNTS
if groupsfile is not None:
# Grab the oldgroups file and the dereplicated groups file
old_groups_files = getInputFiles(groupsfile)
derep_groups_files = getInputFiles(outdir, "*_derep.groups")
printVerbose("Updating .groups files with dereplicated data")
printVerbose("%d Reference (old) groups files to be read:" %
len(old_groups_files))
printVerbose(str(old_groups_files))
printVerbose("%d Dereplicated (new) groups files to be read:" %
len(derep_groups_files))
printVerbose(str(derep_groups_files))
update_groups(old_groups_files, derep_groups_files, outdir,
"dereplicated")
most_recent_groups_files = getInputFiles(outdir,
"dereplicated*",
ignore_empty_files=False)
printVerbose("Done updating .groups files.")
if len(inputs) != len(most_recent_groups_files):
print(
"Error: Number of input fastas (%d) is not equal to the number ofgroups files (%d)."
% (len(inputs), len(most_recent_groups_files)))
exit()
fasta_groups_pairs = zip(inputs, most_recent_groups_files)
# ADD COUNT TO SEQUENCE NAMES AND SORT BY COUNT
# python renameWithReplicantCounts.py
# 8_macse_out/MACSEOUT_MERGED.fasta uc_parsed.out dereplicated_renamed.fasta
printVerbose("Adding dereplication data to unique fasta")
run_parallel([
PythonRunner(renameWithReplicantCounts, [
fasta, groups,
"%s/%s_counts.fasta" % (outdir, strip_ixes(fasta)), 'fasta'
], {"exists": [fasta, groups]})
for fasta, groups in fasta_groups_pairs
], pool)
printVerbose("Done adding data")
aux_dir = makeAuxDir(outdir)
groups_dir = makeDirOrdie("%s_groups_files" % outdir)
bulk_move_to_dir(most_recent_groups_files, groups_dir)
aux_files = getInputFiles(outdir,
'*',
"*_counts.fasta",
ignore_empty_files=False)
bulk_move_to_dir(aux_files, aux_dir)
cleanup_pool(pool)
def cluster_swarm(self, input_f, outdir, groupsfile, processes,
extraargstring):
"""Clusters sequences using SWARM.
:param input_f: A file or folder containing fasta files to cluster.
:param outdir: The output directory results will be written to.
:param groupsfile: A groups file or folder containing groups files that describe the input. Note: if no groups
file is supplied, then entries in the fasta file are assumed to be singleton sequences.
:param processes: The maximum number of processes to use.
:param extraargstring: Advanced program parameter string.
"""
# Grab the fasta file(s) to cluster
inputs = getInputFiles(input_f)
debugPrintInputInfo(inputs, "clustered")
pool = init_pool(min(len(inputs), processes))
# RUN CLUSTERING
run_parallel([
ProgramRunner(ProgramRunnerCommands.CLUSTER_SWARM, [
input_,
"%s/%s_clustered" % (outdir, strip_ixes(input_)),
"%s/%s_clustered_uc" % (outdir, strip_ixes(input_)),
"%s/%s_clustered_seeds" % (outdir, strip_ixes(input_))
], {"exists": [input_]}, extraargstring) for input_ in inputs
], pool)
# PARSE UC FILE TO GROUPS FILE
printVerbose("Parsing the clustered uc files to groups files")
clustered_uc_files = getInputFiles(outdir, "*_clustered_uc")
debugPrintInputInfo(clustered_uc_files, "parsed to groups")
run_parallel([
PythonRunner(
parseUCtoGroups,
[input_, "%s/%s.groups" %
(outdir, strip_ixes(input_))], {"exists": [input_]})
for input_ in clustered_uc_files
], pool)
printVerbose("Done parsing groups files.")
# REMOVE COUNTS FROM CLUSTERING GROUPS FILE
printVerbose("Cleaning the .groups file from clustering")
# Grab the current groups file and the new clustered groups file (which needs to be cleaned)
clustered_groups_files = getInputFiles(outdir, "*_clustered.groups")
debugPrintInputInfo(clustered_groups_files, "cleaned")
run_parallel([
PythonRunner(removeCountsFromGroupsFile, [
input_,
"%s/%s_uncount.groups" % (outdir, strip_ixes(input_))
], {"exists": [input_]}) for input_ in clustered_groups_files
], pool)
printVerbose("Done cleaning groups files.")
printVerbose("Capitalizing sequences")
# Convert the seeds files to uppercase (swarm writes in lowercase)
inputs = getInputFiles(outdir, "*_seeds")
run_parallel([
PythonRunner(capitalize_seqs, [input_, "%s.fasta" % input_],
{"exists": [input_]}) for input_ in inputs
], pool)
printVerbose("Done capitalizing sequences")
# Collect the groups file from clustering with counts removed
cleaned_clustered_groups_files = getInputFiles(
outdir, "*_uncount.groups", ignore_empty_files=False)
# Resolve the user specified names file if necessary
final_groups_files = handle_groups_file_update(
outdir, groupsfile, cleaned_clustered_groups_files)
# Move the final groups file(s) to the groups dir
groups_dir = makeDirOrdie("%s_groups_files" % outdir)
bulk_move_to_dir(final_groups_files, groups_dir)
# Move aux files to the aux dir
aux_files = getInputFiles(outdir,
"*",
"*_seeds.fasta",
ignore_empty_files=False)
aux_dir = makeAuxDir(outdir)
bulk_move_to_dir(aux_files, aux_dir)
# Cleanup the pool
cleanup_pool(pool)
def cluster_crop(self, input_f, outdir, groupsfile, processes, blocksize,
clustpct, maxmcmc, maxsm, rare, blockcount,
extraargstring):
"""Clusters sequences using CROP.
:param input_f: Filepath to the input fasta file to cluster.
:param outdir: Filepath to the output directory.
:param groupsfile: Filepath to the groups file to use as a reference for dereplication counting.
:param blocksize: Size of blocks to be used for all rounds (if -b is specified, then -z will not affect the
first round. For data set with different average sequence length, this parameter should \
be tuned such that it won't take too long for each block to do pariwise alignment. Hint \
for choosing z: z*L<150,000, where L is the average length of the sequences.
:param clustpct: The minimum similarity threshold for clustering. Either 'g' for 95% or 's' for 97%.
:param maxmcmc: This parameter specifies the number of iterations of MCMC. Default value is 2000. Increase \
this value to enhance accuracy (recommended value is at least 10*block size).
:param maxsm: This parameter specifies the maximum number of 'split and merge' process to run. Max is 20.
:param rare: The maximum cluster size allowed to be classified as 'rare'. Clusters are defined as either \
'abundant' or 'rare'. 'Abundant' clusters will be clustered first, then the 'rare' \
clusters are mapped to the 'abundant' clusters. Finally, 'rare' clusters which cannot be \
mapped will be clustered separately. e.g. If r=5, the clusters with size <=5 will be \
considered 'rare' in above procedure. and r=0 will yield the best accuracy. If you \
believe your data is not too diverse to be handled, then r=0 will be the best choice.
:param blockcount: The size of blocks in the first round of clustering. Hint of choosing -b: Each block in the \
first round should contain about 50 sequences. i.e. b=N/50, where N is the number of \
input sequences. Default: # input sequences / z.
:param processes: The maximum number of processes to use.
:param extraargstring: Advanced program parameter string.
"""
# Grab the fasta file(s) to cluster
inputs = getInputFiles(input_f)
debugPrintInputInfo(inputs, "clustered")
pool = init_pool(min(len(inputs), processes))
# RUN CLUSTERING
# crop -i %s -o %s -z %s -c %s -e %s -m %s%s
run_parallel([
ProgramRunner(ProgramRunnerCommands.CLUSTER_CROP, [
input_,
"%s/%s" % (outdir, strip_ixes(input_)), blocksize, clustpct,
maxmcmc, maxsm, rare, blockcount
], {"exists": [input_]}, extraargstring) for input_ in inputs
], pool)
# CLEAN THE OUTPUT GROUPS FILE
printVerbose("Parsing the groups file from clustering")
clustered_groups_files = getInputFiles(outdir, "*.cluster.list")
debugPrintInputInfo(clustered_groups_files,
"converted to groups files")
run_parallel([
PythonRunner(parseCROPoutToGroups, [
input_,
"%s/%s_uncount.groups" % (outdir, strip_ixes(input_))
], {"exists": [input_]}) for input_ in clustered_groups_files
], pool)
printVerbose("Done parsing groups file.")
# Collect the groups file from clustering with counts removed
cleaned_clustered_groups_files = getInputFiles(
outdir, "*_uncount.groups", ignore_empty_files=False)
# Resolve the user specified names file if necessary
final_groups_files = handle_groups_file_update(
outdir, groupsfile, cleaned_clustered_groups_files)
# GATHER AUX FILES
input_dir = getDirName(input_f)
aux_files = cleaned_clustered_groups_files
aux_files += getInputFiles(input_dir,
"*.unique",
ignore_empty_files=False)
aux_files += getInputFiles(input_dir,
"*.unique.list",
ignore_empty_files=False)
aux_files += getInputFiles(input_dir,
"*.unique.TempCenters.Rare",
ignore_empty_files=False)
aux_files += getInputFiles(outdir,
"*.cluster",
ignore_empty_files=False)
aux_files += getInputFiles(outdir,
"*.cluster.list",
ignore_empty_files=False)
aux_files += getInputFiles(outdir, "*.log", ignore_empty_files=False)
aux_files += getInputFiles(".",
"LikelihoodRatio.txt",
ignore_empty_files=False)
# Move the final groups file(s) to the groups dir
groups_dir = makeDirOrdie("%s_groups_files" % outdir)
bulk_move_to_dir(final_groups_files, groups_dir)
# Move aux files to the aux dir
aux_dir = makeAuxDir(outdir)
bulk_move_to_dir(aux_files, aux_dir)
# Cleanup the pool
cleanup_pool(pool)
def cluster_crop(self, input_f, outdir, groupsfile, processes, blocksize, clustpct, maxmcmc, maxsm, rare,
blockcount, extraargstring):
"""Clusters sequences using CROP.
:param input_f: Filepath to the input fasta file to cluster.
:param outdir: Filepath to the output directory.
:param groupsfile: Filepath to the groups file to use as a reference for dereplication counting.
:param blocksize: Size of blocks to be used for all rounds (if -b is specified, then -z will not affect the
first round. For data set with different average sequence length, this parameter should \
be tuned such that it won't take too long for each block to do pariwise alignment. Hint \
for choosing z: z*L<150,000, where L is the average length of the sequences.
:param clustpct: The minimum similarity threshold for clustering. Either 'g' for 95% or 's' for 97%.
:param maxmcmc: This parameter specifies the number of iterations of MCMC. Default value is 2000. Increase \
this value to enhance accuracy (recommended value is at least 10*block size).
:param maxsm: This parameter specifies the maximum number of 'split and merge' process to run. Max is 20.
:param rare: The maximum cluster size allowed to be classified as 'rare'. Clusters are defined as either \
'abundant' or 'rare'. 'Abundant' clusters will be clustered first, then the 'rare' \
clusters are mapped to the 'abundant' clusters. Finally, 'rare' clusters which cannot be \
mapped will be clustered separately. e.g. If r=5, the clusters with size <=5 will be \
considered 'rare' in above procedure. and r=0 will yield the best accuracy. If you \
believe your data is not too diverse to be handled, then r=0 will be the best choice.
:param blockcount: The size of blocks in the first round of clustering. Hint of choosing -b: Each block in the \
first round should contain about 50 sequences. i.e. b=N/50, where N is the number of \
input sequences. Default: # input sequences / z.
:param processes: The maximum number of processes to use.
:param extraargstring: Advanced program parameter string.
"""
# Grab the fasta file(s) to cluster
inputs = getInputFiles(input_f)
debugPrintInputInfo(inputs, "clustered")
pool = init_pool(min(len(inputs), processes))
# RUN CLUSTERING
# crop -i %s -o %s -z %s -c %s -e %s -m %s%s
run_parallel([ProgramRunner(ProgramRunnerCommands.CLUSTER_CROP,
[input_, "%s/%s" % (outdir, strip_ixes(input_)), blocksize, clustpct,
maxmcmc, maxsm, rare, blockcount],
{"exists": [input_]}, extraargstring) for input_ in inputs], pool)
# CLEAN THE OUTPUT GROUPS FILE
printVerbose("Parsing the groups file from clustering")
clustered_groups_files = getInputFiles(outdir, "*.cluster.list")
debugPrintInputInfo(clustered_groups_files, "converted to groups files")
run_parallel([PythonRunner(parseCROPoutToGroups, [input_,
"%s/%s_uncount.groups" % (outdir, strip_ixes(input_))],
{"exists": [input_]})
for input_ in clustered_groups_files], pool)
printVerbose("Done parsing groups file.")
# Collect the groups file from clustering with counts removed
cleaned_clustered_groups_files = getInputFiles(outdir, "*_uncount.groups", ignore_empty_files=False)
# Resolve the user specified names file if necessary
final_groups_files = handle_groups_file_update(outdir, groupsfile, cleaned_clustered_groups_files)
# GATHER AUX FILES
input_dir = getDirName(input_f)
aux_files = cleaned_clustered_groups_files
aux_files += getInputFiles(input_dir, "*.unique", ignore_empty_files=False)
aux_files += getInputFiles(input_dir, "*.unique.list", ignore_empty_files=False)
aux_files += getInputFiles(input_dir, "*.unique.TempCenters.Rare", ignore_empty_files=False)
aux_files += getInputFiles(outdir, "*.cluster", ignore_empty_files=False)
aux_files += getInputFiles(outdir, "*.cluster.list", ignore_empty_files=False)
aux_files += getInputFiles(outdir, "*.log", ignore_empty_files=False)
aux_files += getInputFiles(".", "LikelihoodRatio.txt", ignore_empty_files=False)
# Move the final groups file(s) to the groups dir
groups_dir = makeDirOrdie("%s_groups_files" % outdir)
bulk_move_to_dir(final_groups_files, groups_dir)
# Move aux files to the aux dir
aux_dir = makeAuxDir(outdir)
bulk_move_to_dir(aux_files, aux_dir)
# Cleanup the pool
cleanup_pool(pool)
def dereplicate_vsearch(self, input_f, outdir, groupsfile, processes, stripcounts, extraargstring):
"""Dereplicates with vsearch.
:param input_f: Filepath to the file or folder of files to dereplicate.
:param outdir: Filepath to the output directory.
:param groupsfile: A groups file to use as a reference for replicant counting. If no groups file is
provided, input sequences are conidered singletons (regardless of their name-annotated
dereplication count).
:param processes: The number of processes to use to dereplicate the fileset.
:param stripcounts: If True, strips the trailing dereplication counts from a file before dereplication.
:param extraargstring: Advanced program parameter string.
"""
inputs = getInputFiles(input_f)
pool = init_pool(min(len(inputs), processes))
# REMOVES COUNTS FROM SEQUENCE NAMES IN ORDER TO CLUSTER PROPERLY
# strip counts if we need to.
if stripcounts:
printVerbose("Removing counts from sequence names...")
debugPrintInputInfo(inputs, "renamed")
run_parallel([PythonRunner(removeCountsFromFastFile,
[input_, "%s/%s_uncount.fasta" % (outdir, strip_ixes(input_)), 'fasta'],
{"exists": input_})
for input_ in inputs], pool)
printVerbose("Done removing counts.")
# Grab the cleaned files as input for the next step
inputs = getInputFiles(outdir, "*_uncount.fasta")
# DEREPLICATE
debugPrintInputInfo(inputs, "dereplicated")
printVerbose("Dereplicating...")
run_parallel([ProgramRunner(ProgramRunnerCommands.DEREP_VSEARCH,
[processes, input_,
"%s/%s_derep.fasta" % (outdir, strip_ixes(input_)),
"%s/%s_uc.out" % (outdir, strip_ixes(input_))],
{"exists": [input_], "positive": [processes]},
extraargstring)
for input_ in inputs], pool)
printVerbose("Done dereplicating")
# LOG DEREPLICATED SEQUENCES INTO A .GROUPS FILE
# generates a .groups file named _uc_parsed.out
# python parseUCtoGroups.py uc.out uc_parsed.out
input_ucs = getInputFiles(outdir, "*_uc.out")
printVerbose("Generating a groups file from dereplication.")
debugPrintInputInfo(inputs, "parsed (into a .groups file)")
run_parallel([PythonRunner(parseUCtoGroups, [input_, "%s/%s_derep.groups" % (outdir, strip_ixes(input_))],
{"exists": [input_]})
for input_ in input_ucs], pool)
most_recent_groups_files = getInputFiles(outdir, "*_derep.groups", ignore_empty_files=False)
# UPDATE THE MOST CURRENT GROUPS FILES WITH DEREPLICATION COUNTS
if groupsfile is not None:
# Grab the oldgroups file and the dereplicated groups file
old_groups_files = getInputFiles(groupsfile)
derep_groups_files = getInputFiles(outdir, "*_derep.groups")
printVerbose("Updating .groups files with dereplicated data")
printVerbose("%d Reference (old) groups files to be read:" % len(old_groups_files))
printVerbose(str(old_groups_files))
printVerbose("%d Dereplicated (new) groups files to be read:" % len(derep_groups_files))
printVerbose(str(derep_groups_files))
update_groups(old_groups_files, derep_groups_files, outdir, "dereplicated")
most_recent_groups_files = getInputFiles(outdir, "dereplicated*", ignore_empty_files=False)
printVerbose("Done updating .groups files.")
if len(inputs) != len(most_recent_groups_files):
print ("Error: Number of input fastas (%d) is not equal to the number ofgroups files (%d)." %
(len(inputs), len(most_recent_groups_files)))
exit()
fasta_groups_pairs = zip(inputs, most_recent_groups_files)
# ADD COUNT TO SEQUENCE NAMES AND SORT BY COUNT
# python renameWithReplicantCounts.py
# 8_macse_out/MACSEOUT_MERGED.fasta uc_parsed.out dereplicated_renamed.fasta
printVerbose("Adding dereplication data to unique fasta")
run_parallel([PythonRunner(renameWithReplicantCounts,
[fasta, groups, "%s/%s_counts.fasta" % (outdir, strip_ixes(fasta)), 'fasta'],
{"exists": [fasta, groups]})
for fasta, groups in fasta_groups_pairs], pool)
printVerbose("Done adding data")
aux_dir = makeAuxDir(outdir)
groups_dir = makeDirOrdie("%s_groups_files" % outdir)
bulk_move_to_dir(most_recent_groups_files, groups_dir)
aux_files = getInputFiles(outdir, '*', "*_counts.fasta", ignore_empty_files=False)
bulk_move_to_dir(aux_files, aux_dir)
cleanup_pool(pool)