def summariseGTFs(outfile):
job_memory = str(PARAMS["Merge_memory"]) + "G"
statement = "python {}scripts/annotationSummaryTable.py --gtf-dir combined_annotations.dir --annot-output {} --orf-output {}".format(
os.path.dirname(__file__).rstrip("pipelines"), outfile,
"report.dir/orf_summary.tsv")
P.run(statement)
def run_report(clean=True,
with_pipeline_status=True,
pipeline_status_format="svg"):
'''run cgatreport.
This will also run ruffus to create an svg image of the pipeline
status unless *with_pipeline_status* is set to False. The image
will be saved into the export directory.
'''
params = P.get_params()
if with_pipeline_status:
targetdir = params["exportdir"]
if not os.path.exists(targetdir):
os.mkdir(targetdir)
ruffus.pipeline_printout_graph(
os.path.join(targetdir, "pipeline.%s" % pipeline_status_format),
pipeline_status_format, ["full"],
checksum_level=params["ruffus_checksums_level"])
dirname, basename = os.path.split(P.get_caller().__file__)
report_engine = params.get("report_engine", "cgatreport")
assert report_engine in ('sphinxreport', 'cgatreport')
docdir = os.path.join(dirname, "pipeline_docs",
iotools.snip(basename, ".py"))
themedir = os.path.join(dirname, "pipeline_docs", "themes")
relpath = os.path.relpath(docdir)
trackerdir = os.path.join(docdir, "trackers")
# use a fake X display in order to avoid windows popping up
# from R plots.
xvfb_command = iotools.which("xvfb-run")
# permit multiple servers using -d option
if xvfb_command:
xvfb_command += " -d "
else:
xvfb_command = ""
# if there is no DISPLAY variable set, xvfb runs, but
# exits with error when killing process. Thus, ignore return
# value.
# print os.getenv("DISPLAY"), "command=", xvfb_command
if not os.getenv("DISPLAY"):
erase_return = "|| true"
else:
erase_return = ""
if os.path.exists("conf.py"):
conf_dir = os.path.abspath(".")
else:
conf_dir = os.path.join(os.path.dirname(__file__), "configuration")
# in the current version, xvfb always returns with an error, thus
# ignore these.
erase_return = "|| true"
if clean:
clean = "rm -rf report _cache _static;"
else:
clean = ""
# with sphinx >1.3.1 the PYTHONPATH needs to be set explicitely as
# the virtual environment seems to be stripped. It is thus set to
# the contents of the current sys.path
syspath = ":".join(sys.path)
statement = '''
%(clean)s
(export SPHINX_DOCSDIR=%(docdir)s;
export SPHINX_THEMEDIR=%(themedir)s;
export PYTHONPATH=%(syspath)s;
%(xvfb_command)s
%(report_engine)s-build
--num-jobs=%(report_threads)s
sphinx-build
-b html
-d %(report_doctrees)s
-c %(conf_dir)s
-j %(report_threads)s
%(docdir)s %(report_html)s
>& report.log %(erase_return)s )
'''
P.run(statement)
E.info(
'the report is available at %s' %
os.path.abspath(os.path.join(params['report_html'], "contents.html")))
def runGOFromFiles(outfile,
outdir,
fg_file,
bg_file=None,
go_file=None,
ontology_file=None,
samples=None,
minimum_counts=0,
pairs=False,
gene2name=None):
"""check for GO enrichment.
The gene lists are supplied by files.
This method is a wrapper for `runGO.py`.
Arguments
---------
outfile : string
Output filename
outdir : string
Output directory for auxiliary files
fg_file : string
Gene list of foreground.
bg_file : string
Gene list for background. If None, all genes
with GO annotations are used as background.
go_file : string
Filename with Gene-to-GO assignments
ontology_file : string
Filename with ontology information.
samples : int
Number of samples for empirical FDR. If not given, use
BH FDR.
minimum_counts : int
Minimum number of observations in a GO category
required in order for using it.
pairs : bool
If True, each category for each pair of gene sets will
be tested for differential enrichment.
gene2name : string
Filename with a gene-to-genename information.
"""
if ontology_file is None:
ontology_file = PARAMS.get("go_ontology", None)
options = []
if ontology_file:
options.append("--filename-ontology=%(ontology_file)s" % locals())
if bg_file is not None:
options.append("--background-tsv-file=%(bg_file)s" % locals())
if samples is not None:
options.append("--fdr")
options.append("--sample-size=%(samples)i" % locals())
options.append("--fdr-method=empirical")
else:
options.append("--fdr")
options.append("--fdr-method=BH")
if pairs:
options.append("--pairwise")
if gene2name:
options.append("--gene2name-map-tsv-file=%s" % gene2name)
options = " ".join(options)
statement = '''
cgat runGO
--filename-input=%(go_file)s
--genes-tsv-file=%(fg_file)s
--output-filename-pattern='%(outdir)s/%%(set)s.%%(go)s.%%(section)s'
--min-counts=%(minimum_counts)i
--log=%(outfile)s.log
%(options)s
> %(outfile)s'''
P.run(statement)
def cellrangerCount(infile, outfile):
'''
Execute the cell ranger pipleline for all samples.
'''
# set key parameters
transcriptome = PARAMS["cellranger_transcriptome"]
if transcriptome is None:
raise ValueError('"cellranger_transcriptome" parameter not set'
' in file "pipeline.yml"')
if not os.path.exists(transcriptome):
raise ValueError('The specified "cellranger_transcriptome"'
' file does not exist')
# set the maximum number of jobs for cellranger
max_jobs = PARAMS["cellranger_maxjobs"]
# parse the sample name and expected cell number
library_id, cellnumber, batch, trash = os.path.basename(infile).split(".")
# build lists of the sample files
seq_folders = []
sample_ids = []
# Parse the list of sequencing runs (i.e., paths) for the sample
with open(infile, "r") as sample_list:
for line in sample_list:
seq_folder_path = line.strip()
if seq_folder_path != "":
seq_folders.append(seq_folder_path)
sample_ids.append(os.path.basename(seq_folder_path))
input_fastqs = ",".join(seq_folders)
input_samples = ",".join(sample_ids)
id_tag = library_id + "-count"
log_file = id_tag + ".log"
## send one job script to slurm queue which arranges cellranger run
## hard-coded to ensure enough resources
job_threads = 6
job_memory = "24000M"
statement = (
'''cellranger count
--id %(id_tag)s
--fastqs %(input_fastqs)s
--sample %(input_samples)s
--transcriptome %(transcriptome)s
--expect-cells %(cellnumber)s
--chemistry %(cellranger_chemistry)s
--jobmode=slurm
--maxjobs=%(max_jobs)s
--nopreflight
&> %(log_file)s
''')
P.run(statement)
IOTools.touch_file(outfile)
def subsetAndDownsample(infiles, outfile):
'''
Generate datasets that include subsets of the 10x samples.
Optionally downsample UMI counts to normalise between samples.
'''
outdir = os.path.dirname(outfile)
if not os.path.exists(outdir):
os.mkdir(outdir)
agg_matrix_dir = os.path.join(os.path.dirname(infiles[0]),
"agg.processed.dir")
sample_table = pd.read_csv(infiles[1], sep="\t")
subsets = [k.split("_", 1)[1] for k in PARAMS.keys()
if k.startswith("datasets_")]
# Titles of fields encoded in filenames
name_field_titles = PARAMS["name_field_titles"]
if PARAMS["downsampling_enabled"]:
downsampling_function = PARAMS['downsampling_function']
else:
downsampling_function = "no"
downsampling_apply = PARAMS["downsampling_apply"]
job_memory = PARAMS["postprocess_memory"]
statements = []
for subset in subsets:
if subset == "all":
if not PARAMS["datasets_all"]:
continue
sample_ids = set(sample_table["sample_id"].values)
sample_ids_str = ",".join(sample_ids)
else:
sample_ids = PARAMS["datasets" + "_" + subset]
sample_ids_str = ",".join([x.strip() for x in
sample_ids.split(",")])
out_dir = os.path.join(os.path.dirname(outfile),
subset)
tenx_dir = PARAMS["tenx_dir"]
log_file = outfile.replace(".sentinel",
"." + subset + ".log")
statement = '''Rscript %(tenx_dir)s/R/cellranger_subsetAndDownsample.R
--tenxdir=%(agg_matrix_dir)s
--sampleids=%(sample_ids_str)s
--downsample=%(downsampling_function)s
--apply=%(downsampling_apply)s
--samplenamefields=%(name_field_titles)s
--outdir=%(out_dir)s
&> %(log_file)s
''' % locals()
statements.append(statement)
P.run(statements)
IOTools.touch_file(outfile)
def build_report():
scriptloc = "/".join(os.path.dirname(
sys.argv[0]).split("/")[0:-1]) + "/scripts/assembly_report.Rmd"
statement = 'R -e "rmarkdown::render(\'{}\',output_file=\'{}/report.dir/assembly_report.html\')" --args {}/contigs.dir/Contigs.Summary'.format(
scriptloc, os.getcwd(), os.getcwd())
P.run(statement)
def run(self, infile, outfile, params):
options = []
reference_fasta = params.reference_fasta
reference_fasta_map = build_reference_fasta_map(
params.reference_fasta_map)
reference_label = None
use_target_regions = True
if params.reference_fasta:
map_path2name = dict([(x[1], x[0])
for x in list(reference_fasta_map.items())])
if params.reference_fasta == "auto":
fasta = resolve_argument(list(reference_fasta_map.values()),
",").split(",")
reference_fasta, diffs = get_reference_for_bam(
infile, fastafiles=fasta)
if reference_fasta:
options.append("--ref-seq {}".format(reference_fasta))
reference_label = map_path2name[reference_fasta]
elif diffs:
E.warn(
"attempted to detect reference fasta, but unable to do so. "
"diffs: {}".format(diffs))
else:
E.warn("sequence dict is empty, BAM likely to be empty. "
"target_regions will be ignored")
use_target_regions = False
else:
options.append("--ref-seq {}".format(params.reference_fasta))
reference_label = map_path2name.get(params.reference_fasta,
None)
if params.target_regions and use_target_regions:
target_regions = get_associated_file(params, reference_label,
"target_regions")
# convert to 1-based coordinates and decompress
if target_regions.endswith(".bed.gz"):
target_regions = (
"<(zcat {} "
"| awk '{{printf(\"%%s\\t%%i\\t%%i\\n\", $1, $2+1, $3)}}')"
.format(target_regions))
options.append("--target-regions {}".format(target_regions))
options = " ".join(options)
if not os.path.exists(outfile + ".tmp"):
try:
retval = P.run("{params.path} stats "
"{self.options} "
"{options} "
"{infile} "
"2> {outfile}.log "
"> {outfile}.tmp; ".format(**locals()),
job_memory="16G")
except OSError as e:
E.warn("input file {} gave the following errors: {}".format(
infile, str(e)))
return None
else:
retval = None
def split_output(lines):
is_comment = True
section, body = None, []
for line in lines:
if line.startswith("#"):
if body:
yield section, body
body = []
is_comment = True
else:
# the following preserves new-line
line = re.sub("\t#.*", "", line)
fields = line[:-1].split("\t")
section = fields[0]
body.append(fields[1:])
is_comment = False
if body:
yield section, body
# split into separate files for upload
with IOTools.open_file(outfile + ".tmp") as inf:
for section, body in split_output(inf):
try:
tablename, columns = self._map_section_to_table[section]
except KeyError:
continue
output_file = self.map_table_to_file(tablename, outfile)
with IOTools.open_file(output_file, "w") as outf:
if len(columns) > 1 and columns[1].startswith("VAR_"):
outf.write("{}\t{}\n".format(columns[0],
columns[1][4:]))
for data in body:
outf.write("{}\t{}\n".format(
data[0], ",".join(data)))
else:
outf.write("\t".join(columns) + "\n")
# remove first column, which contains the identifier
outf.write("\n".join(["\t".join(x)
for x in body]) + "\n")
os.rename(outfile + ".tmp", outfile)
return retval
def mergeTables(outfile):
statement=PipelineHumann2.humann2Merge(outfile,PARAMS)
P.run(statement)
def normTables(infile,outfile):
if re.search("coverage",infile):
statement="ln -s ../{} {}".format(infile,outfile)
else:
statement=PipelineHumann2.humann2Norm(infile,outfile,PARAMS)
P.run(statement)
def flagstat(infile, outfile):
statement = '''samtools flagstat %(infile)s > %(outfile)s'''
P.run(statement)
def runHumann2(infile, outfile):
job_threads = int(PARAMS["Humann2_threads"])
job_memory = PARAMS["Humann2_memory"]+"G"
statement = PipelineHumann2.humann2Call(infile,PARAMS)
P.run(statement)
def idxstats(infile, outfile):
statement = '''samtools idxstats %(infile)s > %(outfile)s'''
P.run(statement)
def multiqc(infiles, outfile):
statement = ''' export LC_ALL=en_US.UTF-8 && export LANG=en_US.UTF-8
&& multiqc . -f -n %(outfile)s '''
P.run(statement)
def fastqc(infile, outfile):
statement = '''fastqc %(infile)s > %(outfile)s.log'''
P.run(statement)
def mapReads(infiles, outfile):
'''Map reads to the genome using BWA '''
job_threads = PARAMS["bwa_threads"]
m = PipelineMapping.BWA()
statement = m.build((infiles, ), outfile)
P.run()
def picard_rmdup(infile, outfile):
final_memory = str(int(params['picard_memory']) + 2) + 'g'
statement = """picard -Xmx%(picard_memory)sg MarkDuplicates REMOVE_DUPLICATES = true
I= %(infile)s O=%(outfile)s M= %(outfile)s.metrics
&& samtools index %(outfile)s"""
P.run(statement, job_queue=params['q'], job_memory=final_memory)
def summariseContigs(infile, outfile):
#summarise each contigs file
statement = PipelineAssembly.SummariseContigs(infile, outfile)
P.run(statement)
def filter_read(infile, outfile):
statement = """samtools view -h -b -f1 -f2 -F 4 -F 0x100%(infile)s chr{1..19} > %(outfile)s
&& samtools index %(outfile)s"""
P.run(statement, job_queue=params['q'])
def indexBams(infile, outfile):
'''index merged bams'''
statement = f'''samtools index -b {infile} {outfile}'''
P.run(statement)
def macs2(infiles, outfile):
treatment, control = infiles
name = outfile.replace('_peaks.narrowPeak', "").replace('peaks/', '')
statement = """macs2 callpeak -t %(treatment)s -c %(control)s
-n %(name)s -f BAM -g %(macs2_genome)s --outdir peaks"""
P.run(statement, job_queue=params["q"])
def run(self, infile, outfile, params):
# TODO: bam_fastqc_sequence_length_distribution.tsv may
# contain ranges such as '30-31'. Convert to beginning of
# range like in this perl command:
#
# perl -p -i -e "s/\-\d+//"
# *.dir/bam_fastqc.dir/bam_fastqc.tsv.bam_fastqc_sequence_length_distribution.tsv
if infile.endswith(".gz"):
prefix = IOTools.snip(os.path.basename(infile[:-3]))
else:
prefix = IOTools.snip(os.path.basename(infile))
outdir = os.path.dirname(outfile)
datafile = os.path.join(outdir, "{}_fastqc".format(prefix),
"fastqc_data.txt")
if not os.path.exists(datafile):
if not os.path.exists(outdir):
os.makedirs(outdir)
retval = P.run(
"{params.path} "
"{params.options} "
"--extract "
"--outdir {outdir} "
"{infile} "
">& {outfile} ".format(**locals()), **params._asdict())
else:
IOTools.touch_file(outfile)
retval = None
def _split_output(lines):
body, header, section, status = [], None, None, None
for line in lines:
if line.startswith("##FastQC"):
continue
elif line.startswith("#"):
header, body = line[1:-1].split("\t"), []
elif line.startswith(">>END_MODULE"):
yield section, header, body, status
body, header, section, status = [], None, None, None
elif line.startswith(">>"):
section, status = line[2:-1].split("\t")
else:
fields = line[:-1].split("\t")
body.append(fields)
# split into separate files for upload
summary_data = []
with IOTools.open_file(datafile) as inf:
for section, header, body, status in _split_output(inf):
if len(body) == 0:
continue
summary_data.append((section, status))
tablename = "{}_".format(self.name) + re.sub(
" ", "_", section).lower()
if tablename not in self.tablenames:
raise ValueError(
"unknown tablename {}, expected one of {}".format(
tablename, self.tablenames))
output_file = ".".join((outfile, tablename, "tsv"))
with open(output_file, "w") as outf:
outf.write("\t".join([x.lower() for x in header]) + "\n")
# remove first column, which contains the identifier
outf.write("\n".join(["\t".join(x) for x in body]) + "\n")
output_file = ".".join(
(outfile, "{}_summary".format(self.name), "tsv"))
with IOTools.open_file(output_file, "w") as outf:
outf.write("section\tstatus\n")
for section, status in summary_data:
outf.write("{}\t{}\n".format(section, status))
return retval
def fastqc(infile, outfile):
statement = "fastqc --nogroup -o fastqc %(infile)s > %(outfile)s.log"
P.run(statement, job_queue=params["q"])
def postprocessAggrMatrix(infiles, outfile):
'''
Post-process the cellranger aggr count matrix.
Batch, sample_name and aggregation ID metadata are added.
Optionally cells with barcodes shared (within sequencing batch)
between samples can be removed (known index hopping on Illumina 4000).
'''
outdir = os.path.dirname(outfile)
if not os.path.exists(outdir):
os.mkdir(outdir)
infile = infiles[0]
sample_table = infiles[1]
agg_dir = os.path.dirname(infile)
out_dir = os.path.dirname(outfile)
# Clean barcode hopping
if PARAMS["postprocess_barcodes"]:
hopping = "--hopping"
else:
hopping = ""
# Additional options
options = PARAMS["postprocess_options"]
mexdir = PARAMS["postprocess_mexdir"]
if mexdir is None:
raise ValueError('"postprocess_mexdir" parameter not set'
' in file "pipeline.yml"')
tenxdir = os.path.join(agg_dir, mexdir)
if not os.path.exists(tenxdir):
raise ValueError('The specified "postprocess_mexdir"'
' directory does not exist in directory ' + agg_dir)
job_memory = PARAMS["postprocess_memory"]
blacklist = PARAMS["postprocess_blacklist"]
log_file = outfile.replace(".sentinel", ".log")
statement = '''Rscript %(tenx_dir)s/R/cellranger_postprocessAggrMatrix.R
--tenxdir=%(tenxdir)s
--sampletable=%(sample_table)s
--samplenamefields=%(name_field_titles)s
--downsample=no
%(hopping)s
--blacklist=%(blacklist)s
%(options)s
--outdir=%(out_dir)s
&> %(log_file)s
'''
P.run(statement)
IOTools.touch_file(outfile)
def multiqc(infiles, outfile):
statement = """export LC_ALL=en_US.UTF-8; export LANG=en_US.UTF-8;
multiqc fastqc/ -f -n %(outfile)s"""
P.run(statement, job_queue=params["q"])
def combine_means(infiles, outfile):
infiles = " ".join(infiles)
statement = ("cat %(infiles)s " "> %(outfile)s ".format(**locals()))
P.run(statement)
def picard(infile, outfile):
final_memory = str(int(params['picard_memory']) + 2) + 'g'
statement = """picard -Xmx%(picard_memory)sg CollectAlignmentSummaryMetrics
R=%(picard_genome)s I= %(infile)s O=%(outfile)s"""
P.run(statement, job_queue=params['q'], job_memory=final_memory)
def processReads(infile, outfiles):
'''process reads from .fastq and other sequence files.
'''
trimmomatic_options = P.get_params()["trimmomatic_options"]
if P.get_params()["auto_remove"]:
trimmomatic_options = " ILLUMINACLIP:%s:%s:%s:%s:%s:%s " % (
"contaminants.fasta",
P.get_params()["trimmomatic_mismatches"],
P.get_params()["trimmomatic_p_thresh"],
P.get_params()["trimmomatic_c_thresh"],
P.get_params()["trimmomatic_min_adapter_len"],
P.get_params()["trimmomatic_keep_both_reads"]) + trimmomatic_options
elif P.get_params()["trimmomatic_adapter"]:
trimmomatic_options = " ILLUMINACLIP:%s:%s:%s:%s:%s:%s " % (
P.get_params()["trimmomatic_adapter"],
P.get_params()["trimmomatic_mismatches"],
P.get_params()["trimmomatic_p_thresh"],
P.get_params()["trimmomatic_c_thresh"],
P.get_params()["trimmomatic_min_adapter_len"],
P.get_params()["trimmomatic_keep_both_reads"]) + trimmomatic_options
job_threads = P.get_params()["threads"]
job_memory = "12G"
track = re.match(REGEX_TRACK, infile).groups()[0]
m = preprocess.MasterProcessor(
save=P.get_params()["save"],
summarize=P.get_params()["summarize"],
threads=P.get_params()["threads"],
qual_format=P.get_params()['qual_format'])
for tool in P.as_list(P.get_params()["preprocessors"]):
if tool == "fastx_trimmer":
m.add(preprocess.FastxTrimmer(
P.get_params()["fastx_trimmer_options"],
threads=P.get_params()["threads"]))
elif tool == "trimmomatic":
m.add(preprocess.Trimmomatic(
trimmomatic_options,
threads=P.get_params()["threads"]))
elif tool == "sickle":
m.add(preprocess.Sickle(
P.get_params()["sickle_options"],
threads=P.get_params()["threads"]))
elif tool == "trimgalore":
m.add(preprocess.Trimgalore(
P.get_params()["trimgalore_options"],
threads=P.get_params()["threads"]))
elif tool == "flash":
m.add(preprocess.Flash(
P.get_params()["flash_options"],
threads=P.get_params()["threads"]))
elif tool == "reversecomplement":
m.add(preprocess.ReverseComplement(
P.get_params()["reversecomplement_options"]))
elif tool == "pandaseq":
m.add(preprocess.Pandaseq(
P.get_params()["pandaseq_options"],
threads=P.get_params()["threads"]))
elif tool == "cutadapt":
cutadapt_options = P.get_params()["cutadapt_options"]
if P.get_params()["auto_remove"]:
cutadapt_options += " -a file:contaminants.fasta "
m.add(preprocess.Cutadapt(
cutadapt_options,
threads=P.get_params()["threads"],
untrimmed=P.get_params()['cutadapt_reroute_untrimmed'],
process_paired=P.get_params()["cutadapt_process_paired"]))
else:
raise NotImplementedError("tool '%s' not implemented" % tool)
statement = m.build((infile,), "processed.dir/trimmed-", track)
P.run(statement)
def flagstats(infile, outfile):
statement = """samtools flagstats %(infile)s > %(outfile)s"""
P.run(statement, job_queue=params['q'])
def createGOFromGeneOntology(infile, outfile):
"""get GO assignments from Geneontology.org
GO terms are mapped to ensembl gene names via uniprot identifiers.
Configuration
-------------
geneontology_file
Filename on geneontology database, e.g.,
gene_association.goa_human.gz
database_name
Pipeline database name
Arguments
---------
infile : string
Unused
outfile : string
Output filename
"""
filename = os.path.join(os.path.dirname(outfile), "geneontology.goa.gz")
if not os.path.exists(filename):
statement = '''
wget -O %(filename)s http://cvsweb.geneontology.org/cgi-bin/cvsweb.cgi/go/gene-associations/%(go_geneontology_file)s?rev=HEAD
'''
P.run(statement)
# see http://www.geneontology.org/gene-associations/readme/goa.README
Data = collections.namedtuple(
"Data",
"db db_object_id db_object_symbol qualifier goid dbreference evidence "
" with_id aspect "
" db_object_name synonym db_object_type "
" taxon_id date assigned_by "
" annotation_extension"
" gene_product_form_id")
dbh = sqlite3.connect(PARAMS["database_name"])
cc = dbh.cursor()
map_uniprot2ensembl = dict(
cc.execute("SELECT DISTINCT gene_name, gene_id FROM transcript_info").
fetchall())
map_goid2description = dict(
cc.execute("SELECT DISTINCT go_id, description FROM go_assignments").
fetchall())
aspect2name = {
"P": "biol_process",
"F": "mol_function",
"C": "cell_location"
}
c = E.Counter()
found_uniprot, found_genes, notfound_uniprot = set(), set(), set()
outf = iotools.open_file(outfile, "w")
outf.write("go_type\tgene_id\tgo_id\tdescription\tevidence\n")
for line in iotools.open_file(filename):
if line.startswith("!"):
continue
c.input += 1
data = Data._make(line[:-1].split("\t"))
if data.db_object_symbol in map_uniprot2ensembl:
gene_id = map_uniprot2ensembl[data.db_object_symbol]
found_uniprot.add(data.db_object_symbol)
found_genes.add(gene_id)
outf.write(
"%s\t%s\t%s\t%s\t%s\n" %
(aspect2name[data.aspect], gene_id, data.goid,
map_goid2description.get(data.goid, ""), data.evidence))
c.output += 1
else:
c.notfound += 1
notfound_uniprot.add(data.db_object_symbol)
c.found_genes = len(found_genes)
c.found_uniprot = len(found_uniprot)
c.notfound_uniprot = len(notfound_uniprot)
E.info("%s" % str(c))
E.info("not found=%s" % str(notfound_uniprot))
outf.close()
def meganAnnot(infile, outfile):
job_memory = str(PARAMS["Blast2lca_memory"]) + "G"
job_threads = int(PARAMS["Blast2lca_threads"])
#generate call to blast2lca
statement = PipelineAnnotate.runBlast2Lca(infile, outfile, PARAMS)
P.run(statement)
