def calculateFalsePositiveRate(infiles, outfile):
'''
taxonomy false positives and negatives etc
'''
# connect to database
dbh = sqlite3.connect(PARAMS["database"])
cc = dbh.cursor()
levels = ["phylum", "class", "order", "family", "genus", "species"]
tablename_true = P.toTable(infiles[0])
# get corresponding estimate file
tablename_estimate = P.toTable(os.path.basename([inf for inf in infiles[
1:] if os.path.basename(inf)[len("metaphlan_"):] == os.path.basename(infiles[0])][0]))
outf = open(outfile, "w")
track = P.snip(os.path.basename(infiles[0]), ".taxonomy.relab.load")
for level in levels:
for cutoff in [0, 1]:
true_set = set()
estimate_set = set()
for taxa in cc.execute("""SELECT taxa FROM %s WHERE level == '%s' AND relab > %f""" % (tablename_true, level, float(cutoff) / 100)):
true_set.add(taxa[0])
for taxa in cc.execute("""SELECT taxon FROM %s WHERE taxon_level == '%s' AND rel_abundance > %f""" % (tablename_estimate, level, float(cutoff))):
estimate_set.add(taxa[0])
total_true = len(true_set)
total_estimate = len(estimate_set)
tp = true_set.intersection(estimate_set)
fp = estimate_set.difference(true_set)
fp_rate = float(len(fp)) / total_estimate
tp_rate = float(len(tp)) / total_true
outf.write("%s\t%f\t%f\t%s\t%s\n" %
(level, fp_rate, tp_rate, track, str(cutoff)))
outf.close()
def calculateFalsePositiveRate(infiles, outfile):
'''
taxonomy false positives and negatives etc
'''
# connect to database
dbh = sqlite3.connect(PARAMS["database"])
cc = dbh.cursor()
levels = ["phylum", "class", "order", "family", "genus", "species"]
tablename_true = P.toTable(infiles[0])
# get corresponding estimate file
tablename_estimate = P.toTable(os.path.basename([inf for inf in infiles[1:] if os.path.basename(inf)[len("metaphlan_"):] == os.path.basename(infiles[0])][0]))
outf = open(outfile, "w")
track = P.snip(os.path.basename(infiles[0]), ".taxonomy.relab.load")
for level in levels:
for cutoff in [0, 1]:
true_set = set()
estimate_set = set()
for taxa in cc.execute("""SELECT taxa FROM %s WHERE level == '%s' AND relab > %f""" % (tablename_true, level, float(cutoff)/100)):
true_set.add(taxa[0])
for taxa in cc.execute("""SELECT taxon FROM %s WHERE taxon_level == '%s' AND rel_abundance > %f""" % (tablename_estimate, level, float(cutoff))):
estimate_set.add(taxa[0])
total_true = len(true_set)
total_estimate = len(estimate_set)
tp = true_set.intersection(estimate_set)
fp = estimate_set.difference(true_set)
fp_rate = float(len(fp))/total_estimate
tp_rate = float(len(tp))/total_true
outf.write("%s\t%f\t%f\t%s\t%s\n" % (level, fp_rate, tp_rate, track, str(cutoff)))
outf.close()
def compareAbundanceOfFalsePositiveSpecies(infiles, outfile):
'''
boxplot the relative abundance of false positive
species compared to true positives
'''
tablename_estimate = P.toTable(infiles[0])
track = P.snip(
os.path.basename(infiles[0]).replace("metaphlan_", ""), ".load")
tablename_true = [
P.toTable(x) for x in infiles[1:]
if P.snip(os.path.basename(x), ".load") == track
][0]
dbh = sqlite3.connect("csvdb")
cc = dbh.cursor()
tmp = P.getTempFile(".")
tmp.write("taxa\tabundance\tstatus\n")
estimate = {}
true = set()
for data in cc.execute(
"""SELECT taxon, rel_abundance FROM %s WHERE taxon_level == 'species'"""
% tablename_estimate).fetchall():
estimate[data[0]] = data[1]
for data in cc.execute("""SELECT taxa FROM %s WHERE level == 'species'""" %
tablename_true).fetchall():
true.add(data[0])
for taxa, abundance in estimate.iteritems():
if taxa in true:
tmp.write("%s\t%f\ttp\n" % (taxa, abundance))
else:
tmp.write("%s\t%f\tfp\n" % (taxa, abundance))
tmp.close()
inf = tmp.name
if track.find("15M") != -1:
col = "cadetblue"
elif track.find("30M") != -1:
col = "lightblue"
elif track.find("50M") != -1:
col = "slategray"
R('''dat <- read.csv("%s", header = T, stringsAsFactors = F, sep = "\t")'''
% inf)
R('''library(ggplot2)''')
R('''ggplot(dat, aes(x = status, y = log2(abundance))) + geom_boxplot(colour = "%s") + geom_hline(yintersect=0, linetype="dashed")'''
% col)
R('''ggsave("%s")''' % outfile)
os.unlink(inf)
def loadGeneInformation(infile, outfile, only_proteincoding=False):
'''load gene information gleaned from the attributes
in the gene set gtf file.
*infile* is an ENSEMBL gtf file.
'''
table = P.toTable(outfile)
if only_proteincoding:
filter_cmd = ''' awk '$2 == "protein_coding"' '''
else:
filter_cmd = "cat"
statement = '''
gunzip < %(infile)s
| %(filter_cmd)s
| grep "transcript_id"
| python %(scriptsdir)s/gtf2gtf.py --sort=gene+transcript
| python %(scriptsdir)s/gtf2tsv.py --full --only-attributes -v 0
| python %(toolsdir)s/csv_cut.py
--remove exon_id transcript_id transcript_name protein_id exon_number
| %(scriptsdir)s/hsort 1 | uniq
| python %(scriptsdir)s/csv2db.py %(csv2db_options)s
--index=gene_id
--index=gene_name
--map=gene_name:str
--table=%(table)s
> %(outfile)s'''
P.run()
def loadNumberExonsLengthSummaryStats(infile, outfile):
'''
load the table of exon counts and transcript lengths
'''
tablename = P.toTable(outfile.replace("/", "_")) + "_stats"
statement = '''python %(scriptsdir)s/csv2db.py -t %(tablename)s --log=%(outfile)s.log < %(infile)s > %(outfile)s'''
P.run()
def loadSummariseReadsContributingToTranscripts(infile, outfile):
'''
loads the summary of reads contributing to transcripts
'''
tablename = P.toTable(outfile.replace("/", "_"))
statement = '''python %(scriptsdir)s/csv2db.py -t %(tablename)s --log=%(outfile)s.log < %(infile)s > %(outfile)s'''
P.run()
def loadCountSingleAndMultiExonLincRNA(infile, outfile):
'''
load the counts for the multi and single exon lincRNA
'''
tablename = P.toTable(outfile.replace("/", "_")) + ".count"
statement = '''python %(scriptsdir)s/csv2db.py -t %(tablename)s --log=%(outfile)s.log < %(infile)s > %(outfile)s'''
P.run()
def loadHypergeometricAnalysis(infile, outfile):
'''load GO results.'''
track = P.toTable(outfile)
tablename = 'hypergeometric_%s_summary' % track
P.load(infile, outfile, tablename=tablename)
dbh = connect()
ontologies = [x[0] for x in Database.executewait(
dbh,
'''SELECT DISTINCT ontology FROM %s''' % tablename).fetchall()]
genelists = [x[0] for x in Database.executewait(
dbh,
'''SELECT DISTINCT genelist FROM %s''' % tablename).fetchall()]
# output files from runGO.py
sections = ('results', 'parameters', 'withgenes')
for section in sections:
tablename = 'hypergeometric_%s_%s' % (track, section)
statement = '''
python %(scriptsdir)s/combine_tables.py
--cat=track
--regex-filename="hypergeometric.dir/%(track)s.tsv.dir/(\S+).%(section)s"
hypergeometric.dir/%(track)s.tsv.dir/*.%(section)s
| python %(scriptsdir)s/csv2db.py
%(csv2db_options)s
--table=%(tablename)s
>> %(outfile)s'''
P.run()
for ontology in ontologies:
fn = os.path.join(infile + ".dir", "all_alldesc.%s.l2fold" % ontology)
if not os.path.exists(fn):
E.warn("file %s does not exist" % fn)
continue
P.load(fn,
outfile,
tablename='hypergeometric_%s_%s_l2fold' % (track, ontology),
options='--allow-empty')
fn = os.path.join(
infile + ".dir", "all_alldesc.%s.l10pvalue" % ontology)
P.load(fn,
outfile,
tablename='hypergeometric_%s_%s_l10pvalue' % (track, ontology),
options='--allow-empty')
fn = os.path.join(
infile + ".dir", "all_alldesc.%s.l10qvalue" % ontology)
P.load(fn,
outfile,
tablename='hypergeometric_%s_%s_l10qvalue' % (track, ontology),
options='--allow-empty')
def loadPicardDuplicateStats(infiles, outfile):
'''Merge Picard duplicate stats into single table and load into SQLite.'''
# Join data for all tracks into single file
outf = open('dupstats.txt', 'w')
first = True
for f in infiles:
track = P.snip(os.path.basename(f), ".dedup.bam")
statfile = P.snip(f, ".bam") + ".dupstats"
if not os.path.exists(statfile):
E.warn("File %s missing" % statfile)
continue
lines = [x for x in open(
statfile, "r").readlines() if not x.startswith("#") and x.strip()]
if first:
outf.write("%s\t%s" % ("track", lines[0]))
first = False
outf.write("%s\t%s" % (track, lines[1]))
outf.close()
tmpfilename = outf.name
# Load into database
tablename = P.toTable(outfile)
statement = '''cat %(tmpfilename)s
| python %(scriptsdir)s/csv2db.py
--index=track
--table=%(tablename)s
> %(outfile)s '''
P.run()
def loadTranscriptSummary(infile, outfile):
'''summarize binding information per transcript.'''
dbh = connect()
table = P.toTable(outfile)
cc = dbh.cursor()
# sqlite can not do full outer join
cc.execute( """DROP TABLE IF EXISTS %(table)s""" % locals() )
transcripts = [x[0] for x in cc.execute(
"SELECT DISTINCT(transcript_id) FROM annotations.transcript_info").fetchall()]
tmpf = P.getTempFile()
tables = ("tata", "cpg")
titles = tables
vals = []
for table in tables:
t = set([x[0] for x in cc.execute(
"SELECT DISTINCT(transcript_id) FROM %(table)s" % locals()).fetchall()])
vals.append(t)
tmpf.write("transcript_id\t%s\n" % "\t".join(titles))
for transcript_id in transcripts:
tmpf.write("%s\t%s\n" % (transcript_id,
"\t".join([str(int(transcript_id in v)) for v in vals])))
tmpf.close()
P.load(tmpf.name, outfile)
os.unlink(tmpf.name)
def buildGeneOntology(infile, outfile):
'''create an output file akin to GO ontology files to be
used with GO.py
'''
table = P.toTable(infile)
columns = ("cpg", "tata")
dbh = connect()
cc = dbh.cursor()
outf = IOTools.openFile(outfile, "w")
outf.write("go_type\tgene_id\tgo_id\tdescription\tevidence\n")
i = 1
for c in columns:
cc.execute(
"SELECT DISTINCT gene_id FROM %(table)s WHERE %(c)s" % locals())
outf.write(
"".join(["promotor\t%s\tGO:%07i\twith_%s\tNA\n" % (x[0], i, c) for x in cc]))
i += 1
cc.execute(
"SELECT DISTINCT gene_id FROM %(table)s WHERE %(c)s = 0" % locals())
outf.write(
"".join(["promotor\t%s\tGO:%07i\twithout_%s\tNA\n" % (x[0], i, c) for x in cc]))
i += 1
outf.close()
def createViewMapping(infile, outfile):
'''create view in database for alignment stats.
This view aggregates all information on a per-track basis.
The table is built from the following tracks:
mapping_stats
bam_stats
'''
tablename = P.toTable(outfile)
# can not create views across multiple database, so use table
view_type = "TABLE"
dbhandle = connect()
Database.executewait(
dbhandle, "DROP %(view_type)s IF EXISTS %(tablename)s" % locals())
statement = '''
CREATE %(view_type)s %(tablename)s AS
SELECT *
FROM bam_stats AS b
'''
Database.executewait(dbhandle, statement % locals())
def loadPicardAlignStats(infiles, outfile):
'''Merge Picard alignment stats into single table and load into SQLite.'''
# Join data for all tracks into single file
outf = P.getTempFile()
first = True
for f in infiles:
track = P.snip(os.path.basename(f), ".alignstats")
if not os.path.exists(f):
E.warn("File %s missing" % f)
continue
lines = [
x for x in open(f, "r").readlines() if not x.startswith("#") and x.strip()]
if first:
outf.write("%s\t%s" % ("track", lines[0]))
first = False
for i in range(1, len(lines)):
outf.write("%s\t%s" % (track, lines[i]))
outf.close()
tmpfilename = outf.name
# Load into database
tablename = P.toTable(outfile)
statement = '''cat %(tmpfilename)s
| python %(scriptsdir)s/csv2db.py
--index=track
--table=%(tablename)s
> %(outfile)s'''
P.run()
os.unlink(tmpfilename)
def mergeAndLoad(infiles, outfile, suffix):
'''load categorical tables (two columns) into a database.
The tables are merged and entered row-wise.
'''
header = ",".join([P.quote(P.snip(x, suffix)) for x in infiles])
if suffix.endswith(".gz"):
filenames = " ".join(
["<( zcat %s | cut -f 1,2 )" % x for x in infiles])
else:
filenames = " ".join(["<( cat %s | cut -f 1,2 )" % x for x in infiles])
tablename = P.toTable(outfile)
statement = """python %(scriptsdir)s/combine_tables.py
--headers=%(header)s
--missing=0
--ignore-empty
%(filenames)s
| perl -p -e "s/bin/track/"
| python %(scriptsdir)s/table2table.py --transpose
| python %(scriptsdir)s/csv2db.py
--index=track
--table=%(tablename)s
> %(outfile)s
"""
P.run()
def loadPicardGCStats(infiles, outfile):
'''Merge Picard insert size stats into single table and load into SQLite.'''
tablename = P.toTable(outfile)
outf = P.getTempFile()
first = True
for f in infiles:
track = P.snip(os.path.basename(f), ".gcstats")
if not os.path.exists(f):
E.warn("File %s missing" % f)
continue
lines = [
x for x in open(f, "r").readlines() if not x.startswith("#") and x.strip()]
if first:
outf.write("%s\t%s" % ("track", lines[0]))
first = False
outf.write("%s\t%s" % (track, lines[1]))
outf.close()
tmpfilename = outf.name
statement = '''cat %(tmpfilename)s
| python %(scriptsdir)s/csv2db.py
%(csv2db_options)s
--index=track
--table=%(tablename)s
> %(outfile)s '''
P.run()
os.unlink(tmpfilename)
def loadPicardAlignStats(infiles, outfile):
'''Merge Picard alignment stats into single table and load into SQLite.'''
# Join data for all tracks into single file
outf = P.getTempFile()
first = True
for f in infiles:
track = P.snip(os.path.basename(f), ".alignstats")
if not os.path.exists(f):
E.warn("File %s missing" % f)
continue
lines = [
x for x in open(f, "r").readlines()
if not x.startswith("#") and x.strip()
]
if first:
outf.write("%s\t%s" % ("track", lines[0]))
first = False
for i in range(1, len(lines)):
outf.write("%s\t%s" % (track, lines[i]))
outf.close()
tmpfilename = outf.name
# Load into database
tablename = P.toTable(outfile)
statement = '''cat %(tmpfilename)s
| python %(scriptsdir)s/csv2db.py
--index=track
--table=%(tablename)s
> %(outfile)s'''
P.run()
os.unlink(tmpfilename)
def loadLowerStringencyDeNovos(infile, outfile):
'''Load lower stringency de novos into database'''
scriptsdir = PARAMS["general_scriptsdir"]
tablename = P.toTable(outfile)
statement = '''cat %(infile)s | python %(scriptsdir)s/csv2db.py --table %(tablename)s --retry --ignore-empty --allow-empty > %(outfile)s''' % locals(
)
P.run()
def loadPicardDuplicateStats(infiles, outfile):
'''Merge Picard duplicate stats into single table and load into SQLite.'''
# Join data for all tracks into single file
outf = open('dupstats.txt', 'w')
first = True
for f in infiles:
track = P.snip(os.path.basename(f), ".dedup.bam")
statfile = P.snip(f, ".bam") + ".dupstats"
if not os.path.exists(statfile):
E.warn("File %s missing" % statfile)
continue
lines = [
x for x in open(statfile, "r").readlines()
if not x.startswith("#") and x.strip()
]
if first:
outf.write("%s\t%s" % ("track", lines[0]))
first = False
outf.write("%s\t%s" % (track, lines[1]))
outf.close()
tmpfilename = outf.name
# Load into database
tablename = P.toTable(outfile)
statement = '''cat %(tmpfilename)s
| python %(scriptsdir)s/csv2db.py
--index=track
--table=%(tablename)s
> %(outfile)s '''
P.run()
def loadGeneListStats(infiles, outfile):
'''Merge gene list stats into single table and load into SQLite.'''
tablename = P.toTable(outfile)
outf = open("genelist_stats.txt", "w")
first = True
for f in infiles:
track = P.snip(os.path.basename(f), ".genelist.stats")
if not os.path.exists(f):
E.warn("File %s missing" % f)
continue
lines = [
x for x in open(f, "r").readlines()
if not x.startswith("#") and x.strip()
]
if first: outf.write("%s\t%s" % ("track", lines[0]))
first = False
outf.write("%s\t%s" % (track, lines[1]))
outf.close()
tmpfilename = outf.name
statement = '''cat %(tmpfilename)s
| python %(scriptsdir)s/csv2db.py
--index=track
--table=%(tablename)s
> %(outfile)s '''
P.run()
def filterByCoverage(infiles, outfile):
fcoverage = PARAMS["coverage_filter"]
contig_file = infiles[0]
dbh = sqlite3.connect(
os.path.join(PARAMS["results_resultsdir"], PARAMS["database"]))
cc = dbh.cursor()
contigs = set()
for infile in infiles[1:]:
dirsplit = infile.split("/")
infile = os.path.join(
PARAMS["results_resultsdir"], dirsplit[-2].split(".dir")[0] + "-" + dirsplit[-1])
tablename = P.toTable(os.path.basename(infile))
if P.snip(contig_file, ".fa") == P.snip(os.path.basename(infile), ".coverage.load"):
statement = """SELECT contig_id ave FROM
(SELECT contig_id, AVG(coverage) as ave FROM %s GROUP BY contig_id)
WHERE ave > %i""" % (tablename, PARAMS["coverage_filter"])
for data in cc.execute(statement).fetchall():
contigs.add(data[0])
outf = open(outfile, "w")
print contigs
for fasta in FastaIterator.iterate(IOTools.openFile(contig_file)):
identifier = fasta.title.split(" ")[0]
if identifier in contigs:
outf.write(">%s\n%s\n" % (identifier, fasta.sequence))
outf.close()
def loadCodingPotential(infile, outfile):
'''load annotations'''
table = P.toTable(outfile)
statement = '''
gunzip < %(infile)s
| python %(scriptsdir)s/csv2db.py
%(csv2db_options)s
--allow-empty
--index=gene_id
--map=gene_id:str
--table=%(table)s
> %(outfile)s'''
P.run()
# set the is_coding flag
dbhandle = sqlite3.connect(PARAMS["database"])
Database.executewait(
dbhandle,
'''ALTER TABLE %(table)s ADD COLUMN is_coding INTEGER''' % locals())
Database.executewait(
dbhandle,
'''UPDATE %(table)s SET is_coding = (result == 'coding')''' % locals())
dbhandle.commit()
def filterByCoverage(infiles, outfile):
fcoverage = PARAMS["coverage_filter"]
contig_file = infiles[0]
dbh = sqlite3.connect(
os.path.join(PARAMS["results_resultsdir"], PARAMS["database"]))
cc = dbh.cursor()
contigs = set()
for infile in infiles[1:]:
dirsplit = infile.split("/")
infile = os.path.join(
PARAMS["results_resultsdir"],
dirsplit[-2].split(".dir")[0] + "-" + dirsplit[-1])
tablename = P.toTable(os.path.basename(infile))
if P.snip(contig_file, ".fa") == P.snip(os.path.basename(infile),
".coverage.load"):
statement = """SELECT contig_id ave FROM
(SELECT contig_id, AVG(coverage) as ave FROM %s GROUP BY contig_id)
WHERE ave > %i""" % (tablename,
PARAMS["coverage_filter"])
for data in cc.execute(statement).fetchall():
contigs.add(data[0])
outf = open(outfile, "w")
print contigs
for fasta in FastaIterator.iterate(IOTools.openFile(contig_file)):
identifier = fasta.title.split(" ")[0]
if identifier in contigs:
outf.write(">%s\n%s\n" % (identifier, fasta.sequence))
outf.close()
def loadGeneInformation(infile, outfile, only_proteincoding=False):
'''load gene information gleaned from the attributes
in the gene set gtf file.
*infile* is an ENSEMBL gtf file.
'''
table = P.toTable(outfile)
if only_proteincoding:
filter_cmd = ''' awk '$2 == "protein_coding"' '''
else:
filter_cmd = "cat"
statement = '''
gunzip < %(infile)s
| %(filter_cmd)s
| grep "transcript_id"
| python %(scriptsdir)s/gtf2gtf.py --sort=gene+transcript
| python %(scriptsdir)s/gtf2tsv.py --full --only-attributes -v 0
| python %(toolsdir)s/csv_cut.py
--remove exon_id transcript_id transcript_name protein_id exon_number
| %(scriptsdir)s/hsort 1 | uniq
| python %(scriptsdir)s/csv2db.py %(csv2db_options)s
--index=gene_id
--index=gene_name
--map=gene_name:str
--table=%(table)s
> %(outfile)s'''
P.run()
def loadProteinStats( infile, outfile ):
'''load protein statistics to database.
The *infile* is an ENSEMBL peptide file.
'''
to_cluster = True
table = P.toTable(outfile)
statement = '''
gunzip < %(infile)s |
python %(scriptsdir)s/fasta2table.py
--log=%(outfile)s
--type=aa
--section=length
--section=hid
--section=aa
--regex-identifier="(\S+)" |
sed "s/^id/protein_id/" |
python %(scriptsdir)s/csv2db.py %(csv2db_options)s
--index=protein_id
--map=protein_id:str
--table=%(table)s
> %(outfile)s'''
P.run()
def loadTranscriptStats( infile, outfile ):
'''load gene statistics to database.
The *infile* is the *outfile* from :meth:`buildTranscripts`
'''
to_cluster = True
table = P.toTable(outfile)
statement = '''
gunzip < %(infile)s |\
python %(scriptsdir)s/gtf2table.py \
--log=%(outfile)s.log \
--genome=%(genome_dir)s/%(genome)s \
--reporter=transcripts \
--counter=position \
--counter=length \
--counter=composition-na |\
python %(scriptsdir)s/csv2db.py %(csv2db_options)s \
--index=gene_id \
--map=gene_id:str \
--table=%(table)s \
> %(outfile)s'''
P.run()
def loadRecs(infile, outfile):
'''Load homozygous recessive disease candidates into database'''
scriptsdir = PARAMS["general_scriptsdir"]
tablename = P.toTable(outfile)
statement = '''cat %(infile)s | python %(scriptsdir)s/csv2db.py --table %(tablename)s --retry --ignore-empty --allow-empty > %(outfile)s''' % locals(
)
P.run()
def loadSummariseReadsContributingToTranscripts(infile, outfile):
'''
loads the summary of reads contributing to transcripts
'''
tablename = P.toTable(outfile.replace("/", "_"))
statement = '''python %(scriptsdir)s/csv2db.py -t %(tablename)s --log=%(outfile)s.log < %(infile)s > %(outfile)s'''
P.run()
def loadTranscriptInformation(infile, outfile,
only_proteincoding=False):
'''load the transcript set.
*infile* is an ENSEMBL gtf file.
'''
to_cluster = True
table = P.toTable(outfile)
if only_proteincoding:
filter_cmd = ''' awk '$2 == "protein_coding"' '''
else:
filter_cmd = "cat"
statement = '''gunzip
< %(infile)s
| %(filter_cmd)s
| awk '$3 == "CDS"'
| python %(scriptsdir)s/gtf2gtf.py --sort=gene
| python %(scriptsdir)s/gtf2tsv.py --full --only-attributes -v 0
| python %(toolsdir)s/csv_cut.py --remove exon_id exon_number
| %(scriptsdir)s/hsort 1 | uniq
| python %(scriptsdir)s/csv2db.py %(csv2db_options)s
--index=transcript_id
--index=gene_id
--index=protein_id
--index=gene_name
--map=transcript_name:str
--map=gene_name:str
--table=%(table)s
> %(outfile)s'''
P.run()
def loadCoverageStats(infiles, outfile):
'''Import coverage statistics into SQLite'''
scriptsdir = PARAMS["general_scriptsdir"]
tablename = P.toTable(outfile)
outf = open('coverage.txt', 'w')
first = True
for f in infiles:
track = P.snip(os.path.basename(f), ".cov")
lines = [
x for x in open(f, "r").readlines()
if not x.startswith("#") and x.strip()
]
if first:
outf.write("%s\t%s" % ("track", lines[0]))
first = False
outf.write("%s\t%s" % (track, lines[1]))
outf.close()
tmpfilename = outf.name
statement = '''cat %(tmpfilename)s
| python %(scriptsdir)s/csv2db.py
--index=track
--table=%(tablename)s
--ignore-empty
--retry
> %(outfile)s '''
P.run()
def exportMotifLocations( infiles, outfile ):
'''export motif locations. There will be a bed-file per motif.
Overlapping motif matches in different tracks will be merged.
'''
dbh = connect()
cc = dbh.cursor()
motifs = [ x[0] for x in cc.execute( "SELECT motif FROM motif_info" ).fetchall()]
for motif in motifs:
tmpf = P.getTempFile(".")
for infile in infiles:
table = P.toTable(infile)
track = P.snip( table, "_mast" )
for x in cc.execute( """SELECT contig, start, end, '%(track)s', evalue
FROM %(table)s WHERE motif = '%(motif)s' AND start IS NOT NULL""" % locals() ):
tmpf.write( "\t".join( map(str, x) ) + "\n" )
tmpf.close()
outfile = os.path.join( PARAMS["exportdir"], "motifs", "%s.bed.gz" % motif )
tmpfname = tmpf.name
statement = '''mergeBed -i %(tmpfname)s -nms | gzip > %(outfile)s'''
P.run()
os.unlink( tmpf.name )
def loadCompoundHets(infile, outfile):
'''Load compound heterozygous variants into database'''
scriptsdir = PARAMS["general_scriptsdir"]
tablename = P.toTable(outfile)
statement = '''cat %(infile)s | python %(scriptsdir)s/csv2db.py --table %(tablename)s --retry --ignore-empty --allow-empty > %(outfile)s''' % locals(
)
P.run()
def buildGeneOntology(infile, outfile):
'''create an output file akin to GO ontology files to be
used with GO.py
'''
table = P.toTable(infile)
columns = ("cpg", "tata")
dbh = connect()
cc = dbh.cursor()
outf = IOTools.openFile(outfile, "w")
outf.write("go_type\tgene_id\tgo_id\tdescription\tevidence\n")
i = 1
for c in columns:
cc.execute("SELECT DISTINCT gene_id FROM %(table)s WHERE %(c)s" %
locals())
outf.write("".join([
"promotor\t%s\tGO:%07i\twith_%s\tNA\n" % (x[0], i, c) for x in cc
]))
i += 1
cc.execute("SELECT DISTINCT gene_id FROM %(table)s WHERE %(c)s = 0" %
locals())
outf.write("".join([
"promotor\t%s\tGO:%07i\twithout_%s\tNA\n" % (x[0], i, c)
for x in cc
]))
i += 1
outf.close()
def buildContigSummary(infiles, outfile):
'''
merge the contig summary statistics
'''
stats = collections.defaultdict(list)
for filepath in infiles:
dirname = os.path.dirname(filepath)
stats[dirname].append(os.path.basename(filepath))
N = PARAMS["scaffold_n"]
# connect to database
dbh = connect()
cc = dbh.cursor()
for dirname in stats.keys():
outfname = os.path.join(dirname, "contig.summary.tsv")
outf = open(outfname, "w")
outf.write(
"track\tnscaffolds\tscaffold_length\tN%i\tmean_length\tmedian_length\tmax_length\n" % N)
for infile in stats[dirname]:
track = P.snip(
infile.split(dirname.split(".dir")[0])[1][1:], ".summary.load")
table = P.toTable(infile)
data = cc.execute("""SELECT nscaffolds
, scaffold_length
, N50
, mean_length
, median_length
, max_length FROM %s""" % table).fetchone()
outf.write("\t".join(
map(str, [track, data[0], data[1], data[2],
data[3], data[4], data[5]])) + "\n")
outf.close()
def exportMotifLocations(infiles, outfile):
'''export motif locations. There will be a bed-file per motif.
Overlapping motif matches in different tracks will be merged.
'''
dbh = connect()
cc = dbh.cursor()
motifs = [
x[0] for x in cc.execute("SELECT motif FROM motif_info").fetchall()
]
for motif in motifs:
tmpf = P.getTempFile(".")
for infile in infiles:
table = P.toTable(infile)
track = P.snip(table, "_mast")
for x in cc.execute(
"""SELECT contig, start, end, '%(track)s', evalue
FROM %(table)s WHERE motif = '%(motif)s' AND start IS NOT NULL"""
% locals()):
tmpf.write("\t".join(map(str, x)) + "\n")
tmpf.close()
outfile = os.path.join(PARAMS["exportdir"], "motifs",
"%s.bed.gz" % motif)
tmpfname = tmpf.name
statement = '''mergeBed -i %(tmpfname)s -nms | gzip > %(outfile)s'''
P.run()
os.unlink(tmpf.name)
def createViewMapping(infile, outfile):
"""create view in database for alignment stats.
This view aggregates all information on a per-track basis.
The table is built from the following tracks:
mapping_stats
bam_stats
"""
tablename = P.toTable(outfile)
# can not create views across multiple database, so use table
view_type = "TABLE"
dbhandle = connect()
Database.executewait(dbhandle, "DROP %(view_type)s IF EXISTS %(tablename)s" % locals())
statement = """
CREATE %(view_type)s %(tablename)s AS
SELECT *
FROM bam_stats AS b
"""
Database.executewait(dbhandle, statement % locals())
def loadProteinStats(infile, outfile):
'''load protein statistics to database.
The *infile* is an ENSEMBL peptide file.
'''
to_cluster = True
table = P.toTable(outfile)
statement = '''
gunzip < %(infile)s |
python %(scriptsdir)s/fasta2table.py
--log=%(outfile)s
--type=aa
--section=length
--section=hid
--section=aa
--regex-identifier="(\S+)" |
sed "s/^id/protein_id/" |
python %(scriptsdir)s/csv2db.py %(csv2db_options)s
--index=protein_id
--map=protein_id:str
--table=%(table)s
> %(outfile)s'''
P.run()
def loadDeNovos(infile, outfile):
'''load de novo variants into the database'''
scriptsdir = PARAMS["general_scriptsdir"]
tablename = P.toTable(outfile)
statement = '''cat %(infile)s | sed 's/#CHROM/CHROM/g;s/EFF\[\*\]/EFF/g;s/GEN\[0\]/GEN/g' | python %(scriptsdir)s/csv2db.py --table %(tablename)s --retry --ignore-empty > %(outfile)s''' % locals(
)
P.run()
def loadPicardHistogram( infiles, outfile, suffix, column, pipeline_suffix = ".picard_stats" ):
'''extract a histogram from a picard output file and load it into database.'''
tablename = P.toTable( outfile )
tname = "%s_%s" % (tablename, suffix)
tname = P.snip( tname, "_metrics") + "_histogram"
# some files might be missing
xfiles = [ x for x in infiles if os.path.exists( "%s.%s" % (x, suffix) ) ]
if len(xfiles) == 0:
E.warn ( "no files for %s" % tname )
return
header = ",".join( [P.snip( os.path.basename(x), pipeline_suffix) for x in xfiles ] )
filenames = " ".join( [ "%s.%s" % (x, suffix) for x in xfiles ] )
# there might be a variable number of columns in the tables
# only take the first ignoring the rest
statement = """python %(scriptsdir)s/combine_tables.py
--regex-start="## HISTOGRAM"
--missing=0
--take=2
%(filenames)s
| python %(scriptsdir)s/csv2db.py
--header=%(column)s,%(header)s
--replace-header
--index=track
--table=%(tname)s
>> %(outfile)s
"""
P.run()
def loadTranscriptStats(infile, outfile):
'''load gene statistics to database.
The *infile* is the *outfile* from :meth:`buildTranscripts`
'''
to_cluster = True
table = P.toTable(outfile)
statement = '''
gunzip < %(infile)s |\
python %(scriptsdir)s/gtf2table.py \
--log=%(outfile)s.log \
--genome=%(genome_dir)s/%(genome)s \
--reporter=transcripts \
--counter=position \
--counter=length \
--counter=composition-na |\
python %(scriptsdir)s/csv2db.py %(csv2db_options)s \
--index=gene_id \
--map=gene_id:str \
--table=%(table)s \
> %(outfile)s'''
P.run()
def loadNumberExonsLengthSummaryStats(infile, outfile):
'''
load the table of exon counts and transcript lengths
'''
tablename = P.toTable(outfile.replace("/", "_")) + "_stats"
statement = '''python %(scriptsdir)s/csv2db.py -t %(tablename)s --log=%(outfile)s.log < %(infile)s > %(outfile)s'''
P.run()
def loadTranscriptInformation( infile, outfile,
only_proteincoding = False ):
'''load the transcript set.
*infile* is an ENSEMBL gtf file.
'''
to_cluster = True
table = P.toTable(outfile)
if only_proteincoding: filter_cmd = ''' awk '$2 == "protein_coding"' '''
else: filter_cmd = "cat"
statement = '''gunzip
< %(infile)s
| %(filter_cmd)s
| awk '$3 == "CDS"'
| python %(scriptsdir)s/gtf2gtf.py --sort=gene
| python %(scriptsdir)s/gtf2tsv.py --full --only-attributes -v 0
| python %(toolsdir)s/csv_cut.py --remove exon_id exon_number
| %(scriptsdir)s/hsort 1 | uniq
| python %(scriptsdir)s/csv2db.py %(csv2db_options)s
--index=transcript_id
--index=gene_id
--index=protein_id
--index=gene_name
--map=transcript_name:str
--map=gene_name:str
--table=%(table)s
> %(outfile)s'''
P.run()
def loadCountSingleAndMultiExonLincRNA(infile, outfile):
'''
load the counts for the multi and single exon lincRNA
'''
tablename = P.toTable(outfile.replace("/", "_")) + ".count"
statement = '''python %(scriptsdir)s/csv2db.py -t %(tablename)s --log=%(outfile)s.log < %(infile)s > %(outfile)s'''
P.run()
def loadVariantAnnotation(infile, outfile):
'''Load VCF annotations into database'''
scriptsdir = PARAMS["general_scriptsdir"]
tablename = P.toTable(outfile)
statement = '''cat %(infile)s | python %(scriptsdir)s/csv2db.py --table %(tablename)s --retry --ignore-empty > %(outfile)s''' % locals(
)
P.run()
def mergeAndLoad(infiles, outfile, suffix):
"""load categorical tables (two columns) into a database.
The tables are merged and entered row-wise.
"""
header = ",".join([P.quote(P.snip(x, suffix)) for x in infiles])
if suffix.endswith(".gz"):
filenames = " ".join(["<( zcat %s | cut -f 1,2 )" % x for x in infiles])
else:
filenames = " ".join(["<( cat %s | cut -f 1,2 )" % x for x in infiles])
tablename = P.toTable(outfile)
statement = """python %(scriptsdir)s/combine_tables.py
--headers=%(header)s
--missing=0
--ignore-empty
%(filenames)s
| perl -p -e "s/bin/track/"
| python %(scriptsdir)s/table2table.py --transpose
| python %(scriptsdir)s/csv2db.py
--index=track
--table=%(tablename)s
> %(outfile)s
"""
P.run()
def loadEdgeRResults(infile, outfile):
tableName = P.toTable(outfile)
statement = ''' python %(scriptsdir)s/csv2db.py
--table=%(tableName)s
--index=id < infile
>outfile '''
P.run()
def loadSnpeffAnnotation(infile, outfile):
'''Load snpeff annotations into database'''
scriptsdir = PARAMS["general_scriptsdir"]
tablename = P.toTable(outfile)
statement = '''cat %(infile)s
| python %(scriptsdir)s/csv2db.py --table %(tablename)s
--retry --ignore-empty > %(outfile)s''' % locals()
P.run()
def loadEdgeRResults(infile,outfile):
tableName = P.toTable(outfile)
statement = ''' python %(scriptsdir)s/csv2db.py
--table=%(tableName)s
--index=id < infile
>outfile '''
P.run()
def loadAlignmentStats(infiles, outfile):
'''merge alignment stats into single tables.'''
tablename = P.toTable(outfile)
outf = P.getTempFile()
first = True
for f in infiles:
track = P.snip(f, ".bam.stats")
fn = f + ".alignment_summary_metrics"
if not os.path.exists(fn):
E.warn("file %s missing" % fn)
continue
lines = [
x for x in open(fn, "r").readlines()
if not x.startswith("#") and x.strip()
]
if first: outf.write("%s\t%s" % ("track", lines[0]))
first = False
outf.write("%s\t%s" % (track, lines[1]))
outf.close()
tmpfilename = outf.name
statement = '''cat %(tmpfilename)s
| python %(scriptsdir)s/csv2db.py
--index=track
--table=%(tablename)s
> %(outfile)s
'''
P.run()
for suffix, column in (("quality_by_cycle_metrics", "cycle"),
("quality_distribution_metrics", "quality")):
# some files might be missing - bugs in Picard
xfiles = [x for x in infiles if os.path.exists("%s.%s" % (x, suffix))]
header = ",".join([P.snip(x, ".bam.stats") for x in xfiles])
filenames = " ".join(["%s.%s" % (x, suffix) for x in xfiles])
tname = "%s_%s" % (tablename, suffix)
statement = """python %(scriptsdir)s/combine_tables.py
--missing=0
%(filenames)s
| python %(scriptsdir)s/csv2db.py
--header=%(column)s,%(header)s
--replace-header
--index=track
--table=%(tname)s
>> %(outfile)s
"""
P.run()
os.unlink(tmpfilename)
def loadAlignmentStats(infiles, outfile):
'''merge alignment stats into single tables.'''
tablename = P.toTable(outfile)
outf = P.getTempFile()
first = True
for f in infiles:
track = P.snip(f, ".bam.stats")
fn = f + ".alignment_summary_metrics"
if not os.path.exists(fn):
E.warn("file %s missing" % fn)
continue
lines = [
x for x in open(fn, "r").readlines() if not x.startswith("#") and x.strip()]
if first:
outf.write("%s\t%s" % ("track", lines[0]))
first = False
outf.write("%s\t%s" % (track, lines[1]))
outf.close()
tmpfilename = outf.name
statement = '''cat %(tmpfilename)s
| python %(scriptsdir)s/csv2db.py
--index=track
--table=%(tablename)s
> %(outfile)s
'''
P.run()
for suffix, column in (("quality_by_cycle_metrics", "cycle"),
("quality_distribution_metrics", "quality")):
# some files might be missing - bugs in Picard
xfiles = [x for x in infiles if os.path.exists("%s.%s" % (x, suffix))]
header = ",".join([P.snip(x, ".bam.stats") for x in xfiles])
filenames = " ".join(["%s.%s" % (x, suffix) for x in xfiles])
tname = "%s_%s" % (tablename, suffix)
statement = """python %(scriptsdir)s/combine_tables.py
--missing=0
%(filenames)s
| python %(scriptsdir)s/csv2db.py
--header=%(column)s,%(header)s
--replace-header
--index=track
--table=%(tname)s
>> %(outfile)s
"""
P.run()
os.unlink(tmpfilename)
def compareAbundanceOfFalsePositiveSpecies(infiles, outfile):
'''
boxplot the relative abundance of false positive
species compared to true positives
'''
tablename_estimate = P.toTable(infiles[0])
track = P.snip(
os.path.basename(infiles[0]).replace("metaphlan_", ""), ".load")
tablename_true = [P.toTable(x) for x in infiles[1:] if P.snip(
os.path.basename(x), ".load") == track][0]
dbh = sqlite3.connect("csvdb")
cc = dbh.cursor()
tmp = P.getTempFile(".")
tmp.write("taxa\tabundance\tstatus\n")
estimate = {}
true = set()
for data in cc.execute("""SELECT taxon, rel_abundance FROM %s WHERE taxon_level == 'species'""" % tablename_estimate).fetchall():
estimate[data[0]] = data[1]
for data in cc.execute("""SELECT taxa FROM %s WHERE level == 'species'""" % tablename_true).fetchall():
true.add(data[0])
for taxa, abundance in estimate.iteritems():
if taxa in true:
tmp.write("%s\t%f\ttp\n" % (taxa, abundance))
else:
tmp.write("%s\t%f\tfp\n" % (taxa, abundance))
tmp.close()
inf = tmp.name
if track.find("15M") != -1:
col = "cadetblue"
elif track.find("30M") != -1:
col = "lightblue"
elif track.find("50M") != -1:
col = "slategray"
R('''dat <- read.csv("%s", header = T, stringsAsFactors = F, sep = "\t")''' %
inf)
R('''library(ggplot2)''')
R('''ggplot(dat, aes(x = status, y = log2(abundance))) + geom_boxplot(colour = "%s") + geom_hline(yintersect=0, linetype="dashed")''' %
col)
R('''ggsave("%s")''' % outfile)
os.unlink(inf)
def mergeEffects(infiles, outfile):
'''load transcript effects into single table.'''
tablename = P.toTable(outfile)
outf = open('effects.txt', 'w')
first = True
for f in infiles:
track = P.snip(os.path.basename(f), ".effects.gz")
if not os.path.exists(f):
E.warn("File %s missing" % f)
continue
lines = [x for x in gzip.open(f, "r").readlines()]
if first:
outf.write("%s\t%s" % ("track", lines[0]))
first = False
for i in range(1, len(lines)):
outf.write("%s\t%s" % (track, lines[i]))
outf.close()
statement = '''cat effect.txt |
python %(scriptsdir)s/csv2db.py %(csv2db_options)s \
--index=transcript_id \
--table=%(tablename)s \
> %(outfile)s'''
P.run()
for suffix in ("cds", "intron", "splicing", "translation", "genes"):
outf = open('effects.' + suffix + '.txt', 'w')
first = True
for f in infiles:
track = P.snip(os.path.basename(f), ".effects.gz")
statfile = f + "." + suffix + ".gz"
print(statfile)
if not os.path.exists(statfile):
E.warn("File %s missing" % statfile)
continue
lines = [x for x in gzip.open(statfile, "r").readlines()]
if first:
outf.write("%s\t%s" % ("track", lines[0]))
first = False
for i in range(1, len(lines)):
outf.write("%s\t%s" % (track, lines[i]))
outf.close()
tmpfilename = outf.name
statement = '''cat %(tmpfilename)s |
python %(scriptsdir)s/csv2db.py %(csv2db_options)s
--allow-empty
--index=transcript_id
--table=%(tablename)s_%(suffix)s
--ignore-column=seq_na
--ignore-column=seq_aa
>> %(outfile)s'''
P.run()
def mergeEffects(infiles, outfile):
'''load transcript effects into single table.'''
tablename = P.toTable(outfile)
outf = open('effects.txt', 'w')
first = True
for f in infiles:
track = P.snip(os.path.basename(f), ".effects.gz")
if not os.path.exists(f):
E.warn("File %s missing" % f)
continue
lines = [x for x in gzip.open(f, "r").readlines()]
if first:
outf.write("%s\t%s" % ("track", lines[0]))
first = False
for i in range(1, len(lines)):
outf.write("%s\t%s" % (track, lines[i]))
outf.close()
statement = '''cat effect.txt |
python %(scriptsdir)s/csv2db.py %(csv2db_options)s \
--index=transcript_id \
--table=%(tablename)s \
> %(outfile)s'''
P.run()
for suffix in ("cds", "intron", "splicing", "translation", "genes"):
outf = open('effects.' + suffix + '.txt', 'w')
first = True
for f in infiles:
track = P.snip(os.path.basename(f), ".effects.gz")
statfile = f + "." + suffix + ".gz"
print(statfile)
if not os.path.exists(statfile):
E.warn("File %s missing" % statfile)
continue
lines = [x for x in gzip.open(statfile, "r").readlines()]
if first:
outf.write("%s\t%s" % ("track", lines[0]))
first = False
for i in range(1, len(lines)):
outf.write("%s\t%s" % (track, lines[i]))
outf.close()
tmpfilename = outf.name
statement = '''cat %(tmpfilename)s |
python %(scriptsdir)s/csv2db.py %(csv2db_options)s
--allow-empty
--index=transcript_id
--table=%(tablename)s_%(suffix)s
--ignore-column=seq_na
--ignore-column=seq_aa
>> %(outfile)s'''
P.run()
def loadNCG(infile, outfile):
'''Load NCG into database'''
dbh = connect()
scriptsdir = PARAMS["general_scriptsdir"]
tablename = P.toTable(outfile)
statement = '''cat %(infile)s |
python %(scriptsdir)s/csv2db.py
--table %(tablename)s --retry --ignore-empty
> %(outfile)s''' % locals()
P.run()
def loadMutectFilteringSummary(infile, outfile):
'''Load mutect extended output into database'''
dbh = connect()
scriptsdir = PARAMS["general_scriptsdir"]
tablename = P.toTable(outfile)
statement = '''cat %(infile)s |
python %(scriptsdir)s/csv2db.py
--table %(tablename)s --retry --ignore-empty
> %(outfile)s''' % locals()
P.run()
def loadROI2Gene(infile, outfile):
'''Import genes mapping to regions of interest bed file into SQLite.'''
scriptsdir = PARAMS["general_scriptsdir"]
tablename = P.toTable(outfile)
statement = '''cat %(infile)s
| python %(scriptsdir)s/csv2db.py %(csv2db_options)s
--ignore-empty
--retry
--table=%(tablename)s
> %(outfile)s '''
P.run()
def loadSamples(infile, outfile):
'''Import sample information into SQLite.'''
scriptsdir = PARAMS["general_scriptsdir"]
tablename = P.toTable(outfile)
statement = '''cat %(infile)s
| python %(scriptsdir)s/csv2db.py %(csv2db_options)s
--ignore-empty
--retry
--table=%(tablename)s
> %(outfile)s '''
P.run()
def loadCoverageStats(infile, outfile):
'''
load coverage stats
'''
tablename = P.toTable(
P.snip(os.path.dirname(infile), ".dir") + "_%s" % os.path.basename(outfile))
statement = '''zcat %(infile)s | python %(scriptsdir)s/csv2db.py
-t %(tablename)s
--index=contig
--log=%(outfile)s.log > %(outfile)s'''
P.run()
def loadSamples(infile, outfile):
'''Import sample information into SQLite.'''
scriptsdir = PARAMS["general_scriptsdir"]
tablename = P.toTable(outfile)
statement = '''cat %(infile)s
| python %(scriptsdir)s/csv2db.py %(csv2db_options)s
--ignore-empty
--retry
--table=%(tablename)s
> %(outfile)s '''
P.run()