def pack_datasets(project):
log = task.logger
config = GlobalConfig(task.conf)
project_id = project["id"]
log.info("--- [{0}] --------------------------------------------".format(project_id))
if not config.results.create_zip:
log.info("Creation of the results compressed file is deactivated. Skipped.")
return
project_path = project["path"]
temp_path = project["temp_path"]
dest_path = os.path.join(project_path, "results.zip")
sigdb = SigDb(config.sigdb_path)
sigdb.open()
projdb = ProjectDb(project["db"])
projres = ProjectResults(project)
gene_sym = projdb.get_gene_symbols()
total_samples = projdb.get_total_affected_samples()
log.info("Compressing files ...")
arc = None
try:
arc = Archive(dest_path, mode="w", fmt="zip")
log.info(" Variant genes ...")
with ArcFile(task, arc, project_id, "variant_genes", "w") as vf:
write_line(vf, "PROJECT_ID", "CHR", "STRAND", "START", "ALLELE",
"GENE_ID", "SYMBOL", "VAR_IMPACT", "VAR_IMPACT_DESC",
"SAMPLE_FREQ", "SAMPLE_TOTAL", "SAMPLE_PROP",
"CODING_REGION", "PROTEIN_CHANGES", "INTOGEN_DRIVER", "XREFS")
for afg in projdb.affected_genes(join_variant=True, join_xrefs=True, join_rec=True):
var = afg.var
rec = afg.rec
start, end, ref, alt = var_to_tab(var)
xrefs = [xref for xref in var.xrefs]
if sigdb.exists_variant(var.chr, start):
xrefs += ["I:1"]
xrefs = ",".join(xrefs)
intogen_driver = 1 if sigdb.exists_gene(afg.gene_id) else 0
write_line(vf, project_id, var.chr, var.strand, start, "{0}/{1}".format(ref, alt),
afg.gene_id, gene_sym.get(afg.gene_id),
afg.impact, TransFIC.class_name(afg.impact),
rec.sample_freq or 0, total_samples, rec.sample_prop or 0,
afg.coding_region, afg.prot_changes, intogen_driver, xrefs)
log.info(" Variant samples ...")
with ArcFile(task, arc, project_id, "variant_samples", "w") as vf:
write_line(vf, "PROJECT_ID", "CHR", "STRAND", "START", "ALLELE", "SAMPLES")
for var in projdb.variants(join_samples=True):
start, end, ref, alt = var_to_tab(var)
write_line(vf, project_id, var.chr, var.strand, start, "{0}/{1}".format(ref, alt),
",".join([s.name for s in var.samples]))
log.info(" Consequences ...")
with ArcFile(task, arc, project_id, "consequences", "w") as cf:
write_line(cf, "PROJECT_ID", "CHR", "STRAND", "START", "ALLELE", "TRANSCRIPT_ID", "CT",
"GENE_ID", "SYMBOL", "UNIPROT_ID", "PROTEIN_ID", "PROTEIN_POS", "AA_CHANGE",
"SIFT_SCORE", "SIFT_TRANSFIC", "SIFT_TRANSFIC_CLASS",
"PPH2_SCORE", "PPH2_TRANSFIC", "PPH2_TRANSFIC_CLASS",
"MA_SCORE", "MA_TRANSFIC", "MA_TRANSFIC_CLASS",
"IMPACT", "IMPACT_CLASS")
for csq in projdb.consequences(join_variant=True):
var = csq.var
start, end, ref, alt = var_to_tab(var)
allele = "{0}/{1}".format(ref, alt)
uniprot = protein = protein_pos = aa_change = None
sift_score = sift_tfic = sift_tfic_class = None
pph2_score = pph2_tfic = pph2_tfic_class = None
ma_score = ma_tfic = ma_tfic_class = None
if so.match(csq.ctypes, so.ONCODRIVEFM):
uniprot, protein = csq.uniprot, csq.protein
if so.match(csq.ctypes, so.NON_SYNONYMOUS):
protein_pos, aa_change = csq.protein_pos, csq.aa_change
sift_score, sift_tfic, sift_tfic_class = csq.sift_score, csq.sift_tfic, TransFIC.class_name(csq.sift_tfic_class)
pph2_score, pph2_tfic, pph2_tfic_class = csq.pph2_score, csq.pph2_tfic, TransFIC.class_name(csq.pph2_tfic_class)
ma_score, ma_tfic, ma_tfic_class = csq.ma_score, csq.ma_tfic, TransFIC.class_name(csq.ma_tfic_class)
write_line(cf, project_id, var.chr, var.strand, start, allele, csq.transcript,
",".join(csq.ctypes), csq.gene, gene_sym.get(csq.gene),
uniprot, protein, protein_pos, aa_change,
sift_score, sift_tfic, sift_tfic_class,
pph2_score, pph2_tfic, pph2_tfic_class,
ma_score, ma_tfic, ma_tfic_class,
csq.impact, TransFIC.class_name(csq.impact))
log.info(" Genes ...")
with ArcFile(task, arc, project_id, "genes", "w") as gf:
write_line(gf, "PROJECT_ID", "GENE_ID", "SYMBOL", "FM_PVALUE", "FM_QVALUE", "FM_EXC_CAUSE",
"SAMPLE_FREQ", "SAMPLE_TOTAL", "SAMPLE_PROP",
"CLUST_ZSCORE", "CLUST_PVALUE", "CLUST_QVALUE", "CLUST_EXC_CAUSE", "CLUST_COORDS",
"INTOGEN_DRIVER", "XREFS")
for gene in projdb.genes(join_xrefs=True, join_rec=True):
if gene.rec.sample_freq is not None and gene.rec.sample_freq > 0:
intogen_driver = 1 if sigdb.exists_gene(gene.id) else 0
write_line(gf, project_id, gene.id, gene.symbol, gene.fm_pvalue, gene.fm_qvalue, gene.fm_exc_cause,
gene.rec.sample_freq, total_samples, gene.rec.sample_prop or 0,
gene.clust_zscore, gene.clust_pvalue, gene.clust_qvalue, gene.clust_exc_cause,
gene.clust_coords, intogen_driver, ",".join(gene.xrefs))
log.info(" Pathways ...")
with ArcFile(task, arc, project_id, "pathways", "w") as pf:
write_line(pf, "PROJECT_ID", "PATHWAY_ID", "GENE_COUNT", "FM_ZSCORE", "FM_PVALUE", "FM_QVALUE",
"SAMPLE_FREQ", "SAMPLE_TOTAL", "SAMPLE_PROP", "GENE_FREQ", "GENE_TOTAL", "GENE_PROP")
for pathway in projdb.pathways(join_rec=True):
if pathway.rec.sample_freq is not None and pathway.rec.sample_freq > 0:
write_line(pf, project_id, pathway.id, pathway.gene_count, pathway.fm_zscore, pathway.fm_pvalue, pathway.fm_qvalue,
pathway.rec.sample_freq or 0, total_samples, pathway.rec.sample_prop or 0,
pathway.rec.gene_freq or 0, pathway.gene_count, pathway.rec.gene_prop or 0)
if not config.skip_oncodrivefm:
log.info(" Genes per sample functional impact ...")
with ArcFile(task, arc, project_id, "fimpact.gitools.tdm", "w") as f:
write_line(f, "SAMPLE", "GENE_ID",
"SIFT_SCORE", "SIFT_TRANSFIC", "SIFT_TRANSFIC_CLASS",
"PPH2_SCORE", "PPH2_TRANSFIC", "PPH2_TRANSFIC_CLASS",
"MA_SCORE", "MA_TRANSFIC", "MA_TRANSFIC_CLASS")
for fields in projdb.sample_gene_fimpacts():
(gene, sample,
sift_score, sift_tfic, sift_tfic_class,
pph2_score, pph2_tfic, pph2_tfic_class,
ma_score, ma_tfic, ma_tfic_class) = fields
write_line(f, sample, gene,
sift_score, sift_tfic, TransFIC.class_name(sift_tfic_class),
pph2_score, pph2_tfic, TransFIC.class_name(pph2_tfic_class),
ma_score, ma_tfic, TransFIC.class_name(ma_tfic_class))
log.info("Saving project configuration ...")
with ArcFile(task, arc, project_id, "project", "w") as f:
names = ["PROJECT_ID", "ASSEMBLY", "SAMPLES_TOTAL"]
values = [project_id, project["assembly"], total_samples]
names, values = projres.get_annotations_to_save(config.project.annotations, project["annotations"], names=names, values=values)
tsv.write_line(f, *names)
tsv.write_line(f, *values, null_value="-")
finally:
if arc is not None:
arc.close()
projdb.close()
sigdb.close()
def combination_recurrences(projects_set):
log = task.logger
config = GlobalConfig(task.conf)
paths = PathsConfig(config)
classifier, projects = projects_set
classifier_id = classifier["id"]
group_values = classifier["group_values"]
short_values = classifier["group_short_values"]
long_values = classifier["group_long_values"]
group_name = classifier["group_name"]
group_short_name = classifier["group_short_name"]
group_long_name = classifier["group_long_name"]
if len(group_values) == 0:
group_file_prefix = classifier_id
else:
group_file_prefix = "{0}-{1}".format(classifier_id, group_short_name)
group_file_prefix = normalize_id(group_file_prefix)
log.info(
"--- [{0} ({1}) ({2}) ({3})] {4}".format(
classifier["name"], group_long_name, group_short_name, group_name, "-" * 30
)
)
log.info("Creating database ...")
db_path = make_temp_file(task, suffix="-{0}.db".format(group_file_prefix))
log.debug(" > {0}".format(db_path))
conn = sqlite3.connect(db_path)
conn.row_factory = sqlite3.Row
create_db(conn)
log.info("Combining recurrences ...")
c = conn.cursor()
sample_total = 0
project_ids = []
for project in projects:
project_ids += [project["id"]]
log.info(" Project {0}:".format(project["id"]))
projdb = ProjectDb(project["db"])
project_sample_total = projdb.get_total_affected_samples()
sample_total += project_sample_total
log.info(" Total samples = {0}".format(project_sample_total))
log.info(" Variant genes ...")
count = 0
for afg in projdb.affected_genes(join_variant=True, join_xrefs=True, join_rec=True):
var = afg.var
rec = afg.rec
if rec.sample_freq is None:
log.warn("Discarding variant gene without sample frequency: {0}".format(repr(afg)))
continue
start, end, ref, alt = var_to_tab(var)
try:
c.execute(
"INSERT INTO variants (chr, strand, start, ref, alt, xrefs) VALUES (?,?,?,?,?,?)",
(var.chr, var.strand, start, ref, alt, ",".join(var.xrefs)),
)
var_id = c.lastrowid
except sqlite3.IntegrityError:
c.execute(
"SELECT var_id FROM variants WHERE chr=? AND strand=? AND start=? AND ref=? AND alt=?",
(var.chr, var.strand, start, ref, alt),
)
r = c.fetchone()
var_id = r[0]
try:
c.execute(
"INSERT INTO variant_genes (var_id, gene_id, impact, coding_region, prot_changes, sample_freq) VALUES (?,?,?,?,?,?)",
(var_id, afg.gene_id, afg.impact, afg.coding_region, afg.prot_changes, rec.sample_freq),
)
except sqlite3.IntegrityError:
c.execute(
"""
UPDATE variant_genes
SET sample_freq=sample_freq + ?
WHERE var_id=? AND gene_id=?""",
(rec.sample_freq, var_id, afg.gene_id),
)
count += 1
log.info(" {0} variant genes".format(count))
log.info(" Genes ...")
count = 0
for gene in projdb.genes(join_xrefs=True, join_rec=True):
rec = gene.rec
if rec.sample_freq is None:
continue
c.execute("SELECT COUNT(*) FROM genes WHERE gene_id=?", (gene.id,))
r = c.fetchone()
if r[0] == 0:
c.execute("INSERT INTO genes (gene_id, sample_freq) VALUES (?,?)", (gene.id, rec.sample_freq))
else:
c.execute("UPDATE genes SET sample_freq=sample_freq + ? WHERE gene_id=?", (rec.sample_freq, gene.id))
count += 1
log.info(" {0} genes".format(count))
log.info(" Pathways ...")
count = 0
for pathway in projdb.pathways(join_rec=True):
rec = pathway.rec
if rec.sample_freq is None:
continue
c.execute("SELECT COUNT(*) FROM pathways WHERE pathway_id=?", (pathway.id,))
r = c.fetchone()
if r[0] == 0:
c.execute("INSERT INTO pathways (pathway_id, sample_freq) VALUES (?,?)", (pathway.id, rec.sample_freq))
else:
c.execute(
"UPDATE pathways SET sample_freq=sample_freq + ? WHERE pathway_id=?", (rec.sample_freq, pathway.id)
)
count += 1
log.info(" {0} pathways".format(count))
projdb.close()
log.info("Calculating proportions with {0} samples in total among projects ...".format(sample_total))
if sample_total > 0:
c.execute("UPDATE variant_genes SET sample_prop=CAST(sample_freq AS REAL)/{0}.0".format(sample_total))
c.execute("UPDATE genes SET sample_prop=CAST(sample_freq AS REAL)/{0}.0".format(sample_total))
c.execute("UPDATE pathways SET sample_prop=CAST(sample_freq AS REAL)/{0}.0".format(sample_total))
c.close()
conn.commit()
log.info("Saving results ...")
c = conn.cursor()
base_path = paths.combination_path("recurrences")
log.info(" Variant genes ...")
with tsv.open(os.path.join(base_path, "variant_gene-{0}.tsv.gz".format(group_file_prefix)), "w") as f:
tsv.write_param(f, "classifier", classifier["id"])
tsv.write_param(f, "group_id", group_name)
tsv.write_param(f, "group_short_name", group_short_name)
tsv.write_param(f, "group_long_name", group_long_name)
tsv.write_param(f, "projects", ",".join(project_ids))
tsv.write_param(f, "SAMPLE_TOTAL", sample_total)
tsv.write_line(
f,
"CHR",
"STRAND",
"START",
"ALLELE",
"GENE_ID",
"IMPACT",
"IMPACT_CLASS",
"SAMPLE_FREQ",
"SAMPLE_PROP",
"PROT_CHANGES",
"XREFS",
)
for r in c.execute(
"SELECT * FROM variant_genes JOIN variants USING (var_id) ORDER BY chr*1, chr, strand, start, gene_id"
):
strand, ref, alt = r["strand"], r["ref"], r["alt"]
allele = "{0}/{1}".format(ref, alt)
tsv.write_line(
f,
r["chr"],
strand,
r["start"],
allele,
r["gene_id"],
r["impact"],
TransFIC.class_name(r["impact"]),
r["sample_freq"],
r["sample_prop"],
r["prot_changes"],
r["xrefs"],
null_value="-",
)
log.info(" Genes ...")
with tsv.open(os.path.join(base_path, "gene-{0}.tsv.gz".format(group_file_prefix)), "w") as f:
tsv.write_param(f, "classifier", classifier["id"])
tsv.write_param(f, "group_id", group_name)
tsv.write_param(f, "group_short_name", group_short_name)
tsv.write_param(f, "group_long_name", group_long_name)
tsv.write_param(f, "projects", ",".join(project_ids))
tsv.write_param(f, "SAMPLE_TOTAL", sample_total)
tsv.write_line(f, "GENE_ID", "SAMPLE_FREQ", "SAMPLE_PROP")
for r in c.execute("SELECT * FROM genes ORDER BY gene_id"):
tsv.write_line(f, r["gene_id"], r["sample_freq"], r["sample_prop"], null_value="-")
log.info(" Pathways ...")
with tsv.open(os.path.join(base_path, "pathway-{0}.tsv.gz".format(group_file_prefix)), "w") as f:
tsv.write_param(f, "classifier", classifier["id"])
tsv.write_param(f, "group_id", group_name)
tsv.write_param(f, "group_short_name", group_short_name)
tsv.write_param(f, "group_long_name", group_long_name)
tsv.write_param(f, "projects", ",".join(project_ids))
tsv.write_param(f, "SAMPLE_TOTAL", sample_total)
tsv.write_line(f, "PATHWAY_ID", "SAMPLE_FREQ", "SAMPLE_PROP")
for r in c.execute("SELECT * FROM pathways ORDER BY pathway_id"):
tsv.write_line(f, r["pathway_id"], r["sample_freq"], r["sample_prop"], null_value="-")
conn.close()
remove_temp(task, db_path)
def fimpact_run(partition):
log = task.logger
config = GlobalConfig(task.conf)
paths = PathsConfig(config)
results_port = task.ports("results")
project = partition["project"]
log.info("--- [{0} @ {1}] --------------------------------------------".format(project["id"], partition["index"]))
log.info("Reading MA scores ...")
ma_uniprot = {}
ma_scores = {}
with open(partition["ma_path"], "r") as f:
for var_id, uniprot, fi_score in tsv.lines(f, (int, str, float), null_value="-"):
ma_uniprot[var_id] = uniprot
ma_scores[var_id] = fi_score
log.info("Reading VEP results and calculating functional impact ...")
tfic = TransFIC(data_path=paths.data_transfic_path())
tfi_path = os.path.join(partition["base_path"], "{0:08d}.tfi".format(partition["index"]))
cf = open(tfi_path, "w")
with open(partition["vep_path"], "r") as f:
for fields in tsv.lines(f, (int, str, str, str, str, str, str, float, float), null_value="-"):
(var_id, gene, transcript, ct,
protein_pos, aa_change, protein,
sift_score, pph2_score) = fields
ct = (ct or "").split(",")
# Invert sift score
if sift_score is not None:
sift_score = 1.0 - sift_score
ma_score = None
uniprot = ma_uniprot.get(var_id)
sift_impact = pph2_impact = ma_impact = None # TransFIC.UNKNOWN_IMPACT_CLASS
coding_region = 1 if so.match(ct, so.CODING_REGION) else 0
sift_tfic, sift_class, pph2_tfic, pph2_class, ma_tfic, ma_class = (None, None, None, None, None, None)
ct_type = None
if so.match(ct, so.NON_SYNONYMOUS): # missense
ct_type = TransFIC.CT_NON_SYNONYMOUS
ma_score = ma_scores.get(var_id)
(sift_tfic, sift_class,
pph2_tfic, pph2_class,
ma_tfic, ma_class) = tfic.calculate("gosmf", gene, ct_type, sift_score, pph2_score, ma_score)
sift_impact = sift_class if sift_class in IMPACT_CLASSES else sift_impact
pph2_impact = pph2_class if pph2_class in IMPACT_CLASSES else pph2_impact
ma_impact = ma_class if ma_class in IMPACT_CLASSES else ma_impact
elif so.match(ct, so.STOP): # stop
sift_impact = pph2_impact = ma_impact = TransFIC.HIGH_IMPACT_CLASS
sift_score = pph2_score = 1.0
ma_score = 3.5
elif so.match(ct, so.FRAMESHIFT): # frameshift
sift_impact = pph2_impact = ma_impact = TransFIC.HIGH_IMPACT_CLASS
sift_score = pph2_score = 1.0
ma_score = 3.5
elif so.match(ct, so.SPLICE_JUNCTION): # splice junction
sift_impact = pph2_impact = ma_impact = TransFIC.HIGH_IMPACT_CLASS
sift_score = pph2_score = 1.0
ma_score = 3.5
elif so.match(ct, so.SPLICE_REGION): # splice region
sift_impact = pph2_impact = ma_impact = TransFIC.UNKNOWN_IMPACT_CLASS
sift_score = pph2_score = 1.0
ma_score = 3.5
elif so.match(ct, so.SYNONYMOUS): # synonymous
sift_impact = pph2_impact = ma_impact = TransFIC.NONE_IMPACT_CLASS
sift_score = pph2_score = 0.0
ma_score = -2
else:
sift_impact = pph2_impact = ma_impact = TransFIC.NONE_IMPACT_CLASS
aff_gene = (var_id, gene)
# try to follow the convention http://www.hgvs.org/mutnomen/recs-prot.html
prot_change = None
if ct_type == TransFIC.CT_FRAMESHIFT:
if protein_pos is None:
prot_change = "fs"
else:
prot_change = "fs {0}".format(protein_pos)
#log.debug("FRAMESHIFT: gene={}, protein={}, pos={}, change={}".format(gene, protein, protein_pos, aa_change))
elif ct_type == "splice":
prot_change = "r.spl?"
#log.debug("SPLICE: gene={}, protein={}, pos={}, change={}".format(gene, protein, protein_pos, aa_change))
elif protein_pos is not None and aa_change is not None:
rc = ReContext()
if rc.match(SIMPLE_AA_CHANGE_RE, aa_change):
prot_change = "{ref}{pos}{alt}".format(pos=protein_pos, ref=rc.group(1), alt=rc.group(2) or "=")
elif rc.match(COMPLEX_AA_CHANGE_RE, aa_change):
prot_change = "{0} {1}".format(aa_change, protein_pos)
else:
log.warn("Unmatched aa change: gene={}, protein={}, pos={}, change={}, ct=[{}]".format(
gene, protein, protein_pos, aa_change, ", ".join(ct)))
tr_impact = ma_impact or pph2_impact or sift_impact or TransFIC.UNKNOWN_IMPACT_CLASS
tsv.write_line(cf, var_id, transcript, gene, uniprot, prot_change, coding_region, tr_impact,
sift_score, sift_tfic, sift_class, sift_impact,
pph2_score, pph2_tfic, pph2_class, pph2_impact,
ma_score, ma_tfic, ma_class, ma_impact,
null_value="-")
cf.close()
# Send results to the next module
partition["tfi_path"] = tfi_path
results_port.send(partition)
def create_datasets(project):
log = task.logger
config = GlobalConfig(task.conf)
paths = PathsConfig(config)
project_id = project["id"]
log.info("--- [{0}] --------------------------------------------".format(project_id))
project_path = project["path"]
temp_path = project["temp_path"]
datasets_path = paths.project_results_path(project_path)
ensure_path_exists(datasets_path)
sigdb = SigDb(config.sigdb_path)
sigdb.open()
projdb = ProjectDb(project["db"])
gene_sym = projdb.get_gene_symbols()
total_samples = projdb.get_total_affected_samples()
log.info("Exporting variant genes ...")
vf = open_dataset(project_id, project_path, datasets_path, "variant_gene", "w", log)
tsv.write_param(vf, "SAMPLE_TOTAL", total_samples)
tsv.write_line(vf, "VAR_ID", "CHR", "STRAND", "START", "ALLELE",
"GENE_ID", "IMPACT", "IMPACT_CLASS",
"SAMPLE_FREQ", "SAMPLE_PROP",
"CODING_REGION", "PROTEIN_CHANGES", "INTOGEN_DRIVER", "XREFS")
sf = open_dataset(project_id, project_path, datasets_path, "variant-samples", "w", log)
tsv.write_line(sf, "VAR_ID", "CHR", "STRAND", "START", "ALLELE", "SAMPLE")
count = 0
for afg in projdb.affected_genes(join_variant=True, join_samples=True, join_xrefs=True, join_rec=True):
var = afg.var
rec = afg.rec
start, end, ref, alt = var_to_tab(var)
allele = "{0}/{1}".format(ref, alt)
xrefs = [xref for xref in var.xrefs]
if sigdb.exists_variant(var.chr, start):
xrefs += ["I:1"]
xrefs = ",".join(xrefs)
intogen_driver = 1 if sigdb.exists_gene(afg.gene_id) else 0
tsv.write_line(vf, var.id, var.chr, var.strand, start, allele,
afg.gene_id, afg.impact, TransFIC.class_name(afg.impact),
rec.sample_freq, rec.sample_prop,
afg.coding_region, afg.prot_changes, intogen_driver, xrefs, null_value="\N")
for sample in var.samples:
tsv.write_line(sf, var.id, var.chr, var.strand, start, allele, sample.name, null_value="\N")
count += 1
vf.close()
sf.close()
log.info(" {0} variant genes".format(count))
log.info("Exporting consequences ...")
cf = open_dataset(project_id, project_path, datasets_path, "consequence", "w", log)
tsv.write_line(cf, "VAR_ID", "CHR", "STRAND", "START", "ALLELE", "TRANSCRIPT_ID",
"CT", "GENE_ID", "SYMBOL", "UNIPROT_ID", "PROTEIN_ID", "PROTEIN_POS", "AA_CHANGE",
"SIFT_SCORE", "SIFT_TRANSFIC", "SIFT_TRANSFIC_CLASS",
"PPH2_SCORE", "PPH2_TRANSFIC", "PPH2_TRANSFIC_CLASS",
"MA_SCORE", "MA_TRANSFIC", "MA_TRANSFIC_CLASS",
"IMPACT", "IMPACT_CLASS")
count = 0
for csq in projdb.consequences(join_variant=True):
var = csq.var
start, end, ref, alt = var_to_tab(var)
allele = "{0}/{1}".format(ref, alt)
uniprot = protein = protein_pos = aa_change = None
sift_score = sift_tfic = sift_tfic_class = None
pph2_score = pph2_tfic = pph2_tfic_class = None
ma_score = ma_tfic = ma_tfic_class = None
if so.match(csq.ctypes, so.ONCODRIVEFM):
uniprot, protein = csq.uniprot, csq.protein
if so.match(csq.ctypes, so.NON_SYNONYMOUS):
protein_pos, aa_change = csq.protein_pos, csq.aa_change
sift_score, sift_tfic, sift_tfic_class = csq.sift_score, csq.sift_tfic, TransFIC.class_name(csq.sift_tfic_class)
pph2_score, pph2_tfic, pph2_tfic_class = csq.pph2_score, csq.pph2_tfic, TransFIC.class_name(csq.pph2_tfic_class)
ma_score, ma_tfic, ma_tfic_class = csq.ma_score, csq.ma_tfic, TransFIC.class_name(csq.ma_tfic_class)
tsv.write_line(cf, var.id, var.chr, var.strand, start, allele, csq.transcript,
",".join(csq.ctypes), csq.gene, gene_sym.get(csq.gene),
uniprot, protein, protein_pos, aa_change,
sift_score, sift_tfic, sift_tfic_class,
pph2_score, pph2_tfic, pph2_tfic_class,
ma_score, ma_tfic, ma_tfic_class,
csq.impact, TransFIC.class_name(csq.impact), null_value="\N")
count += 1
cf.close()
log.info(" {0} consequences".format(count))
log.info("Exporting genes ...")
gf = open_dataset(project_id, project_path, datasets_path, "gene", "w", log)
tsv.write_param(gf, "SAMPLE_TOTAL", total_samples)
tsv.write_line(gf, "GENE_ID", "FM_PVALUE", "FM_QVALUE", "FM_EXC_CAUSE",
"CLUST_ZSCORE", "CLUST_PVALUE", "CLUST_QVALUE", "CLUST_EXC_CAUSE", "CLUST_COORDS",
"SAMPLE_FREQ", "SAMPLE_PROP", "INTOGEN_DRIVER")
for gene in projdb.genes(join_rec=True):
rec = gene.rec
if rec.sample_freq is None or rec.sample_freq == 0:
continue
intogen_driver = 1 if sigdb.exists_gene(gene.id) else 0
tsv.write_line(gf, gene.id, gene.fm_pvalue, gene.fm_qvalue, gene.fm_exc_cause,
gene.clust_zscore, gene.clust_pvalue, gene.clust_qvalue, gene.clust_exc_cause, gene.clust_coords,
rec.sample_freq or 0, rec.sample_prop or 0,
intogen_driver, null_value="\N")
gf.close()
log.info("Exporting pathways ...")
pf = open_dataset(project_id, project_path, datasets_path, "pathway", "w", log)
tsv.write_param(pf, "SAMPLE_TOTAL", total_samples)
tsv.write_line(pf, "PATHWAY_ID", "GENE_COUNT", "FM_ZSCORE", "FM_PVALUE", "FM_QVALUE",
"SAMPLE_FREQ", "SAMPLE_PROP", "GENE_FREQ", "GENE_TOTAL", "GENE_PROP")
for pathway in projdb.pathways(join_rec=True):
rec = pathway.rec
if rec.sample_freq is None or rec.sample_freq == 0:
continue
tsv.write_line(pf, pathway.id, pathway.gene_count, pathway.fm_zscore, pathway.fm_pvalue, pathway.fm_qvalue,
rec.sample_freq or 0, rec.sample_prop or 0, rec.gene_freq or 0, pathway.gene_count, rec.gene_prop or 0, null_value="\N")
pf.close()
if not config.skip_oncodrivefm:
log.info("Exporting genes per sample functional impact ...")
with open_dataset(project_id, project_path, datasets_path, "gene_sample-fimpact", "w", log) as f:
tsv.write_line(f, "GENE_ID", "SAMPLE",
"SIFT_SCORE", "SIFT_TRANSFIC", "SIFT_TRANSFIC_CLASS",
"PPH2_SCORE", "PPH2_TRANSFIC", "PPH2_TRANSFIC_CLASS",
"MA_SCORE", "MA_TRANSFIC", "MA_TRANSFIC_CLASS")
for fields in projdb.sample_gene_fimpacts():
(gene, sample,
sift_score, sift_tfic, sift_tfic_class,
pph2_score, pph2_tfic, pph2_tfic_class,
ma_score, ma_tfic, ma_tfic_class) = fields
tsv.write_line(f, gene, sample,
sift_score, sift_tfic, TransFIC.class_name(sift_tfic_class),
pph2_score, pph2_tfic, TransFIC.class_name(pph2_tfic_class),
ma_score, ma_tfic, TransFIC.class_name(ma_tfic_class), null_value="\N")
projdb.close()
sigdb.close()
log.info("Saving project configuration ...")
projres = ProjectResults(project)
with open_dataset(project_id, project_path, datasets_path, "project.tsv", "w", log) as f:
names = ["ASSEMBLY", "SAMPLES_TOTAL"]
values = [project["assembly"], total_samples]
names, values = projres.get_annotations_to_save(config.project.annotations, project["annotations"], names=names, values=values)
tsv.write_line(f, *names)
tsv.write_line(f, *values, null_value="\N")
projects_port = task.ports("projects_out")
projects_port.send(project)
def fimpact_run(partition):
log = task.logger
conf = task.conf
results_port = task.ports("results")
project = partition["project"]
log.info("--- [{0} @ {1}] --------------------------------------------".format(project["id"], partition["index"]))
log.info("Reading MA scores ...")
ma_uniprot = {}
ma_scores = {}
with open(partition["ma_path"], "r") as f:
for var_id, uniprot, fi_score in tsv.lines(f, (int, str, float), null_value="-"):
ma_uniprot[var_id] = uniprot
ma_scores[var_id] = fi_score
log.info("Reading VEP results and calculating functional impact ...")
tfic = TransFIC(data_path=os.path.join(conf["data_path"], "TransFIC"))
tfi_path = os.path.join(partition["base_path"], "{0:08d}.tfi".format(partition["index"]))
cf = open(tfi_path, "w")
aff_gene_attrs = {}
with open(partition["vep_path"], "r") as f:
for fields in tsv.lines(f, (int, str, str, str, str, str, str, float, float), null_value="-"):
(var_id, gene, transcript, ct,
protein_pos, aa_change, protein,
sift_score, pph2_score) = fields
if ct is not None:
ct = ct.split(",")
else:
ct = []
# Invert sift score
if sift_score is not None:
sift_score = 1.0 - sift_score
ma_score = None
uniprot = ma_uniprot[var_id] if var_id in ma_uniprot else None
sift_impact = pph2_impact = ma_impact = None # TransFIC.UNKNOWN_IMPACT_CLASS
coding_region = so.match(ct, so.CODING_REGION)
calculate_transfic = True
ct_type = None
if so.match(ct, so.NON_SYNONYMOUS): # missense
ct_type = TransFIC.CT_NON_SYNONYMOUS
ma_score = ma_scores[var_id] if var_id in ma_scores else None
elif so.match(ct, so.STOP): # stop
ct_type = TransFIC.CT_STOP
sift_impact = pph2_impact = ma_impact = TransFIC.HIGH_IMPACT_CLASS
sift_score = pph2_score = 1.0
ma_score = 3.5
elif so.match(ct, so.FRAMESHIFT): # frameshift
ct_type = TransFIC.CT_FRAMESHIFT
sift_impact = pph2_impact = ma_impact = TransFIC.HIGH_IMPACT_CLASS
sift_score = pph2_score = 1.0
ma_score = 3.5
elif so.match(ct, so.SPLICE): # splice
ct_type = "splice"
sift_impact = pph2_impact = ma_impact = TransFIC.HIGH_IMPACT_CLASS if so.match(ct, so.SPLICE_JUNCTION) else TransFIC.UNKNOWN_IMPACT_CLASS
calculate_transfic = False
elif so.match(ct, so.SYNONYMOUS): # synonymous
ct_type = TransFIC.CT_SYNONYMOUS
sift_impact = pph2_impact = ma_impact = TransFIC.NONE_IMPACT_CLASS
sift_score = pph2_score = 0.0
ma_score = -2
else:
sift_impact = pph2_impact = ma_impact = TransFIC.NONE_IMPACT_CLASS
calculate_transfic = False
if calculate_transfic:
(sift_tfic, sift_class,
pph2_tfic, pph2_class,
ma_tfic, ma_class) = tfic.calculate("gosmf", gene, ct_type, sift_score, pph2_score, ma_score)
# if the impact was not preassigned get it from the transFIC calculated class
sift_impact = sift_class if sift_impact is None and sift_class in IMPACT_CLASSES else sift_impact
pph2_impact = pph2_class if pph2_impact is None and pph2_class in IMPACT_CLASSES else pph2_impact
ma_impact = ma_class if ma_impact is None and ma_class in IMPACT_CLASSES else ma_impact
else:
sift_tfic, sift_class, pph2_tfic, pph2_class, ma_tfic, ma_class = (None, None, None, None, None, None)
aff_gene = (var_id, gene)
# update aggregated impact for all the predictors
update_attr(aff_gene_attrs, aff_gene, "sift_impact", sift_impact, update=TransFIC.higher_impact)
update_attr(aff_gene_attrs, aff_gene, "pph2_impact", pph2_impact, update=TransFIC.higher_impact)
update_attr(aff_gene_attrs, aff_gene, "ma_impact", ma_impact, update=TransFIC.higher_impact)
# update whether the affected gene is a coding region or not
update_attr(aff_gene_attrs, aff_gene, "coding_region", coding_region,
update=lambda prev_value, value: prev_value or value)
# aggregate protein changes per affected_gene
# try to follow the convention http://www.hgvs.org/mutnomen/recs-prot.html
prot_change = None
if ct_type == TransFIC.CT_FRAMESHIFT:
if protein_pos is None:
prot_change = "fs"
else:
prot_change = "fs {0}".format(protein_pos)
#log.debug("FRAMESHIFT: gene={}, protein={}, pos={}, change={}".format(gene, protein, protein_pos, aa_change))
elif ct_type == "splice":
prot_change = "r.spl?"
#log.debug("SPLICE: gene={}, protein={}, pos={}, change={}".format(gene, protein, protein_pos, aa_change))
elif protein_pos is not None and aa_change is not None:
rc = ReContext()
if rc.match(SIMPLE_AA_CHANGE_RE, aa_change):
prot_change = "{ref}{pos}{alt}".format(pos=protein_pos, ref=rc.group(1), alt=rc.group(2) or "=")
elif rc.match(COMPLEX_AA_CHANGE_RE, aa_change):
prot_change = "{0} {1}".format(aa_change, protein_pos)
else:
log.warn("Unmatched aa change: gene={}, protein={}, pos={}, change={}, ct=[{}]".format(
gene, protein, protein_pos, aa_change, ", ".join(ct)))
if prot_change is not None:
update_attr(aff_gene_attrs, aff_gene, "prot_changes", prot_change,
new=lambda value: set([value]),
update=lambda prev_value, value: prev_value | set([value]))
impact = ma_impact or pph2_impact or sift_impact or TransFIC.UNKNOWN_IMPACT_CLASS
tsv.write_line(cf, var_id, transcript, uniprot,
sift_score, sift_tfic, sift_class,
pph2_score, pph2_tfic, pph2_class,
ma_score, ma_tfic, ma_class,
impact, null_value="-")
cf.close()
log.info("Saving variant impacts ...")
gfi_path = os.path.join(partition["base_path"], "{0:08d}.gfi".format(partition["index"]))
vf = open(gfi_path, "w")
for aff_gene, attrs in aff_gene_attrs.items():
var_id, gene = aff_gene
# get the impact by trust priority: ma, pph2, sift
impact = attrs.get("ma_impact") or attrs.get("pph2_impact") or attrs.get("sift_impact") or TransFIC.UNKNOWN_IMPACT_CLASS
coding_region = attrs.get("coding_region", False)
coding_region = 1 if coding_region else 0
prot_changes = attrs.get("prot_changes")
prot_changes = ",".join(prot_changes) if prot_changes is not None else None
tsv.write_line(vf, var_id, gene, impact, coding_region, prot_changes, null_value="-")
vf.close()
# Send results to the next module
partition["tfi_path"] = tfi_path
partition["gfi_path"] = gfi_path
results_port.send(partition)
