def run(self, variants_path): """ Run the VEP service and save results in a temporary file. :param variants_path: File with variants. In BED format. http://www.ensembl.org/info/docs/variation/vep/vep_script.html#custom_formats :return: True if successfull or False otherwise """ if self.results_path is None: self.results_path = tempfile.mkstemp()[1] with open(self.results_path, "w") as rf: with open(variants_path, "r") as vf: column_types = (str, int, int, str, str, int) for fields in tsv.lines(vf, column_types): chr, start, end, allele, strand, var_id = fields alt = allele[allele.find("/") + 1:] results = self.get(chr, start, end, strand, alt, var_id) if results is None: continue for r in results: rf.write(tsv.line_text( var_id, chr, start, allele, r.gene, r.transcript, ",".join(sorted(r.consequences)), r.protein_pos, r.aa_change, r.protein, r.sift, r.polyphen, null_value="-"))
def update_db(project): log = task.logger conf = task.conf projects_out_port = task.ports("projects_out") project_id = project["id"] log.info("--- [{0}] --------------------------------------------".format(project_id)) oclust = project["oncodriveclust"] del project["oncodriveclust"] if not os.path.exists(oclust["results"]): log.warn("No results have been found. Skipping it.") return log.info("Updating the project database ...") projdb = ProjectDb(project["db"]) exc_path = os.path.join(project["temp_path"], "oncodriveclust-excluded-cause.tsv") log.info(" Excluded gene causes ...") log.debug(" > {0}".format(exc_path)) count = 0 with tsv.open(exc_path, "r") as exf: for gene, cause in tsv.lines(exf, (str, str), header=True): projdb.update_gene(Gene(id=gene, clust_exc_cause=cause)) count += 1 log.debug(" {0} genes excluded".format(count)) log.info(" OncodriveCLUST results ...") with tsv.open(oclust["results"], "r") as f: types = (str, str, float, float, float) columns = ("GENE", "CLUST_COORDS", "ZSCORE", "PVALUE", "QVALUE") for gene, coords, zscore, pvalue, qvalue in tsv.lines(f, types, columns=columns, header=True, null_value="NA"): projdb.update_gene(Gene(id=gene, clust_coords=coords, clust_zscore=zscore, clust_pvalue=pvalue, clust_qvalue=qvalue, clust_exc_cause=ProjectDb.NO_GENE_EXC)) projdb.commit() projdb.close() projects_out_port.send(project)
def main(): parser = argparse.ArgumentParser(description="Extract mutations in VCF and save as simple tabulated file") parser.add_argument("vcf_paths", metavar="PATH", nargs="+", help="The VCF files") parser.add_argument("-o", dest="out_path", metavar="PATH", help="Output file. Use - for standard output.") bglogging.add_logging_arguments(self._parser) args = parser.parse_args() bglogging.initialize(self.args) log = bglogging.get_logger("vcf-to-snvs") if args.out_path is None: names = [] for path in args.vcf_paths: if path != "-": base_path, name, ext = tsv.split_path(path) names += [name] prefix = os.path.commonprefix(*names) if len(names) > 0 else "" prefix = prefix.rstrip(".") if len(prefix) == 0: prefix = "genome" args.out_path = "{}.tsv.gz".format(prefix) with tsv.open(args.out_path, "w") as outf: tsv.write_line(outf, "CHR", "POS", "REF", "ALT") for path in args.vcf_paths: log.info("Reading {} ...".format(path)) with tsv.open(path) as inf: types = (str, str, str, str) columns = [0, 1, 3, 4] for fields in tsv.lines(inf, types, columns=columns): chrom, pos, ref, alt = fields # ref = ref.upper().strip("N") # alt = alt.upper().strip("N") ref_len = len(ref) alt_len = len(alt) if ref_len != alt_len or ref_len == 0 or alt_len == 0: continue try: pos = int(pos) except: continue if ref_len == 1: tsv.write_line(outf, chrom, pos, ref, alt) else: for i in range(ref_len): tsv.write_line(outf, chrom, pos + i, ref[i], alt[i])
def load_cds_len(self, path): self.logger.info("Loading transcripts CDS length ...") self.logger.debug("> {}".format(path)) cds_len = {} with tsv.open(path, "r") as f: for gene, transcript, transcript_len in tsv.lines(f, (str, str, int), header=True): cds_len[transcript] = transcript_len return cds_len
def main(): parser = argparse.ArgumentParser(description="Add annotations") cmd = DefaultCommandHelper(parser) cmd.add_db_args() parser.add_argument("id", metavar="ID", help="Annotation identifier.") parser.add_argument("name", metavar="NAME", help="Annotation name.") parser.add_argument( "type", metavar="TYPE", choices=["transcript", "protein"], help="Annotation type: transcript, protein" ) parser.add_argument("path", metavar="PATH", help="Annotation items") parser.add_argument( "--priority", dest="priority", default=0, help="Priority for translating input annotations. 0 means not considered for translation. Default 0.", ) parser.add_argument( "--header", dest="header", action="store_true", default=False, help="Specify that the annotation items file have a header.", ) args, logger = cmd.parse_args("ann-add") db = cmd.open_db() try: logger.info("Creating annotation {} ...".format(args.name)) db.add_map(args.id, args.name, args.type, args.priority) logger.info("Loading items ...") with tsv.open(args.path) as f: for source, value in tsv.lines(f, (str, str), header=args.header): if len(source) > 0 and len(value) > 0: db.add_map_item(args.id, source, value) db.commit() except: return cmd.handle_error() finally: db.close() return 0
def results(self): """ Iterator that parses the results temporary file and yields VepResult's """ with open(self.results_path, "r") as f: column_types = (int, str, int, str, str, str, _ctype, str, str, str, float, float) for fields in tsv.lines(f, column_types, null_value="-"): var_id, chr, start, allele, gene, transcript, consequences, protein_pos, aa_change, protein, sift, polyphen = fields yield VepResult(var_id=var_id, chr=chr, start=start, allele=allele, gene=gene, transcript=transcript, consequences=consequences, protein_pos = protein_pos, aa_change=aa_change, protein=protein, sift=sift, polyphen=polyphen)
def load_data(self, data_paths, method=None): columns = [] col_names = [] row_name_index = {} for col_index, data_file in enumerate(data_paths): self.log.debug(" > {0}".format(data_file)) names = [] values = [] with tsv.open(data_file, "r") as f: col_name, ext = os.path.splitext(os.path.basename(data_file)) params = tsv.params(f) if "slice" in params: col_name = params["slice"] if "method" in params: if method is None: method = params["method"] elif method != params["method"]: self.log.warn("Different method of computation used for file {0}".format(data_file)) for name, value in tsv.lines(f, (str, float), header=True, null_value="-"): if len(name) == 0: self.log.warn("Empty identifier detected") continue if name not in row_name_index: row_name_index[name] = len(row_name_index) names += [name] values += [value] col_names += [col_name] columns += [(names, values)] num_cols = len(columns) num_rows = len(row_name_index) row_names = [None] * num_rows for name, index in row_name_index.items(): row_names[index] = name data = np.empty((num_rows, num_cols)) data[:] = np.nan for col_index, (names, values) in enumerate(columns): for i, name in enumerate(names): data[row_name_index[name], col_index] = values[i] return row_names, col_names, data, method
def add_map(db, id, name, type, priority, path, header=True): """ :param id: map identifier :param name: map name :param type: xref maps to type: transcript, protein :param path: map file :param priority: priority for translating input xrefs. 0 means not considered for translation. Default 0. :param header: specify that the map file have a header. """ logger = logging.getLogger("fannsdb.map-add") logger.info("Creating map {} ...".format(name)) db.add_map(id, name, type, priority) logger.info("Loading items ...") with tsv.open(path) as f: for source, value in tsv.lines(f, (str, str), header=header): if len(source) > 0 and len(value) > 0: db.add_map_item(id, source, value)
def main(): parser = argparse.ArgumentParser( description="Export dbNSFP scores") cmd = DefaultCommandHelper(parser) parser.add_argument("source_path", metavar="SOURCE", help="The original zip file") parser.add_argument("ensp_map_path", metavar="MAP", help="The mapping between Ensembl protein id's and Ensembl transcript id's and Uniprot id's") parser.add_argument("uniprot_map_path", metavar="MAP", help="The mapping between Ensembl protein id's and Uniprot id's") parser.add_argument("-o", "--output", dest="out_path", metavar="OUT_PATH", help="The output file") parser.add_argument("--temp", dest="temp_path", metavar="TEMP_PATH", help="A temporary path for zip extraction") parser.add_argument("--chr", dest="chr", metavar="CHROMOSOMES", help="Chromosomes to include: list separated by commas.") parser.add_argument("--skip-empty-scores", dest="skip_empty_scores", action="store_true", default=False, help="Skip SNV's where all the scores are empty") args, logger = cmd.parse_args("dbnsfp-export") if args.out_path is None: basename = os.path.basename(args.source_path) prefix = os.path.splitext(basename)[0] args.out_path = "{}.tsv.gz".format(prefix) logger.info("Loading maps ...") uniprot_map = {} trs_map = {} with tsv.open(args.ensp_map_path) as f: for ensp, enst in tsv.lines(f, (str, str)): if len(enst) > 0: trs_map[enst] = ensp with tsv.open(args.uniprot_map_path) as f: for ensp, uniprot_id in tsv.lines(f, (str, str)): if len(uniprot_id) > 0: uniprot_map[uniprot_id] = ensp logger.info("Opening {} ...".format(args.source_path)) chromosomes = None if args.chr is not None: chromosomes = [c.strip().upper() for c in args.chr.split(",") if len(c.strip()) > 0] logger.info("Selected chromosomes: {}".format(", ".join(chromosomes))) chromosomes = set(chromosomes) name_pattern = re.compile(r"dbNSFP.+_variant.chr(.+)") COLUMNS = [ "#chr", "pos(1-coor)", "ref", "alt", "cds_strand", "genename", "Uniprot_id", "Uniprot_aapos", "aaref", "aaalt", "Ensembl_geneid", "Ensembl_transcriptid", "aapos", "SIFT_score", "Polyphen2_HVAR_score", "MutationAssessor_score", "FATHMM_score", "MutationTaster_score", # "GERP_RS", "GERP++_RS", # "PhyloP_score" "phyloP" ] tmp_prefix = args.temp_path or tempfile.gettempdir() if not os.path.exists(tmp_prefix): os.makedirs(tmp_prefix) if tmp_prefix[-1] != "/": tmp_prefix += "/" extract_path = tempfile.mkdtemp(prefix=tmp_prefix) try: logger.info("Output: {}".format(args.out_path if args.out_path != "-" else "standard output")) total_start_time = time.time() total_lines = 0 with ZipFile(args.source_path, "r") as zf,\ tsv.open(args.out_path, "w") as of: #,\ #tsv.open(args.noprot_path, "w") as npf: tsv.write_line(of, "CHR", "STRAND", "START", "REF", "ALT", "TRANSCRIPT", "PROTEIN", "AA_POS", "AA_REF", "AA_ALT", "SIFT", "PPH2", "MA", "FATHMM", "MT", "GERPRS", "PHYLOP") #tsv.write_line(npf, "#CHR", "POS", "ID", "REF", "ALT", "QUAL", "FILTER", "INFO") entries = [] for entry in zf.infolist(): m = name_pattern.match(entry.filename) if not m: continue chr = m.group(1) index = CHR_INDEX[chr] if chr in CHR_INDEX else 99 if chromosomes is not None and chr not in chromosomes: logger.debug("Skipping chromosome {} ...".format(chr)) continue entries += [(index, chr, entry)] for index, chr, entry in sorted(entries, key=lambda x: x[0]): logger.info("Reading chromosome {} ...".format(chr)) zf.extract(entry, extract_path) fpath = os.path.join(extract_path, entry.filename) with open(fpath) as f: # Parse header hdr_line = f.readline() hdr = {} for index, name in enumerate(hdr_line.rstrip("\n").split("\t")): hdr[name] = index columns = [hdr[name] if name in hdr else None for name in COLUMNS] read = set() start_time = time.time() partial_start_time = start_time for line_num, line in enumerate(f, start=2): fields = line.rstrip("\n").split("\t") try: fields = [fields[i] if i is not None and i < len(fields) else None for i in columns] (chr, start, ref, alt, strand, symbol, uniprot, uniprot_aapos, aa_ref, aa_alt, gene, transcript, aapos, sift, pph2, ma, fathmm, mt, gerprs, phylop) = fields start = safe_int(start) ref = ref.upper() if ref is not None else None alt = alt.upper() if alt is not None else None aa_ref = aa_ref.upper() if aa_ref is not None else None aa_alt = aa_alt.upper() if aa_alt is not None else None sift = safe_float(sift) ma = safe_float(ma) fathmm = safe_float(fathmm) mt = safe_float(mt) gerprs = safe_float(gerprs) phylop = safe_float(phylop) if start is None or ref is None or alt is None: logger.warn("None value for pos or ref or alt at line {}: {}".format(line_num, fields)) continue elif ref not in BASE_INDEX or alt not in BASE_INDEX: logger.warn("Unknown ref or alt at line {}: {}".format(line_num, fields)) continue elif len(ref) != 1 or len(alt) != 1: logger.warn("Length != 1 for ref or alt len at line {}: {}".format(line_num, fields)) continue #elif aa_ref not in AA_INDEX or aa_alt not in AA_INDEX: # logger.warn("Unknown aa_ref or aa_alt at line {}: {}".format(line_num, fields)) # continue elif transcript is None or aapos is None or uniprot is None or uniprot_aapos is None: logger.warn("None value for transcript or aapos or uniprot or uniprot_aapos at line {}: {}".format(line_num, fields)) continue if aa_ref not in AA_INDEX: aa_ref = None if aa_alt not in AA_INDEX: aa_alt = None trs_values = transcript.split(";") aapos_values = [safe_int(v) for v in aapos.split(";")] l = len(trs_values) - len(aapos_values) if l > 0: aapos_values += [aapos_values[-1]] * l uniprot_values = uniprot.split(";") uniprot_aapos_values = [safe_int(v) for v in uniprot_aapos.split(";")] l = len(uniprot_values) - len(uniprot_aapos_values) if l > 0: uniprot_aapos_values += [uniprot_aapos_values[-1]] * l pph2_values = [safe_float(v) for v in pph2.split(";")] if pph2 is not None else [None] l = len(uniprot_values) - len(pph2_values) if l > 0: pph2_values += [pph2_values[-1]] * l uniprot_index = {} for i, id in enumerate(uniprot_values): if uniprot_aapos_values[i] is not None: uniprot_index[uniprot_aapos_values[i]] = i for i, trs in enumerate(trs_values): pos = aapos_values[i] if pos < 0: pos = None if pos is not None and pos in uniprot_index: j = uniprot_index[pos] uniprot_value = uniprot_values[j] pph2_value = pph2_values[j] else: uniprot_value = pph2_value = None if trs in trs_map: prot_id = trs_map[trs] elif uniprot_value in uniprot_map: prot_id = uniprot_map[uniprot_value] else: logger.warn("Couldn't map neither protein {} or transcript {} at line {}: {}".format(uniprot_value, trs, line_num, "|".join([str(v) for v in fields]))) continue #if pos < 0: # logger.warn("Negative protein position at line {}: {}".format(line_num, pos)) # continue #elif ... if pph2_value is not None and (pph2_value < 0.0 or pph2_value > 1.0): logger.warn("PPH2 score {} out of range at line {}: {}".format(pph2_value, line_num, fields)) continue if aa_alt == "X": # fix stop codons having a sift score sift = None if args.skip_empty_scores and sift is None and pph2_value is None and ma is None \ and mt is None and gerprs is None and phylop is None: continue #log.info((chr, strand, start, ref, alt, aapos_values[i], aa_ref, aa_alt, trs, sift, pph2_value, ma)) if pos is None or aa_ref is None or aa_alt is None: pass #tsv.write_line(npf, chr, start, ".", ref, alt, ".", "PASS", # "dbNSFP={}|{}|{}|{}|{}|{}".format(trs, prot_id, # sift or "", pph2_value or "", ma or "", fathmm or "")) else: tsv.write_line(of, chr, strand, start, ref, alt, trs, prot_id, pos, aa_ref, aa_alt, sift, pph2_value, ma, fathmm, mt, gerprs, phylop) except KeyboardInterrupt: raise except: logger.warn("Malformed line {}: {}".format(line_num, "|".join([str(v) for v in fields]))) raise #continue partial_time = time.time() - partial_start_time if partial_time >= 5.0: partial_start_time = time.time() elapsed_time = time.time() - start_time logger.debug(" {} lines, {:.1f} lines/second".format(hsize(line_num-1), (line_num-1) / float(elapsed_time))) total_lines += line_num logger.info(" > {} lines, {:.1f} lines/second".format(hsize(line_num), line_num / float(time.time() - start_time))) logger.info(" >> {} lines, {:.1f} lines/second".format(hsize(total_lines), total_lines / float(time.time() - total_start_time))) os.remove(fpath) total_elapsed_time = timedelta(seconds=time.time() - total_start_time) logger.info("Finished successfully. Elapsed time: {}".format(total_elapsed_time)) except: return cmd.handle_error() finally: shutil.rmtree(extract_path) return 0
def main(): parser = argparse.ArgumentParser( description="Calculate Baseline Tolerance statistics") cmd = DefaultCommandHelper(parser) parser.add_argument("tree_path", metavar="TREE_PATH", help="The groups descendant tree") parser.add_argument("root_group", metavar="ROOT_GROUP", help="Tree root group") parser.add_argument("group_genes_path", metavar="GROUP_FEATS_PATH", help="Map between groups and features") parser.add_argument("stats_path", metavar="STATS_PATH", help="Partial feature statistics") parser.add_argument("out_path", metavar="OUTPUT_PATH", help="Output feature statistics") parser.add_argument("--tsv", dest="tsv_path", metavar="PATH", help="Store baseline tolerance in tsv format too.") parser.add_argument("-c", "--count-threshold", dest="count_threshold", metavar="N", default=DEFAULT_COUNT_THRESHOLD, help="Minimum number of features per group") parser.add_argument("--stdev-threshold", dest="stdev_threshold", metavar="V", default=DEFAULT_STDEV_THRESHOLD, help="Skip feature statistics with a standard deviation less than V (it will be calculated at the level of groups)") args, logger = cmd.parse_args("blt-groups") logger.info("Loading groups tree ...") tree = Tree() with tsv.open(args.tree_path) as f: for group, children in tsv.lines(f, (str, lambda v: set(v.split(",")))): tree.add_node(group, children) logger.info(" Nodes: {}".format(tree.node_count)) logger.info("Loading mappings between groups and features ...") all_groups = set() all_features = set() with tsv.open(args.group_genes_path) as f: for group, features in tsv.lines(f, (str, lambda v: set(v.split(",")))): tree.add_node(group, features) all_groups.add(group) all_features.update(features) logger.info(" Nodes: {}".format(tree.node_count)) logger.info(" Groups: {}".format(len(all_groups))) logger.info(" Features: {}".format(len(all_features))) logger.info("Loading partial statistics ...") with tsv.open(args.stats_path) as f: predictors = f.readline().rstrip("\n").split("\t")[1:] num_predictors = len(predictors) num_features = 0 for line in f: try: fields = line.rstrip("\n").split("\t") feature = fields[0] node = tree.get_or_create_node(feature) for p, ss in zip(predictors, fields[1:]): try: s0, s1, s2 = [float(v) if i > 0 else int(v) for i, v in enumerate(ss.split("/"))] node.set_pblt(p, PartialBLT(s0, s1, s2, sources=set([feature]))) except: import traceback traceback.print_exc() logger.warn("Failed to parse partial baseline tolerance" " for {}/{} from {}".format(feature, p, ss)) exit(-1) continue num_features += 1 except: logger.warn("Failed to parse partial baseline tolerance" " for {} from {}".format(feature, line)) continue logger.info(" Nodes: {}".format(tree.node_count)) logger.info(" Features: {}".format(num_features)) logger.info(" Predictors: {}".format(", ".join(predictors))) logger.info("Calculating baseline tolerance ...") for predictor in predictors: logger.info("For {} ...".format(predictor)) calculate_blt( parent=None, node=tree.get_or_create_node(args.root_group), predictor=predictor, count_threshold=args.count_threshold, stdev_threshold=args.stdev_threshold, logger=logger) # TODO log summary info logger.info("Writing results into {} ...".format(os.path.basename(args.out_path))) if args.tsv_path is not None: with tsv.open(args.tsv_path, "w") as of: tsv.write_line(of, "FEATURE", *predictors) for feature in all_features: sb = [feature] node = tree.get_node(feature) predictors_with_blt = 0 for predictor in predictors: blt = node.get_blt(predictor) if blt is None or blt.n < args.count_threshold: sb += ["/".join(["-"] * 5)] continue predictors_with_blt += 1 sb += ["/".join(map(str, [blt.from_node, blt.scope, blt.n, blt.mean, blt.stdev]))] if predictors_with_blt > 0: tsv.write_line(of, *sb) with tsv.open(args.out_path, "w") as of: tree_blt = {} for node_name, node in tree.nodes.items(): predictors_blt = {} for predictor in predictors: pred_blt = node.get_blt(predictor) if pred_blt is None or pred_blt.n < args.count_threshold: continue predictors_blt[predictor] = dict( from_node=pred_blt.from_node, scope=pred_blt.scope, N=pred_blt.n, mean=pred_blt.mean, stdev=pred_blt.stdev) if len(predictors_blt) > 0: tree_blt[node.name] = predictors_blt doc = dict( created=str(datetime.now()), predictors=predictors, count_threshold=args.count_threshold, stdev_threshold=args.stdev_threshold, tree=None, # tree relations features=list(all_features), pblt=None, # TODO blt=tree_blt ) json.dump(doc, of, indent=True) return 0
def end(): log = task.logger projects_out_port = task.ports("projects_out") log.info("Updating the projects database ...") for project_id, projects in task.context.items(): log.info("[{0}]".format(project_id)) for index, project in enumerate(projects): projdb = ProjectDb(project["db"]) if index == 0: log.info(" Functional impact ...") projdb.delete_sample_gene_fimpact() with tsv.open(project["sample_gene_fi_data"], "r") as f: types = (int, str, float, float, int, float, float, int, float, float, int) for fields in tsv.lines(f, types, header=True, null_value="-"): projdb.add_sample_gene_fimpact(*fields) ofm = project["oncodrivefm"] del project["oncodrivefm"] exc_path = os.path.join(project["temp_path"], "oncodrivefm-excluded-cause.tsv") log.info(" Excluded gene causes ...") log.debug(" > {0}".format(exc_path)) count = 0 with tsv.open(exc_path, "r") as exf: for gene, cause in tsv.lines(exf, (str, str), header=True): projdb.update_gene(Gene(id=gene, fm_exc_cause=cause)) count += 1 log.debug(" {0} genes excluded".format(count)) for feature, results_path in ofm: log.info(" {0} ...".format(feature)) log.debug(" > {0}".format(results_path)) if feature == "genes": with tsv.open(results_path, "r") as f: count = 0 for gene, pvalue, qvalue in tsv.lines(f, (str, float, float), header=True): projdb.update_gene(Gene(id=gene, fm_pvalue=pvalue, fm_qvalue=qvalue, fm_exc_cause=ProjectDb.NO_GENE_EXC)) count += 1 log.info(" {0} genes".format(count)) elif feature == "pathways": with tsv.open(results_path, "r") as f: count = 0 for pathway, zscore, pvalue, qvalue in tsv.lines(f, (str, float, float, float), header=True): projdb.update_pathway(Pathway(id=pathway, fm_zscore=zscore, fm_pvalue=pvalue, fm_qvalue=qvalue)) count += 1 log.info(" {0} pathways".format(count)) projdb.commit() projdb.close() projects_out_port.send(projects[0])
def main(): parser = argparse.ArgumentParser( description="Prepare SNV's dataset from individual training sets") parser.add_argument("pos_path", metavar="POS_SET", help="The positive training set file") parser.add_argument("neg_path", metavar="NEG_SET", help="The negative training set file") parser.add_argument("-m", "--map", dest="map_path", metavar="MAP", help="Optional mapping file for feature id's. Format: DST SRC") parser.add_argument("-o", dest="out_path", metavar="PATH", help="Output file. Use - for standard output.") bglogging.add_logging_arguments(parser) args = parser.parse_args() bglogging.initialize(args) logger = bglogging.get_logger("training-sets") if args.out_path is None: prefix = os.path.commonprefix([ os.path.splitext(os.path.basename(args.pos_path))[0], os.path.splitext(os.path.basename(args.neg_path))[0]]) prefix = prefix.rstrip(".") args.out_path = os.path.join(os.getcwd(), "{}-training.tsv".format(prefix)) if args.map_path is not None: logger.info("Loading map ...") prot_map = {} with tsv.open(args.map_path) as f: for dst_feature, src_feature in tsv.lines(f, (str, str)): if len(src_feature) > 0: if src_feature not in prot_map: prot_map[src_feature] = set([dst_feature]) else: prot_map[src_feature].add(dst_feature) else: prot_map = None logger.info("Processing ...") hits = dict(POS=0, NEG=0) fails = dict(POS=0, NEG=0) start_time = datetime.now() with tsv.open(args.out_path, "w") as wf: for event_type, path in (("POS", args.pos_path), ("NEG", args.neg_path)): logger.info(" [{}] Reading {} ...".format(event_type, path)) with tsv.open(path) as f: types = (str, int, str, str) for protein, pos, aa1, aa2 in tsv.lines(f, types): protein = protein.strip() if prot_map is not None: if protein not in prot_map: logger.debug("[{}] Unmapped protein: {}".format(event_type, protein)) fails[event_type] += 1 continue proteins = prot_map[protein] else: proteins = [protein] hits[event_type] += 1 for p in proteins: tsv.write_line(wf, p, pos, aa1.strip(), aa2.strip(), event_type) logger.info(" POS NEG") logger.info("SNVs {POS:>8} {NEG:>8}".format(**hits)) if args.map_path is not None: logger.info("unmapped {POS:>8} {NEG:>8}".format(**fails)) logger.info("Finished. Elapsed time: {}".format(datetime.now() - start_time))
def load_cds_len(conf): cds_len = {} with tsv.open(get_data_ensembl_gene_transcripts_path(conf), "r") as f: for gene, transcript, transcript_len in tsv.lines(f, (str, str, int), header=True): cds_len[transcript] = transcript_len return cds_len
def drivers(): log = task.logger config = GlobalConfig(task.conf) paths = PathsConfig(config) db_path = paths.results_path("drivers.db") db = SigDb(db_path) db.open() log.info("Variants ...") path = paths.combination_path("recurrences", "variant_gene-global-all.tsv.gz") with tsv.open(path, "r") as f: types = (str, str, int, str) for fields in tsv.lines(f, types, columns=("CHR", "STRAND", "START", "ALLELE"), header=True): chr, strand, start, allele = fields[:4] db.add_variant(chr, start) log.info("Genes ...") gene_sites = {} gene_fm = set() gene_clust = set() #SPECIAL_THRESHOLD = ["C18", "C34"] SPECIAL_THRESHOLD = [] log.info(" OncodriveFM ...") filename_re = re.compile(r"gene-cancer_site-(.+)\.tsv.gz") base_path = paths.combination_path("oncodrivefm") for path in os.listdir(base_path): m = filename_re.match(path) if not m: continue cancer_site_code = m.group(1) if cancer_site_code in SPECIAL_THRESHOLD: threshold = 1e-6 else: threshold = 0.01 with tsv.open(os.path.join(base_path, path), "r") as f: params = tsv.params(f) cancer_site_name = params["group_long_name"] for fields in tsv.lines(f, (str, float), columns=("ID", "QVALUE"), header=True): gene, qvalue = fields if qvalue < threshold: add_cancer_site(gene_sites, gene, cancer_site_code, cancer_site_name) gene_fm.add(gene) log.info(" OncodriveCLUST ...") filename_re = re.compile(r"cancer_site-(.+)\.tsv.gz") base_path = paths.combination_path("oncodriveclust") for path in os.listdir(base_path): m = filename_re.match(path) if not m: continue cancer_site_code = m.group(1) with tsv.open(os.path.join(base_path, path), "r") as f: params = tsv.params(f) cancer_site_name = params["group_long_name"] for fields in tsv.lines(f, (str, float), columns=("ID", "QVALUE"), header=True): gene, qvalue = fields if qvalue < 0.05: add_cancer_site(gene_sites, gene, cancer_site_code, cancer_site_name) gene_clust.add(gene) log.info(" Updating db ...") sig_genes = gene_fm | gene_clust for gene in sig_genes: db.add_gene(gene, gene in gene_fm, gene in gene_clust) log.info("Saving driver genes cancer sites dataset ...") path = paths.results_path("gene-driver_cancer_sites.tsv") log.debug("> {}".format(path)) with open(path, "w") as f: tsv.write_param(f, "date", datetime.now()) tsv.write_line(f, "GENE_ID", "FM", "CLUST", "CANCER_SITES_COUNT", "CANCER_SITE_CODES", "CANCER_SITE_NAMES") for gene, sites in gene_sites.items(): tsv.write_line(f, gene, 1 if gene in gene_fm else 0, 1 if gene in gene_clust else 0, len(sites), ", ".join(sorted([code for code, name in sites])), ", ".join(sorted([name for code, name in sites]))) db.commit() db.close()
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)
def gene_impact(project): log = task.logger config = GlobalConfig(task.conf) paths = PathsConfig(config) projects_port = task.ports("projects") log.info("--- [{0}] --------------------------------------------".format(project["id"])) partitions = project["partitions"] log.info("Reading {} partitions ...".format(len(partitions))) aff_gene_attrs = {} for partition in partitions: log.info(" Partition {} ...".format(partition["index"])) with open(partition["tfi_path"], "r") as f: bool_type = lambda val: bool(int(val)) if val is not None else False types = (int, str, str, bool_type, int, int, int, int) columns = [0, 2, 4, 5, 6, 10, 14, 18] for fields in tsv.lines(f, types, columns=columns, null_value="-"): (var_id, gene, prot_change, coding_region, tr_impact, sift_impact, pph2_impact, ma_impact) = fields coding_region = coding_region == 1 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 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])) num_vars = len(set([var_id for var_id, gene in aff_gene_attrs.keys()])) num_genes = len(set([gene for var_id, gene in aff_gene_attrs.keys()])) log.info("Saving {} variant-gene impacts ({} variants and {} genes) ...".format(len(aff_gene_attrs), num_vars, num_genes)) gfi_path = os.path.join(project["csq_path"], "variant-gene_impact.tsv") with open(gfi_path, "w") as vf: 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="-") # Send results to the next module project["gfi_path"] = gfi_path projects_port.send(project)
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 main(): parser = argparse.ArgumentParser(description="Calculate Baseline Tolerance statistics per gene") cmd = DefaultCommandHelper(parser) parser.add_argument("tree_path", metavar="TREE_PATH", help="The groups descendant tree") parser.add_argument("root_group", metavar="ROOT_GROUP", help="Tree root group") parser.add_argument("group_genes_path", metavar="GROUP_FEATS_PATH", help="Map between groups and features") parser.add_argument("stats_path", metavar="STATS_PATH", help="Partial gene statistics") parser.add_argument("out_path", metavar="OUTPUT_PATH", help="Output gene statistics") parser.add_argument( "-c", "--count-threshold", dest="count_threshold", metavar="N", default=DEFAULT_COUNT_THRESHOLD, help="Minimum number of features per group", ) parser.add_argument( "--stdev-threshold", dest="stdev_threshold", metavar="V", default=DEFAULT_STDEV_THRESHOLD, help="Skip feature statistics with a standard deviation less than V (it will be calculated at the level of groups)", ) args, logger = cmd.parse_args("blt-groups") logger.info("Loading groups tree ...") group_children = defaultdict(set) with tsv.open(args.tree_path) as f: for group, children in tsv.lines(f, (str, lambda v: set(v.split(",")))): group_children[group] |= children logger.info("Loading mappings between groups and features ...") group_genes = defaultdict(set) with tsv.open(args.group_genes_path) as f: for group, genes in tsv.lines(f, (str, lambda v: set(v.split(",")))): group_genes[group] |= genes logger.info("Loading partial statistics ...") partial_stats = {} with tsv.open(args.stats_path) as f: predictors = f.readline().rstrip("\n").split("\t")[1:] num_predictors = len(predictors) for line in f: fields = line.rstrip("\n").split("\t") gene = fields[0] gene_stats = [[float(v) if i > 0 else int(v) for i, v in enumerate(ss.split("/"))] for ss in fields[1:]] partial_stats[gene] = gene_stats logger.info(" Predictors: {}".format(", ".join(predictors))) logger.info(" Features: {}".format(len(partial_stats.keys()))) logger.info("Calculating features ...") stats = {} feat_count = 0 feat_partial_count = [0] * num_predictors for feature, feat_partial_stats in partial_stats.items(): feat_with_stats = False feat_stats = [None] * (num_predictors + 1) for i in range(num_predictors): s0, s1, s2 = feat_partial_stats[i] if s0 == 0.0: continue if s0 < args.count_threshold: continue x = (s0 * s2 - s1 * s1) / (s0 * (s0 - 1)) if x < -1e-12: continue mean = s1 / s0 std = math.sqrt(abs(x)) if std < args.stdev_threshold: continue feat_stats[i] = (int(s0), mean, std) feat_partial_count[i] += 1 feat_with_stats = True if feat_with_stats: feat_count += 1 stats[feature] = feat_stats # print feature, "\t".join(["/".join([str(v) for v in feat_stats[i] or []]) for i in range(num_predictors)]) logger.info( " {} ({}) features out of {} calculated directly from partial statistics".format( feat_count, "/".join(map(str, feat_partial_count)), len(partial_stats) ) ) logger.info("Calculating groups ...") calculate_group( logger, args.root_group, args.count_threshold, group_children, group_genes, partial_stats, num_predictors, stats ) logger.info(" {} features calculated in total".format(len(stats))) with tsv.open(args.out_path, "w") as of: tsv.write_line(of, "GENE", "GROUP", *predictors) for gene in sorted(stats.keys()): gene_stats = stats[gene] sb = [gene] stats_group = gene_stats[num_predictors] if stats_group is not None: sb += [stats_group] else: sb += ["|" + ("-" * num_predictors)] for i in range(num_predictors): if gene_stats[i] is not None: sb += ["/".join([str(v) for v in gene_stats[i]])] else: sb += ["-/-/-"] tsv.write_line(of, *sb) return 0
def update_db(project): log = task.logger config = GlobalConfig(task.conf) projects_port = task.ports("projects_out") log.info("--- [{0}] --------------------------------------------".format(project["id"])) partitions = project["partitions"] if not os.path.exists(config.vardb_path): log.warn("Database for variation external references not found") log.debug("> {0}".format(conf["vardb_path"])) varxdb = VarXrefsDb(config.vardb_path) varxdb.open() projdb = ProjectDb(project["db"]) updated_variants = set() plen = len(partitions) gene_xrefs = defaultdict(set) for part in partitions: log.info("Updating database with partition data ({0} out of {1}) ...".format(part["index"] + 1, plen)) log.info(" VEP results ...") ctype = lambda v: v.split(",") with open(part["vep_path"], "r") as vf: for fields in tsv.lines(vf, (int, str, str, ctype, str, str, str, float, float), null_value="-"): ( var_id, gene, transcript, consequences, protein_pos, aa_change, protein, sift_score, pph2_score, ) = fields var = projdb.get_variant(var_id) xrefs = varxdb.get_xrefs(var.chr, var.start, var.ref, var.alt, var.strand) if xrefs is not None: xrefs = ["{0}:{1}".format(source, xref) for source, xref in xrefs] gene_xrefs[gene].update(xrefs) if len(xrefs) == 0: xrefs = None projdb.update_variant(Variant(id=var_id, xrefs=xrefs)) projdb.add_consequence( Consequence( var=Variant(id=var_id), transcript=transcript, gene=gene, ctypes=consequences, protein_pos=protein_pos, aa_change=aa_change, protein=protein, ) ) log.info(" Transcript functional impacts ...") with open(part["tfi_path"], "r") as f: types = (int, str, str, int, float, float, int, float, float, int, float, float, int) columns = [0, 1, 3, 6, 7, 8, 9, 11, 12, 13, 15, 16, 17] for fields in tsv.lines(f, types, columns=columns, null_value="-"): ( var_id, transcript, uniprot, impact, sift_score, sift_tfic, sift_class, pph2_score, pph2_tfic, pph2_class, ma_score, ma_tfic, ma_class, ) = fields print fields projdb.update_consequence( Consequence( var=Variant(id=var_id), transcript=transcript, uniprot=uniprot, sift_score=sift_score, sift_tfic=sift_tfic, sift_tfic_class=sift_class, pph2_score=pph2_score, pph2_tfic=pph2_tfic, pph2_tfic_class=pph2_class, ma_score=ma_score, ma_tfic=ma_tfic, ma_tfic_class=ma_class, impact=impact, ) ) log.info("Updating variant-gene functional impacts ...") with open(project["gfi_path"], "r") as f: types = (int, str, float, int, str) for var_id, gene, impact, coding_region, prot_changes in tsv.lines(f, types, null_value="-"): projdb.add_affected_gene( AffectedGene( var=Variant(id=var_id), gene_id=gene, impact=impact, coding_region=coding_region, prot_changes=prot_changes, ) ) log.info("Updating database with gene external variant references ...") for gene, xrefs in gene_xrefs.items(): projdb.update_gene(Gene(id=gene, xrefs=xrefs)) projdb.commit() projdb.close() varxdb.close() del project["partitions"] projects_port.send(project)