Filter fusion artifacts (resolves #29)

resolves #29
related to BD2KGenomics/protect#237

Fusion calling often has FPS related to homology, especially among
mitochondrial  and (lnc,etc) RNA genes. Furthermore, transcriptional
readthroughs are usually oncologically irrelevant. This PR adds
filtering flags for mitochondrial, Immunoglobulin, RP11- (lncRNA) and
transcriptional readthrough filtering.

common.py

@@ -180,4 +180,86 @@ def __eq__(self, other):
     # The number of reads at the position
     'coverage',
     # The variant allele frequency
-    'vaf'))
+    'vaf'))
+
+# Standard Genetic Code from NCBI
+amino = 'FFLLSSSSYY**CC*WLLLLPPPPHHQQRRRRIIIMTTTTNNKKSSRRVVVVAAAADDEEGGGG'
+base1 = 'TTTTTTTTTTTTTTTTCCCCCCCCCCCCCCCCAAAAAAAAAAAAAAAAGGGGGGGGGGGGGGGG'
+base2 = 'TTTTCCCCAAAAGGGGTTTTCCCCAAAAGGGGTTTTCCCCAAAAGGGGTTTTCCCCAAAAGGGG'
+base3 = 'TCAGTCAGTCAGTCAGTCAGTCAGTCAGTCAGTCAGTCAGTCAGTCAGTCAGTCAGTCAGTCAG'
+genetic_code = {''.join([b1, b2, b3]): aa
+                for aa, b1, b2, b3 in zip(amino, base1, base2, base3)}
+
+
+BEDPE = collections.namedtuple('BEDPE',
+                               'chrom1, start1, end1, '
+                               'chrom2, start2, end2, '
+                               'name, '
+                               'score, '
+                               'strand1, strand2, '
+                               'junctionSeq1, junctionSeq2, '
+                               'hugo1, hugo2')
+
+
+def get_exons(genome_file, annotation_file, genes_of_interest):
+    """
+    Generates list of GTFRecord objects for each transcript
+
+    :param file genome_file: Reference genome FASTA file
+    :param file annotation_file: Genome annotation file (GTF)
+    :param set(str) genes_of_interest: The genes in this sample that might need translation.
+    :return: GTFRecord exons
+    :rtype: dict
+    """
+    annotation_file.seek(0)
+    chroms = {}
+    exons = collections.defaultdict(list)
+    for header, comment, seq in read_fasta(genome_file, 'ACGTN'):
+        chroms[header] = seq
+
+    for line in annotation_file:
+        if line.startswith('#'):
+            continue
+        else:
+            gtf = GTFRecord(line)
+            if gtf.feature == 'exon' and gtf.gene_name in genes_of_interest:
+                gtf.sequence = chroms[gtf.seqname][gtf.start - 1: gtf.end]
+                exons[gtf.transcript_id].append(gtf)
+    return exons
+
+
+def read_genes_from_gtf(gtf_file):
+    """
+    Read the gene annotations into a dict
+
+    :param file gtf_file: A file handle ot the annotation file.
+    :returns:  A dict of a gtf record for each gene
+    :rtype: dict(string, GTFRecord)
+    """
+    gene_annotations = {}
+    for line in gtf_file:
+        if line.startswith('#'):
+            continue
+        else:
+            gtf = GTFRecord(line)
+            if gtf.feature == 'gene':
+                gene_annotations[gtf.gene_name] = gtf
+    return gene_annotations
+
+
+def translate(seq):
+    """
+    Translates DNA sequence into protein sequence using globally defined genetic code
+
+    :param str seq: DNA sequence
+    :returns: Translated sequence
+    :rtype: str
+
+    >>> translate('ATGTTTCGTT')
+    'MFR'
+    """
+    start = 0
+    n = len(seq)
+    codons = (seq[i: i+3] for i in range(start, n - n % 3, 3))
+    protein = [genetic_code[codon] for codon in codons]
+    return ''.join(protein)

fusion.py

@@ -1,23 +1,14 @@
 from __future__ import print_function
+
 import collections
 import csv
 import logging
 import pickle
 import re
-import string
 from cStringIO import StringIO
 
 import swalign
-
-from common import read_fasta, GTFRecord, trans
-
-# Standard Genetic Code from NCBI
-amino = 'FFLLSSSSYY**CC*WLLLLPPPPHHQQRRRRIIIMTTTTNNKKSSRRVVVVAAAADDEEGGGG'
-base1 = 'TTTTTTTTTTTTTTTTCCCCCCCCCCCCCCCCAAAAAAAAAAAAAAAAGGGGGGGGGGGGGGGG'
-base2 = 'TTTTCCCCAAAAGGGGTTTTCCCCAAAAGGGGTTTTCCCCAAAAGGGGTTTTCCCCAAAAGGGG'
-base3 = 'TCAGTCAGTCAGTCAGTCAGTCAGTCAGTCAGTCAGTCAGTCAGTCAGTCAGTCAGTCAGTCAG'
-genetic_code = {''.join([b1, b2, b3]): aa
-                for aa, b1, b2, b3 in zip(amino, base1, base2, base3)}
+from common import read_fasta, trans, BEDPE, translate
 
 
 def get_transcriptome_data(infile):
@@ -49,56 +40,53 @@ def get_transcriptome_data(infile):
     return transcript_cds, gene_transcripts
 
 
-def get_exons(genome_file, annotation_file):
+def rna_gene_in_bedpe(record):
     """
-    Generates list of GTFRecord objects for each transcript
+    Determine if one of the two candidates in a BEDPE line is an rna gene.
 
-    :param file genome_file: Reference genome FASTA file
-    :param file annotation_file: Genome annotation file (GTF)
-    :return: GTFRecord exons
-    :rtype: dict
+    :param BEDPE record: A BEDPE line from the input file
+    :returns: True if one of the candidates is an RNA gene and False if not
+    :rtype: bool
     """
-    chroms = {}
-    exons = collections.defaultdict(list)
-    for header, comment, seq in read_fasta(genome_file, 'ACGTN'):
-        chroms[header] = seq
+    #  We will accept fusions that have an RP11- (lncRNA) 3' partner since they can still be
+    # translated. This is a heuristic.
+    return 'RP11-' in record.hugo1
 
-    for line in annotation_file:
-        if line.startswith('#'):
-            continue
-        else:
-            gtf = GTFRecord(line)
-            if gtf.feature == 'exon':
-                gtf.sequence = chroms[gtf.seqname][gtf.start - 1: gtf.end]
-                exons[gtf.transcript_id].append(gtf)
-    return exons
 
-
-def translate(seq):
+def readthrough_in_bedpe(record, annotation, rt_threshold):
     """
-    Translates DNA sequence into protein sequence using globally defined genetic code
-
-    :param str seq: DNA sequence
-    :returns: Translated sequence
-    :rtype: str
-
-    >>> translate('ATGTTTCGTT')
-    'MFR'
+    Determine if the two genes in the record are within `rt_threshold` bp of each other on the same
+    chromosome.
+
+    :param BEDPE record: A BEDPE line from the input file
+    :param dict(str, GTFRecord) annotation: see `read_fusions:gene_annotations`
+    :param rt_threshold: The genomic distance on the same chromosome below which we will call a
+           candidate fusion a readthrough.
+    :returns: True if the pair is considered a readthrough and False if not
+    :rtype: bool
     """
-    start = 0
-    n = len(seq)
-    codons = (seq[i: i+3] for i in range(start, n - n % 3, 3))
-    protein = [genetic_code[codon] for codon in codons]
-    return ''.join(protein)
+    return (record.chrom1 == record.chrom2 and
+            ((annotation[record.hugo1].start <= annotation[record.hugo2].start <=
+                annotation[record.hugo1].end + rt_threshold) or
+             (annotation[record.hugo2].start <= annotation[record.hugo1].start <=
+                annotation[record.hugo2].end + rt_threshold)))
 
 
-def read_fusions(fusion_file):
+def read_fusions(fusion_file, gene_annotations, filter_mt, filter_ig, filter_rg, filter_rt,
+                 rt_threshold, out_bedpe):
     """
     Reads in gene fusion predictions in modified BEDPE format.
     In addition to the basic BEDPE features, this function requires the fusion
     junction sequences and HUGO names for the donor and acceptor genes.
 
     :param file fusion_file: Fusion calls in BEDPE format
+    :param dict(str, GTFRecord) gene_annotations: The gene annotations from the gtf
+    :param bool filter_mt: Filter mitochondrial events?
+    :param bool filter_ig: Filter immunoglobulin pairs?
+    :param bool filter_rg: Filter RNA-Gene events?
+    :param bool filter_rt: Filter transcriptional read-throughs?
+    :param int rt_threshold: Distance threshold to call a readthrough
+    :param file out_bedpe: A file handle to an output BEDPE file
     :returns: list of BEDPE namedtuples
     :rtype: list
 
@@ -118,31 +106,49 @@ def read_fusions(fusion_file):
     hugo1:          HUGO name for first feature
     hugo2:          HUGO name for second feature
     """
-    BEDPE = collections.namedtuple('BEDPE',
-                                   'chrom1, start1, end1, '
-                                   'chrom2, start2, end2, '
-                                   'name, score, '
-                                   'strand1, strand2, '
-                                   'junctionSeq1, junctionSeq2, '
-                                   'hugo1, hugo2')
 
     calls = []
+
     for line in csv.reader(fusion_file, delimiter='\t'):
         if line[0].startswith('#'):
+            print('\t'.join(line), file=out_bedpe)
             continue
         try:
-            calls.append(BEDPE(*line))
-
+            record = BEDPE(*line)
         except TypeError:
             raise ValueError("ERROR: fusion file is malformed.\n{}".format(read_fusions.__doc__))
 
+        if filter_mt and 'M' in record.chrom1 or 'M' in record.chrom2:
+            logging.warning("Rejecting %s-%s for containing a Mitochondrial gene.", record.hugo1,
+                            record.hugo2)
+            continue
+        elif filter_ig and record.hugo1.startswith('IG') and record.hugo2.startswith('IG'):
+            # This will drop some Insulin-like growth factor (IGF) proteins but they have a lot of
+            # homology too so its ok.
+            logging.warning("Rejecting %s-%s an an Immunoglobulin gene pair.", record.hugo1,
+                            record.hugo2)
+            continue
+        elif filter_rg and rna_gene_in_bedpe(record):
+            logging.warning("Rejecting %s-%s for containing a 5' RNA gene.", record.hugo1,
+                            record.hugo2)
+            continue
+        elif filter_rt and readthrough_in_bedpe(record, gene_annotations, rt_threshold):
+            logging.warning("Rejecting %s-%s as a potential readthrough.", record.hugo1,
+                            record.hugo2)
+            continue
+        else:
+            logging.info("Accepting %s-%s for further study.", record.hugo1, record.hugo2)
+            print('\t'.join(line), file=out_bedpe)
+            calls.append(record)
+
     return calls
 
 # Namedtuple for storing alignment metrics
-# Neeeds to be global for pickling
+# Needs to be global for pickling
 AlignStats = collections.namedtuple('AlignStats',
                                     'qstart, qstop, rstart, rstop, insertions, deletions')
 
+
 def align_filter(ref, query, mode, mismatches_per_kb=1):
     """
     Aligns query to reference CDS sequence using the Smith-Waterman algorithm.

test/test_input/test_fusions.bedpe

@@ -4,3 +4,6 @@ chr8	.	42968214	chr10	43116584	.	ENSG00000168172-ENSG00000165731	1_1	+	+	TTGGAGG
 chr21	.	41494381	chr7	13935844	.	ENSG00000184012-ENSG00000006468	1_1	-	-	CCGGAAAACCCCTATCCCGCACAGCCCACTGTGGTCCCCACTGTCTACGAGGTGCATCCGGCTCAGTACTACCCGTCCCCCGTGCCCCAGTACGCCCCGAGGGTCCTGACGCAGGCTTCCAACCCCGTCGTCTGCACGCAGCCCAAATCC	CCATCCAGCACGCCAGTGTCCCCACTGCATCATGCATCTCCAAACTCAACTCATACACCGAAACCTGACCGGGCCTTCCCAGCTCACCTCCCTCCATCGCAGTCCATACCAGATAGCAGCTACCCCATGGACCACAGATTTCGCCGCCAGCTTTCTGAACCCTGTAACTCCTTTCCTCCTTTGCCGACGATGCCAAGGGAAGGACGTCCTATGTACCAACGCCAGATGTCTGAGCCAAACATCCCCTTCCCACCACAAGGCTTTAAGCAGGAGTACCACGACCCAGTGTATGAACACAACACCATGGTTGGCAGTGCGGCCAGCCAAAGCTTTCCCCCTCCTCTGATGATTAAACAGGAACCCAGAGATTTTGCATATGACTCAGAAGTGCCTAGCTGCCACTCCATTTATATGAGGCAAGAAGGCTTCCTGGCTCATCCCAGCAGAACAGAAGGCTGTATGTTTGAAAAGGGCCCCAGGCAGTTTTATGATGACACCTGTGTTGTCCCAGAAAAATTCGATGGAGACATCAAACAAGAGCCAGGAATGTATCGGGAAGGACCCACATACCAACGGCGAGGATCACTTCAGCTCTGGCAGTTTTTGGTAGCTCTTCTGGATGACCCTTCAAATTCTCATTTTATTGCCTGGACTGGTCGAGGCATGGAATTTAAACTGATTGAGCCTGAAGAGGTGGCCCGACGTTGGGGCATTCAGAAAAACAGGCCAGCTATGAACTATGATAAACTTAGCCGTTCACTCCGCTATTACTATGAGAAAGGAATTATGCAAAAGGTGGCTGGAGAGAGATATGTCTACAAGTTTGTGTGTGATCCAGAAGCCCTTTTCTCCATGGCCTTTCCAGATAATCAGCGTCCACTGCTGAAGACAGACATGGAACGTCACATCAACGAGGAGGACACAGTGCCTCTTTCTCACTTTGATGAGAGCATGGCCTACATGCCGGAAGGGGGCTGCTGCAACCCCCACCCCTACAACGAAGGCTACGTGTATTAACACAAGTGACAGTCAAGCAGGGCGTT	TMPRSS2	ETV1
 chr21	.	41494381	chr7	13935844	.	ENSG00000184012-ENSG00000006468	1_1	-	-	CCGGAAAACCCCTATCCCGCACAGCCCACTGTGGTCCCCACTGTCTACGAGGTGCATCCGGCTCAGTACTACCCGTCCCCCGTGCCCCAGTACGCCCCGAGGGTCCTGACGCAGGCTTCCAACCCCGTCGTCTGCACGCAGCCCAAATCC	CCATCCAGCACGCCAGTGTCCCCACTGCATCATGCATCTCCAAACTCAACTCATACACCGAAACCTGACCGGGCCTTCCCAGCTCACCTCCCTCCATCGCAGTCCATACCAGATAGCAGCTACCCCATGGACCACAGATTTCGCCGCCAGCTTTCTGAACCCTGTAACTCCTTTCCTCCTTTGCCGACGATGCCAAGGGAAGGACGTCCTATGTACCAACGCCAGATGTCTGAGCCAAACATCCCCTTCCCACCACAAGGCTTTAAGCAGGAGTACCACGACCCAGTGTATGAACACAACACCATGGTTGGCAGTGCGGCCA	TMPRSS2	ETV1
 chr21	.	42870046	chr21	39817544	.	ENSG00000184012-ENSG00000157554	1_1	-	-	.	.	TMPRSS2	ERG
+chr21	.	46493768	chr21	46511596	.	ENSG00000197381-ENSG00000268805	1_1	-	-	THIS_IS_A_READTHROUGH	.	ADARB1	PRED57
+chr21	.	16904219	chr21	16914235	.	ENSG00000230481-ENSG00000253691	1_1	-	-	THIS_IS_AN_IG	.	IGKV1OR22-5	IGKV2OR22-4
+chr21	.	10397644	chr21	39817544	.	ENSG00000278106-ENSG00000157554	1_1	-	-	THIS_IS_AN_RP11	.	RP11-589M2.1	ERG

test/test_transgene.py

@@ -107,6 +107,9 @@ def _test_transgene(self, use_RNA=False, use_DNA=False, fusions=False):
                                                            'annotation.gtf')) if fusions else None
         params.genome_file = open(self._get_input_path('test_input/chr21.fa')) if fusions else None
 
+        params.filter_mt = params.filter_rg = params.filter_ig = params.filter_rt = True
+        params.rt_threshold = 100000
+
         params.cores = 3
         transgene.main(params)
         output = {'9mer': {'fasta': 'unit_test_tumor_9_mer_snpeffed.faa',