Add --input_variant parameter for handling single variant

Dockerfile

@@ -9,7 +9,7 @@
 #FROM ubuntu:18.04
 FROM ensemblorg/ensembl-vep:release_104.3
 USER root
-ENV DEBIAN_FRONTEND noninteractive
+ENV DEBIAN_FRONTEND=noninteractive
 
 # Install dependencies
 RUN apt-get update && apt-get install -y \
@@ -38,7 +38,7 @@ RUN pip3 install -r /opt/requirements.txt
 RUN pip3 install bgzip
 
 # Install bcftools
-RUN wget https://github.com/samtools/bcftools/releases/download/1.20/bcftools-1.20.tar.bz2
+RUN curl -OL https://github.com/samtools/bcftools/releases/download/1.20/bcftools-1.20.tar.bz2
 RUN mv bcftools-1.20.tar.bz2 /opt/bcftools-1.20.tar.bz2
 RUN tar -xf /opt/bcftools-1.20.tar.bz2 -C /opt/ && \
   rm /opt/bcftools-1.20.tar.bz2 && \
@@ -49,7 +49,7 @@ RUN tar -xf /opt/bcftools-1.20.tar.bz2 -C /opt/ && \
   rm -rf /opt/bcftools-1.20
 
 # Install bedtools
-RUN wget https://github.com/arq5x/bedtools2/releases/download/v2.30.0/bedtools.static.binary
+RUN curl -OL https://github.com/arq5x/bedtools2/releases/download/v2.30.0/bedtools.static.binary
 RUN mv bedtools.static.binary /run/bedtools
 RUN chmod a+x /run/bedtools
 

bin/add_c_nc.py

@@ -10,13 +10,13 @@ def add_c_nc(score, ref):
     if ref == "hg38":
         clin_c = pd.read_csv("merge_expand/hg38/clin_c.tsv.gz", sep="\t")
         clin_nc = pd.read_csv("merge_expand/hg38/clin_nc.tsv.gz", sep="\t")
-        hgmd_nc = pd.read_csv("merge_expand/hg38/hgmd_nc.tsv.gz", sep="\t")
         hgmd_c = pd.read_csv("merge_expand/hg38/hgmd_c.tsv.gz", sep="\t")
+        hgmd_nc = pd.read_csv("merge_expand/hg38/hgmd_nc.tsv.gz", sep="\t")
     else:
         clin_c = pd.read_csv("merge_expand/hg19/clin_c.tsv.gz", sep="\t")
         clin_nc = pd.read_csv("merge_expand/hg19/clin_nc.tsv.gz", sep="\t")
-        hgmd_nc = pd.read_csv("merge_expand/hg19/hgmd_nc.tsv.gz", sep="\t")
         hgmd_c = pd.read_csv("merge_expand/hg19/hgmd_c.tsv.gz", sep="\t")
+        hgmd_nc = pd.read_csv("merge_expand/hg19/hgmd_nc.tsv.gz", sep="\t")
 
     a = score.pos.values
     ac = score.chrom.values
@@ -28,7 +28,7 @@ def add_c_nc(score, ref):
 
     i, j = np.where((a[:, None] >= bl) & (a[:, None] <= bh) & (ac[:, None] == bc))
 
-    cln = pd.concat(
+    clin = pd.concat(
         [
             temp.loc[i, :].reset_index(drop=True),
             clin_nc.loc[j, :].reset_index(drop=True),
@@ -38,7 +38,7 @@ def add_c_nc(score, ref):
 
     # Take into account When HGMD data base is empty
     if hgmd_nc.shape[0] == 0:
-        pass
+        hgmd = hgmd_nc
 
     else:
         # merge by region
@@ -58,29 +58,28 @@ def add_c_nc(score, ref):
             axis=1,
         )
 
-    merged = score.merge(cln, how="left", on="varId")
-    if hgmd_nc.shape[0] == 0:
-        merged["nc_HGMD_Exp"] = np.NaN
-        merged["nc_RANKSCORE"] = np.NaN
-    else:
-        merged = merged.merge(hgmd, how="left", on="varId")
+    merged = score.merge(
+        clin_c.rename(columns={'new_chr': 'chrom', 'new_pos': 'pos'}),
+        how="left",
+        on=["chrom", "pos", "ref", "alt"],
+    )
+    merged = merged.merge(clin, how="left", on="varId")
 
     if hgmd_c.shape[0] == 0:
         merged["c_HGMD_Exp"] = np.NaN
         merged["c_RANKSCORE"] = np.NaN
         merged["CLASS"] = np.NaN
     else:
         merged = merged.merge(
-            hgmd_c,
+            hgmd_c.rename(columns={'new_chr': 'chrom', 'new_pos': 'pos'}),
             how="left",
-            left_on=["chrom", "pos", "ref", "alt"],
-            right_on=["new_chr", "new_pos", "ref", "alt"],
+            on=["chrom", "pos", "ref", "alt"],
         )
-    merged = merged.merge(
-        clin_c,
-        how="left",
-        left_on=["chrom", "pos", "ref", "alt"],
-        right_on=["new_chr", "new_pos", "ref", "alt"],
-    )
+
+    if hgmd_nc.shape[0] == 0:
+        merged["nc_HGMD_Exp"] = np.NaN
+        merged["nc_RANKSCORE"] = np.NaN
+    else:
+        merged = merged.merge(hgmd, how="left", on="varId")
 
     return merged

bin/extraModel/generate_bivar_data.py

@@ -68,7 +68,7 @@ def process_sample(data_folder, sample_id, default_pred, labeling=False):
         ]
 
     # Remove all the duplicated variant pairs.
-    gene_var_pairs_df = pd.DataFrame(gene_var_pairs)
+    gene_var_pairs_df = pd.DataFrame(gene_var_pairs, columns=['geneEnsId', 'varId1', 'varId2'])
     # gene_var_pairs_df = gene_var_pairs_df.drop_duplicates(['varId1', 'varId2'])
 
     # Use only subset columns of features
@@ -130,4 +130,7 @@ def process_sample(data_folder, sample_id, default_pred, labeling=False):
     # Sort before saving
     recessive_feature_df = recessive_feature_df.sort_index()
 
+    if recessive_feature_df.shape[0] == 0:
+        return
+
     recessive_feature_df.to_csv(f"{recessive_folder}/{sample_id}.csv")

bin/feature.py

@@ -258,7 +258,7 @@ def main():
         print("input annoatated varFile:", args.varFile)
         t1 = time.time()
         varDf = pd.read_csv(
-            args.varFile, sep="\t", skiprows=numHeaderSkip, error_bad_lines=False
+            args.varFile, sep="\t", skiprows=numHeaderSkip,
         )
         # varDf=varDf[0:10]
         # #do this if need to have a small test

bin/generate_input_vcf.py

@@ -0,0 +1,35 @@
+#!/usr/bin/env python3.8
+
+import string
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument("variant", type=str)
+
+args = parser.parse_args()
+
+vcf_template = string.Template("""
+##fileformat=VCFv4.2
+##FORMAT=<ID=AD,Number=.,Type=Integer,Description="Allelic depths for the ref and alt alleles in the order listed">
+##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth (reads with MQ=255 or with bad mates are filtered)">
+##FORMAT=<ID=GQ,Number=1,Type=Integer,Description="Genotype Quality">
+##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
+##FORMAT=<ID=PL,Number=G,Type=Integer,Description="Normalized, Phred-scaled likelihoods for genotypes as defined in the VCF specification">
+##FORMAT=<ID=SB,Number=4,Type=Integer,Description="Per-sample component statistics which comprise the Fisher's Exact Test to detect strand bias.">
+##FILTER=<ID=PASS,Description="All filters passed">
+#CHROM	POS	ID	REF	ALT	QUAL	FILTER	INFO	FORMAT	SAMPLE
+$chrom	$pos	.	$ref	$alt	.	.	.	GT:AD:DP:GQ:PL	0/1:7,5:12:99:142,0,214
+""".strip())
+
+def main(variant):
+    chrom, pos, ref, alt = variant.split("-")
+    with open("input.vcf", "w", encoding="ascii") as text_file:
+        text_file.write(vcf_template.substitute(
+            chrom=chrom,
+            pos=pos,
+            ref=ref,
+            alt=alt,
+        ))
+
+if __name__ == "__main__":
+    main(**vars(args))

docs/source/nf_usage.txt

@@ -18,6 +18,7 @@ Args:
   --outdir                  Output directory.
   --run_id                  Unique identifier for this run. (default: 1)
   --ref_ver                 Reference genome version [hg38 or hg19] (default: hg19)
+  --input_variant           Variant ID Formatted in 'chr-pos-alt-ref' (optional)
   --bed_filter              Path to a BED file to perform an analysis only for regions of interest (optional)
   --exome_filter            Enable exonic filter. Addition will filter out variants outside of exonic region  (default: false)