[dev] succesfully run annotator one

.coveragerc

+[run]
+omit = */__init__.py

.gitignore

 # Project specific
-/data/
-/results/
+/examples/
+/htmlcov/
 
 # Logs 
 log/

README.md

@@ -12,7 +12,7 @@ see [VEP's options page](https://www.ensembl.org/info/docs/tools/vep/script/vep_
 
 ### 1.1. Manual parsing
 
-Relies on tags specified by the user to extract relevant info like genotype (GT), somatic status (SS), quality and filter info (QUAL, INFO) and most importantly reads information (AD, DP, FA, DP4, TAR, TIR). The parser has been tested on TCGA VCF files as produced by
+Relies on tags specified by the user to extract relevant info like genotype (GT), somatic status (SS), quality and filter info (QUAL, INFO) and most importantly reads information (AD, DP, FA, DP4, TAR, TIR). The parser has been tested on VCF files as produced by
 - Mutect v.1 (TCGA GA SNV) and Strelka (TCGA GA Indel)
 - sets of callers VarScanSomatic-Strelka-Sniper-Samtools (TCGA HS SNP), GatkSomaticIndel-Pindel-Strelka-VarScanSomatic (TCGA HS Indel).
 - Mutect v.1.1.7 with no header

src/main.py

@@ -10,18 +10,38 @@ Created on Thu Aug 13 2020
 
 Main functions for running each step and the assembling step.
 """
+import os
+import numpy  as np
+import pandas as pd
 
-def run_annotator(i_split: int, n_split: int, vcf2maf: str, vep_folder: str, vep_data: str, fasta: str):
-    """
-    Run the manual, vcf2maf and vep annotations and assemble.
+from   .manual  import run_manual_annotator
+from   .vcf2maf import run_vcf2maf_annotator
+from   .vep     import run_vep_annotator
+
+DataFrame = pd.core.frame.DataFrame
 
+#### # FUNCTION FOR ONE VCF
+#### # #######################################################################################################
 
-    Paramters
+def run_annotator_one(vcf_folder: str, vcf_file: str, col_normal: str, col_tumor: str, tumor_id: str, normal_id: str,
+infos_n_reads: list, infos_other: list, vcf2maf: str, vep_folder: str, vep_data: str, fasta: str, dt_folders: dict):
+    """
+    Run the manual, vcf2maf and vep annotations on one VCF file and assemble.
+
+    Parameters
     --------
-    i_split: int
-        the split processed.
-    n_split: int
-        the number of splits for processing a set of vcf files
+    vcf_file: str
+        name of the vcf file
+    vcf_folder: str
+        path to the folder where the vcf is
+    col_normal: str
+        name of the column in the vcf for the normal sample
+    col_tumor: str
+        name of the column in the vcf for the tumor sample
+    infos_n_reads: list
+        list of sigles that contain read info
+    infos_other: list
+        list of sigles that need extraction
     vcf2maf: str
         path to the vcf2maf perl script
     vep_folder: str
@@ -30,47 +50,243 @@ def run_annotator(i_split: int, n_split: int, vcf2maf: str, vep_folder: str, vep
         path to the .vep data where the reference genome is located
     fasta: str
         relative path to fasta file from vep_folder
+    vcf_folder: str
+        path to the folder where the vcf files are
+    dt_folders: dict
+        dict with the following keys:
+            * manual_out_folder
+            * vcf2maf_tmp_folder
+            * vcf2maf_out_folder
+            * vep_out_folder
+            * maf_folder
     """
+    vcf_path = os.path.join(vcf_folder, vcf_file)
+
+    out_file         = vcf_file.replace(".vcf", ".txt")
+    manual_out_path  = os.path.join(dt_folders["manual_out_folder"], out_file)
+    vcf2maf_out_path = os.path.join(dt_folders["vcf2maf_out_folder"], out_file)
+    vep_out_path     = os.path.join(dt_folders["vep_out_folder"], out_file)
+
+    #### # 1. RUN EACH ANNOTATOR
+    #### # ###################################################################################################
+
+    run_manual_annotator(
+        vcf_path      = vcf_path,
+        out_path      = manual_out_path,
+        col_normal    = col_normal,
+        col_tumor     = col_tumor,
+        infos_n_reads = infos_n_reads,
+        infos_other   = infos_other
+    )
 
+    run_vcf2maf_annotator(
+        vcf2maf    = vcf2maf,
+        vep_folder = vep_folder,
+        vep_data   = vep_data,
+        vcf_path   = vcf_path,
+        out_path   = vcf2maf_out_path,
+        tmp_folder = dt_folders["vcf2maf_tmp_folder"],
+        tumor_id   = tumor_id,
+        normal_id  = normal_id,
+        fasta      = fasta
+    )
 
-#### # SCRIPT PARAMETERS 
-#### #####################################################################################################
+    run_vep_annotator(
+        vep_folder = vep_folder,
+        vep_data   = vep_data,
+        vcf_path   = vcf_path,
+        out_path   = vep_out_path,
+        fasta      = fasta
+    )
 
-parser = argparse.ArgumentParser()
-parser.add_argument('--i_split'    , type=int , default=1  , help='the split processed')
-parser.add_argument('--n_split'    , type=int , default=1  , help='total number of splits')
-parser.add_argument('--vcf2maf'    , type=str , default="" , help='path to the vcf2maf perl script')
-parser.add_argument('--vep_folder' , type=str , default="" , help='path to the folder of the vep command')
-parser.add_argument('--vep_data'   , type=str , default="" , help='path to the .vep data folder')
-parser.add_argument('--fasta'      , type=str , default="" , help='path to reference genome FASTA file')
-args = parser.parse_args()
+    #### # 2. ASSEMBLE ANNOTATIONS
+    #### ######################################################################################################
 
-print("Parameters", flush=True)
-for arg in vars(args):
-    print("%s: %s" % (arg, getattr(args, arg)), flush=True)
+    ddf_vcf = {}
 
+    #### vep manual
+    ddf_vcf["manual"] = pd.read_csv(
+        filepath_or_buffer = manual_out_path,
+        sep                = "\t"
+    )
+
+    #### vep alone
+    skipsymbol = "##"
+    with open(vep_out_path) as file:
+        skiprows = sum(line.startswith(skipsymbol) for line in file.readlines())
+
+    df_alone = pd.read_csv(
+        filepath_or_buffer = vep_out_path,
+        sep                = "\t",
+        skiprows           = skiprows
+    )
+
+    df_alone_extra = df_alone.Extra.apply(lambda x: dict(item.split("=") for item in x.split(";")))
+    df_alone_extra = df_alone_extra.apply(pd.Series)
+    df_alone = pd.concat((df_alone, df_alone_extra), axis=1)
 
-if __name__ == "__main__":
+    ddf_vcf["alone"] = df_alone
 
-    #### change working dir
-    current_wd = setwd_to_data()
+    #### maf vcf2maf
+    skipsymbol = "#"
+    with open(vcf2maf_out_path) as file:
+        skiprows = sum(line.startswith(skipsymbol) for line in file.readlines())
 
-    #### Parameters
-    dt_paths = {
-        'vcf_meta'        : './raw/vcf/mbc/info/mbc_vcf_identifiers.txt',
-        'vcf_dir'         : './raw/vcf/mbc/files_filtered/',
-        'vcf_list'        : './raw/vcf/mbc/info/list_vcf_mbc.txt',
-        'vep_manual_dir'  : './raw/vep/vep_manual/mbc',
-        'vep_alone_dir'   : './raw/vep/vep_alone/mbc',
-        'vep_vcf2maf_dir' : './raw/vep/vep_vcf2maf/mbc/vep',
-        'maf_vcf2maf_dir' : './raw/vep/vep_vcf2maf/mbc/maf',
-        'maf_one_dir'     : './raw/maf/mbc',
+    ddf_vcf["vcf2maf"] = pd.read_csv(
+        filepath_or_buffer = vcf2maf_out_path,
+        sep                = "\t",
+        skiprows           = skiprows
+    )
+
+    #### checked that all scripts ran ok the vcf file
+    n_manual  = ddf_vcf["manual"].shape[0]
+    n_vcf2maf = ddf_vcf["vcf2maf"].shape[0]
+    n_alone   = ddf_vcf["alone"].shape[0]
+
+    if n_manual != n_vcf2maf:
+        print("SHAPE error: manual %d vs vcf2maf %d " % (n_manual, n_vcf2maf), flush=True)
+
+    if n_manual != n_alone:
+        print("SHAPE error: manual %d vs alone %d " % (n_manual, n_alone), flush=True)
+
+    #### choose column per source
+    choice_columns = {
+        "Hugo_Symbol"                 : "vcf2maf",
+        "Entrez_Gene_Id"              : "vcf2maf",
+        "NCBI_Build"                  : "vcf2maf",
+        "Chromosome"                  : "vcf2maf",
+        "Start_Position"              : "vcf2maf",
+        "End_Position"                : "vcf2maf",
+        "Variant_Quality"             : "manual",
+        "Filter"                      : "manual",
+        "Variant_Classification"      : "vcf2maf",
+        "Variant_Type"                : "vcf2maf",
+        "Reference_Allele"            : "vcf2maf",
+        "Tumor_Seq_Allele1"           : "vcf2maf",
+        "Tumor_Seq_Allele2"           : "vcf2maf",
+        "dbSNP_RS"                    : "vcf2maf",
+        "Tumor_Sample"                : "manual",
+        "Tumor_Sample_Barcode"        : "manual",
+        "Tumor_Sample_Site"           : "manual",
+        "Matched_Norm_Sample_Barcode" : "manual",
+        "Match_Norm_Seq_Allele1"      : "vcf2maf",
+        "Match_Norm_Seq_Allele2"      : "vcf2maf",
+        "Tumor_Sample_UUID"           : "manual",
+        "Matched_Norm_Sample_UUID"    : "manual",
+        "HGVSc"                       : "vcf2maf",
+        "HGVSp"                       : "vcf2maf",
+        "HGVSp_Short"                 : "vcf2maf",
+        "all_effects"                 : "vcf2maf",
+        "Location"                    : "alone",
+        "Gene"                        : "alone",
+        "Feature"                     : "alone",
+        "Feature_type"                : "alone",
+        "cDNA_position"               : "alone",
+        "CDS_position"                : "alone",
+        "Protein_position"            : "alone",
+        "Amino_acids"                 : "alone",
+        "Codons"                      : "alone",
+        "Existing_variation"          : "alone",
+        "Consequence"                 : "alone",
+        "IMPACT"                      : "alone",
+        "SIFT"                        : "alone",
+        "PolyPhen"                    : "alone",
+        "CLIN_SIG"                    : "alone",
+        "STRAND"                      : "alone",
+        "SYMBOL_SOURCE"               : "alone",
+        "HGNC_ID"                     : "alone",
+        "BIOTYPE"                     : "alone",
+        "CCDS"                        : "alone",
+        "ENSP"                        : "alone",
+        "SWISSPROT"                   : "alone",
+        "TREMBL"                      : "alone",
+        "UNIPARC"                     : "alone",
+        "EXON"                        : "alone",
+        "INTRON"                      : "alone",
+        "AF"                          : "alone",  #### AF from 1000 Genomes Phase 3
+        "AA_AF"                       : "alone",  #### NHLBI-ESP populations
+        "EA_AF"                       : "alone",  #### NHLBI-ESP populations
+        "gnomAD_AF"                   : "alone",  #### gnomAD exome populations
+        "MAX_AF"                      : "alone",  #### highest AF from any of 1000 genomes, ESP or gnomAD
+        "MAX_AF_POPS"                 : "alone",  #### highest AF from any of 1000 genomes, ESP or gnomAD
     }
 
-    #### make dir if does not exist already
-    for k, v in dt_paths.items():
-        if "dir" in k:
-            os.makedirs(v, exist_ok=True)
+    maf_columns = []
+    cols_reads = [x for x in ddf_vcf["manual"].columns if x.startswith(("n_", "t_"))]
+
+    #### add column from dict
+    for column,source in choice_columns.items():
+        if column not in ddf_vcf[source].columns:
+            print("WARNING: %s is not in %s" % (column, source), flush=True)
+        else:
+            maf_columns.append(ddf_vcf[source][column])
+
+    #### add read columns from manual extraction of reads
+    for column in cols_reads:
+        if column not in ddf_vcf["manual"].columns:
+            print("WARNING: %s is not in %s" % (column, source), flush=True)
+        else:
+            maf_columns.append(ddf_vcf["manual"][column])
+
+    #### # 3. SAVE
+    #### ######################################################################################################
+
+    maf_out_path = os.path.join(dt_folders["maf_folder"], out_file)
+    df_maf = pd.concat(maf_columns, axis=1)
+    df_maf.to_csv(
+        path_or_buf = maf_out_path,
+        sep         = "\t",
+        header      = True,
+        index       = False
+    )
+    print("maf file saved at %s" % maf_out_path, flush=True)
+
+
+def run_annotator(i_split: int, n_split: int, vcf2maf: str, vep_folder: str, vep_data: str, fasta: str, vcf_folder: str,
+out_folder: str, vcf_list_path: str=None, vcf_meta_path: str=None):
+    """
+    Run the manual, vcf2maf and vep annotations and assemble.
+
+
+    Parameters
+    --------
+    i_split: int
+        the split processed.
+    n_split: int
+        the number of splits for processing a set of vcf files
+    vcf2maf: str
+        path to the vcf2maf perl script
+    vep_folder: str
+        path to the folder where the vep command is
+    vep_data: str
+        path to the .vep data where the reference genome is located
+    fasta: str
+        relative path to fasta file from vep_folder
+    vcf_folder: str
+        path to the folder where the vcf files are
+    out_folder: str
+        path to the folder where subfolders with output results will be saved
+    vcf_list_path: str, optional
+        path to the file containing the list of vcf files to be processed. If not specified, all vcf files found in the
+        vcf_folder are processed.
+    vcf_meta_path: str, optional
+        path to the file that contain the names of the tumor and normal columns for each vcf file. The file must be a
+        .txt file containing 3 columns: "vcf_name", "normal_column", "tumor_column". If not specified,
+        all vcf files must have columns with the names NORMAL and PRIMARY.
+    """
+
+    #### paths to results folders
+    dt_folders = {
+        'manual_out_folder'  : os.path.join(out_folder, "tmp/manual/out"),
+        'vcf2maf_tmp_folder' : os.path.join(out_folder, "tmp/vcf2maf/tmp"),
+        'vcf2maf_out_folder' : os.path.join(out_folder, "tmp/vcf2maf/out"),
+        'vep_out_folder'     : os.path.join(out_folder, "tmp/vep/out"),
+        'maf_folder'         : os.path.join(out_folder, "tmp/maf"),
+    }
+
+    #### make folders if they do not exist already
+    for k, v in dt_folders.items():
+        os.makedirs(v, exist_ok=True)
 
     #### load meta data
     df_meta = pd.read_csv(
@@ -80,76 +296,44 @@ if __name__ == "__main__":
 
     if os.path.exists(dt_paths["vcf_list"]):
         with open(dt_paths["vcf_list"]) as file:
-            vcf_names_orig = file.read().splitlines()
+            vcf_files = file.read().splitlines()
     else:
-        vcf_names_orig = df_meta.loc[df_meta.keep == True, "vcf"].tolist()
+        #### what if compressed ?
+        vcf_files = [x for x in os.listdir(vcf_folder) if x.endswith(".vcf")]
 
     #### get list of vcfs for the split
-    count_one_split = len(vcf_names_orig)//args.n_split
+    count_one_split = len(vcf_files)//args.n_split
 
     if args.i_split == args.n_split:
-        vcf_names_orig  = vcf_names_orig[(args.i_split-1)*count_one_split:]
+        vcf_files  = vcf_files[(args.i_split-1)*count_one_split:]
     else:
-        vcf_names_orig  = vcf_names_orig[(args.i_split-1)*count_one_split:args.i_split*count_one_split]
+        vcf_files  = vcf_files[(args.i_split-1)*count_one_split:args.i_split*count_one_split]
 
     count = 0
-    count_total = len(vcf_names_orig)
+    count_total = len(vcf_files)
 
     #### loop over the list
-    for vcf_name_orig in vcf_names_orig:
-        vcf_name = vcf_name_orig
-        vcf_path = os.path.join(dt_paths['vcf_dir'], vcf_name)
-
-        count += 1
-
-        print("="*80, flush=True)
-        print("vcf %d/%d" % (count, count_total), flush=True)
-        print("processing %s\n" % vcf_name, flush=True)
-
-        #### get vcf type
-        if "mutect" in vcf_name_orig:
-            vcf_type = "snv"
-        elif "indel" in vcf_name_orig:
-            vcf_type = "indel"
-
-        #### get vcf identifiers
-        mask_vcf_name = df_meta.vcf == vcf_name_orig
-        index_vcf_name = mask_vcf_name[mask_vcf_name].index[0]
-
-        vcf_identifiers = {
-            "Tumor_Sample"                : df_meta.loc[index_vcf_name, "Subject.ID"],
-            "Tumor_Sample_Barcode"        : df_meta.loc[index_vcf_name, "tumor_sample"],
-            "Matched_Norm_Sample_Barcode" : df_meta.loc[index_vcf_name, "normal_sample"],
-        }
-
-        if vcf_type == "snv":
-            infos_n_reads = ["RC", "AC"]
-            infos_other = ["RS", "AS", "GT"]
-
-            col_tumor  = vcf_identifiers["Tumor_Sample_Barcode"]
-            col_normal = vcf_identifiers["Matched_Norm_Sample_Barcode"]
-
-        elif vcf_type == "indel":
-            infos_n_reads = ["DP", "TAR", "TIR"]
-            infos_other = ["SGT"]
-
-            col_tumor  = "TUMOR"
-            col_normal = "NORMAL"
-
-        process_vcf(
-            vcf_identifiers = vcf_identifiers,
-            vcf_path        = vcf_path,
-            vcf_name        = vcf_name,
-            vcf2maf         = args.vcf2maf,
-            vep_folder      = args.vep_folder,
-            vep_data        = args.vep_data,
-            fasta           = args.fasta,
-            dt_paths        = dt_paths,
-            col_tumor       = col_tumor,
-            col_normal      = col_normal,
-            infos_n_reads   = infos_n_reads,
-            infos_other     = infos_other
+    for vcf_file in vcf_files:
+
+        col_normal    = "NORMAL"
+        col_tumor     = "PRIMARY"
+        normal_id     = "TCGA-A1-A0SD-10A-01D-A110-09"
+        tumor_id      = "TCGA-A1-A0SD-01A-11D-A10Y-09"
+        infos_n_reads = ["AD", "DP4", "DP", "TAR", "TIR"]
+        infos_other   = ["SS", "GT"]
+
+        run_annotator_one(
+            vcf_folder    = vcf_folder,
+            vcf_file      = vcf_file,
+            col_normal    = col_normal,
+            col_tumor     = col_tumor,
+            normal_id     = normal_id,
+            tumor_id      = tumor_id,
+            infos_n_reads = infos_n_reads,
+            infos_other   = infos_other,
+            vcf2maf       = vcf2maf,
+            vep_folder    = vep_folder,
+            vep_data      = vep_data,
+            fasta         = fasta,
+            dt_folders    = dt_folders
         )
-
-    #### revert to previous work dir
-    os.chdir(current_wd)

src/manual.py

@@ -291,8 +291,7 @@ def process_assemble(df_vcf: DataFrame, df_vcf_info: DataFrame, df_vcf_reads: Da
 #### # MAIN FUNCTION
 #### #####################################################################################################
 
-def run_manual_annotator(vcf_path: str, out_path:str,  col_normal: str, col_tumor: str, infos_n_reads: list,
-                         infos_other: list):
+def run_manual_annotator(vcf_path: str, out_path:str,  col_normal: str, col_tumor: str, infos_n_reads: list, infos_other: list):
     """
     Manually parse VCF file and save at the path specified.
 

src/tests/test_main.py

+# -*- coding: utf-8 -*-
+"""
+Created on Thu Aug 13 2020
+
+@author: Yoann Pradat
+
+    CentraleSupelec
+    MICS laboratory
+    9 rue Juliot Curie, Gif-Sur-Yvette, 91190 France
+
+Test functions from vep module.
+"""
+
+import os
+from ..main import run_annotator_one
+
+def test_main():
+    vcf2maf    = "~/Documents/biotools/informatics/VCF/mskcc-vcf2maf-5453f80/vcf2maf.pl"
+    vep_folder = "~/Documents/biotools/informatics/VCF/ensembl-vep"
+    vep_data   = "~/.vep"
+    fasta      = "~/.vep/homo_sapiens/99_GRCh37/Homo_sapiens.GRCh37.75.dna.primary_assembly.fa"
+
+    #### # 1. TCGA GA
+    #### # ########################################################################################################
+
+    vcf_folder = "./examples/data/TCGA_GA/"
+    out_folder = "./examples/results/TCGA_GA/"
+
+    #### paths to results folders
+    dt_folders = {
+        'manual_out_folder'  : os.path.join(out_folder, "tmp/out_manual"),
+        'vcf2maf_tmp_folder' : os.path.join(out_folder, "tmp/tmp_vcf2maf"),
+        'vcf2maf_out_folder' : os.path.join(out_folder, "tmp/out_vcf2maf"),
+        'vep_out_folder'     : os.path.join(out_folder, "tmp/out_vep"),
+        'maf_folder'         : os.path.join(out_folder, "maf"),
+    }
+
+    #### make folders if they do not exist already
+    for k, v in dt_folders.items():
+        os.makedirs(v, exist_ok=True)
+
+    #### Indel TCGA_GA
+    vcf_file      = "TCGA-A1-A0SB_db9d40fb-bfce-4c3b-a6c2-41c5c88982f1_a3254f8e-3bbd-42fc-abea-a5f25b7648b3.indel.capture.tcga.vcf"
+    col_normal    = "NORMAL"
+    col_tumor     = "PRIMARY"
+    normal_id     = "TCGA-A1-A0SD-10A-01D-A110-09"
+    tumor_id      = "TCGA-A1-A0SD-01A-11D-A10Y-09"
+    infos_n_reads = ["AD", "DP4", "DP", "TAR", "TIR"]
+    infos_other   = ["SS", "GT"]
+
+    run_annotator_one(
+        vcf_folder    = vcf_folder,
+        vcf_file      = vcf_file,
+        col_normal    = col_normal,
+        col_tumor     = col_tumor,
+        normal_id     = normal_id,
+        tumor_id      = tumor_id,
+        infos_n_reads = infos_n_reads,
+        infos_other   = infos_other,
+        vcf2maf       = vcf2maf,
+        vep_folder    = vep_folder,
+        vep_data      = vep_data,
+        fasta         = fasta,
+        dt_folders    = dt_folders
+    )
+
+    #### SNP TCGA_GA
+    vcf_file = "TCGA-A1-A0SB_db9d40fb-bfce-4c3b-a6c2-41c5c88982f1_a3254f8e-3bbd-42fc-abea-a5f25b7648b3.oxoG.snp.capture.tcga.vcf"
+
+    #### # 2. TCGA HS
+    #### # ########################################################################################################
+
+    vcf_folder = "./examples/data/TCGA_HS/"
+
+    #### Indel TCGA_HS
+    vcf_file =  "genome.wustl.edu.TCGA-A1-A0SD.indel.0e81f9c986154ce89e59240c3f09534f.vcf"
+
+    #### SNP TCGA_HS
+    vcf_file =  "genome.wustl.edu.TCGA-A1-A0SD.snv.0e81f9c986154ce89e59240c3f09534f.vcf"

src/tests/test_manual.py

@@ -20,7 +20,7 @@ def test_manual():
     #### # ########################################################################################################
 
     vcf_folder = "./examples/data/TCGA_GA/"
-    out_folder = "./examples/results/TCGA_GA/out_manual"
+    out_folder = "./examples/results/TCGA_GA/tmp/out_manual"
     os.makedirs(out_folder, exist_ok=True)
 
     #### Indel TCGA_GA
@@ -61,7 +61,7 @@ def test_manual():
     #### # ########################################################################################################
 
     vcf_folder = "./examples/data/TCGA_HS/"
-    out_folder = "./examples/results/TCGA_HS/out_manual"
+    out_folder = "./examples/results/TCGA_HS/tmp/out_manual"
     os.makedirs(out_folder, exist_ok=True)
 
     #### Indel TCGA_HS

src/tests/test_vcf2maf.py

@@ -24,8 +24,8 @@ def test_vcf2maf():
     #### # ########################################################################################################
 
     vcf_folder = "./examples/data/TCGA_GA/"
-    tmp_folder = "./examples/results/TCGA_GA/tmp_vcf2maf"
-    out_folder = "./examples/results/TCGA_GA/out_vcf2maf"
+    tmp_folder = "./examples/results/TCGA_GA/tmp/tmp_vcf2maf"
+    out_folder = "./examples/results/TCGA_GA/tmp/out_vcf2maf"
     os.makedirs(out_folder, exist_ok=True)
 
     #### Indel TCGA_GA
@@ -68,8 +68,8 @@ def test_vcf2maf():
     #### # ########################################################################################################
 
     vcf_folder = "./examples/data/TCGA_HS/"
-    tmp_folder = "./examples/results/TCGA_HS/tmp_vcf2maf"
-    out_folder = "./examples/results/TCGA_HS/out_vcf2maf"
+    tmp_folder = "./examples/results/TCGA_HS/tmp/tmp_vcf2maf"
+    out_folder = "./examples/results/TCGA_HS/tmp/out_vcf2maf"
     os.makedirs(out_folder, exist_ok=True)
 
     #### Indel TCGA_HS

src/tests/test_vep.py

@@ -23,7 +23,7 @@ def test_vep():
     #### # ########################################################################################################
 
     vcf_folder = "./examples/data/TCGA_GA/"
-    out_folder = "./examples/results/TCGA_GA/out_vep"
+    out_folder = "./examples/results/TCGA_GA/tmp/out_vep"
     os.makedirs(out_folder, exist_ok=True)
 
     #### Indel TCGA_GA
@@ -54,7 +54,7 @@ def test_vep():
     #### # ########################################################################################################
 
     vcf_folder = "./examples/data/TCGA_HS/"
-    out_folder = "./examples/results/TCGA_HS/out_vep"
+    out_folder = "./examples/results/TCGA_HS/tmp/out_vep"
     os.makedirs(out_folder, exist_ok=True)
 
     #### Indel TCGA_HS