SNV Pipeline

SNV calling pipeline developed explicitly to process individual or trio vcf files obtained from Illumina based pipeline (grch37/grch38). The pipeline requires user defined datasets & annotation sources, available tools and input set of vcf files. It generates analysis scripts that can be incorporated into high performance cluster (HPC) computing to process the samples. This results in list of filtered variants per family that can be used for interpreation, reporting and further downstream analysis.

For demonstration purpose below example is presented for GRCh37. However, the same can be replicated for GRCh38.

Installation

git clone https://github.com/ajaarma/snv.git

Required Installation packages

Install anaconda v2.0

Follow this link for installation: https://docs.anaconda.com/anaconda/install/linux/

Conda environment commands

$ conda create --name snv
$ source activate snv
$ conda install python=2.7.16
$ pip install xmltodict
$ pip install dicttoxml

$ conda install -c bioconda gvcfgenotyper
$ conda install -c anaconda gawk	
$ conda install samtools=1.3
$ conda install vcftools=0.1.14
$ conda install bcftools=1.9
$ conda install gcc #(OSX)
$ conda install gcc_linux-64 #(Linux)
$ conda install parallel
$ conda install -c mvdbeek ucsc_tools
** conda-develop -n 
    
    
     /demo/softwares/vep/Plugins/

$ conda install -c r r-optparse
$ conda install -c r r-dplyr
$ conda install -c r r-plyr
$ conda install -c r r-data.table
$ conda install -c aakumar r-readbulk
$ conda install -c bioconda ensembl-vep=100.4
$ vep_install -a cf -s homo_sapiens -y GRCh37 -c 
     
      /demo/softwares/vep/grch37 --CONVERT
$ vep_install -a cf -s homo_sapiens -y GRCh38 -c 
      
       /demo/softwares/vep/grch38 --CONVERT

Data directory and datasets

Default datasets provided

1. exac_pli: demo/resources/gnomad/grch37/gnomad.v2.1.1.lof_metrics.by_transcript_forVEP.txt
2. ensembl: demo/resources/ensembl/grch37/ensBioMart_grch37_v98_ENST_lengths_191208.txt
3. region-exons: demo/resources/regions/grch37/hg19_refseq_ensembl_exons_50bp_allMT_hgmd_clinvar_20200519.txt
4. region-pseudo-autosomal: demo/resources/regions/grch37/hg19_non_pseudoautosomal_regions_X.txt
5. HPO: demo/resources/hpo/phenotype_to_genes.tar.gz

Other datasets that require no entry to user-configuration file

6. Curated: 
	6.1. Genelist: demo/resources/curated/NGC_genelist_allNamesOnly-20200519.txt
	6.2. Somatic mosaicism genes: demo/resources/curated/haem_somatic_mosaicism_genes_20191015.txt
	6.3. Imprinted gene list: demo/resources/curated/imprinted_genes_20200424.txt
	6.4. Polymorphic gene list: demo/resources/curated/polymorphic_genes_20200509.txt
7. OMIM: demo/resources/omim/omim_20200421_geneInfoBase.txt

Download link for following dataset and place them in corresponding directories as shown

' | awk -v OFS="\t" '{ if(/^#/){ print }else{ print $1,$2,$3,$4,$5,$6,$7,"ID="$3";"$8 } }' | bgzip -c > hgmd_pro_2019.4_hg19_wID.vcf.gz $ bcftools index -t hgmd_pro_2019.4_hg19_wID.vcf.gz $ bcftools index hgmd_pro_2019.4_hg19_wID.vcf.gz Put it in this directory: demo/resources/hgmd/grch37/hgmd_pro_2019.4_hg19_wID.vcf.gz Edit the user config flat file CONFIG/UserConfig.txt : hgmd=hgmd/grch37/hgmd_pro_2019.4_hg19_wID.vcf.gz 8. CLINVAR: Download link: https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh37/weekly/clinvar_20200506.vcf.gz https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh37/weekly/clinvar_20200506.vcf.gz.tbi Put it in this directory: demo/resources/clinvar/grch37/clinvar_20200506.vcf.gz Edit the user config flat file CONFIG/UserConfig.txt : clinvar=clinvar/grch37/clinvar_20200506.vcf.gz ">

1. HPO: Extract HPO phenotypes mapping:
	$ cd 
   
    /demo/resources/hpo/
	$ tar -zxvf phenotypes_to_genes.tar.gz 

2. REFERENCE SEQUENCE GENOME (FASTA file alongwith Index)
	Download link: https://drive.google.com/drive/folders/1Ro3pEYhVdYkMmteSr8YRPFeTvb_K0lVf?usp=sharing
	Download file: Homo_sapiens.GRCh37.74.dna.fasta
		Get the corresponding index and dict files: *.fai and *.dict
	Put this in folder: demo/resources/genomes/grch37/Homo_sapiens.GRCh37.74.dna.fasta

3. GNOMAD
	Download link (use wget): 
	Genomes: https://storage.googleapis.com/gnomad-public/release/2.1.1/vcf/genomes/gnomad.genomes.r2.1.1.sites.vcf.bgz
	Exomes: https://storage.googleapis.com/gnomad-public/release/2.1.1/vcf/exomes/gnomad.exomes.r2.1.1.sites.vcf.bgz
	Put it in this folder: 
		demo/resources/gnomad/grch37/gnomad.genomes.r2.1.1.sites.vcf.bgz
		demo/resources/gnomad/grch37/gnomad.exomes.r2.1.1.sites.vcf.bgz
	Edit User config flat file CONFIG/UserConfig.txt : 
		gnomad_g=gnomad/grch37/gnomad.genomes.r2.1.1.sites.vcf.bgz
		gnomad_e=gnomad/grch37/gnomad.exomes.r2.1.1.sites.vcf.bgz

4. ExAC:
	Download Link: https://drive.google.com/drive/folders/11Ya8XfAxOYmlKZ9mN8A16IDTLHdHba_0?usp=sharing
	Download file: ExAC.r0.3.1.sites.vep.decompose.norm.prefixed_PASS-only.vcf.gz
		also the index files (*.csi and *.tbi)
	Put it in this folder as: 
		demo/resources/exac/grch37/ExAC.r0.3.1.sites.vep.decompose.norm.prefixed_PASS-only.vcf.gz
	Edit User config flat file CONFIG/UserConfig.txt : 
		exac=exac/grch37/ExAC.r0.3.1.sites.vep.decompose.norm.prefixed_PASS-only.vcf.gz
		exac_t=exac/grch37/ExAC.r0.3.1.sites.vep.decompose.norm.prefixed_PASS-only.vcf.gz

5. CADD:
	Download link (use wget):
		https://krishna.gs.washington.edu/download/CADD/v1.6/GRCh37/whole_genome_SNVs.tsv.gz
		https://krishna.gs.washington.edu/download/CADD/v1.6/GRCh37/InDels.tsv.gz
		(Also download the corresponding tabix index files as well)
	Put it in this directory: 
		demo/resources/cadd/grch37/whole_genome_SNVs.tsv.gz
		demo/resource/cadd/grch37/InDels.tsv.gz
	Edit the user config flat file CONFIG/UserConfig.txt :
		cadd_snv=cadd/grch37/whole_genome_SNVs.tsv.gz
		cadd_indel=cadd/grch37/InDels.tsv.gz

6. REVEL:
	Download link: https://drive.google.com/drive/folders/12Tl1YU5bI-By_VawTPVWHef7AXzn4LuP?usp=sharing
	Download file: new_tabbed_revel.tsv.gz
	         Also the index file: *.tbi
	Put it in this directory: demo/resources/revel/grch37/new_tabbed_revel.tsv.gz
	Edit the user config flat file CONFIG/UserConfig.txt : 
		revel=revel/grch37/new_tabbed_revel.tsv.gz

7. HGMD:
	Download link: http://www.hgmd.cf.ac.uk/ac/index.php (Require personal access login)
	Put it in this directory: demo/resources/hgmd/grch37/hgmd_pro_2019.4_hg19.vcf

	Use this command to process HGMD file inside this directory:
		$ cat hgmd_pro_2019.4_hg19.vcf | sed '/##comment=.*\"/a  ##INFO=
    
     ' | awk -v OFS="\t" '{ if(/^#/){ print }else{ print $1,$2,$3,$4,$5,$6,$7,"ID="$3";"$8 } }' | bgzip -c  > hgmd_pro_2019.4_hg19_wID.vcf.gz
		$ bcftools index -t hgmd_pro_2019.4_hg19_wID.vcf.gz
		$ bcftools index hgmd_pro_2019.4_hg19_wID.vcf.gz	

	Put it in this directory: demo/resources/hgmd/grch37/hgmd_pro_2019.4_hg19_wID.vcf.gz
	Edit the user config flat file CONFIG/UserConfig.txt :
		hgmd=hgmd/grch37/hgmd_pro_2019.4_hg19_wID.vcf.gz

8. CLINVAR:
	Download link: 
		https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh37/weekly/clinvar_20200506.vcf.gz
		https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh37/weekly/clinvar_20200506.vcf.gz.tbi
	Put it in this directory: demo/resources/clinvar/grch37/clinvar_20200506.vcf.gz
	Edit the user config flat file CONFIG/UserConfig.txt :
		clinvar=clinvar/grch37/clinvar_20200506.vcf.gz

Customized Curated Annotation sets

Default present with this distribution. Can be found in XML file with these tags:
	(1) GeneList: 
   
    
	(2) Somatic mosaicism genes: 
    
     
	(3) Imprinted genes: 
     
      
	(4) Polymorphic genes: 
      
       
	(5) HPO terms: 
       
         (6) OMIM:

Activate the conda environment

$ source activate snv

Step - 1:

1. Edit CONFIG/UserConfig.txt: 
	(a) Add the absolute path prefix for the resources directory with tag: resourceDir. 
	    An example can be seen in CONFIG/Example-UserConfig.txt file.
	(b) Manually check if datasets corresponding to other field tags are correctly downloaded and 
	    put in respective folders.
2. Create user defined XML file from input User Configuration flat file and Base-XML file

Command:

$ python createAnalysisXML.py -u 
   
     
		    	      -b 
    
      
		              -o

Example:

$ python createAnalysisXML.py -u CONFIG/UserConfig.txt 
		       	      -b CONFIG/Analysis_base_grch37.xml 
		              -o CONFIG/Analysis_user_grch37.xml

Outputs:

CONFIG/Analysis_user_grch37.xml

Step-2:

1. Put the respective vcf files in the directory. For example: demo/example/vcf/ 
2. Create manifest file in same format as shown in demo/example/example_manifest.txt
3. Assign gender to each family members (illumina or sample id). For example: demo/example/example_genders.txt
4. List of all the family ids that needed to be analyzed.
         For e.g: demo/example/manifest/example_family_analysis.txt

Step -3:

Generate all the shell scripts that can be incorporated into user specific HPC cluster network. For e.g: Slurm/PBS/LSF network.

Command:

$ python processSNV.py 	-a 
   
    
	    		-p 
    
     
	      		-m 
     
      
	     		-e 
      
       
			-w 
       
         -g 
        
          -d 
         
           -f 
          
            -s 
           
             -r

Example:

$ python processSNV.py 	-a CONFIG/Analysis_user_grch37.xml \ 
			-p 20210326 \
			-m 
   
    /demo/example/example_manifest.txt \
			-e gvcfGT \
			-w 
    
     /demo/example/ \
			-g 
     
      /demo/example/example_genders.txt \
			-d 
      
       /demo/example/exeter_samples_norm.fof \
			-f 
       
        /demo/example/manifest/example_family_analysis.txt \ -s 
        
         /demo/example/manifest/example_family.fof \ -r 
         
          /demo/example/manifest/example_family_header.txt (optional)

Outputs:

Two scripts in the directory: 
   
    /demo/example/20210326/tmp_binaries/
Launch the scripts in these 2 stages sequentially after each of them gets finished.

   (1) genotypeAndAnnotate_chr%.sh where %=1..22,X,Y and MT
	scatter the annotation and frequency filtering per chromosome for all families.
   (2) mergeAndFilter.sh:
	Merge all the chromosome and apply inheritance filtering.

Step-4

Final output of list of filtered variant is present in:

   
    /demo/example/20210326/fam_filter/
    
     /
     
      .filt_
      
       .txt

For any questions/issues/bugs please mail us at:

Ajay: [email protected]
Courtney: [email protected]

SNV calling pipeline developed explicitly to process individual or trio vcf files obtained from Illumina based pipeline (grch37/grch38).

Related tags

Overview

SNV Pipeline

Installation

Required Installation packages

Install anaconda v2.0

Conda environment commands

Data directory and datasets

Default datasets provided

Other datasets that require no entry to user-configuration file

Download link for following dataset and place them in corresponding directories as shown

Customized Curated Annotation sets

Activate the conda environment

Step - 1:

Command:

Example:

Outputs:

Step-2:

Step -3:

Command:

Example:

Outputs:

Step-4

For any questions/issues/bugs please mail us at:

Owner

East Genomics

Python utility to extract differences between two pandas dataframes.

Detailed analysis on fraud claims in insurance companies, gives you information as to why huge loss take place in insurance companies

Demonstrate a Dataflow pipeline that saves data from an API into BigQuery table

The lastest all in one bombing tool coded in python uses tbomb api

International Space Station data with Python research 🌎

In this tutorial, raster models of soil depth and soil water holding capacity for the United States will be sampled at random geographic coordinates within the state of Colorado.

Average time per match by division

Analytical view of olist e-commerce in Brazil

A real data analysis and modeling project - restaurant inspections

Predictive Modeling & Analytics on Home Equity Line of Credit

Lale is a Python library for semi-automated data science.

A multi-platform GUI for bit-based analysis, processing, and visualization

Churn prediction with PySpark

Python dataset creator to construct datasets composed of OpenFace extracted features and Shimmer3 GSR+ Sensor datas

Spectral Analysis in Python

Pizza Orders Data Pipeline Usecase Solved by SQL, Sqoop, HDFS, Hive, Airflow.

Parses data out of your Google Takeout (History, Activity, Youtube, Locations, etc...)

This mini project showcase how to build and debug Apache Spark application using Python

Get mutations in cluster by querying from LAPIS API

Meltano: ELT for the DataOps era. Meltano is open source, self-hosted, CLI-first, debuggable, and extensible.