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

Overview

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:
Owner
East Genomics
Bringing together genomic medicine across the East Midlands and East of England
East Genomics
simple way to build the declarative and destributed data pipelines with python

unipipeline simple way to build the declarative and distributed data pipelines. Why you should use it Declarative strict config Scaffolding Fully type

aliaksandr-master 0 Jan 26, 2022
This is a tool for speculation of ancestral allel, calculation of sfs and drawing its bar plot.

superSFS This is a tool for speculation of ancestral allel, calculation of sfs and drawing its bar plot. It is easy-to-use and runing fast. What you s

3 Dec 16, 2022
pyETT: Python library for Eleven VR Table Tennis data

pyETT: Python library for Eleven VR Table Tennis data Documentation Documentation for pyETT is located at https://pyett.readthedocs.io/. Installation

Tharsis Souza 5 Nov 19, 2022
Functional tensors for probabilistic programming

Funsor Funsor is a tensor-like library for functions and distributions. See Functional tensors for probabilistic programming for a system description.

208 Dec 29, 2022
Flexible HDF5 saving/loading and other data science tools from the University of Chicago

deepdish Flexible HDF5 saving/loading and other data science tools from the University of Chicago. This repository also host a Deep Learning blog: htt

UChicago - Department of Computer Science 255 Dec 10, 2022
A highly efficient and modular implementation of Gaussian Processes in PyTorch

GPyTorch GPyTorch is a Gaussian process library implemented using PyTorch. GPyTorch is designed for creating scalable, flexible, and modular Gaussian

3k Jan 02, 2023
Pypeln is a simple yet powerful Python library for creating concurrent data pipelines.

Pypeln Pypeln (pronounced as "pypeline") is a simple yet powerful Python library for creating concurrent data pipelines. Main Features Simple: Pypeln

Cristian Garcia 1.4k Dec 31, 2022
Geospatial data-science analysis on reasons behind delay in Grab ride-share services

Grab x Pulis Detailed analysis done to investigate possible reasons for delay in Grab services for NUS Data Analytics Competition 2022, to be found in

Keng Hwee 6 Jun 07, 2022
Unsub is a collection analysis tool that assists libraries in analyzing their journal subscriptions.

About Unsub is a collection analysis tool that assists libraries in analyzing their journal subscriptions. The tool provides rich data and a summary g

9 Nov 16, 2022
Pyspark project that able to do joins on the spark data frames.

SPARK JOINS This project is to perform inner, all outer joins and semi joins. create_df.py: load_data.py : helps to put data into Spark data frames. d

Joshua 1 Dec 14, 2021
University Challenge 2021 With Python

University Challenge 2021 This repository contains: The TeX file of the technical write-up describing the University / HYPER Challenge 2021 under late

2 Nov 27, 2021
VHub - An API that permits uploading of vulnerability datasets and return of the serialized data

VHub - An API that permits uploading of vulnerability datasets and return of the serialized data

André Rodrigues 2 Feb 14, 2022
Modular analysis tools for neurophysiology data

Neuroanalysis Modular and interactive tools for analysis of neurophysiology data, with emphasis on patch-clamp electrophysiology. Functions for runnin

Allen Institute 5 Dec 22, 2021
Data and code accompanying the paper Politics and Virality in the Time of Twitter

Politics and Virality in the Time of Twitter Data and code accompanying the paper Politics and Virality in the Time of Twitter. In specific: the code

Cardiff NLP 3 Jul 02, 2022
pipeline for migrating lichess data into postgresql

How Long Does It Take Ordinary People To "Get Good" At Chess? TL;DR: According to 5.5 years of data from 2.3 million players and 450 million games, mo

Joseph Wong 182 Nov 11, 2022
PyNHD is a part of HyRiver software stack that is designed to aid in watershed analysis through web services.

A part of HyRiver software stack that provides access to NHD+ V2 data through NLDI and WaterData web services

Taher Chegini 23 Dec 14, 2022
Airflow ETL With EKS EFS Sagemaker

Airflow ETL With EKS EFS & Sagemaker (en desarrollo) Diagrama de la solución Imp

1 Feb 14, 2022
Picka: A Python module for data generation and randomization.

Picka: A Python module for data generation and randomization. Author: Anthony Long Version: 1.0.1 - Fixed the broken image stuff. Whoops What is Picka

Anthony 108 Nov 30, 2021
Office365 (Microsoft365) audit log analysis tool

Office365 (Microsoft365) audit log analysis tool The header describes it all WHY?? The first line of code was written long time before other colleague

Anatoly 1 Jul 27, 2022