An adaptable Snakemake workflow which uses GATKs best practice recommendations to perform germline mutation calling starting with BAM files

Overview

Germline Mutation Calling

This Snakemake workflow follows the GATK best-practice recommandations to call small germline variants.

The pipeline requires as inputs aligned BAM files (e.g. with BWA) where the duplicates are already marked (e.g. with Picard or sambamba). It then performed Base Quality Score Recalibration and joint genotyping of multiple samples, which is automatically parallized over user defined intervals (for examples see intervals.txt) and chromosomes.

Filtering is performed using GATKs state-of-the-art Variant Quality Score Recalibration

At the end of the worklow, the Variant Effect Predictor is used to annotate the identified germline mutations.

A high level overview of the performed steps can be seen below:

DAG

As seen by the execution graph, an arbitrary number of samples/BAM files can be processed in parallel up to the joint variant calling.

Installation

Required tools:

The majority of the listed tools can be quite easily installed with conda which is recommanded.

Usage

First, modify the config_wgs.yaml and resources.yaml files. Both files contain detailed description what is expected. The config_wgs.yaml also contains links to some reference resources. Be careful, they are all specific for the GRCh37/hg19/b37 genome assembly.

After setting up all the config files and installing all tools, you can simply run:

snakemake --latency-wait 300 -j 5 --cluster "sbatch --mem={resources.mem_mb} --time {resources.runtime_min} --cpus-per-task {threads} --job-name={rule}.%j --output snakemake_cluster_submit_log/{rule}.%j.out --mail-type=FAIL"

This assumes that the cluster you are using is running SLURM. If this is not the case, you have to adjust the command after --cluster. The log information of each job will be safed in the snakemake_cluster_submit_log directory. This directory will not be created automatically.

-j specifies the number of jobs/rules should be submitted in parallel.

I recommand running this command in a detached session with tmux or screen.

Output

Below is the output of the tree command, after the workflow has finished for one patient H005-00ML. Usually you would include many patients simultaneously (>50). This is just to illustrate the created output files.

.
├── cohort
│ ├── benchmark
│ │ ├── ApplyVQSR_indel.txt
│ │ ├── ApplyVQSR_snp.txt
│ │ ├── CombineGVCFs.txt
│ │ ├── GenotypeGVCFs.txt
│ │ ├── MergeCohortVCFs.txt
│ │ ├── SelectVariants.txt
│ │ ├── VEP.txt
│ │ ├── VQSR_indel.txt
│ │ └── VQSR_snp.txt
│ ├── cohort.recalibrated.pass.vep.vcf.gz
│ ├── cohort.recalibrated.pass.vep.vcf.gz_summary.html
│ ├── cohort.recalibrated.vcf.gz
│ ├── cohort.recalibrated.vcf.gz.tbi
│ └── logs
│     ├── ApplyVQSR_indel.out
│     ├── ApplyVQSR_snp.out
│     ├── CombineGVCFs
│     ├── CombineGVCFs.1.out
│     ├── CombineGVCFs.2.out
│     ├── ...
│     ├── ...
│     ├── CombineGVCFs.Y.out
│     ├── GenotypeGVCFs.1.out
│     ├── GenotypeGVCFs.2.out
│     ├── ...
│     ├── ...
│     ├── GenotypeGVCFs.Y.out
│     ├── MakeSitesOnly.out
│     ├── MergeCohortVCFs.out
│     ├── SelectVariants.err
│     ├── VEP.out
│     ├── VQSR_indel.out
│     └── VQSR_snp.out
├── config
│ ├── config_wgs.yaml
│ └── resources.yaml
├── H005-00ML
│ ├── benchmark
│ │ ├── ApplyBQSR.txt
│ │ ├── BaseRecalibrator.txt
│ │ ├── GatherBQSRReports.txt
│ │ ├── GatherRecalBamFiles.txt
│ │ ├── HaplotypeCaller.txt
│ │ ├── IndexBam.txt
│ │ ├── MergeHaplotypeCaller.txt
│ │ └── SortBam.txt
│ ├── H005-00ML.germline.merged.g.vcf.gz
│ ├── H005-00ML.germline.merged.g.vcf.gz.tbi
│ └── logs
│     ├── ApplyBQSR
│     ├── ApplyBQSR.0000-scattered.interval_list.out
│     ├── ApplyBQSR.0001-scattered.interval_list.out
│     ├── ...
│     ├── ...
│     ├── ApplyBQSR.0049-scattered.interval_list.out
│     ├── BaseRecalibrator
│     ├── BaseRecalibrator.0000-scattered.interval_list.out
│     ├── BaseRecalibrator.0001-scattered.interval_list.out
│     ├── ...
│     ├── ...
│     ├── BaseRecalibrator.0049-scattered.interval_list.out
│     ├── GatherBQSRReports.out
│     ├── GatherRecalBamFiles.out
│     ├── HaplotypeCaller
│     ├── HaplotypeCaller.0000-scattered.interval_list.out
│     ├── HaplotypeCaller.0001-scattered.interval_list.out
│     ├── ...
│     ├── ...
│     ├── HaplotypeCaller.0049-scattered.interval_list.out
│     ├── IndexBam.out
│     ├── MergeHaplotypeCaller.out
│     └── SortBam.out
├── rules
│ ├── BaseQualityScoreRecalibration.smk
│ ├── JointGenotyping.smk
│ ├── VEP.smk
│ └── VQSR.smk
├── Snakefile
├── snakemake_cluster_submit_log
│ ├── ApplyBQSR.24720887.out
│ ├── ApplyVQSR_snp.24777265.out
│ ├── BaseRecalibrator.24710227.out
│ ├── CombineGVCFs.24772984.out
│ ├── GatherBQSRReports.24715726.out
│ ├── GatherRecalBamFiles.24722478.out
│ ├── GenotypeGVCFs.24773026.out
│ ├── HaplotypeCaller.24769848.out
│ ├── IndexBam.24768728.out
│ ├── MergeCohortVCFs.24776018.out
│ ├── MergeHaplotypeCaller.24772183.out
│ ├── SelectVariants.24777733.out
│ ├── SortBam.24768066.out
│ ├── VEP.24777739.out
│ ├── VQSR_indel.24776035.out
│ └── VQSR_snp.24776036.out

For each analyzed patient, a seperate directory gets created. Along with the patient specific gvcf file, this directory contains log files for all the processing steps that were performed for that patient (log directory) as well as benchmarks for each rule, e.g. how long the step took or how much CPU/RAM was used (benchmark directory).

The cohort directory contains the multi-sample VCF file, which gets created after performing the joint variant calling. The cohort.recalibrated.vcf.gz is the product of GATKs Variant Quality Score Recalibration. The cohort.recalibrated.pass.vep.vcf.gz is the filtered and VEP annotated version of cohort.recalibrated.vcf.gz (only variants with PASS are kept).

For most applications, the cohort.recalibrated.pass.vep.vcf.gz file, is the file you want to continue working with.

Data Analysis: Data Visualization of Airlines

Data Analysis: Data Visualization of Airlines Anderson Cruz | London-UK | Linkedin | Nowa Capital Project: Traffic Airlines Airline Reporting Carrier

Anderson Cruz 1 Feb 10, 2022
erdantic is a simple tool for drawing entity relationship diagrams (ERDs) for Python data model classes

erdantic is a simple tool for drawing entity relationship diagrams (ERDs) for Python data model classes. Diagrams are rendered using the venerable Graphviz library.

DrivenData 129 Jan 04, 2023
Cryptocurrency Centralized Exchange Visualization

This is a simple one that uses Grafina to visualize cryptocurrency from the Bitkub exchange. This service will make a request to the Bitkub API from your wallet and save the response to Postgresql. G

Popboon Mahachanawong 1 Nov 24, 2021
Piglet-shaders - PoC of custom shaders for Piglet

Piglet custom shader PoC This is a PoC for compiling Piglet fragment shaders usi

6 Mar 10, 2022
This is a super simple visualization toolbox (script) for transformer attention visualization ✌

Trans_attention_vis This is a super simple visualization toolbox (script) for transformer attention visualization ✌ 1. How to prepare your attention m

Mingyu Wang 3 Jul 09, 2022
TensorDebugger (TDB) is a visual debugger for deep learning. It extends TensorFlow with breakpoints + real-time visualization of the data flowing through the computational graph

TensorDebugger (TDB) is a visual debugger for deep learning. It extends TensorFlow (Google's Deep Learning framework) with breakpoints + real-time visualization of the data flowing through the comput

Eric Jang 1.4k Dec 15, 2022
Draw tree diagrams from indented text input

Draw tree diagrams This repository contains two very different scripts to produce hierarchical tree diagrams like this one: $ ./classtree.py collectio

Luciano Ramalho 8 Dec 14, 2022
Type-safe YAML parser and validator.

StrictYAML StrictYAML is a type-safe YAML parser that parses and validates a restricted subset of the YAML specification. Priorities: Beautiful API Re

Colm O'Connor 1.2k Jan 04, 2023
Collection of scripts for making high quality beautiful math-related posters.

Poster Collection of scripts for making high quality beautiful math-related posters. The poster can have as large printing size as 3x2 square feet wit

Nattawut Phetmak 3 Jun 09, 2022
Process dataframe in a easily way.

Popanda Written by Shengxuan Wang at OSU. Used for processing dataframe, especially for machine learning. The name is from "Po" in the movie Kung Fu P

ShawnWang 1 Dec 24, 2021
Simple addon for snapping active object to mesh ground

Snap to Ground Simple addon for snapping active object to mesh ground How to install: install the Python file as an addon use shortcut "D" in 3D view

Iyad Ahmed 12 Nov 07, 2022
📊 Extensions for Matplotlib

📊 Extensions for Matplotlib

Nico Schlömer 519 Dec 30, 2022
📊 Charts with pure python

A zero-dependency python package that prints basic charts to a Jupyter output Charts supported: Bar graphs Scatter plots Histograms 🍑 📊 👏 Examples

Max Humber 54 Oct 04, 2022
Learning Convolutional Neural Networks with Interactive Visualization.

CNN Explainer An interactive visualization system designed to help non-experts learn about Convolutional Neural Networks (CNNs) For more information,

Polo Club of Data Science 6.3k Jan 01, 2023
A simple Monte Carlo simulation using Python and matplotlib library

Monte Carlo python simulation Install linux dependencies sudo apt update sudo apt install build-essential \ software-properties-commo

Samuel Terra 2 Dec 13, 2021
The plottify package is makes matplotlib plots more legible

plottify The plottify package is makes matplotlib plots more legible. It's a thin wrapper around matplotlib that automatically adjusts font sizes, sca

Andy Jones 97 Nov 04, 2022
Extract data from ThousandEyes REST API and visualize it on your customized Grafana Dashboard.

ThousandEyes Grafana Dashboard Extract data from the ThousandEyes REST API and visualize it on your customized Grafana Dashboard. Deploy Grafana, Infl

Flo Pachinger 16 Nov 26, 2022
A dashboard built using Plotly-Dash for interactive visualization of Dex-connected individuals across the country.

Dashboard For The DexConnect Platform of Dexterity Global Working prototype submission for internship at Dexterity Global Group. Dashboard for real ti

Yashasvi Misra 2 Jun 15, 2021
Jupyter notebook and datasets from the pandas Q&A video series

Python pandas Q&A video series Read about the series, and view all of the videos on one page: Easier data analysis in Python with pandas. Jupyter Note

Kevin Markham 2k Jan 05, 2023