EXOME-seek 🔬 
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GATK4 Whole Exome-sequencing Pipeline. This is the home of the pipeline, exome-seek. Its long-term goals: to accurately call germline and somatic variants, to infer CNVs, and to boldly annotate variants like no pipeline before!
Overview¶
Welcome to exome-seek's documentation! This guide is the main source of documentation for users that are getting started with the GATK4 WES pipeline.
The ./exome-seek pipeline is composed several inter-related sub commands to setup and run the pipeline across different systems. Each of the available sub commands perform different functions:
exome-seek run: Run the GATK4 WES pipeline with your input files.exome-seek unlock: Unlocks a previous runs output directory.exome-seek cache: Cache remote resources locally, coming soon!
EXOME-seek is a comprehensive whole exome-sequencing pipeline following the Broad's set of best practices. It relies on technologies like Singularity1 to maintain the highest-level of reproducibility. The pipeline consists of a series of data processing and quality-control steps orchestrated by Snakemake2, a flexible and scalable workflow management system, to submit jobs to a cluster or cloud provider.
The pipeline is compatible with data generated from Illumina short-read sequencing technologies. As input, it accepts a set of FastQ or BAM files and can be run locally on a compute instance, on-premise using a cluster, or on the cloud (feature coming soon!). A user can define the method or mode of execution. The pipeline can submit jobs to a cluster using a job scheduler like SLURM, or run on AWS using Tibanna (feature coming soon!). A hybrid approach ensures the pipeline is accessible to all users.
Before getting started, we highly recommend reading through the usage section of each available sub command.
For more information about issues or trouble-shooting a problem, please checkout our FAQ prior to opening an issue on Github.
Contribute¶
This site is a living document, created for and by members like you. EXOME-seek is maintained by the members of CCBR and is improved by continous feedback! We encourage you to contribute new content and make improvements to existing content via pull request to our GitHub repository .
References¶
1. Kurtzer GM, Sochat V, Bauer MW (2017). Singularity: Scientific containers for mobility of compute. PLoS ONE 12(5): e0177459.
2. Koster, J. and S. Rahmann (2018). "Snakemake-a scalable bioinformatics workflow engine." Bioinformatics 34(20): 3600.