JWES: a new pipeline for whole genome/exome sequence data processing, management, and gene‐variant discovery, annotation, prediction, and genotyping

• Published in: FEBS open bio Vol. 11; no. 9; pp. 2441 - 2452
• Main Authors: Ahmed, Zeeshan, Renart, Eduard Gibert, Mishra, Deepshikha, Zeeshan, Saman
• Format: Journal Article
• Language: English
• Published: England John Wiley & Sons, Inc 01.09.2021; John Wiley and Sons Inc; Wiley
• Subjects: Application programming interface; Bioinformatics; bioinformatics application; Computational Biology - methods; Computer science; Data Management; Data processing; Data storage; Databases, Genetic; Exome; gene; Gene expression; Genetic diversity; Genetic Variation; Genome; Genomes; Genomics; Genomics - methods; Genotyping; Haplotypes; High performance computing; Humans; Java; Machine learning; Method; Methods; Molecular Sequence Annotation; Nucleotide sequence; Reproducibility of Results; Sequence Analysis, DNA - methods; Severe acute respiratory syndrome coronavirus 2; Single-nucleotide polymorphism; Software; variants; Visualization; whole exome; Whole Exome Sequencing; whole genome; Whole Genome Sequencing; Workflow; bioinformatics application; database; whole exome; variants; gene; whole genome
• ISSN: 2211-5463; 2211-5463
• DOI: 10.1002/2211-5463.13261

Whole genome and exome sequencing (WGS/WES) are the most popular next‐generation sequencing (NGS) methodologies and are at present often used to detect rare and common genetic variants of clinical significance. We emphasize that automated sequence data processing, management, and visualization should be an indispensable component of modern WGS and WES data analysis for sequence assembly, variant detection (SNPs, SVs), imputation, and resolution of haplotypes. In this manuscript, we present a newly developed findable, accessible, interoperable, and reusable (FAIR) bioinformatics‐genomics pipeline Java based Whole Genome/Exome Sequence Data Processing Pipeline (JWES) for efficient variant discovery and interpretation, and big data modeling and visualization. JWES is a cross‐platform, user‐friendly, product line application, that entails three modules: (a) data processing, (b) storage, and (c) visualization. The data processing module performs a series of different tasks for variant calling, the data storage module efficiently manages high‐volume gene‐variant data, and the data visualization module supports variant data interpretation with Circos graphs. The performance of JWES was tested and validated in‐house with different experiments, using Microsoft Windows, macOS Big Sur, and UNIX operating systems. JWES is an open‐source and freely available pipeline, allowing scientists to take full advantage of all the computing resources available, without requiring much computer science knowledge. We have successfully applied JWES for processing, management, and gene‐variant discovery, annotation, prediction, and genotyping of WGS and WES data to analyze variable complex disorders. In summary, we report the performance of JWES with some reproducible case studies, using open access and in‐house generated, high‐quality datasets. In this manuscript, we present JWES, a newly developed FAIR bioinformatics‐genomics pipeline for gene‐variant discovery, annotation, prediction, and genotyping, and big data modelling and visualization. JWES is an open‐source and freely available application, allowing scientists to take full advantage of all the computing resources available, without requiring much computer science knowledge.