This article is part of the supplement: Italian Society of Bioinformatics (BITS): Annual Meeting 2012
WEP: a high-performance analysis pipeline for whole-exome data
1 Dipartimento di Bioscienze, Biotecnologie e Scienze Farmacologiche, Università degli Studi di Bari, Bari, Italy
2 CASPUR, Consorzio interuniversitario per le Applicazioni di Supercalcolo per Università e Ricerca, Rome, Italy
3 Istituto di Biomembrane e Bioenergetica, Consiglio Nazionale delle Ricerche, Bari, Italy
4 Center of Excellence in Genomics (CEGBA), Bari, Italy
5 Cineca, Consorzio Interuniversitario di Supercalcolo, Bologna, Italy
6 Dipartimento di Biotecnologie ed Ematologia, Sapienza Università di Roma, Rome, Italy
BMC Bioinformatics 2013, 14(Suppl 7):S11 doi:10.1186/1471-2105-14-S7-S11Published: 22 April 2013
The advent of massively parallel sequencing technologies (Next Generation Sequencing, NGS) profoundly modified the landscape of human genetics.
In particular, Whole Exome Sequencing (WES) is the NGS branch that focuses on the exonic regions of the eukaryotic genomes; exomes are ideal to help us understanding high-penetrance allelic variation and its relationship to phenotype. A complete WES analysis involves several steps which need to be suitably designed and arranged into an efficient pipeline.
Managing a NGS analysis pipeline and its huge amount of produced data requires non trivial IT skills and computational power.
Our web resource WEP (Whole-Exome sequencing Pipeline web tool) performs a complete WES pipeline and provides easy access through interface to intermediate and final results. The WEP pipeline is composed of several steps:
1) verification of input integrity and quality checks, read trimming and filtering; 2) gapped alignment; 3) BAM conversion, sorting and indexing; 4) duplicates removal; 5) alignment optimization around insertion/deletion (indel) positions; 6) recalibration of quality scores; 7) single nucleotide and deletion/insertion polymorphism (SNP and DIP) variant calling; 8) variant annotation; 9) result storage into custom databases to allow cross-linking and intersections, statistics and much more. In order to overcome the challenge of managing large amount of data and maximize the biological information extracted from them, our tool restricts the number of final results filtering data by customizable thresholds, facilitating the identification of functionally significant variants. Default threshold values are also provided at the analysis computation completion, tuned with the most common literature work published in recent years.
Through our tool a user can perform the whole analysis without knowing the underlying hardware and software architecture, dealing with both paired and single end data. The interface provides an easy and intuitive access for data submission and a user-friendly web interface for annotated variant visualization.
Non-IT mastered users can access through WEP to the most updated and tested WES algorithms, tuned to maximize the quality of called variants while minimizing artifacts and false positives.