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ExScalibur: A High-Performance Cloud-Enabled Suite for Whole Exome Germline and Somatic Mutation Identification

Whole exome sequencing has facilitated the discovery of causal genetic variants associated with human diseases at deep coverage and low cost. In particular, the detection of somatic mutations from tumor/normal pairs has provided insights into the cancer genome. Although there is an abundance of publ...

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Autores principales: Bao, Riyue, Hernandez, Kyle, Huang, Lei, Kang, Wenjun, Bartom, Elizabeth, Onel, Kenan, Volchenboum, Samuel, Andrade, Jorge
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4535852/
https://www.ncbi.nlm.nih.gov/pubmed/26271043
http://dx.doi.org/10.1371/journal.pone.0135800
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author Bao, Riyue
Hernandez, Kyle
Huang, Lei
Kang, Wenjun
Bartom, Elizabeth
Onel, Kenan
Volchenboum, Samuel
Andrade, Jorge
author_facet Bao, Riyue
Hernandez, Kyle
Huang, Lei
Kang, Wenjun
Bartom, Elizabeth
Onel, Kenan
Volchenboum, Samuel
Andrade, Jorge
author_sort Bao, Riyue
collection PubMed
description Whole exome sequencing has facilitated the discovery of causal genetic variants associated with human diseases at deep coverage and low cost. In particular, the detection of somatic mutations from tumor/normal pairs has provided insights into the cancer genome. Although there is an abundance of publicly-available software for the detection of germline and somatic variants, concordance is generally limited among variant callers and alignment algorithms. Successful integration of variants detected by multiple methods requires in-depth knowledge of the software, access to high-performance computing resources, and advanced programming techniques. We present ExScalibur, a set of fully automated, highly scalable and modulated pipelines for whole exome data analysis. The suite integrates multiple alignment and variant calling algorithms for the accurate detection of germline and somatic mutations with close to 99% sensitivity and specificity. ExScalibur implements streamlined execution of analytical modules, real-time monitoring of pipeline progress, robust handling of errors and intuitive documentation that allows for increased reproducibility and sharing of results and workflows. It runs on local computers, high-performance computing clusters and cloud environments. In addition, we provide a data analysis report utility to facilitate visualization of the results that offers interactive exploration of quality control files, read alignment and variant calls, assisting downstream customization of potential disease-causing mutations. ExScalibur is open-source and is also available as a public image on Amazon cloud.
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spelling pubmed-45358522015-08-20 ExScalibur: A High-Performance Cloud-Enabled Suite for Whole Exome Germline and Somatic Mutation Identification Bao, Riyue Hernandez, Kyle Huang, Lei Kang, Wenjun Bartom, Elizabeth Onel, Kenan Volchenboum, Samuel Andrade, Jorge PLoS One Research Article Whole exome sequencing has facilitated the discovery of causal genetic variants associated with human diseases at deep coverage and low cost. In particular, the detection of somatic mutations from tumor/normal pairs has provided insights into the cancer genome. Although there is an abundance of publicly-available software for the detection of germline and somatic variants, concordance is generally limited among variant callers and alignment algorithms. Successful integration of variants detected by multiple methods requires in-depth knowledge of the software, access to high-performance computing resources, and advanced programming techniques. We present ExScalibur, a set of fully automated, highly scalable and modulated pipelines for whole exome data analysis. The suite integrates multiple alignment and variant calling algorithms for the accurate detection of germline and somatic mutations with close to 99% sensitivity and specificity. ExScalibur implements streamlined execution of analytical modules, real-time monitoring of pipeline progress, robust handling of errors and intuitive documentation that allows for increased reproducibility and sharing of results and workflows. It runs on local computers, high-performance computing clusters and cloud environments. In addition, we provide a data analysis report utility to facilitate visualization of the results that offers interactive exploration of quality control files, read alignment and variant calls, assisting downstream customization of potential disease-causing mutations. ExScalibur is open-source and is also available as a public image on Amazon cloud. Public Library of Science 2015-08-13 /pmc/articles/PMC4535852/ /pubmed/26271043 http://dx.doi.org/10.1371/journal.pone.0135800 Text en © 2015 Bao et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Bao, Riyue
Hernandez, Kyle
Huang, Lei
Kang, Wenjun
Bartom, Elizabeth
Onel, Kenan
Volchenboum, Samuel
Andrade, Jorge
ExScalibur: A High-Performance Cloud-Enabled Suite for Whole Exome Germline and Somatic Mutation Identification
title ExScalibur: A High-Performance Cloud-Enabled Suite for Whole Exome Germline and Somatic Mutation Identification
title_full ExScalibur: A High-Performance Cloud-Enabled Suite for Whole Exome Germline and Somatic Mutation Identification
title_fullStr ExScalibur: A High-Performance Cloud-Enabled Suite for Whole Exome Germline and Somatic Mutation Identification
title_full_unstemmed ExScalibur: A High-Performance Cloud-Enabled Suite for Whole Exome Germline and Somatic Mutation Identification
title_short ExScalibur: A High-Performance Cloud-Enabled Suite for Whole Exome Germline and Somatic Mutation Identification
title_sort exscalibur: a high-performance cloud-enabled suite for whole exome germline and somatic mutation identification
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4535852/
https://www.ncbi.nlm.nih.gov/pubmed/26271043
http://dx.doi.org/10.1371/journal.pone.0135800
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