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AMLVaran: a software approach to implement variant analysis of targeted NGS sequencing data in an oncological care setting

BACKGROUND: Next-Generation Sequencing (NGS) enables large-scale and cost-effective sequencing of genetic samples in order to detect genetic variants. After successful use in research-oriented projects, NGS is now entering clinical practice. Consequently, variant analysis is increasingly important t...

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Autores principales: Wünsch, Christian, Banck, Henrik, Müller-Tidow, Carsten, Dugas, Martin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7001226/
https://www.ncbi.nlm.nih.gov/pubmed/32019565
http://dx.doi.org/10.1186/s12920-020-0668-3
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author Wünsch, Christian
Banck, Henrik
Müller-Tidow, Carsten
Dugas, Martin
author_facet Wünsch, Christian
Banck, Henrik
Müller-Tidow, Carsten
Dugas, Martin
author_sort Wünsch, Christian
collection PubMed
description BACKGROUND: Next-Generation Sequencing (NGS) enables large-scale and cost-effective sequencing of genetic samples in order to detect genetic variants. After successful use in research-oriented projects, NGS is now entering clinical practice. Consequently, variant analysis is increasingly important to facilitate a better understanding of disease entities and prognoses. Furthermore, variant calling allows to adapt and optimize specific treatments of individual patients, and thus is an integral part of personalized medicine.However, the analysis of NGS data typically requires a number of complex bioinformatics processing steps. A flexible and reliable software that combines the variant analysis process with a simple, user-friendly interface is therefore highly desirable, but still lacking. RESULTS: With AMLVaran (AML Variant Analyzer), we present a web-based software, that covers the complete variant analysis workflow of targeted NGS samples. The software provides a generic pipeline that allows free choice of variant calling tools and a flexible language (SSDL) for filtering variant lists. AMLVaran’s interactive website presents comprehensive annotation data and includes curated information on relevant hotspot regions and driver mutations. A concise clinical report with rule-based diagnostic recommendations is generated.An AMLVaran configuration with eight variant calling tools and a complex scoring scheme, based on the somatic variant calling pipeline appreci8, was used to analyze three datasets from AML and MDS studies with 402 samples in total. Maximum sensitivity and positive predictive values were 1.0 and 0.96, respectively. The tool’s usability was found to be satisfactory by medical professionals. CONCLUSION: Coverage analysis, reproducible variant filtering and software usability are important for clinical assessment of variants. AMLVaran performs reliable NGS variant analyses and generates reports fulfilling the requirements of a clinical setting. Due to its generic design, the software can easily be adapted for use with different targeted panels for other tumor entities, or even for whole-exome data. AMLVaran has been deployed to a public web server and is distributed with Docker scripts for local use.
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spelling pubmed-70012262020-02-10 AMLVaran: a software approach to implement variant analysis of targeted NGS sequencing data in an oncological care setting Wünsch, Christian Banck, Henrik Müller-Tidow, Carsten Dugas, Martin BMC Med Genomics Software BACKGROUND: Next-Generation Sequencing (NGS) enables large-scale and cost-effective sequencing of genetic samples in order to detect genetic variants. After successful use in research-oriented projects, NGS is now entering clinical practice. Consequently, variant analysis is increasingly important to facilitate a better understanding of disease entities and prognoses. Furthermore, variant calling allows to adapt and optimize specific treatments of individual patients, and thus is an integral part of personalized medicine.However, the analysis of NGS data typically requires a number of complex bioinformatics processing steps. A flexible and reliable software that combines the variant analysis process with a simple, user-friendly interface is therefore highly desirable, but still lacking. RESULTS: With AMLVaran (AML Variant Analyzer), we present a web-based software, that covers the complete variant analysis workflow of targeted NGS samples. The software provides a generic pipeline that allows free choice of variant calling tools and a flexible language (SSDL) for filtering variant lists. AMLVaran’s interactive website presents comprehensive annotation data and includes curated information on relevant hotspot regions and driver mutations. A concise clinical report with rule-based diagnostic recommendations is generated.An AMLVaran configuration with eight variant calling tools and a complex scoring scheme, based on the somatic variant calling pipeline appreci8, was used to analyze three datasets from AML and MDS studies with 402 samples in total. Maximum sensitivity and positive predictive values were 1.0 and 0.96, respectively. The tool’s usability was found to be satisfactory by medical professionals. CONCLUSION: Coverage analysis, reproducible variant filtering and software usability are important for clinical assessment of variants. AMLVaran performs reliable NGS variant analyses and generates reports fulfilling the requirements of a clinical setting. Due to its generic design, the software can easily be adapted for use with different targeted panels for other tumor entities, or even for whole-exome data. AMLVaran has been deployed to a public web server and is distributed with Docker scripts for local use. BioMed Central 2020-02-04 /pmc/articles/PMC7001226/ /pubmed/32019565 http://dx.doi.org/10.1186/s12920-020-0668-3 Text en © The Author(s) 2020 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Software
Wünsch, Christian
Banck, Henrik
Müller-Tidow, Carsten
Dugas, Martin
AMLVaran: a software approach to implement variant analysis of targeted NGS sequencing data in an oncological care setting
title AMLVaran: a software approach to implement variant analysis of targeted NGS sequencing data in an oncological care setting
title_full AMLVaran: a software approach to implement variant analysis of targeted NGS sequencing data in an oncological care setting
title_fullStr AMLVaran: a software approach to implement variant analysis of targeted NGS sequencing data in an oncological care setting
title_full_unstemmed AMLVaran: a software approach to implement variant analysis of targeted NGS sequencing data in an oncological care setting
title_short AMLVaran: a software approach to implement variant analysis of targeted NGS sequencing data in an oncological care setting
title_sort amlvaran: a software approach to implement variant analysis of targeted ngs sequencing data in an oncological care setting
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7001226/
https://www.ncbi.nlm.nih.gov/pubmed/32019565
http://dx.doi.org/10.1186/s12920-020-0668-3
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