seqQscorer: automated quality control of next-generation sequencing data using machine learning

Controlling quality of next-generation sequencing (NGS) data files is a necessary but complex task. To address this problem, we statistically characterize common NGS quality features and develop a novel quality control procedure involving tree-based and deep learning classification algorithms. Predi...

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Autores principales: Albrecht, Steffen, Sprang, Maximilian, Andrade-Navarro, Miguel A., Fontaine, Jean-Fred
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Software
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7934511/
https://www.ncbi.nlm.nih.gov/pubmed/33673854
http://dx.doi.org/10.1186/s13059-021-02294-2
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author Albrecht, Steffen
Sprang, Maximilian
Andrade-Navarro, Miguel A.
Fontaine, Jean-Fred
author_facet Albrecht, Steffen
Sprang, Maximilian
Andrade-Navarro, Miguel A.
Fontaine, Jean-Fred
author_sort Albrecht, Steffen
collection PubMed
description Controlling quality of next-generation sequencing (NGS) data files is a necessary but complex task. To address this problem, we statistically characterize common NGS quality features and develop a novel quality control procedure involving tree-based and deep learning classification algorithms. Predictive models, validated on internal and external functional genomics datasets, are to some extent generalizable to data from unseen species. The derived statistical guidelines and predictive models represent a valuable resource for users of NGS data to better understand quality issues and perform automatic quality control. Our guidelines and software are available at https://github.com/salbrec/seqQscorer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02294-2.
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spelling pubmed-79345112021-03-08 seqQscorer: automated quality control of next-generation sequencing data using machine learning Albrecht, Steffen Sprang, Maximilian Andrade-Navarro, Miguel A. Fontaine, Jean-Fred Genome Biol Software Controlling quality of next-generation sequencing (NGS) data files is a necessary but complex task. To address this problem, we statistically characterize common NGS quality features and develop a novel quality control procedure involving tree-based and deep learning classification algorithms. Predictive models, validated on internal and external functional genomics datasets, are to some extent generalizable to data from unseen species. The derived statistical guidelines and predictive models represent a valuable resource for users of NGS data to better understand quality issues and perform automatic quality control. Our guidelines and software are available at https://github.com/salbrec/seqQscorer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02294-2. BioMed Central 2021-03-05 /pmc/articles/PMC7934511/ /pubmed/33673854 http://dx.doi.org/10.1186/s13059-021-02294-2 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.
spellingShingle Software
Albrecht, Steffen
Sprang, Maximilian
Andrade-Navarro, Miguel A.
Fontaine, Jean-Fred
seqQscorer: automated quality control of next-generation sequencing data using machine learning
title seqQscorer: automated quality control of next-generation sequencing data using machine learning
title_full seqQscorer: automated quality control of next-generation sequencing data using machine learning
title_fullStr seqQscorer: automated quality control of next-generation sequencing data using machine learning
title_full_unstemmed seqQscorer: automated quality control of next-generation sequencing data using machine learning
title_short seqQscorer: automated quality control of next-generation sequencing data using machine learning
title_sort seqqscorer: automated quality control of next-generation sequencing data using machine learning
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7934511/
https://www.ncbi.nlm.nih.gov/pubmed/33673854
http://dx.doi.org/10.1186/s13059-021-02294-2
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