Please use this identifier to cite or link to this item:
http://doi.org/10.25358/openscience-6351
Authors: | Albrecht, Steffen Sprang, Maximilian Andrade, Miguel Fontaine, Jean-Fred |
Title: | seqQscorer: automated quality control of next-generation sequencing data using machine learning |
Online publication date: | 20-Sep-2021 |
Year of first publication: | 2021 |
Language: | english |
Abstract: | 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. |
DDC: | 004 Informatik 004 Data processing 570 Biowissenschaften 570 Life sciences |
Institution: | Johannes Gutenberg-Universität Mainz |
Department: | FB 10 Biologie |
Place: | Mainz |
ROR: | https://ror.org/023b0x485 |
DOI: | http://doi.org/10.25358/openscience-6351 |
Version: | Published version |
Publication type: | Zeitschriftenaufsatz |
License: | CC BY |
Information on rights of use: | https://creativecommons.org/licenses/by/4.0/ |
Journal: | Genome biology 22 |
Pages or article number: | 75 |
Publisher: | BioMed Central |
Publisher place: | London |
Issue date: | 2021 |
ISSN: | 1474-760X |
Publisher URL: | https://doi.org/10.1186/s13059-021-02294-2 |
Publisher DOI: | 10.1186/s13059-021-02294-2 |
Annotation: | Andrade, Miguel veröffentlicht unter: Andrade-Navarro, Miguel A. |
Appears in collections: | JGU-Publikationen |
Files in This Item:
File | Description | Size | Format | ||
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albrecht_steffen-seqqscorer__au-20210917111217183.pdf | 2 MB | Adobe PDF | View/Open |