CARE 2.0: reducing false-positive sequencing error corrections using machine learning

Background Next-generation sequencing pipelines often perform error correction as a preprocessing step to obtain cleaned input data. State-of-the-art error correction programs are able to reliably detect and correct the majority of sequencing errors. However, they also introduce new errors by making...

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Veröffentlicht in:	BMC bioinformatics Jg. 23; H. 1; S. 227 - 17
Hauptverfasser:	Kallenborn, Felix, Cascitti, Julian, Schmidt, Bertil
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 13.06.2022 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Algorithms Analysis Assembly Bioinformatics Biomedical and Life Sciences Candidates Computational Biology/Bioinformatics Computer Appl. in Life Sciences Construction Context Datasets Decision trees DNA sequencing Error correction Error correction & detection False positive reactions High-Throughput Nucleotide Sequencing - methods Humans Impact analysis Learning algorithms Life Sciences Machine Learning Methods Microarrays Next-generation sequencing Nucleotide sequence Nucleotide sequencing Pipelining (computers) Prevention Sequence Alignment Sequence Analysis, DNA - methods Software Statistical analysis Germany Next-generation sequencing Machine learning Error correction
ISSN:	1471-2105, 1471-2105
Online-Zugang:	Volltext
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