LanceOtron: a deep learning peak caller for genome sequencing experiments

Abstract Motivation Genome sequencing experiments have revolutionized molecular biology by allowing researchers to identify important DNA-encoded elements genome wide. Regions where these elements are found appear as peaks in the analog signal of an assay’s coverage track, and despite the ease with...

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Vydáno v:Bioinformatics Ročník 38; číslo 18; s. 4255 - 4263
Hlavní autoři: Hentges, Lance D, Sergeant, Martin J, Cole, Christopher B, Downes, Damien J, Hughes, Jim R, Taylor, Stephen
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Oxford University Press 15.09.2022
Témata:
Base Sequence
Chromatin Immunoprecipitation Sequencing
Deep Learning
High-Throughput Nucleotide Sequencing - methods
Humans
Original Papers
Sequence Analysis, DNA - methods
Software
ISSN:1367-4803, 1367-4811, 1460-2059, 1367-4811
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Shrnutí:Abstract Motivation Genome sequencing experiments have revolutionized molecular biology by allowing researchers to identify important DNA-encoded elements genome wide. Regions where these elements are found appear as peaks in the analog signal of an assay’s coverage track, and despite the ease with which humans can visually categorize these patterns, the size of many genomes necessitates algorithmic implementations. Commonly used methods focus on statistical tests to classify peaks, discounting that the background signal does not completely follow any known probability distribution and reducing the information-dense peak shapes to simply maximum height. Deep learning has been shown to be highly accurate for many pattern recognition tasks, on par or even exceeding human capabilities, providing an opportunity to reimagine and improve peak calling. Results We present the peak calling framework LanceOtron, which combines deep learning for recognizing peak shape with multifaceted enrichment calculations for assessing significance. In benchmarking ATAC-seq, ChIP-seq and DNase-seq, LanceOtron outperforms long-standing, gold-standard peak callers through its improved selectivity and near-perfect sensitivity. Availability and implementation A fully featured web application is freely available from LanceOtron.molbiol.ox.ac.uk, command line interface via python is pip installable from PyPI at https://pypi.org/project/lanceotron/, and source code and benchmarking tests are available at https://github.com/LHentges/LanceOtron. Supplementary information Supplementary data are available at Bioinformatics online.
Bibliografie:ObjectType-Article-1
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ISSN:1367-4803
1367-4811
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btac525