Nevin Manimala Statistics

LanceOtron: a deep learning peak caller for genome sequencing experiments

Bioinformatics. 2022 Jul 22:btac525. doi: 10.1093/bioinformatics/btac525. Online ahead of print.


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 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: A fully featured web application is freely available from, command line interface via python is pip installable from PyPI at, and source code and benchmarking tests available at

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:35866989 | DOI:10.1093/bioinformatics/btac525

By Nevin Manimala

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