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esloco: simulation-based estimation of local coverage in long-read DNA sequencing.

Adrian Weich·Christopher Lischer·Julio Vera
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Abstract

SUMMARY: Long-read DNA sequencing is increasingly applied for whole-genome studies, yet experimental planning often lacks reliable estimates of target region coverage, leading to costly and time-consuming pilot studies and replicates. We present esloco, a Monte Carlo-based simulation framework for estimating local coverage in long-read sequencing experiments, including scenarios with unknown target regions (e.g. viral integration, CRISPR-Cas9) or PCR-free designs (e.g. base modifications). By modeling coverage as a function of sequencing depth and read length distribution, esloco enables informed predictions of local sequencing outcomes. Benchmarking across a 45-gene panel demonstrated close agreement with empirical data, underscoring the framework's reliability. AVAILABILITY AND IMPLEMENTATION: esloco is a Python package available on PyPI (https://pypi.org/project/esloco/), GitHub (https://github.com/aweich/esloco), and Zenodo (https://doi.org/10.5281/zenodo.17776161).

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Sequence Analysis, DNASoftwareHigh-Throughput Nucleotide SequencingMonte Carlo MethodComputer SimulationJournal ArticleResearch Support, Non-U.S. Gov't

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Adrian Weich, Christopher Lischer, Julio Vera. 2026-01-03. esloco: simulation-based estimation of local coverage in long-read DNA sequencing.. https://doi.org/10.1093/bioinformatics%2Fbtag009

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