biostudies-literatureWeich ABundesministerium für Bildung und ForschungEuropean Union's Horizon Europe Research and InnovationUK Erlangen IZKFEuropean Union’s Horizon Europe Research and InnovationBundesministerium für Bildung und Forschung (BMBF)btag009https://www.ebi.ac.uk/biostudies/studies/S-EPMC12866673biostudies-literatureUnknown42(2)<h4>Summary</h4>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.<h4>Availability and implementation</h4>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).Bioinformatics (Oxford, England)esloco: simulation-based estimation of local coverage in long-read DNA sequencing.PMC1286667301ZX1905A26011443101057250DE IZKF-D043Lischer CWeich AVera Jfalseesloco: simulation-based estimation of local coverage in long-read DNA sequencing.<h4>Summary</h4>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.<h4>Availability and implementation</h4>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).2026-01-01T00:00:00Z2026 Jan2026-06-30T03:28:57.982Z2026-06-30T03:22:31.123ZS-EPMC128666734151228710.1093/bioinformatics/btag009