{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Weich A"],"funding":["Bundesministerium für Bildung und Forschung","European Union's Horizon Europe Research and Innovation","UK Erlangen IZKF","European Union’s Horizon Europe Research and Innovation","Bundesministerium für Bildung und Forschung (BMBF)"],"pagination":["btag009"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12866673"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["42(2)"],"pubmed_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)."],"journal":["Bioinformatics (Oxford, England)"],"pubmed_title":["esloco: simulation-based estimation of local coverage in long-read DNA sequencing."],"pmcid":["PMC12866673"],"funding_grant_id":["01ZX1905A","26011443","101057250","DE IZKF-D043"],"pubmed_authors":["Lischer C","Weich A","Vera J"],"additional_accession":[]},"is_claimable":false,"name":"esloco: simulation-based estimation of local coverage in long-read DNA sequencing.","description":"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).","dates":{"release":"2026-01-01T00:00:00Z","publication":"2026 Jan","modification":"2026-06-30T03:28:57.982Z","creation":"2026-06-30T03:22:31.123Z"},"accession":"S-EPMC12866673","cross_references":{"pubmed":["41512287"],"doi":["10.1093/bioinformatics/btag009"]}}