esloco: simulation-based estimation of local coverage in long-read DNA sequencing.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 41512287.
- Also identified by DOI 10.1093/bioinformatics/btag009 and PMC identifier 12866673.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
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. 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).
Medical subject headings
- Software
- Sequence Analysis, DNA
- High-Throughput Nucleotide Sequencing