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

Area of Science:

• Genomics
• Bioinformatics

Background:

• Long-read DNA sequencing is vital for whole-genome studies.
• Accurate coverage estimation is essential for experimental planning.
• Current methods often lack reliable local coverage predictions.

Purpose of the Study:

• To introduce esloco, a novel simulation framework.
• To enable accurate estimation of local coverage in long-read sequencing.
• To support experimental design for various sequencing scenarios.

Main Methods:

• esloco utilizes Monte Carlo simulations.
• It models coverage based on sequencing depth and read length distribution.
• The framework handles unknown target regions and PCR-free designs.

Main Results:

• Benchmarking showed close agreement between esloco predictions and empirical data.
• The framework accurately estimates local sequencing outcomes.
• esloco demonstrated reliability across a 45-gene panel.

Conclusions:

• esloco offers a reliable solution for predicting local coverage in long-read sequencing.
• It facilitates informed experimental planning, saving time and resources.
• The framework supports diverse applications, including viral integration and base modification studies.