DepthFinder: a tool to determine the optimal read depth for reduced-representation sequencing.

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Single nucleotide polymorphisms (SNPs) are crucial for genetic studies.
• Reduced-representation sequencing, including restriction site associated sequencing (RSAS), offers cost-effective SNP discovery and genotyping.
• Estimating optimal read depth for RSAS has historically relied on trial and error.

Purpose of the Study:

• To introduce DepthFinder, a bioinformatics tool for estimating required read counts for RSAS methods.
• To evaluate DepthFinder's performance across diverse species and technical factors.
• To assess the accuracy and utility of DepthFinder in predicting effective RSAS parameters.

Main Methods:

• Developed the DepthFinder bioinformatics tool to calculate necessary read depths for RSAS.
• Applied DepthFinder to six species (human, cattle, spruce budworm, salmon, barley, soybean) with varying biological and technical characteristics.
• Validated DepthFinder's predictions against independent RSAS experimental data.

Main Results:

• DepthFinder accurately estimated required read counts for RSAS across diverse species.
• Prediction accuracy reached nearly 94% when compared to experimental datasets.
• DepthFinder also proved effective in predicting optimal size selection intervals for RSAS.

Conclusions:

• DepthFinder provides an efficient and reliable method for determining optimal sequencing depth in RSAS.
• The tool supports cost-effective and high-throughput SNP genotyping.
• DepthFinder is a valuable resource for researchers across various scientific communities utilizing RSAS.