Using false discovery rates to benchmark SNP-callers in next-generation sequencing projects

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Sequence alignments are fundamental to comparative and population genomics.
• Current alignment tools have variable accuracy based on sequence divergence and methods.
• A standardized method for assessing post-resequencing alignment accuracy is lacking.

Purpose of the Study:

• To present a framework and tool for evaluating the accuracy of read datasets, alignment strategies, and SNP-calling methods.
• To provide a method for determining homozygous and heterozygous positions using binomial probabilities.
• To benchmark the developed method against existing SNP callers.

Main Methods:

• Developed a framework to assess the accuracy of read datasets and alignment strategies when a reference sequence is available.
• Implemented a tool for comparing False Discovery Rates (FDR).
• Utilized binomial probabilities to determine homozygous and heterozygous positions based on an expected error rate.

Main Results:

• The developed method achieved a high level of accuracy when benchmarked against other SNP callers.
• Demonstrated the utility of the FDR method across three fungal genomes.
• Provided a tool for assessing the accuracy of SNP-calling pipelines.

Conclusions:

• The presented framework and tool offer a standardized approach to assess genomic data and alignment accuracy.
• The method effectively determines SNP positions and False Discovery Rates.
• These tools enhance the reliability of comparative and population genomic studies.