Comparison of mapping algorithms used in high-throughput sequencing: application to Ion Torrent data

Area of Science:

• Bioinformatics
• Genomics
• Computational Biology

Background:

• High-throughput sequencing (HTS) generates vast amounts of data, necessitating efficient read mapping.
• Selecting appropriate mapping algorithms is crucial but challenging due to the complexity of evaluating their performance.

Purpose of the Study:

• To introduce a comprehensive benchmark procedure for comparing HTS data mapping algorithms.
• To provide tools for simulating sequencing reads and evaluating mapping quality.

Main Methods:

• Developed a benchmark procedure evaluating mappers on criteria including computational resources, mapping robustness, repetitive region handling, and genetic variation accuracy.
• Introduced a novel definition for mapping correctness considering read start/end positions and sequence variations (indels, substitutions).
• Created CuReSim, a customizable read simulator, and CuReSimEval for mapping quality assessment.

Main Results:

• Evaluated 14 mapping algorithms using whole genome sequencing data from Ion Torrent technology for small genomes.
• Demonstrated the benchmark procedure's effectiveness in comparing mapper performance across various criteria.
• Validated the utility of CuReSim and CuReSimEval in generating and assessing benchmark data.

Conclusions:

• The presented benchmark procedure offers a standardized method for HTS mapper evaluation.
• The tools and methodology aid in selecting optimal mappers based on specific applications, research questions, and sequencing platforms.
• This approach is valuable for assessing current mappers, developing new ones, and optimizing mapper parameters.