Suitability of Different Mapping Algorithms for Genome-Wide Polymorphism Scans with Pool-Seq Data.

Area of Science:

• Genomics
• Bioinformatics
• Population Genetics

Background:

• Pool-Seq is a cost-effective method for genetic research, widely used for complex traits and cancer evolution.
• Previous studies addressed Pool-Seq errors from sequencing technology, library prep, and mapping parameters.
• The influence of mapping tools on Pool-Seq accuracy remained unevaluated.

Purpose of the Study:

• To evaluate the impact of different mapping tools on Pool-Seq data accuracy.
• To identify mapping algorithms suitable for Pool-Seq data analysis.
• To develop strategies for mitigating mapping-induced errors in Pool-Seq.

Main Methods:

• Utilized simulated and real Pool-Seq data for evaluation.
• Assessed the performance of 14 different mapping algorithms.
• Compared mapping results across varying read lengths and insert sizes.

Main Results:

• Mapping tools have a substantial impact on Pool-Seq data, introducing false positives in genome-wide scans.
• False positives were more pronounced when comparing data with different read lengths and insert sizes.
• Novoalign, BWA-MEM, and CLC4 were identified as the most suitable mapping algorithms.

Conclusions:

• No single mapping algorithm can completely avoid false positives in Pool-Seq data.
• Intersecting results from two mapping algorithms is an effective strategy to eliminate false positives.
• A consistent Pool-Seq bioinformatics pipeline, based on study recommendations, enhances result reliability, especially for inter-protocol comparisons.