inGAP-family: Accurate Detection of Meiotic Recombination Loci and Causal Mutations by Filtering Out Artificial Variants due to Genome Complexities.

To identify DNA polymorphisms accurately can bridge the gap between phenotypes and genotypes and is essential for molecular marker assisted genetic studies. Genome complexities, including large-scale structural variation, bring great challenge to bioinformatic analysis for obtaining high-confident genomic variants, as sequence difference between non-allelic loci of two or more genomes can be misinterpreted as polymorphisms. It is important to correctly filter out artificial variants to avoid false genotyping or estimation of allele frequencies. Here we present an efficient and effective framework (inGAP-family) to discover, filter and visualize DNA polymorphisms and structural variants from alignment of short reads. Applying this method on polymorphism detection on real datasets shows that elimination of artificial variants greatly facilitates the precise identification of meiotic recombination points, recognizing causal mutations in mutant genomes or QTL loci. In addition, inGAP-family provides user-friendly graphical interface for detecting polymorphisms and structural variants, further evaluating predicted variants and identifying mutations related to genotypes. It is accessible at https://sourceforge.net/projects/ingap-family/.