Mapping high throughput sequencing data to a reference genome is an essential step for most analysis pipelines aiming at the computational analysis of genome and transcriptome sequencing data. Breaking ties between equally well mapping locations poses a severe problem not only during the alignment phase, but also has significant impact on the results of downstream analyses. We present the multimapper resolution (MMR) tool that infers optimal mapping locations from the coverage densities of other mapped reads in vicinity.
Filtering alignments with MMR can significantly improve the performance of downstream analyses like transcript quantitation and differential testing. We illustrate that the accuracy (Spearman correlation) of transcript quantification increases by 17% when using reads of length 51. In addition, MMR decreases the alignment file sizes and this leads to a reduction of running time of the quantification tool. Our efficient implementation of the MMR algorithm is easily applicable as a post-processing step to existing alignment files in BAM format. Its complexity scales linearly with the number of alignments and requires no further inputs.
The source code is public under GPLv3 and can be downloaded from GitHub (https://github.com/ratschlab/mmr). Supplemental text and figures, comprehensive testing results and further information can be found at http://bioweb.me/mmr.
The user documentation and a description of the commandline parameters are available in file DOCUMENTATION.