# MMR: A tool for Read Multi-Mapper Resolution

## TL;DR

The MMR tool improves the accuracy of genome and transcriptome sequencing data analysis by resolving ambiguous read mappings.

## Contribution

MMR introduces a novel method for resolving multi-mapped reads using coverage density to enhance downstream analysis accuracy.

## Key findings

- Using MMR increases transcript quantification accuracy by 17% for reads of length 51.
- MMR reduces alignment file sizes by over 50%, decreasing quantification tool runtime.
- The MMR algorithm is efficient, linear in complexity, and compatible with BAM files.

## Abstract

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 density of other mapped reads.

Filtering alignments with MMR can significantly improve the performance of downstream analyses like transcript quantitation and differential testing. We illustrate that the accuracy (Spear-man correlation) of transcript quantification increases by 17% when using reads of length 51. In addition, MMR decreases the alignment file sizes by more than 50% and this leads to a reduced 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.

Source code and documentation are available for download at github.com/ratschlab/mmr. Supplementary text and figures, comprehensive testing results and further information can be found at bioweb.me/mmr.

akahles@cbio.mskcc.org and raetsch@cbio.mskcc.org