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Journal Article

GenMap: Ultra-fast Computation of Genome Mappability

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Pockrandt,  Christopher M.
Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Reinert,  Knut
Efficient Algorithms for Omics Data (Knut Reinert), Max Planck Fellow Group, Max Planck Institute for Molecular Genetics, Max Planck Society;

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Pockrandt_2020.pdf
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Citation

Pockrandt, C. M., Alzamel, M., Iliopoulos, C. S., & Reinert, K. (2020). GenMap: Ultra-fast Computation of Genome Mappability. Bioinformatics, btaa222. doi:10.1093/bioinformatics/btaa222.


Cite as: http://hdl.handle.net/21.11116/0000-0006-4E29-6
Abstract
Motivation:Computing the uniqueness ofk-mers for each position of a genome while allowing for up toemismatches is computationally challenging. However, it is crucial for many biological applications suchas the design of guide RNA for CRISPR experiments. More formally, the uniqueness or(k, e)-mappabilitycan be described for every position as the reciprocal value of how often thisk-mer occurs approximatelyin the genome, i.e., with up toemismatches.Results:We present a fast method GenMap to compute the(k, e)-mappability. We extend the mappabilityalgorithm, such that it can also be computed across multiple genomes where ak-mer occurrence is onlycounted once per genome. This allows for the computation of marker sequences or finding candidatesfor probe design by identifying approximatek-mers that are unique to a genome or that are present in allgenomes. GenMap supports different formats such as binary output, wig and bed files as well as csv filesto export the location of all approximatek-mers for each genomic position.Availability:GenMap can be installed via bioconda. Binaries and C++ source code are available onhttps://github.com/cpockrandt/genmap.