Motivation

Computing the uniqueness of k-mers for each position of a genome while allowing for up to e mismatches is computationally challenging. However, it is crucial for many biological applications such as the design of guide RNA for CRISPR experiments. More formally, the uniqueness or (k, e)-mappability can be described for every position as the reciprocal value of how often this k-mer occurs approximately in the genome, i.e., with up to e mismatches.

Results

We present a fast method GenMap to compute the (k, e)-mappability. We extend the mappability algorithm, such that it can also be computed across multiple genomes where a k-mer occurrence is only counted once per genome. This allows for the computation of marker sequences or finding candidates for probe design by identifying approximate k-mers that are unique to a genome or that are present in all genomes. GenMap supports different formats such as binary output, wig and bed files as well as csv files to export the location of all approximate k-mers for each genomic position.

Availability

GenMap can be installed via bioconda. Binaries and C ++ source code are available on https://github.com/cpockrandt/genmap.

中文翻译：

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GenMap：基因组可映射性的超快速计算。

动机

计算基因组每个位置的k聚体的唯一性，同时允许多达e个错配，这在计算上具有挑战性。然而，对于许多生物学应用来说至关重要，例如用于CRISPR实验的指导RNA的设计。更正式地说，可以将每个位置的唯一性或（k，e）映射性描述为该k-mer在基因组中大约多长时间出现一次的倒数，即具有多达e个错配。

结果

我们提出了一种快速的GenMap方法来计算（k，e）-可映射性。我们扩展了可映射性算法，使得它也可以跨多个基因组进行计算，其中每个基因组仅对k-mer发生一次进行计数。这允许计算标记序列或通过鉴定对于基因组唯一或存在于所有基因组中的近似k聚体来寻找探针设计的候选者。GenMap支持不同的格式，例如二进制输出，假发和床文件以及csv文件，以导出每个基因组位置的所有近似k-mer的位置。

可用性

可以通过bioconda安装GenMap。二进制文件和C ++源代码可在https://github.com/cpockrandt/genmap上获得。