RapMap - Rob Patro's Reimplements Kallisto's Pseudoalignment Code

RapMap - Rob Patro's Reimplements Kallisto's Pseudoalignment Code


If software license is the only thing that stops you from using wonderful Kallisto algorithm/program, maybe this github code can help. As another advantage, it comes with GPL license (could be BSD if not for Jellyfish dependence) and you can build your code on top of it by using RapMap as a module. Pseudoalignment is a powerful lightweight concept and we can expect more applications to use this module.

What is RapMap?

RapMap is a testing ground for ideas in lightweight / pseudo / quasi transcriptome alignment. That means that, at this point, it is very experimental and there are no guarantees on stability / compatibility between commits. Eventually, I hope that RapMap will become a stand-alone lightweight / pseudo / quasi-aligner that can be used with other tools.

Lightweight / pseudo / quasi-alignment is the term I’m using here for the type of information required for certain tasks (e.g. transcript quantification) that is less “heavyweight” than what is provided by traditional alignment. For example, one may only need to know the transcripts / contigs to which a read aligns and, perhaps, the position within those transcripts rather than the optimal alignment and base-for-base CIGAR string that aligns the read and substring of the transcript.

There are a number of different ways to collect such information, and the idea of RapMap (as the repository grows) will be to explore multiple different strategies in how to most rapidly determine all feasible / compatible locations for a read within the transcriptome. In this sense, it is like an all-mapper; the alignments it outputs are intended to be (eventually) disambiguated (Really, it’s more like an “all-best” mapper, since it returns all hits in the top “stratum” of lightweight/pseudo/quasi alignments). If there is a need for it, best-mapper functionality may be added in the future.

How fast is RapMap?

It’s currently too early in development for a comprehensive benchmark suite, but, on a synthetic test dataset comprised of 75 million 76bp paired-end reads, mapping to a human transcriptome with ~213,000 transcripts, RapMap takes ~ 10 minutes to align all of the reads on a single core (on an Intel Xeon E5-2690 @ 3.00 GHz) — if you actually want to write out the alignments — it depends on you disk speed, but for us it’s ~15 minutes. Again, these


Written by M. //

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