“Pseudoalignment” means aligning the reads on a reference based on minimal sampling of k-mers from the reads. This, coupled with de Bruijn graph representation of the reference, has become a powerful lightweight method for RNAseq analysis (Kallisto, Salmon) and now metagenomics. Readers may enjoy the following papers from two top groups working on this approach.
Rob Patro and colleagues -
Motivation: The alignment of sequencing reads to a transcriptome is a common and important step in many RNA-seq analysis tasks. When aligning RNA-seq reads directly to a transcriptome (as is common in the de novo setting or when a trusted reference annotation is available), care must be taken to report the potentially large number of multi-mapping locations per read. This can pose a substantial computational burden for existing aligners, and can considerably slow downstream analysis. Results: We introduce a novel algorithm, quasi- mappin, for mapping sequencing reads to a transcriptome. By attempting only to report the potential loci of origin of a sequencing read, and not the base-to- base alignment by which it derives from the reference, RapMap — the tool implementing this quasi-mappin algorithm — is capable of mapping sequencing reads to a target transcriptome substantially faster than existing alignment tools. The quasi-mapping algorithm itself uses several efficient data structures and takes advantage of the special structure of shared sequence prevalent in transcriptomes to rapidly provide highly-accurate mapping information. Availability: RapMap is implemented in C++11 and is available as open-source software, under GPL v3, at https://github.com/COMBINE-lab/RapMap.
Lior Pachter and colleagues -
We explore connections between metagenomic read assignment and the quantification of transcripts from RNA-Seq data. In particular, we show that the recent idea of pseudoalignment introduced in the RNA-Seq context is suitable in the metagenomics setting. When coupled with the Expectation- Maximization (EM) algorithm, reads can be assigned far more accurately and quickly than is currently possible with state of the art software.
Readers are also encouraged to read Lior Pachter’s blog post -
In response to the development of reference-based bioinformatics possibilities for metagenomics, about three years ago my student Lorian Schaeffer started looking at the suitability of RNA-Seq tools for metagenomic read assignment. Although the metagenomic and RNA-Seq assignment problems are conceptually similar and methodologically related, there are various technical issues involved in applying RNA-Seq tools in the metagenomic setting (e.g. the need to carefully account for taxonomy in the metagenomics setting). After developing the computational infrastructure to benchmark RNA-Seq programs in the metagenomic setting, she proceeded to evaluate the accuracy of eXpress, a streaming algorithm for RNA-Seq quantification. Although the quantification of eXpress was specifically designed to be suitable for large numbers of reads, the program requires read alignments to a reference transcriptome (or in Lorians experiments a genome) database. In the metagenomic setting realistic databases are huge, and she found that it took days just to map the reads. Nevertheless, her initial benchmarks revealed that eXpress was significantly more accurate than the available metagenomic read assignment tools of the time.
When Kraken (Wood and Salzberg 2014), and later CLARK (Ounit et al. 2015) were published in 2014 and 2015 respectively, we took note because by circumventing the alignment step they dramatically altered the tractability of metagenomic read assignment. In parallel, in my group, Nicolas Bray and later Pll Melsted and Harold Pimentel were developing what is now kallisto (Bray et al. 2015). Like Kraken, kallisto avoided the need for aligning reads, but with the introduction of the concept of pseudoalignment, allowed for accurate read assignments based on joint analysis of exact k-mer matches. What we showed earlier this year is that unlike nave k-mer based approaches to quantification, kallisto is as accurate as eXpress and other read alignment based quantification tools, and this observation led Lorian to immediately proceed to benchmark it on metagenomic data. The result of her work was just posted as a preprint:
Lorian Schaeffer, Harold Pimentel, Nicolas Bray, Pll Melsted and Lior Pachter, Pseudoalignment for metagenomic read assignment, arXiv 1510.07371, 2015.
With this paper we demonstrate a technology transfer from RNA-Seq bioinformatics to metagenomics, one that achieves dramatic improvements in read assignment accuracy in the metagenomics setting. The main result of her work is Table 1 in our preprint: