UC Berkeley

The first step in many metagenomic and metatranscriptomic analysis workflows is assigning high-throughput sequencing reads to specific strains or transcripts, providing the basis for identification and later quantification. However, the high degree of similarity between the sequences of many strains and genes makes it difficult to assign reads at the lowest level of taxonomy, and reads are typically assigned to more general taxonomic levels where they are unambiguous. Recent developments in RNA-Seq analysis have found direct-match k-mer based methods to be extremely accurate and fast when comparing sequenced RNA-Seq reads to transcriptomes. While similar methods have been used in metagenomics before now, none are highly accurate at distinguishing similar strains, and none have been applied to metatranscriptomic data. We explore connections between metagenomic and metatranscriptomic read assignment and the quantification of transcripts from RNA-Seq data to develop novel methods for rapid and accurate quantification of microbiome strains and transcripts.

We find that the recent idea of pseudoalignment introduced in the RNA-Seq context is highly applicable in the metagenomics and metatranscriptomics settings as well. 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, making it possible and practical for the first time to analyze abundances of individual genomes in metagenomics and metatranscriptomics projects.