Multiple comparative metagenomics using multisetk-mer counting

Abstract

Background. Large scale metagenomic projects aim to extract biodiversity knowledge between different environmental conditions. Current methods for comparing microbial communities face important limitations. Those based on taxonomical or functional assignation rely on a small subset of the sequences that can be associated to known organisms. On the other hand, de novo methods, that compare the whole sets of sequences, either do not scale up on ambitious metagenomic projects or do not provide precise and exhaustive results. Methods. These limitations motivated the development of a new de novo metagenomic comparative method, called Simka. This method computes a large collection of standard ecological distances by replacing species counts by k-mer counts. Simka scales-up today's metagenomic projects thanks to a new parallel k-mer counting strategy on multiple datasets. Results. Experiments on public Human Microbiome Project datasets demonstrate that Simka captures the essential underlying biological structure. Simka was able to compute in a few hours both qualitative and quantitative ecological distances on hundreds of metagenomic samples (690 samples, 32 billions of reads). We also demonstrate that analyzing metagenomes at the k-mer level is highly correlated with extremely precise de novo comparison techniques which rely on all-versus-all sequences alignment strategy or which are based on taxonomic profiling.

City-wide metagenomics uncover antibiotic resistance reservoirs in urban beach and sewage waters

“…First, we explored the structure of microbial communities present in our beach and sewage samples using a multiset k-mer counting approach. This strategy provides an 111 unbiased view that is not affected by taxonomic or functional assignment, conversely, it just evaluates the 112 differential abundance of unique DNA segments [13]. Figure 1A shows a clustering analysis based on this 113 methodology that shows a complete discrimination between sewage and beach samples, suggesting 114 substantial differences in the composition of communities in these environments.…”

“…First, an unbiased description of the variability among communities in sewage and beach was 289 obtained by running Simka [13] with default parameters. Second, MetaPhlan2 [14] was used to identify 290 species and to determine their relative abundances across samples.…”

Read-SpaM: assembly-free and alignment-free comparison of bacterial genomes with low sequencing coverage

“…Alignment-free methods, including Co-phylog [49], Mash [35], Simka [2], AAF [14] and Skmer [43], have been successfully applied to unassembled reads. Co-phylog estimates distances using so-called micro alignments.…”

Assembly-free and alignment-free sample identification using genome skims

“…Several assembly-free methods also exist. Co-phylog [39] makes microalignments and calculates distances to reconstruct phylogenetic trees; Mash [45] computes the Jaccard index and an evolutionary distance using the k-mers; Simka [46] computes several distance measures based on the whole k-mer content of reads. However, these methods all assume high coverage, enough to cover most of the genome with at least one read.…”