Additional file 6 of Assessment of k-mer spectrum applicability for metagenomic dissimilarity analysis

Figure S4. Comparison of dissimilarity measures obtained by k-mer and 3 reference-based methods: BC MetaPhlAn genus, BC MetaPhlAn org and WG UniFrac. For each plot, Y-axis represents k-mer dissimilarity, X-axis - dissimilarity using one of reference-based methods. (PNG 925 kb

Additional file 5 of Assessment of k-mer spectrum applicability for metagenomic dissimilarity analysis

Figure S3. Correlation between k-mer and taxonomic composition dissimilarity matrices, as well as k-mer dissimilarity matrix computation time with varying values of k. All computations were performed on a compute node with CPU Opteron 6176 2.3 GHz (24 cores) and 64 Gb RAM. (PNG 185 kb

Additional file 1 of Assessment of k-mer spectrum applicability for metagenomic dissimilarity analysis

Supplementary tables. Supplementary Table S1. Bacterial abundances in Simulation 1 (high-diversity communities). Supplementary Table S2. Bacterial abundances in Simulation 2 (low-diversity communities). Supplementary Table S3. List of genomes in taxonomic catalog for human gut. Supplementary Table S4. Taxonomic composition for real dataset (organism level). Supplementary Table S5. Taxonomic composition for real dataset (genus level). Supplementary Table S6. Functional composition for real dataset (COG). Supplementary Table S7. Taxonomic composition for real dataset by MetaPhlAn (organism level). Supplementary Table S8. Mapped read counts and percentage of mapping on taxonomic and functional catalog and phage genome. (XLS 2693 kb

Additional file 4 of Assessment of k-mer spectrum applicability for metagenomic dissimilarity analysis

Figure S2. Comparison of pairwise dissimilarity measures obtained by k-mer and taxonomic composition for simulated for high- and low-diversity metagenomes. As seen, satisfactory correlation of k-mers with taxonomic composition can be obtained only at relatively high values of k. (PNG 233 kb

Abundance profiling of specific gene groups using precomputed gut metagenomes yields novel biological hypotheses

<div><p>The gut microbiota is essentially a multifunctional bioreactor within a human being. The exploration of its enormous metabolic potential provides insights into the mechanisms underlying microbial ecology and interactions with the host. The data obtained using “shotgun” metagenomics capture information about the whole spectrum of microbial functions. However, each new study presenting new sequencing data tends to extract only a little of the information concerning the metabolic potential and often omits specific functions. A meta-analysis of the available data with an emphasis on biomedically relevant gene groups can unveil new global trends in the gut microbiota. As a step toward the reuse of metagenomic data, we developed a method for the quantitative profiling of user-defined groups of genes in human gut metagenomes. This method is based on the quick analysis of a gene coverage matrix obtained by pre-mapping the metagenomic reads to a global gut microbial catalogue. The method was applied to profile the abundance of several gene groups related to antibiotic resistance, phages, biosynthesis clusters and carbohydrate degradation in 784 metagenomes from healthy populations worldwide and patients with inflammatory bowel diseases and obesity. We discovered country-wise functional specifics in gut resistome and virome compositions. The most distinct features of the disease microbiota were found for Crohn’s disease, followed by ulcerative colitis and obesity. Profiling of the genes belonging to crAssphage showed that its abundance varied across the world populations and was not associated with clinical status. We demonstrated temporal resilience of crAssphage and the influence of the sample preparation protocol on its detected abundance. Our approach offers a convenient method to add value to accumulated “shotgun” metagenomic data by helping researchers state and assess novel biological hypotheses.</p></div

Methods comparison table.

<p>Methods comparison table.</p

Prevalent genes encoding carbohydrate-active enzymes.

<p>Prevalent genes encoding carbohydrate-active enzymes.</p

Prevalence of the detected RiPP synthesis clusters in the metagenomes of healthy populations.

<p>The cluster names (along X axis) are indicated as in the original article [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0176154#pone.0176154.ref007" target="_blank">7</a>]. For each cluster, there are 5 colored bars indicating the prevalence of the cluster for each of the groups.</p

Distribution of the metagenomes by the abundance of the genes associated with starch degradation.

<p>On the PCA plot, for each sample, the color shows the most abundant taxon. The color legend represents the three major taxa (the <i>Bacteroidaceae</i> and <i>Prevotellaceae</i> families and the <i>Clostridiales</i> order). The latter taxon was taken at a higher level of phylogenetic hierarchy because it includes multiple starch-degrading families (i.e., <i>Eubacteriaceae</i> and <i>Ruminococcaceae</i>), which together contribute to the observed gradient without a clear single driver. The percent of total variance explained by each of the first two principal components is included in the respective axes labels.</p

Distribution of discrepancy ratio for each gene set.

<p>Distribution of discrepancy ratio for each gene set.</p