Mibianto: ultra-efficient online microbiome analysis through k-mer based metagenomics

Abstract

Quantifying microbiome species and composition from metagenomic assays is often challenging due to its time-consuming nature and computational complexity. In Bioinformatics, k-mer-based approaches were long established to expedite the analysis of large sequencing data and are now widely used to annotate metagenomic data. We make use of k-mer counting techniques for efficient and accurate compositional analysis of microbiota from whole metagenome sequencing. Mibianto solves this problem by operating directly on read files, without manual preprocessing or complete data exchange. It handles diverse sequencing platforms, including short single-end, paired-end, and long read technologies. Our sketch-based workflow significantly reduces the data volume transferred from the user to the server (up to 99.59% size reduction) to subsequently perform taxonomic profiling with enhanced efficiency and privacy. Mibianto offers functionality beyond k-mer quantification; it supports advanced community composition estimation, including diversity, ordination, and differential abundance analysis. Our tool aids in the standardization of computational workflows, thus supporting reproducibility of scientific sequencing studies. It is adaptable to small- and large-scale experimental designs and offers a user-friendly interface, thus making it an invaluable tool for both clinical and research-oriented metagenomic studies. Mibianto is freely available without the need for a login at: https://www.ccb.uni-saarland.de/mibianto.

Graphical Abstract

Figure 1.

Mibianto results of the protocol comparison after minor adjustments to the visualizations downloaded from the server. (A) Assignment rate for all samples. Samples with the prefix SRR18 were sequenced with Oxford nanopore sequencing. (B) Quality control proxy computed on FracMinHash-based dissimilarities without co-embedding of our precomputed samples. (C) Number of observed species in each sample, split by DNA extraction kit. (D) Short-read sequencing samples were embedded with principal coordinate analysis on Bray-Curtis distances computed on the species level.

Figure 2.

Mibianto results of the cohort study after minor adjustments to the visualizations downloaded from the server. (A) Shannon diversity computed on species level grouped by cohort and split by timepoint. (B) Ordination analysis using non-metric multidimensional scaling on Bray-Curtis distances. (C) Abundance of F. prausnitzii_C in the different cohort groups after center log-ratio normalization.

Figure 3.

Scatter plot of normalized base-2 logarithms of k-mer counts vs. kallisto FPKM values; one point per expressed gene per sample from 10 different samples (103.415 points overall), with a trend line (dashed red) obtained by robust regression. Partial transparency was used to visualize regions of low vs. high point density. Overall, a strong correlation is visible (Pearson correlation coefficient 0.983).