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

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.

GSR-DB: a manually curated and optimized taxonomical database for 16S rRNA amplicon analysis.

Amplicon-based 16S ribosomal RNA sequencing remains a widely used method to profile microbial communities, especially in low biomass samples, due to its cost-effectiveness and low-complexity approach. Reference databases are a mainstay for taxonomic assignments, which typically rely on popular databases such as SILVA, Greengenes, Genome Taxonomy Database (GTDB), or Ribosomal Database Project (RDP). However, the inconsistency of the nomenclature across databases and the presence of shortcomings in the annotation of these databases are limiting the resolution of the analysis. To overcome these limitations, we created the GSR database (Greengenes, SILVA, and RDP database), an integrated and manually curated database for bacterial and archaeal 16S amplicon taxonomy analysis. Unlike previous integration approaches, this database creation pipeline includes a taxonomy unification step to ensure consistency in taxonomical annotations. The database was validated with three mock communities, two real data sets, and a 10-fold cross-validation method and compared with existing 16S databases such as Greengenes, Greengenes 2, GTDB, ITGDB, SILVA, RDP, and MetaSquare. Results showed that the GSR database enhances taxonomical annotations of 16S sequences, outperforming current 16S databases at the species level, based on the evaluation of the mock communities. This was confirmed by the 10-fold cross-validation, except for Greengenes 2. The GSR database is available for full-length 16S sequences and the most commonly used hypervariable regions: V4, V1-V3, V3-V4, and V3-V5.IMPORTANCETaxonomic assignments of microorganisms have long been hindered by inconsistent nomenclature and annotation issues in existing databases like SILVA, Greengenes, Greengenes2, Genome Taxonomy Database, or Ribosomal Database Project. To overcome these issues, we created Greengenes-SILVA-RDP database (GSR-DB), accurate and comprehensive taxonomic annotations of 16S amplicon data. Unlike previous approaches, our innovative pipeline includes a unique taxonomy unification step, ensuring consistent and reliable annotations. Our evaluation analyses showed that GSR-DB outperforms existing databases in providing species-level resolution, especially based on mock-community analysis, making it a game-changer for microbiome studies. Moreover, GSR-DB is designed to be accessible to researchers with limited computational resources, making it a powerful tool for scientists across the board. Available for full-length 16S sequences and commonly used hypervariable regions, including V4, V1-V3, V3-V4, and V3-V5, GSR-DB is a go-to database for robust and accurate microbial taxonomy analysis.

Exploring microbial diversity and biosynthetic potential in zoo and wildlife animal microbiomes.

Understanding human, animal, and environmental microbiota is essential for advancing global health and combating antimicrobial resistance (AMR). We investigate the oral and gut microbiota of 48 animal species in captivity, comparing them to those of wildlife animals. Specifically, we characterize the microbiota composition, metabolic pathways, AMR genes, and biosynthetic gene clusters (BGCs) encoding the production of specialized metabolites. Our results reveal a high diversity of microbiota, with 585 novel species-level genome bins (SGBs) and 484 complete BGCs identified. Functional gene analysis of microbiomes shows diet-dependent variations. Furthermore, by comparing our findings to wildlife-derived microbiomes, we observe the impact of captivity on the animal microbiome, including examples of converging microbiome compositions. Importantly, our study identifies AMR genes against commonly used veterinary antibiotics, as well as resistance to vancomycin, a critical antibiotic in human medicine. These findings underscore the importance of the 'One Health' approach and the potential for zoonotic transmission of pathogenic bacteria and AMR. Overall, our study contributes to a better understanding of the complexity of the animal microbiome and highlights its BGC diversity relevant to the discovery of novel antimicrobial compounds.

Decoding the diagnostic and therapeutic potential of microbiota using pan-body pan-disease microbiomics.

The human microbiome emerges as a promising reservoir for diagnostic markers and therapeutics. Since host-associated microbiomes at various body sites differ and diseases do not occur in isolation, a comprehensive analysis strategy highlighting the full potential of microbiomes should include diverse specimen types and various diseases. To ensure robust data quality and comparability across specimen types and diseases, we employ standardized protocols to generate sequencing data from 1931 prospectively collected specimens, including from saliva, plaque, skin, throat, eye, and stool, with an average sequencing depth of 5.3 gigabases. Collected from 515 patients, these samples yield an average of 3.7 metagenomes per patient. Our results suggest significant microbial variations across diseases and specimen types, including unexpected anatomical sites. We identify 583 unexplored species-level genome bins (SGBs) of which 189 are significantly disease-associated. Of note, the existence of microbial resistance genes in one specimen was indicative of the same resistance genes in other specimens of the same patient. Annotated and previously undescribed SGBs collectively harbor 28,315 potential biosynthetic gene clusters (BGCs), with 1050 significant correlations to diseases. Our combinatorial approach identifies distinct SGBs and BGCs, emphasizing the value of pan-body pan-disease microbiomics as a source for diagnostic and therapeutic strategies.