Hi-C metagenomics facilitate comparative genome analysis of bacteria and yeast from spontaneous beer and cider.

Sequence-based analysis of fermented foods and beverages' microbiomes offers insights into their impact on taste and consumer health. High-throughput metagenomics provide detailed taxonomic and functional community profiling, but bacterial and yeast genome reconstruction and mobile genetic elements tracking are to be improved. We established a pipeline for exploring fermented foods microbiomes using metagenomics coupled with chromosome conformation capture (Hi-C metagenomics). The approach was applied to analyze a collection of spontaneously fermented beers and ciders (n = 12). The Hi-C reads were used to reconstruct the metagenome-assembled genomes (MAGs) of bacteria and yeasts facilitating subsequent comparative genomic analysis, assembly scaffolding and exploration of "plasmid-bacteria" links. For a subset of beverages, yeasts were isolated and characterized phenotypically. The reconstructed Hi-C MAGs primarily belonged to the Lactobacillaceae family in beers, along with Acetobacteraceae and Enterobacteriaceae in ciders, exhibiting improved quality compared to conventional metagenomic MAGs. Comparative genomic analysis of Lactobacillaceae Hi-C MAGs revealed clustering by niche and suggested genetic determinants of survival and probiotic potential. For Pediococcus damnosus, Hi-C-based networks of contigs enabled linking bacteria with plasmids. Analyzing phylogeny and accessory genes in the context of known reference genomes offered insights into the niche specialization of beer lactobacilli. The subspecies-level diversity of cider Tatumella spp. was disentangled using a Hi-C-based graph. We obtained highly complete yeast Hi-C MAGs primarily represented by Brettanomyces and Saccharomyces, with Hi-C-facilitated chromosome-level genome assembly for the former. Utilizing Hi-C metagenomics to unravel the genomic content of individual species can provide a deeper understanding of the ecological interactions within the food microbiome, aid in bioprospecting beneficial microorganisms, improving quality control and improving innovative fermented products.

Metagenomic Data Assembly - The Way of Decoding Unknown Microorganisms.

Metagenomics is a segment of conventional microbial genomics dedicated to the sequencing and analysis of combined genomic DNA of entire environmental samples. The most critical step of the metagenomic data analysis is the reconstruction of individual genes and genomes of the microorganisms in the communities using metagenomic assemblers - computational programs that put together small fragments of sequenced DNA generated by sequencing instruments. Here, we describe the challenges of metagenomic assembly, a wide spectrum of applications in which metagenomic assemblies were used to better understand the ecology and evolution of microbial ecosystems, and present one of the most efficient microbial assemblers, SPAdes that was upgraded to become applicable for metagenomics.

BayesHammer: Bayesian clustering for error correction in single-cell sequencing.

Error correction of sequenced reads remains a difficult task, especially in single-cell sequencing projects with extremely non-uniform coverage. While existing error correction tools designed for standard (multi-cell) sequencing data usually come up short in single-cell sequencing projects, algorithms actually used for single-cell error correction have been so far very simplistic.We introduce several novel algorithms based on Hamming graphs and Bayesian subclustering in our new error correction tool BAYESHAMMER. While BAYESHAMMER was designed for single-cell sequencing, we demonstrate that it also improves on existing error correction tools for multi-cell sequencing data while working much faster on real-life datasets. We benchmark BAYESHAMMER on both k-mer counts and actual assembly results with the SPADES genome assembler.