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Biology subjects

Chklovski, A.

Publications and source records attributed to Chklovski, A..

2 recordsLinked to original sources

Capturing global pet dog gut microbial diversity and hundreds of near-finished bacterial genomes by using long-read metagenomics in a Shanghai cohort

Pet dogs are considered part of the family, and understanding their gut microbiomes can provide insights into both animal and household health. Most comprehensive studies, however, relied on short-read sequencing, resulting in fragmented MAGs that miss mobile elements, antimicrobial-resistance genes, and ribosomal genes. Here, we applied deep long-read metagenomics (polished with short-reads) to fecal samples from 51 urban pet dogs in Shanghai, generating 2,676 MAGs--representing 320 bacterial species--, of which [~]72% achieved near-finished quality, often improving on the corresponding reference public genome. Comparisons with external datasets showed that our Shanghai-based MAG catalog is representative of pet dogs worldwide (median read mapping of >90%). Moreover, we recovered circular extrachromosomal elements, including those linked to antimicrobial resistance, which were also detected in external dog gut datasets. In conclusion, we provide a high-quality reference resource and demonstrate the power of deep long-read metagenomics to resolve microbial diversity in complex host-associated microbiomes.

microbiology↗

CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning

Advances in DNA sequencing and bioinformatics have dramatically increased the rate of recovery of microbial genomes from metagenomic data. Assessing the quality of metagenome-assembled genomes (MAGs) is a critical step prior to downstream analysis. Here, we present CheckM2, an improved method of predicting the completeness and contamination of MAGs using machine learning. We demonstrate the effectiveness of CheckM2 on synthetic and experimental data, and show that it outperforms the original version of CheckM in predicting MAG quality. CheckM2 is substantially faster than CheckM and its database can be rapidly updated with new high-quality reference genomes. We show that CheckM2 accurately predicts genome quality for MAGs from novel lineages, even those with sparse genomic representation, or reduced genome size (e.g. symbionts) such as those found in the Patescibacteria and the DPANN superphylum. CheckM2 provides accurate genome quality predictions across the microbial tree of life, giving increased confidence when inferring novel biological conclusions from MAGs.

bioinformatics↗