Biology subjects

Ochkalova, S.

Publications and source records attributed to Ochkalova, S..

2 recordsLinked to original sources

bioRxiv2025

A comprehensive AMR genotype-phenotype database (CABBAGE)

Addressing the growing threat of antimicrobial resistance (AMR) requires the development of large-scale resources that link bacterial genomic data with phenotypic antimicrobial resistance profiles. Such datasets are essential for advancing genotype-based predictions of resistance to uncover novel resistance mechanisms, as well as identifying and tracking global trends. Here, we describe the development of the Comprehensive Assessment of Bacterial-Based AMR prediction from GEnotypes (CABBAGE) database, linking bacterial genomes to associated antibiotic susceptibility data and relevant metadata across WHO Bacterial Priority Pathogens, sourced from both publications and existing databases, and curated into a format that is compatible with, and extends, both NCBI and ENA formats. The resulting CABBAGE database, comprising over 170,000 unique sequenced isolates and approximately 1.7 million genome-phenotype pairs linked to extensive metadata, represents the largest database of its kind, consolidating existing AMR phenotype-genotype data into a single unified format. CABBAGE encompasses a broad range of antimicrobials, facilitating the analysis of global resistance trends as well as benchmarks of genotype-to-phenotype predictive methods, and empowering further research uses. The database is freely accessible at https://www.ebi.ac.uk/amr and is currently being integrated with the BioSample database, enabling easy access for the AMR research community.

genomics↗

bioRxiv2022

BinSPreader: refine binning results for fuller MAG reconstruction

Despite the recent advances in high-throughput sequencing, analysis of the metagenome of the whole microbial population still remains a challenge. In particular, the metagenome-assembled genomes (MAGs) are often fragmented due to interspecies repeats, uneven coverage and vastly different strain abundance. MAGs are usually constructed via a dedicated binning process that uses different features of input data in order to cluster contigs that might belong to the same species. This process has some limitations and therefore binners usually discard input contigs that are shorter than several kilobases. Therefore, binning of even simple metagenome assemblies can miss a decent fraction of contigs and resulting MAGs oftentimes do not contain important conservative sequences that might be of great interest of researcher. In this work we present BO_SCPLOWINC_SCPLOWSPO_SCPLOWREADERC_SCPLOW -- a novel binning refiner tool that exploits the assembly graph topology and other connectivity information to refine the existing binning, correct binning errors, propagate binning from longer contigs to shorter contigs and infer contigs belonging to multiple bins. Furthermore, BO_SCPLOWINC_SCPLOWSPO_SCPLOWREADERC_SCPLOW can split input reads in accordance with the resulting binning, predicting reads potentially belonging to multiple MAGs. We show that BO_SCPLOWINC_SCPLOWSPO_SCPLOWREADERC_SCPLOW could effectively complete the binning, increasing the completeness of the bins without sacrificing the purity and could predict contigs belonging to several MAGs.

bioinformatics↗