Utilizing co-abundances of antimicrobial resistance genes to identify potential co-selection in the resistome.

Unlabelled: The rapid spread of antimicrobial resistance (AMR) is a threat to global health, and the nature of co-occurring antimicrobial resistance genes (ARGs) may cause collateral AMR effects once antimicrobial agents are used. Therefore, it is essential to identify which pairs of ARGs co-occur. Given the wealth of next-generation sequencing data available in public repositories, we have investigated the correlation between ARG abundances in a collection of 214,095 metagenomic data sets.

ARGprofiler-a pipeline for large-scale analysis of antimicrobial resistance genes and their flanking regions in metagenomic datasets.

Motivation: Analyzing metagenomic data can be highly valuable for understanding the function and distribution of antimicrobial resistance genes (ARGs). However, there is a need for standardized and reproducible workflows to ensure the comparability of studies, as the current options involve various tools and reference databases, each designed with a specific purpose in mind.

Results: In this work, we have created the workflow ARGprofiler to process large amounts of raw sequencing reads for studying the composition, distribution, and function of ARGs.

A curated data resource of 214K metagenomes for characterization of the global antimicrobial resistome.

The growing threat of antimicrobial resistance (AMR) calls for new epidemiological surveillance methods, as well as a deeper understanding of how antimicrobial resistance genes (ARGs) have been transmitted around the world. The large pool of sequencing data available in public repositories provides an excellent resource for monitoring the temporal and spatial dissemination of AMR in different ecological settings. However, only a limited number of research groups globally have the computational resources to analyze such data.

Global Distribution of Gene Variants in 214K Metagenomic Samples.

Since the initial discovery of a mobilized colistin resistance gene (), several other variants have been reported, some of which might have circulated a while beforehand. Publicly available metagenomic data provide an opportunity to reanalyze samples to understand the evolutionary history of recently discovered antimicrobial resistance genes (ARGs). Here, we present a large-scale metagenomic study of 442 Tbp of sequencing reads from 214,095 samples to describe the dissemination and emergence of nine gene variants ( to ).

NetSolP: predicting protein solubility in Escherichia coli using language models.

Motivation: Solubility and expression levels of proteins can be a limiting factor for large-scale studies and industrial production. By determining the solubility and expression directly from the protein sequence, the success rate of wet-lab experiments can be increased.

Results: In this study, we focus on predicting the solubility and usability for purification of proteins expressed in Escherichia coli directly from the sequence.