Ecology of prophage-like elements in Bacillus subtilis at global and local geographical scales.

Prophages constitute a substantial portion of bacterial genomes, yet their effects on hosts remain poorly understood. We examine the abundance, distribution, and activity of prophages in Bacillus subtilis using computational and laboratory analyses. Genome sequences from the NCBI database and riverbank soil isolates reveal prophages primarily related to mobile genetic elements in laboratory strains.

Unravelling a Latent Pathobiome Across Coral Reef Biotopes.

Previous studies on disease in coral reef organisms have neglected the natural distribution of potential pathogens and the genetic factors that underlie disease incidence. This study explores the intricate associations between hosts, microbial communities, putative pathogens, antibiotic resistance genes (ARGs) and virulence factors (VFs) across diverse coral reef biotopes. We observed a substantial compositional overlap of putative bacterial pathogens, VFs and ARGs across biotopes, consistent with the 'everything is everywhere, but the environment selects' hypothesis.

Sequential Anaerobic-Aerobic Treatment Enhances Sulfamethoxazole Removal: From Batch Cultures to Observations in a Large-Scale Wastewater Treatment Plant.

Sulfamethoxazole (SMX) passes through conventional wastewater treatment plants (WWTPs) mainly unaltered. Under anoxic conditions sulfate-reducing bacteria can transform SMX but the fate of the transformation products (TPs) and their prevalence in WWTPs remain unknown. Here, we report the anaerobic formation and aerobic degradation of SMX TPs.

BioPrediction-RPI: Democratizing the prediction of interaction between non-coding RNA and protein with end-to-end machine learning.

Machine Learning (ML) algorithms have been important tools for the extraction of useful knowledge from biological sequences, particularly in healthcare, agriculture, and the environment. However, the categorical and unstructured nature of these sequences requiring usually additional feature engineering steps, before an ML algorithm can be efficiently applied. The addition of these steps to the ML algorithm creates a processing pipeline, known as end-to-end ML.

BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification.

The accurate classification of non-coding RNA (ncRNA) sequences is pivotal for advanced non-coding genome annotation and analysis, a fundamental aspect of genomics that facilitates understanding of ncRNA functions and regulatory mechanisms in various biological processes. While traditional machine learning approaches have been employed for distinguishing ncRNA, these often necessitate extensive feature engineering. Recently, deep learning algorithms have provided advancements in ncRNA classification.

Genome-centric analyses of 165 metagenomes show that mobile genetic elements are crucial for the transmission of antimicrobial resistance genes to pathogens in activated sludge and wastewater.

Unlabelled: Wastewater is considered a reservoir of antimicrobial resistance genes (ARGs), where the abundant antimicrobial-resistant bacteria and mobile genetic elements facilitate horizontal gene transfer. However, the prevalence and extent of these phenomena in different taxonomic groups that inhabit wastewater are still not fully understood. Here, we determined the presence of ARGs in metagenome-assembled genomes (MAGs) and evaluated the risks of MAG-carrying ARGs in potential human pathogens.

Identification of Huge Phages from Wastewater Metagenomes.

Huge phages have genomes larger than 200 kilobases, which are particularly interesting for their genetic inventory and evolution. We screened 165 wastewater metagenomes for the presence of viral sequences. After identifying over 600 potential huge phage genomes, we reduced the dataset using manual curation by excluding viral contigs that did not contain viral protein-coding genes or consisted of concatemers of several small phage genomes.

Natural product biosynthetic potential reflects macroevolutionary diversification within a widely distributed bacterial taxon.

This is the most comprehensive study performed thus far on the biosynthetic potential within the family. Our findings reveal intertwined taxonomic and natural product biosynthesis diversification within the family. We posit that the carbohydrate, peptide, and secondary metabolism triad synergistically shaped the evolution of this keystone bacterial taxon, acting as major forces underpinning the broad host range and opportunistic-to-pathogenic behavior encompassed by species in the family.

MuDoGeR: Multi-Domain Genome recovery from metagenomes made easy.

Several computational frameworks and workflows that recover genomes from prokaryotes, eukaryotes and viruses from metagenomes exist. Yet, it is difficult for scientists with little bioinformatics experience to evaluate quality, annotate genes, dereplicate, assign taxonomy and calculate relative abundance and coverage of genomes belonging to different domains. MuDoGeR is a user-friendly tool tailored for those familiar with Unix command-line environment that makes it easy to recover genomes of prokaryotes, eukaryotes and viruses from metagenomes, either alone or in combination.

The AnimalAssociatedMetagenomeDB reveals a bias towards livestock and developed countries and blind spots in functional-potential studies of animal-associated microbiomes.

Background: Metagenomic data can shed light on animal-microbiome relationships and the functional potential of these communities. Over the past years, the generation of metagenomics data has increased exponentially, and so has the availability and reusability of data present in public repositories. However, identifying which datasets and associated metadata are available is not straightforward.

BioAutoML: automated feature engineering and metalearning to predict noncoding RNAs in bacteria.

Recent technological advances have led to an exponential expansion of biological sequence data and extraction of meaningful information through Machine Learning (ML) algorithms. This knowledge has improved the understanding of mechanisms related to several fatal diseases, e.g.