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Brizola Toscan, R.

Publications and source records attributed to Brizola Toscan, R..

3 recordsLinked to original sources

PHI: Prophage-Host Interaction toolkit for automated prediction and comprehensive profiling of prophages and their hosts via Galaxy

BackgroundViruses that infect bacteria, known as bacteriophages or phages, are widespread in nature and play important roles in shaping microbial communities and ecosystem functions. Some phages can integrate into bacterial genomes as "prophages", where they may influence the biology of their host by carrying genes that affect metabolism, virulence, or environmental adaptation. Despite their importance, studying prophages and their interactions with bacterial hosts remains challenging because it typically requires combining many complex computational tools and can be resource-intensive. ResultsIn this study, we introduce the Prophage-Host Interaction Toolkit (PHI), a user-friendly and automated workflow available through the Galaxy platform. PHI brings together multiple established tools into a single, reproducible pipeline that identifies candidate prophages, evaluates their quality, predicts host relationships, and characterizes key functional genes. Importantly, all results are summarized in an interactive report that simplifies interpretation. When applied to a mock community composed of 22 bacteria as a workflow demonstration, PHI detected 41 prophages across 14 hosts, classifying them into high- and medium-quality phage genomes. Host assemblies exhibited > 99 % completeness and < 1 % contamination for most genomes, while DefenseFinder revealed between 3 and 24 antiviral systems per genome. ConclusionsBy removing installation barriers and consolidating the outputs of multiple established tools, PHI lowers the barrier to advanced phage analysis, enabling both specialists and non-experts to explore phage-host interactions and their implications in areas such as microbiome research, biotechnology, and environmental science.

bioinformatics↗

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. However, 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 accessible for non-bioinformaticians that make it easy to recover genomes of prokaryotes, eukaryotes, and viruses from metagenomes, either alone or in combination. We tested MuDoGer using 24 individual-isolated genomes and 574 metagenomes, demonstrating the applicability for a few samples and high throughput. MuDoGeR is open-source software available at https://github.com/mdsufz/MuDoGeR.

bioinformatics↗

PredicTF: a tool to predict bacterial transcription factors in complex microbial communities.

Transcription Factors (TFs) are proteins that control the flow of genetic information by regulating cellular gene expression. Here we describe PredicTF, a first platform supporting the prediction and classification of novel bacterial TF in complex microbial communities. We evaluated PredicTF using a two-step approach. First, we tested PredictTFs ability to predict TFs for the genome of an environmental isolate. In the second evaluation step, PredicTF was used to predict TFs in a metagenome and 11 metatranscriptomes recovered from a community performing anaerobic ammonium oxidation (anammox) in a bioreactor. PredicTF is open source pipeline available at https://github.com/mdsufz/PredicTF.

bioinformatics↗