Biology subjects

Saraiva, J. P.

Publications and source records attributed to Saraiva, J. P..

8 recordsLinked to original sources

bioRxiv2026

A new automated pipeline for whole genome shotgun sequencing analysis and hazard characterization of microbial pesticides

Microbial pesticides are increasingly important for sustainable crop protection, yet hazard analysis and risk assessment remain challenging and require specific characterization of properties relevant to both safety and biological activity. Whole-genome sequencing (WGS) can support the identification of microorganisms at high resolution and characterize potential hazards associated with infectivity, pathogenicity, antimicrobial resistance, and toxic metabolite production. However, the routine use of WGS in this context requires accessible, reproducible, and interpretable workflows. Here, we present a publicly available web-based workflow for WGS-supported hazard analysis of microbial biocontrol agents. The workflow accepts assembled genomes as well as short-read, long-read and hybrid sequencing data, and performs genome quality assessment, taxonomic assignment, genome annotation, AMR detection, mobile-element screening, pathogenicity prediction and secondary-metabolite analysis, and compiles the results into an HTML report. For bacterial agents, we implement a transparent rule-based risk-classification module integrating taxonomic identity, PathogenFinder2 predictions, CARD/RGI resistance evidence, WHO priority taxa, and medically important antimicrobial categories. Secondary-metabolite assessment combines antiSMASH with local BLAST searches and EFSA-aligned identity/coverage thresholds to support product-level interpretation of biosynthetic gene clusters. Comparison with the currently used MOpS workflow developed by EFSA demonstrates the added benefits of the proposed workflow for a guided hazard analysis of microbial pesticides. The workflow is intended as a community-accessible pre-assessment tool that complements regulatory platforms by improving transparency, reproducibility, and early identification of potential hazards in microbial biocontrol candidates, envisioned to make WGS an integral part of the risk assessment of microbial pesticides. Key pointsO_LIThe RATION-GUI provides a public, reproducible workflow for whole-genome sequencing- supported hazard characterization of bacterial and fungal microbial pesticides. C_LIO_LIThe workflow integrates genome quality, taxonomy, annotation, antimicrobial resistance, pathogenicity-related evidence, mobile elements, and secondary-metabolite potential. C_LIO_LIDomain-specific outputs are translated into structured hazard indicators, evidence summaries, and recommended follow-up actions without replacing expert regulatory judgment. C_LIO_LICase studies demonstrate how the workflow improves transparency, traceability, and interpretation of genomic evidence for microbial pesticide risk assessment. C_LI

bioinformatics↗

bioRxiv2025

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.

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bioRxiv2025

HVRLocator: A Computationally Efficient Tool for Identifying Hypervariable Regions in 16S rRNA Big Datasets

BackgroundAmplicon sequencing of the 16S rRNA gene is widely used to assess microbial diversity due to its cost-effectiveness and efficiency. However, public 16S rRNA datasets often lack standardized metadata, particularly information on the sequenced hypervariable regions or primers used, which are critical for accurate analysis and data reuse. To address this, we present the HVRLocator, a computational tool that reliably identifies sequenced hypervariable regions, enhancing metadata quality and enabling more robust large-scale microbiome studies. ResultsThe HVRLocator tool processed samples at an average rate of 0.147 per minute. Validation confirmed 100% accuracy in predicting alignment positions, correctly matching sequences to the expected primer regions based on literature. We demonstrated how to use the tool to select appropriate and comparable sequences for building a global bacterial database from V4 region amplicons of the 16S rRNA gene. Using HVRLocator, we selected 36,217 valid samples out of 45,882 runs, enabling us to identify cases where metadata incorrectly labeled sequences as targeting the V4 region. ConclusionEven when metadata is available, it can be inaccurate or misleading. HVRLocator offers a reliable and efficient method to identify the exact hypervariable sequenced region, ensuring accurate processing of large-scale 16S rRNA amplicon data. By bypassing inconsistent metadata and literature, it streamlines data curation and enhances the reliability of microbial studies, syntheses, and meta-analyses. Its use is essential for critically evaluating published data and enabling accurate and reproducible research in microbial ecology.

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bioRxiv2022

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.

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bioRxiv2022

Recovery of 447 Eukaryotic bins reveals major challenges for Eukaryote genome reconstruction from metagenomes.

An estimated 8.7 million eukaryotic species exist on our planet. However, recent tools for taxonomic classification of eukaryotes only dispose of 734 reference genomes. As most Eukaryotic genomes are yet to be sequenced, the mechanisms underlying their contribution to different ecosystem processes remain untapped. Although approaches to recover Prokaryotic genomes have become common in genome biology, few studies have tackled the recovery of Eukaryotic genomes from metagenomes. This study assessed the reconstruction of Eukaryotic genomes using 215 metagenomes from diverse environments using the EukRep pipeline. We obtained 447 eukaryotic bins from 15 classes (e.g., Saccharomycetes, Sordariomycetes, and Mamiellophyceae) and 16 orders (e.g., Mamiellales, Saccharomycetales, and Hypocreales). More than 73% of the obtained eukaryotic bins were recovered from samples whose biomes were classified as host-associated, aquatic and anthropogenic terrestrial. However, only 93 bins showed taxonomic classification to (9 unique) genera and 17 bins to (6 unique) species. A total of 193 bins contained completeness and contamination measures. Average completeness and contamination were 44.64% ({sigma}=27.41%) and 3.97% ({sigma}=6.53%), respectively. Micromonas commoda was the most frequent taxa found while Saccharomyces cerevisiae presented the highest completeness, possibly resulting from a more significant number of reference genomes. However, mapping eukaryotic bins to the chromosomes of the reference genomes suggests that completeness measures should consider both single-copy genes and chromosome coverage. Recovering eukaryotic genomes will benefit significantly from long-read sequencing, intron removal after assembly, and improved reference genomes databases.

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bioRxiv2021

OrtSuite - from genomes to prediction of microbial interactions within targeted ecosystem processes

The high complexity found in microbial communities makes the identification of microbial interactions challenging. To address this challenge, we present OrtSuite, a flexible workflow to predict putative microbial interactions based on genomic content of microbial communities and targeted to specific ecosystem processes. The pipeline is composed of three user-friendly bash commands. OrtSuite combines ortholog clustering with genome annotation strategies limited to user-defined sets of functions allowing for hypothesis-driven data analysis such as assessing microbial interactions in specific ecosystems. OrtSuite matched, on average, 96 % of experimentally verified KEGG orthologs involved in benzoate degradation in a known group of benzoate degraders. Identification of putative synergistic species interactions was evaluated using the sequenced genomes of an independent study which had previously proposed potential species interactions in benzoate degradation. OrtSuite is an easy to use workflow that allows for rapid functional annotation based on a user curated database and can easily be extended to ecosystem processes where connections between genes and reactions are known. OrtSuite is an open-source software available at https://github.com/mdsufz/OrtSuite.

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bioRxiv2021

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.

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bioRxiv2019

TerrestrialMetagenomeDB: a public repository of curated and standardized metadata for terrestrial metagenomes

Microbiome studies focused on the genetic potential of microbial communities (metagenomics) became standard within microbial ecology. MG-RAST and the Sequence Read Archive (SRA), the two main metagenome repositories, contain over 202 858 public available metagenomes and this number has increased exponentially. However, mining databases can be challenging due to misannotated, misleading and decentralized data. The main goal of TerrestrialMetagenomeDB is to make it easier for scientists to find terrestrial metagenomes of interest that could be compared with novel datasets in meta-analyses. We defined terrestrial metagenomes as those that do not belong to marine environments. Further, we curated the database using text mining to assign potential descriptive keywords that better contextualize environmental aspects of terrestrial metagenomes, such as biomes and materials. TerrestrialMetagenomeDB release 1.0 includes 15 194 terrestrial metagenomes from SRA and MG-RAST. Together, the downloadable data amounts to 68 Tbp. In total, 199 terrestrial terms were divided into 14 categories. These metagenomes span 84 countries, 31 biomes and 7 main source materials. The TerrestrialMetagenomeDB is publicly available at https://webapp.ufz.de/tmdb.

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