Ricardo Silva

This study examines metabolomic diversity in Amazonian Pseudonocardia bacteria, showing that ecological interactions and local adaptation drive chemical variation rather than phylogeny.

This paper presents a low-cost bioreactor system for monitoring microbial metabolic dynamics in lignin degradation studies using two fungal species.

This paper introduces DeepSEA, a deep learning tool that functionally annotates antimicrobial resistance proteins without sequence alignment, achieving higher recall than alignment-based methods.

The paper characterizes a marine bacteria using a metabologenomics approach, linking genomic and metabolomic data to identify a new biosynthetic gene cluster and bioactive compound.

The study tests social learning in Serin birds, finding 40% could learn from conspecifics, with no sex or age influence but wing length and personality traits not linked to learning ability.

ClusterApp provides a web tool for metabolomics data analysis using PCoA, offering preprocessing, visualization, and clustering features to enhance data interpretation.

The paper introduces RegFilter, a tool that enhances metabolite detection in DDA mode by dynamically selecting precursor ions and creating custom spectral libraries.

The paper introduces NPMINE, a tool that extracts chemical and taxonomic information from natural products literature to aid in cataloging and structure-activity relationship analysis.

The paper introduces ChemWalker, a method that improves annotation propagation on molecular networks using random walks, enhancing scalability and efficiency compared to previous approaches.

The paper uses transcriptomic data from Rhodosporidium toruloides to identify novel cis-regulatory elements by predicting transcription factor binding motifs under stress and substrate conditions.

ChiMera is a pipeline that reconstructs, evaluates, and visualizes bacterial genome-scale metabolic models using efficient tools and a user-friendly interface.

NPOmix connects mass spectrometry fragmentation data to biosynthetic gene clusters using a k-nearest neighbor classifier, improving metabolite discovery and structure prediction.

The paper maps chemical modifications in an ant fungus garden as plant material passes through, revealing its role in nutrient uptake.

The paper introduces FBMN, a method for improving molecular networking by enhancing quantitative analysis and isomer distinction using feature detection and alignment tools in the GNPS platform.

MASST enables querying of public small-molecule tandem MS data to discover molecular relationships in metabolomics and natural product datasets.