Biomedical subjects

Alberto J M Martin

Publications and source records attributed to Alberto J M Martin.

3 recordsLinked to original sources

PubMed2026

GRNContext: an interactive web platform for contextualized gene regulatory networks visualization across human cancers.

SUMMARY: While current Gene Regulatory Network (GRN) databases provide comprehensive reference maps of potential interactions between transcription factors and target genes, they do not specify which regulatory interactions are active within specific biological contexts. This limitation is particularly critical in cancer, where transcriptional programs are inherently tissue-specific. To address this gap, we developed GRNContext, an interactive web platform designed for the visualization, exploration, and comparative analysis of gene regulatory networks contextualized across 33 cancer types from The Cancer Genome Atlas (TCGA). Our approach uses the TFLink human reference GRN as a starting point and integrates TCGA transcriptomic profiles to infer cancer-specific regulatory activity. Regulatory relevance was assessed using complementary machine learning and statistical methods, which were unified into a consensus score to prioritize and filter the most relevant candidate regulators for each target gene. By providing both curated context-specific GRNs and a user-friendly platform, GRNContext constitutes a comprehensive and accessible resource that supports mechanistic investigations, hypothesis generation, and translational research focused on transcriptional regulation in cancer. AVAILABILITY AND IMPLEMENTATION: GRNContext is supported by all major browsers and freely available on the web at https://apps.cienciavida.org/grncontext. It is implemented as a client-server web application featuring a FastAPI backend and a React frontend utilizing Cytoscape.js for interactive network visualization, all containerized via Docker for cross-platform compatibility.

Humans↗

PubMed2025

Comparative and systems analyses of Leishmania spp. non-coding RNAs through developmental stages.

Leishmania spp. is the etiological agent of leishmaniases, neglected diseases that seek to be eradicated in the coming years. The life cycle of these parasites involves different host and stress environments. In recent years, many studies have shown that several protein-coding genes are directly involved with the development and host interactions. However, little is still known about the role of non-coding RNAs (ncRNAs) in life cycle progression. In this study, we aimed to identify the genomic structure and function of ncRNAs from Leishmania spp. and to get insights into the repertoire of ncRNAs (RNAome) of this protozoan genus. We studied 26 strains corresponding to 16 different species of Leishmania. Our RNAome analysis revealed the presence of several ncRNAs that are shared among different species, allowing us to differentiate between subgenera as well as between species that are canonically related to visceral leishmaniasis. We also studied co-expression relationships between coding genes and ncRNAs which in the amastigote developmental stage for Leishmania braziliensis and Leishmania donovani revealed the presence of miRNA-like transcripts co-expressed with several coding genes involved in starvation, survival and histone modification. This work represents the first effort to characterize the Leishmania ssp. RNAome, supporting further approaches to better understand the role of ncRNAs in gene regulation, infective process, and host-parasite interaction.

RNA, Untranslated↗

PubMed2006

Distill: a suite of web servers for the prediction of one-, two- and three-dimensional structural features of proteins.

BACKGROUND: We describe Distill, a suite of servers for the prediction of protein structural features: secondary structure; relative solvent accessibility; contact density; backbone structural motifs; residue contact maps at 6, 8 and 12 Angstrom; coarse protein topology. The servers are based on large-scale ensembles of recursive neural networks and trained on large, up-to-date, non-redundant subsets of the Protein Data Bank. Together with structural feature predictions, Distill includes a server for prediction of Calpha traces for short proteins (up to 200 amino acids). RESULTS: The servers are state-of-the-art, with secondary structure predicted correctly for nearly 80% of residues (currently the top performance on EVA), 2-class solvent accessibility nearly 80% correct, and contact maps exceeding 50% precision on the top non-diagonal contacts. A preliminary implementation of the predictor of protein Calpha traces featured among the top 20 Novel Fold predictors at the last CASP6 experiment as group Distill (ID 0348). The majority of the servers, including the Calpha trace predictor, now take into account homology information from the PDB, when available, resulting in greatly improved reliability. CONCLUSION: All predictions are freely available through a simple joint web interface and the results are returned by email. In a single submission the user can send protein sequences for a total of up to 32k residues to all or a selection of the servers. Distill is accessible at the address: http://distill.ucd.ie/distill/.

Algorithms↗