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Cabas-Mora, G.

Publications and source records attributed to Cabas-Mora, G..

2 recordsLinked to original sources

nf-sarcopipe enables integrative discovery of exercise-responsive miRNAs and miRNA-mRNA regulatory networks associated with skeletal muscle adaptation

Skeletal muscle dynamically adapts to physiological stimuli such as exercise through coordinated molecular and structural remodeling processes. Circulating microRNAs (miRNAs) represent promising non-invasive biomarkers of exercise responsiveness and skeletal muscle physiological states; however, most analytical frameworks rely solely on annotated miRNAs and overlook novel candidates. Here, we present nf-sarcopipe, a modular Nextflow pipeline that integrates de novo and reference-guided miRNA discovery with transcriptomic analysis and regulatory network reconstruction. The pipeline is organized into three complementary modules: 1) Preprocessing, 2) miRNA Discovery, and 3) Target Prediction & mRNA Integration. Using publicly available datasets from active and sedentary young women, the pipeline identified reproducible miRNA signatures and prioritized a small set of structurally supported, high-confidence de novo candidates. Previously reported exercise-associated miRNAs compiled from the literature were additionally incorporated for comparative candidate evaluation. Although the available datasets were derived from different tissues, confounding-aware analyses enabled the identification of coherent transcriptional signatures associated with exercise responsiveness. Integrative miRNA-mRNA analysis uncovered consistent regulatory interactions linking circulating miRNAs--both novel and known--to pathways involved in immune response, extracellular matrix remodeling, autophagy, and skeletal muscle adaptation. Together, these results establish nf-sarcopipe as a robust and scalable framework for complementary miRNA discovery and for investigating regulatory mechanisms associated with exercise-induced skeletal muscle adaptation.

bioinformatics↗

RUDEUS, a machine learning classification system to study DNA-Binding proteins

DNA-binding proteins are essential in different biological processes, including DNA replication, transcription, packaging, and chromatin remodelling. Exploring their characteristics and functions has become relevant in diverse scientific domains. Computational biology and bioinformatics have assisted in studying DNA-binding proteins, complementing traditional molecular biology methods. While recent advances in machine learning have enabled the integration of predictive systems with bioinformatic approaches, there still needs to be generalizable pipelines for identifying unknown proteins as DNA-binding and assessing the specific type of DNA strand they recognize. In this work, we introduce RUDEUS, a Python library featuring hierarchical classification models designed to identify DNA-binding proteins and assess the specific interaction type, whether single-stranded or double-stranded. RUDEUS has a versatile pipeline capable of training predictive models, synergizing protein language models with supervised learning algorithms, and integrating Bayesian optimization strategies. The trained models have high performance, achieving a precision rate of 95% for DNA-binding identification and 89% for discerning between single-stranded and doublestranded interactions. RUDEUS includes an exploration tool for evaluating unknown protein sequences, annotating them as DNA-binding, and determining the type of DNA strand they recognize. Moreover, a structural bioinformatic pipeline has been integrated into RUDEUS for validating the identified DNA strand through DNA-protein molecular docking. These comprehensive strategies and straightforward implementation demonstrate comparable performance to high-end models and enhance usability for integration into protein engineering pipelines.

bioinformatics↗