nf-sarcopipe enables integrative discovery of exercise-responsive miRNAs and miRNA-mRNA regulatory networks associated with skeletal muscle adaptation
Poblete-Duran, N.; Gomez-Molina, F.; Cabas-Mora, G.; Di Genova-Bravo, A.; Valladares-Ide, D.; Moraga-Quinteros, C.
Show abstract
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.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- CorrAdjust unveils biologically relevant transcriptomic correlations by efficiently eliminating hidden confounders 91%
- CSsingle: A Unified Tool for Robust Decomposition of Bulk and Spatial Transcriptomic Data Across Diverse Single-Cell References 91%
- miEAA 2.0: Integrating multi-species microRNA enrichment analysis and workflow management systems 91%
Similar papers in this journal
- Repeated Disuse Atrophy Imprints a Molecular Memory in Skeletal Muscle: Transcriptional Resilience in Young Adults and Susceptibility in Aged Muscle 90%
- Distinct stress-dependent signatures of cellular and extracellular tRNA-derived small RNAs (tDRs) 89%
- Organ-specific and conserved regulatory logic orchestrates gene expression in the embryonic mesothelium 89%
Similar papers in this journal
- Identifying similar populations across independent single cell studies without data integration 91%
- Sequence-based chromatin activity modeling and regulatory impact prediction of genetic variants in farmed animals using deep learning 90%
- Kmerator Suite: design of specific k-mer signatures andautomatic metadata discovery in large RNA-Seq datasets. 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.