Biology subjects

Cano-Muniz, S.

Publications and source records attributed to Cano-Muniz, S..

3 recordsLinked to original sources

bioRxiv2026

WGCNA+: AI-powered WGCNA for Integration of Multi-Omics Data

Background: Weighted Gene Co-expression Network Analysis (WGCNA) is a widely adopted systems biology method to discover gene modules and module-trait associations, mostly from transcriptomics. Designed for a single layer, it cannot jointly analyze multi-omics layers, a consequential limitation in modern biomedical research. WGCNA modules are often hard to interpret, requiring vast follow-up for contextualization. Moreover, no integrated framework exists to visualize condition-specific, cross-omics relationships at module or feature level. Results: To address these limitations, we developed WGCNA+, a novel R package extending WGCNA to multi-omics. WGCNA+ offers key innovations: (i) a unified multi-omics pipeline for per-layer network inference and cross-layer module enrichment; (ii) SVD-accelerated topological overlap matrix calculation that greatly reduces computation time; (iii) a consensus framework identifying modules reproducible across independent datasets/conditions; (iv) LASAGNA, a companion R package for phenotype-conditioned, multi-partite graph visualization of cross-omics relationships; (v) AI-powered annotation and infographics offering immediate biological insight. We tested WGCNA+ across public transcriptomics, proteomics, and miRNA datasets. WGCNA+ detects biologically meaningful modules, cross-omics feature and phenotype correlations, and provides AI-powered interpretation that accelerates research. Conclusions: WGCNA+ addresses existing gaps with a principled, efficient framework for co-expression network analysis across omics. It detects cross-omics regulatory modules and their phenotype association to support basic research, biomarker discovery and pathway analysis. It uniquely offers AI-assisted interpretation and infographics, aiding hypothesis generation. Complementing WGCNA+, LASAGNA is a phenotype-aware multi-partite visualization framework to explore cross-omics relationships. Altogether, these features make WGCNA+ an innovative, powerful tool for clinical and translational research. Availability and implementation: WGCNA+ and LASAGNA are implemented in R language for statistical computing, version[≥]3.5. WGCNA+ and LASAGNA are fully and freely available with no restrictions (https://github.com/bigomics/WGCNAplus; https://github.com/bigomics/lasagna)

bioinformatics↗

bioRxiv2026

BaSiCPy: Scalable and Robust Shading Correction for Optical Microscopy Images

Quantitative fluorescence microscopy is frequently confounded by spatially varying illumination and temporal intensity drift. Although BaSiC is a widely adopted retrospective correction method, it can fail when foreground content is strongly correlated across images--a common regime in time-lapse, tiled and volumetric acquisitions--and its application often requires manual parameter tuning that limits reproducibility and scalability. We introduce BaSiCPy, a foreground-aware implementation of BaSiC that improves illumination profile estimation under correlated foreground structures, provides automatic hyperparameter selection and accelerates large-scale processing through GPU support. BaSiCPy is distributed as an open-source Python package with graphical and programmatic interfaces, facilitating integration into contemporary bioimage analysis workflows.

bioengineering↗

bioRxiv2018

Indole Pulse Signalling Regulates the Cytoplasmic pH of E. coli in a Memory-Like Manner

Bacterial cells are critically dependent upon pH regulation. Most proteins function over a limited pH range and the pH gradient across the bacterial cell membrane is central to energy production and transduction1. Here we demonstrate that indole plays a critical role in the regulation of the cytoplasmic pH of E. coli. Indole is an aromatic molecule with diverse signalling roles that in bacteria is produced from tryptophan by the enzyme tryptophanase (TnaA)2. Two modes of indole signalling have been described: persistent and pulse signalling. The latter is illustrated by the brief but intense elevation of intracellular indole during stationary phase entry3,4. We show that E. coli cells growing under conditions where no indole is produced maintain their cytoplasmic pH at 7.8 {+/-} 0.2. In contrast, under conditions permitting indole production, pH is maintained at 7.2 {+/-} 0.2. Experiments where indole was added experimentally to non-producing cultures showed that pH regulation results from pulse, rather than persistent, indole signalling. Furthermore, the application of an artificial pulse of either of two non-biological proton ionophores (DNP or CCCP) caused a similar effect, suggesting that the relevant property of indole in this context is its ability to conduct protons across the cytoplasmic membrane5. Additionally, we show that the effect of the indole pulse that occurs normally during stationary phase entry in rich medium remains as a \"memory\" to maintain the correct cytoplasmic pH until entry into the next stationary phase. The indole-mediated reduction in cytoplasmic pH may explain why indole provides E. coli with a degree of protection against stresses, including some bactericidal antibiotics.

microbiology↗