Biology subjects

Seemab, U.

Publications and source records attributed to Seemab, U..

4 recordsLinked to original sources

bioRxiv2026

CoTRA: an integrated R/Shiny framework for transparent bulk and single-cell RNA-seq analysis

Bulk RNA-seq and single-cell RNA-seq (scRNA-seq) are widely used to investigate gene-expression changes, but downstream analysis often requires multiple statistical,visualization, and reporting tools, creating fragmented workflows that are difficult to configure and reproduce. We developed CoTRA (Comprehensive Toolbox for RNA-seq Analysis), an open-source R/Shiny package providing independent bulk and scRNA-seq workflows within a common graphical environment. CoTRA supports quality control, differential expression, annotation, enrichment, dimensionality reduction, clustering, marker detection, cell-type annotation, differential abundance, trajectory inference, pathway activity, cell-cell communication, and reporting while exposing key analytical parameters. Compared with 14 other platforms across 49 predefined criteria, CoTRA fully supported 46 and partially supported three. Under matched inputs and parameters, CoTRA reproduced direct DESeq2, edgeR, and Seurat implementations, including identical significant bulk gene sets and scRNA-seq clustering (ARI = 1.000; NMI = 1.000). Retinal case studies recapitulated degeneration-associated transcriptional changes and demonstrated cell-type-resolved analysis. Synthetic scRNA-seq benchmarking scaled to 50,000 cells with 5.10 GB peak memory. CoTRA v1.0.0 requires R [≥] 4.4.0, has been tested on Linux, Windows, and macOS, is GPL-3 licensed, and is available at https://github.com/UmairSeemab/CoTRA, and support is provided through GitHub Issues.

bioinformatics↗

bioRxiv2025

ODDB: Ocular Disease Database for integrated analysis of ocular disease gene drug relationships

Ocular diseases such as age-related macular degeneration, glaucoma, diabetic retinopathy, and inherited retinal dystrophies are leading causes of vision loss worldwide, yet existing databases often address only limited aspects of these disorders. To fill this gap, we developed the Ocular Disease Database (ODDB), a web-based resource that integrates genes, biomarkers, variants, and drugs associated to ocular diseases. Data were systematically collected through literature mining of PubMed-indexed journals, the NCBI Gene Expression Omnibus (GEO), and drug regulatory agency datasets. Multi-omics, experimental, and clinical information were harmonized using standardized integration workflows. The database is organized according to two complementary ontologies: one based on the anatomical site of pathology (cornea, retina, optic nerve) and another on gene inheritance pattern. ODDB currently covers over 170 ocular diseases, more than 1190 genes, 2400+ variants, and 386 drugs, including both approved and investigational compounds. Each record includes detailed annotations of associated genes, variants, therapeutic targets, and mechanisms of action. The platform supports interactive querying and network-based visualization of disease-gene-drug relationships. All data was internally validated for accuracy and are compliant with FAIR principles, ensuring accessibility and interoperability. ODDB (https://www.oculardiseases.fi/) provides a comprehensive and standardized reference for exploring molecular mechanisms and therapeutic opportunities in ocular diseases.

bioinformatics↗

bioRxiv2025

Female sex is a risk factor for exacerbated lipid peroxidation and disease in murine retinitis pigmentosa

Oxidative stress is an important aspect in retinal degenerations that could be targeted in various forms of currently untreatable diseases. It is generally believed that males are more predisposed to oxidative stress than females due to their higher metabolic activity and/or lower antioxidant capacity. However, studies using mouse disease models have demonstrated that photoreceptor degeneration progresses faster in females. Sex hormones likely play a role, but the cellular mechanism behind the sex difference is unclear. In the current study, we confirmed that the accelerated disease phenotype in female rd10 and P23H retinitis pigmentosa mice coincides with sexual maturity, and further, we found that it co-occurs with increased retinal lipid peroxidation. Instead, protein oxidation and inflammatory marker levels were similar between the sexes. Retinal lipid profiling revealed higher levels of polyunsaturated fatty acid (PUFA)-containing lipids in healthy 2-month-old female mice compared to males, whereas before puberty the sex difference in retinal PUFAs was absent. Analysis of open bulk retina transcriptomic data from middle-aged humans found supplemental evidence of sex-related differences in retinal energy metabolism pathways. Besides mechanistic study directed to reveal the reasons for differential lipid peroxidation between sexes, more research needs to be directed to study sex differences in retinal metabolism and lipid composition across animal species. The current results highlight the need to consider the impact of sex differences when undertaking preclinical experiments with RP models. Significance statementThis article suggests female sex as a significant risk factor for progressive photoreceptor degenerative disease, based on experiments in two widely used retinitis pigmentosa mouse models. The disease phenotype in females accelerates markedly after sexual maturity, especially in mice carrying the autosomal dominant P23H rhodopsin mutation. This acceleration is associated with intensified retinal lipid peroxidation. Given the retinas high energy demand, continuous photoreceptor cilia renewal, and constant light exposure that generates reactive oxygen species, the susceptibility of photoreceptor membranes to oxidative damage is substantial. Our findings suggest that retinal metabolism may differ between sexes after puberty, potentially influenced by sex hormones, which could contribute to the increased vulnerability of females to photoreceptor degenerative diseases.

physiology↗

bioRxiv2020

Minimal information for Chemosensitivity assays (MICHA): A next-generation pipeline to enable the FAIRification of drug screening experiments

Chemosensitivity assays are commonly used for preclinical drug discovery and clinical trial optimization. However, data from independent assays are often discordant, largely attributed to uncharacterized variation in the experimental materials and protocols. We report here the launching of MICHA (Minimal Information for Chemosensitivity Assays), accessed via https://micha-protocol.org. Distinguished from existing efforts that are often lacking support from data integration tools, MICHA can automatically extract publicly available information to facilitate the assay annotation including: 1) compounds, 2) samples, 3) reagents, and 4) data processing methods. For example, MICHA provides an integrative web server and database to obtain compound annotation including chemical structures, targets, and disease indications. In addition, the annotation of cell line samples, assay protocols and literature references can be greatly eased by retrieving manually curated catalogues. Once the annotation is complete, MICHA can export a report that conforms to the FAIR principle (Findable, Accessible, Interoperable and Reusable) of drug screening studies. To consolidate the utility of MICHA, we provide FAIRified protocols from five major cancer drug screening studies, as well as six recently conducted COVID-19 studies. With the MICHA webserver and database, we envisage a wider adoption of a community-driven effort to improve the open access of drug sensitivity assays.

bioinformatics↗