bioRxiv Science⌕ Search

Biology subjects

Kota, K. P.

Publications and source records attributed to Kota, K. P..

2 recordsLinked to original sources

Re-analysis of Transcriptomic and Proteomic Data Using Multi-Omics Approaches Identifies Biomarkers of Diabetes-Associated Complications in an INS Mutant Pig Model

Mutant Insulin Induced Diabetes of Youth (MIDY) is an established porcine model caused by the INSC94Y mutation, which results in misfolded insulin, leading to severe {beta}-cell loss and hyperglycemia. Understanding disease pathophysiology is critical for identifying biomarkers and therapeutic targets, and animal models play a key role in this process. In this study, we re-analyzed published transcriptomic and proteomic data from the MIDY model using advanced multi-omics approaches and our in-house SurfacOmics tool. This integrative analysis identified ADAMTS17 as a novel biomarker, suggesting a potential association in diabetes-associated immune dysfunction and delayed wound healing through ECM-immune interplay.

bioinformatics↗

SurfacOmics: an R shiny application integrating variable Feature Selection for gene biomarker discovery using Elastic-Net Regularization

Affordable sequencing technologies have resulted in a rapid rise in genomic and proteomic data. As a result, a massive amount of data is being analyzed, and the outcomes must be summarized as relevant clinical biomarkers. One of the major challenges is enabling wet-lab researchers to make meaningful inferences from the data, even in the absence of expertise in pipeline development and statistical training. We present a user-friendly R shiny application, SurfacOmics, which allows the user to perform biomarker identification via penalized regression algorithms. It also enables researchers to choose a crucial binary variable, such as treatment or group, from the study design metadata to anticipate potential biomarkers. We have introduced a novel concept of scoring scheme to characterize potential biomarkers for prediction, prioritization, and ranking. The tool integrates Gene Ontology information for sub-cellular localization together with a manually curated knowledgebase to provide valuable insights on biological processes, molecular functions and antibody resources, offering a comprehensive view in a single interface.

bioinformatics↗