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Ruhela, V.

Publications and source records attributed to Ruhela, V..

2 recordsLinked to original sources
bioRxiv2025

SAGA (Simplified Association Genomewide Analyses): a user-friendly Pipeline to Democratize Genome-Wide Association Studies

Genome-wide association studies (GWAS) have enabled clinicians and researchers to identify genetic variants linked to complex traits and diseases(1-3). However, GWAS still face several challenges, particularly regarding accessibility and reproducibility (4-6). Conducting these analyses often requires substantial bioinformatics expertise for data preprocessing, software installation, and scripting(7-10). We then developed SAGA ("Simplified Association Genome-wide Analyses"), a BASH-based, open-source, fully automated pipeline that integrates three widely adopted tools--PLINK(11), GMMAT(12), and SAIGE(13)--for accessible, robust, and reproducible GWAS. After installation, users simply need to provide genotype and phenotype files in standard formats. The pipeline automates preprocessing, association testing, and visualization, outputting summary statistics, Manhattan plots, and quantile-quantile plots. SAGA enables robust GWAS for users without scripting experience, expanding access to complex genetic analyses.

bioinformatics↗
bioRxiv2023

A Comprehensive Targeted Panel of 282 Genes: Unveiling Key Biomarkers in Multiple Myeloma

Multiple myeloma (MM) is a haematological cancer that evolves from the benign precursor stage termed monoclonal gammopathy of undetermined significance (MGUS). Understanding the pivotal biomarkers, genomic events, and gene interactions distinguishing MM from MGUS can significantly contribute to early detection and an improved understanding of MMs pathogenesis. This study presents a curated, comprehensive, targeted sequencing panel focusing on 295 MM-relevant genes and employing clinically oriented NGS-targeted sequencing approaches. To identify these genes, an innovative AI-powered attention model, the Bio-Inspired Graph Network Learning-based Gene-Gene Interaction (BIO-DGI) model, was devised for identifying Disease-Initiating and Disease-Transformative genes using the genomic profiles of MM and MGUS samples. The BIO-DGI model leverages gene interactions from nine protein-protein interaction (PPI) networks and analyzes the genomic features from 1154 MM and 61 MGUS samples. The proposed model outperformed base-line machine learning (ML) and deep learning (DL) models on quantitative performance metrics. Additionally, the BIO-DGI model identified the highest number of MM-relevant genes in the post-hoc analysis, demonstrating its superior qualitative performance. Pathway analysis highlighted the significance of top-ranked genes, emphasizing their role in MM-related pathways. Encompassing 9417 coding regions with a length of 2.630 Mb, the 295-gene panel exhibited superior performance, surpassing previously published panels in detecting genomic disease-initiating and disease-transformative events. The panel also revealed highly influential genes and their interactions within MM gene communities. Clinical relevance was confirmed through a two-fold univariate survival analysis, affirming the significance of the proposed gene panel in understanding disease progression. The studys findings offer crucial insights into essential gene biomarkers and interactions, shaping our understanding of MM pathophysiology.

bioinformatics↗