Biology subjects

Muruganujan, A.

Publications and source records attributed to Muruganujan, A..

3 recordsLinked to original sources

bioRxiv2026

SNPWay: streamlined SNP-to-function and pathway over-representation analysis

MotivationPost-GWAS interpretation frequently requires translating variant lists (e.g., lead SNPs, clumped loci, credible sets, or curated panels) into pathway and functional hypotheses. In practice, obtaining pathway and functional over-representation results from SNP inputs often requires stitching together multiple tools for variant annotation, regulatory annotation, gene identifier handling, and statistical testing. This integration burden can reduce reproducibility and restrict end-to-end analysis to groups with dedicated bioinformatics support. SummaryWe present SNPWay, a web server and R package that performs end-to-end SNP-to-function and pathway over-representation analysis in a single standardized workflow. SNPWay accepts rsIDs, VCF files, or hg19/GRCh37 genomic coordinates. It queries Annotation Query (AnnoQ) to obtain SNP-to-gene mappings from ANNOVAR, SnpEff, and VEP under both Ensembl and RefSeq gene models, and incorporates enhancer-gene links via PEREGRINE to augment mappings for noncoding variants. SNPWay aggregates mapped genes into a single, non-redundant, combined gene list and submits it to PANTHER for over-representation testing against the Homo sapiens reference list, returning over-represented pathways and functional categories (e.g., Gene Ontology) with direct links for interactive exploration in PANTHER. SNPWays modular architecture is designed for extensibility, enabling incorporation of additional analysis methods in future releases. A step-by-step walkthrough is provided in Supplementary Data. Availability and implementationWeb server: https://snpway.annoq.org/. R package and source code: https://github.com/USCbiostats/Annoq_Overrepr_Workflow. Documentation: https://snpway.annoq.org/about. Examples: The website contains Sample files for the input formats, also provided in Supplementary Data. SNPWay is free to use with no mandatory login. Contacthuaiyumi@usc.edu Supplementary informationSupplementary data are available at Bioinformatics online

bioinformatics↗

bioRxiv2026

From SNPs to Pathways: A genome-wide benchmark of annotation discrepancies and their impact on protein- and pathway-level inference

BackgroundAccurate single-nucleotide polymorphism (SNP) annotation is central to genomic research yet widely used tools and gene models often yield divergent results. Prior studies have shown such discrepancies in small datasets, but the extent of genome-wide variation and its impact on downstream pathway analysis remain unclear. ResultsWe conducted a comprehensive comparison of three commonly used SNP annotation tools, ANNOVAR, SnpEff, and VEP, using both Ensembl and RefSeq gene models to evaluate more than 40 million SNPs from the Haplotype Reference Consortium. At the protein level, annotation output differed significantly across tools and gene models (p-adj < 0.001), with discrepancies present in both genic and intergenic regions. RefSeq produced broader annotation coverage, particularly for intergenic SNPs, while Ensembl showed greater internal consistency. SnpEff provided the most complete coverage overall, whereas no single tool or model configuration achieved full annotation recovery of the union reference. Integration across tools and models maximized coverage and reduced annotation loss. In a case study of 204 colorectal cancer-associated SNPs from the FIGI GWAS, pathway enrichment results varied depending on annotation strategy. The fully integrated approach identified all four significant pathways, whereas several single-tool or single-model strategies missed one or more. ConclusionSNP annotation outcomes are influenced by both the tool and gene model used, and relying on a single approach may result in incomplete coverage. A multi-tool, multi-model strategy provides the most comprehensive annotation and preserves enriched pathways, supporting more robust and reproducible genomic interpretation.

bioinformatics↗

bioRxiv2018

BDQC: a general-purpose analytics tool for domain-blind validation of Big Data

Translational biomedical research is generating exponentially more data: thousands of whole-genome sequences (WGS) are now available; brain data are doubling every two years. Analyses of Big Data, including imaging, genomic, phenotypic, and clinical data, present qualitatively new challenges as well as opportunities. Among the challenges is a proliferation in ways analyses can fail, due largely to the increasing length and complexity of processing pipelines. Anomalies in input data, runtime resource exhaustion or node failure in a distributed computation can all cause pipeline hiccups that are not necessarily obvious in the output. Flaws that can taint results may persist undetected in complex pipelines, a danger amplified by the fact that research is often concurrent with the development of the software on which it depends. On the positive side, the huge sample sizes increase statistical power, which in turn can shed new insight and motivate innovative analytic approaches. We have developed a framework for Big Data Quality Control (BDQC) including an extensible set of heuristic and statistical analyses that identify deviations in data without regard to its meaning (domain-blind analyses). BDQC takes advantage of large sample sizes to classify the samples, estimate distributions and identify outliers. Such outliers may be symptoms of technology failure (e.g., truncated output of one step of a pipeline for a single genome) or may reveal unsuspected \" signal\" in the data (e.g., evidence of aneuploidy in a genome). We have applied the framework to validate real-world WGS analysis pipelines. BDQC successfully identified data outliers representing various failure classes, including genome analyses missing a whole chromosome or part thereof, hidden among thousands of intermediary output files. These failures could then be resolved by reanalyzing the affected samples. BDQC both identified hidden flaws as well as yielded new insights into the data. BDQC is designed to complement quality software development practices. There are multiple benefits from the application of BDQC at all pipeline stages. By verifying input correctness, it can help avoid expensive computations on flawed data. Analysis of intermediary and final results facilitates recovery from aberrant termination of processes. All these computationally inexpensive verifications reduce cryptic analytical artifacts that could seriously preclude clinical-grade genome interpretation. BDQC is available at https://github.com/ini-bdds/bdqc.

bioinformatics↗