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Biomedical subjects

Bimal P Chaudhari

Publications and source records attributed to Bimal P Chaudhari.

2 recordsLinked to original sources

RNU4ATAC-opathy: Clinical, molecular, and transcriptomic insights from a large cohort.

PURPOSE: We aim to better define the genotype and phenotype spectrum of RNU4ATAC-opathy, demonstrate the utility of RNA sequencing (RNA-seq) for variant classification, and highlight the challenges in detecting variants in this noncoding gene. METHODS: Sixty individuals with molecularly confirmed RNU4ATAC-opathy were recruited from multiple clinical and research centers internationally. RNA-seq was available for 7 affected individuals. RESULTS: We report the clinical and molecular findings of 60 individuals, including 42 not previously described, and 33 distinct RNU4ATAC variants, 13 of which are novel. Core features in this cohort-present in most individuals assessed and varying in severity-include microcephaly, short stature, skeletal anomalies, developmental delay, cerebral anomalies, skin conditions, and immune deficiency. Additional findings, such as diabetes, holoprosencephaly, and the absence of various core features in some individuals, highlight the broad phenotypic spectrum. All individuals who underwent RNA-seq showed a consistent pattern of minor intron retention. In 6 individuals, RNA-seq enabled the reclassification of variants of uncertain significance as likely pathogenic. Although RNU4ATAC variants are generally covered by clinical exomes, they are often overlooked in analysis because of their noncoding nature. CONCLUSION: This study highlights the variability of phenotypes and genotypes associated with RNU4ATAC-opathy. Laboratories should ensure RNU4ATAC and other noncoding genes are appropriately assessed by their analysis pipelines.

Lowry-Wood syndrome

PheBee: A Graph-Aware System for Scalable, Traceable, and Semantic Phenotyping.

OBJECTIVES: Phenotype-driven workflows in clinical and translational research require standardized ontology-based representation, ontology-aware cohort discovery, and provenance inspection for each assertion. Existing approaches optimize either for semantic traversal or scalable batch analytics, but not both. We describe PheBee, a hybrid system that links semantic assertions to scalable evidence storage via a deterministic identifier, preserving provenance while supporting ontology-aware discovery at cohort scale. MATERIALS AND METHODS: PheBee represents phenotype assertions in a knowledge graph as ontology-linked nodes with clinical modifier context (e.g., negated, family history), and stores supporting evidence records in a scalable row-oriented evidence table for cohort-scale access. The two layers are connected by a deterministic identifier enabling stable joins across repeated ingestions without duplicating high-volume evidence in the graph. We evaluated PheBee using synthetic datasets designed to exercise end-to-end ingestion and query workflows. RESULTS: Functional evaluation validated hierarchical term expansion, qualifier-aware retrieval, duplicate-free assertion handling under re-ingestion, and privacy-conscious management of subjects shared across multiple research projects. At scale (10,000 subjects producing 12M evidence records) PheBee completed ingestion in ~30 minutes and responded to interactive queries within 6 seconds under concurrent load. DISCUSSION: PheBee exposes a unified API for ontology-aware cohort discovery with hierarchical term expansion, subject-centric retrieval of phenotypes and clinical modifiers, and evidence and provenance queries. Its data model aligns with GA4GH Phenopackets, facilitating interoperability with phenotype exchange standards. CONCLUSION: By combining ontology-aware semantics with scalable, provenance-bearing evidence storage, PheBee provides a practical open-source foundation for phenotype-driven research workflows that demand both semantic precision and cohort-scale traceability.

cohort studies