Scalable Literature Curation, Summarization, and Monitoring of Genomic Variants

About This Grant

PROJECT SUMMARY/ABSTRACT Genomic variant reclassification plays a key role in accurate diagnoses and appropriate clinical decisions, as new evidence can change a variant’s classification, impacting patient treatment options. However, the process of genomic variant reclassification is hindered by the sheer volume of rapidly expanding literature, the labor-intensive and error-prone nature of manual curation, and the lack of efficient mechanisms for continuously tracking and integrating new findings. This project aims to develop scalable and automated informatics solutions for genomic variant curation by extracting key metadata, ranking evidence by reliability, and implementing literature monitoring to support timely revisit to the evidence. Specifically, the project is organized into 3 specific aims. Aim 1 focuses on developing a pipeline to extract evidence and metadata related to genetic variants from the literature, while also categorizing evidence by study type, such as experimental, computational, epidemiological, and case reports. Additionally, a ranking model will be developed to prioritize extracted evidence based on relevance and recency. Aim 2 focuses on analyzing trends in variant reclassifications and their correlations with evidence metadata to improve the understanding of factors influencing classification changes. Based on this analysis, an automated tool will be developed to monitor, summarize, and highlight significant new evidence from the literature. Aim 3 focuses on developing a user-friendly web application to present the extracted metadata, ranking results, and literature updates in a format tailored to the needs of genetic researchers. In collaboration with domain experts, the application will be co-designed to enhance usability and integration into existing variant curation workflows. A pilot study will be conducted to evaluate its effectiveness in improving researchers’ efficiency, reducing costs, and minimizing the manual effort required for variant reclassification. During the K99 phase, Dr. Zhang will develop an automated pipeline for curating metadata of genomic evidence under the supervision of Dr. Chunhua Weng. In the R00 phase, Dr. Zhang will develop real-time genomic literature monitoring and a user-friendly web application for genomic researchers, collaborating with domain experts to co-design and evaluate its impact on variant reclassification. To ensure a successful transition to independence, Dr. Zhang will receive training in genomic medicine through coursework and collaborations with clinical experts. Additionally, Dr. Zhang will strengthen mentorship, leadership, and grant-writing skills through activities including co-mentoring students, managing research projects.