A search-based geographic metadata curation pipeline to refine sequencing institution information and support public health

作者：Kun Zhao, Katie Farrell, Melchizedek Mashiku, Dawit Abay, Kevin Tang, M. Steven Oberste, Cara C. Burns · 发表于：Frontiers in Public Health · 年份：2023 · DOI：10.3389/fpubh.2023.1254976 · 被引用次数：5 · 研究领域：Genomics and Rare Diseases、Species Distribution and Climate Change、Genomics and Phylogenetic Studies

Background: The National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) has amassed a vast reservoir of genetic data since its inception in 2007. These public data hold immense potential for supporting pathogen surveillance and control. However, the lack of standardized metadata and inconsistent submission practices in SRA may impede the data's utility in public health. Methods: To address this issue, we introduce the Search-based Geographic Metadata Curation (SGMC) pipeline. SGMC utilized Python and web scraping to extract geographic data of sequencing institutions from NCBI SRA in the Cloud and its website. It then harnessed ChatGPT to refine the sequencing institution and location assignments. To illustrate the pipeline's utility, we examined the geographic distribution of the sequencing institutions and their countries relevant to polio eradication and categorized them. Results: SGMC successfully identified 7,649 sequencing institutions and their global locations from a random selection of 2,321,044 SRA accessions. These institutions were distributed across 97 countries, with strong representation in the United States, the United Kingdom and China. However, there was a lack of data from African, Central Asian, and Central American countries, indicating potential disparities in sequencing capabilities. Comparison with manually curated data for U.S. institutions reveals SGMC's accuracy rates of 94.8% for institutions, 93.1% for countries, and 74.5% ...