A search-based geographic metadata curation pipeline to refine sequencing institution information and support public health
Bibliographic Data
| ID | 22087005 |
|---|---|
| Authors | Kun Zhao (0000-0002-4947-2418, Centers for Disease Control and Prevention, corresponding author), Katie Farrell (Centers for Disease Control and Prevention), Melchizedek Mashiku (0000-0002-4627-5216, Centers for Disease Control and Prevention), Dawit Abay (Centers for Disease Control and Prevention), Kevin Tang (0000-0002-2908-1954, Centers for Disease Control and Prevention), M Steven Oberste (0000-0002-9170-9845, Centers for Disease Control and Prevention), Cara C Burns (0000-0003-2582-2476, Centers for Disease Control and Prevention) |
| Year | 2023 |
| Volume | 11 |
| Pages | 1254976-1254976 |
| Publication date | 2023-11-14 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2023.1254976 |
| PMID | 38035280 |
| OpenAlex | W4388703754 |
| Language | EN |
| Citations received | 1 |
| References cited | 26 |
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% for geographic coordinates. Conclusion: SGMC may represent a novel approach using a generative AI model to enhance geographic data (country and institution assignments) for large numbers of samples within SRA datasets. This information can be utilized to bolster public health endeavors
Cartography · Data science · Geographic information system · Geography · Geospatial analysis · Metadata · World Wide Web · Computer Science · Genomics and Phylogenetic Studies · Genomics and Rare Diseases · Species Distribution and Climate Change
| Citations per year | 1 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |