Ultrafast search of all deposited bacterial and viral genomic data

Journal article

Exponentially increasing amounts of unprocessed bacterial and viral genomic sequence data are stored in the global archives. The ability to query these data for sequence search terms would facilitate both basic research and applications such as real-time genomic epidemiology and surveillance. However, this is not possible with current methods. To solve this problem, we combine knowledge of microbial population genomics with computational methods devised for web search to produce a searchable data structure named BItsliced Genomic Signature Index (BIGSI). We indexed the entire global corpus of 447,833 bacterial and viral whole-genome sequence datasets using four orders of magnitude less storage than previous methods. We applied our BIGSI search function to rapidly find resistance genes MCR-1, MCR-2, and MCR-3, determine the host-range of 2,827 plasmids, and quantify antibiotic resistance in archived datasets. Our index can grow incrementally as new (unprocessed or assembled) sequence datasets are deposited and can scale to millions of datasets.

Authors: Bradley, P; Den Bakker, H; Rocha, E; McVean, G; Iqbal, Z

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Nature
• Journal: Nature Biotechnology
• Volume: 37
• Issue: 2
• Pages: 152-159
• Publication date: 2019-02-04
• Acceptance date: 2018-12-20
• DOI: 10.1038/s41587-018-0010-1
• EISSN: 1546-1696
• ISSN: 1087-0156
• Pmid: 30718882
• Language: English
• Keywords: computational platforms and environments; microbial genetics; infectious-disease diagnostics; software; data mining