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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.

Original publication

DOI

10.1038/s41587-018-0010-1

Type

Journal article

Journal

Nature biotechnology

Publication Date

04/02/2019

Volume

37

Pages

152 - 159

Addresses

Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.

Keywords

Escherichia coli, Salmonella, Mycobacterium, Staphylococcus, Streptococcus, Transferases (Other Substituted Phosphate Groups), Escherichia coli Proteins, Membrane Proteins, False Positive Reactions, Models, Statistical, Chromosome Mapping, Sequence Analysis, DNA, Computational Biology, Genomics, Phylogeny, Drug Resistance, Bacterial, Genotype, Genome, Bacterial, Genome, Viral, Plasmids, Algorithms, Programming Languages, Databases, Factual, Molecular Epidemiology