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Ultrafast search of all deposited bacterial and viral genomic data


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.

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Fig. 1: Sequence matching methods.
Fig. 2: BIGSI encoding.
Fig. 3: Speed and space trade-offs as index grows.
Fig. 4: Phylogenetic distribution of plasmid sequences.
Fig. 5: Plasmid spread and antibiotic resistance genes.
Fig. 6: Antibiotic resistance gene prevalence in ENA over time.

Code availability

An open source implementation of BIGSI can be found at BIGSI v0.3.0 supports disk-based indexing via Berkeley-DB or rocksDB, as well as distributed in-memory (via redis ( key-value stores, and can be extended to any key-value store. The benchmarking uses the rocksDB key-value store and v0.2.0 of BIGSI, and the all-microbial index uses Berkeley-DB and BIGSI version v0.1.7.

Data availability

All of the underlying genomic data for this study are publicly available at the ENA, and Supplementary Data can be found in the directory Supplementary Data 19 can be found at or at Contents are as follows: Supplementary Data 1, MCR search results; Supplementary Data 2, plasmid search results; Supplementary Data 3, counts of five specific plasmids across genera; Supplementary Data 4, counts of MOB types across genera; Supplementary Data 5, CARD antibiotic resistance gene search results (T = 70%); Supplementary Data 6, benchmarking results; Supplementary Data 7, Bracken taxonomic results; Supplementary Data 8, MOB type definition fasta; Supplementary Data 9, MOB and T4SS search results (T = 100%). The all-microbial index itself is available at In order to facilitate reproducibility for others without having to download and process 170 Tb of raw data, we made the 26 Tb of cleaned de Bruijn (binary) graph files for the entire all-microbial index snapshot of the ENA available at An archive of computational code can be found at An archive of the data underlying the figures can be found at and or as Supplementary Data 10 We have also made a public instance of our index of the ENA available at, where the user can paste sequence and search. This instance uses BIGSI v0.1.7 (using berkeleyDB) and is hosted by CLIMB ( on a 3 Tb RAM server.


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We would like to thank, for critical reading and helpful suggestions: G. Blackwell, R. Ffrancon, M. Hunt, J. Kelleher, J. Thornton, R. Patro, K. Malone, J. Marioni, A. Page, S. Gog, T. Bellman, F. Gauger. For enormous assistance with data download from EBI: R. Esnouf, G. Cochrane. For hosting our BIGSI demonstration: CLIMB. We acknowledge funding from Wellcome Trust Core Award Grant Number 203141/Z/16/Z. Z.I. was funded by a Wellcome Trust/Royal Society Sir Henry Dale Fellowship, grant number 102541/A/13/Z. G.M. was funded by Wellcome Trust grant 100956/Z/13/Z. E.P.C.R. was funded by the ANR MAGISBAC grant number ANR-14-CE10-0007-02. P.B. was funded by Wellcome Trust Studentship H5RZCO00.

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Authors and Affiliations



Z.I., G.M. designed and oversaw the study; P.B. invented the method, developed software and performed analyses; H.C.d.B. performed analyses for the plasmid study; E.P.C.R. codesigned and analyzed the conjugative system and plasmid analysis; Z.I. wrote the paper; all authors gave detailed feedback on the paper.

Corresponding author

Correspondence to Zamin Iqbal.

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Competing interests

G.M. is a cofounder of, holder of shares in, and is a consultant to Genomics PLC.

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Integrated supplementary information

Supplementary Figure 1 Cartoon comparison between human and E. coli genomes.

Cartoon comparison of human genomes (above) and E. coli (below) as a representative bacterium. In humans, genetic variation is dominated by relatively sparse single nucleotide polymorphisms (SNPs), nucleotide diversity π = 0.001, and less than 1% of a typical genome lies in a structural variant (SV) [1]. Human genomes are therefore relatively compressible. In stark contrast, genes make up around 88% of an E. coli genome [2], yet two E. coli genomes may only share around 60% of their genes [3], and conserved genes have much higher nucleotide diversity (0.02) [4]. Thus, bacterial genomes present different compression and indexing challenges to human genomes. 1. The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68-74, doi:10.1038/nature15393 (2015). 2. Blattner, F. R. et al. The complete genome sequence of Escherichia coli K-12. Science 277, 1453-1462 (1997). 3. Touchon, Marie, et al. "Organised genome dynamics in the Escherichia coli species results in highly diverse adaptive paths." PLoS genetics 5.1 (2009): e1000344. 4. Kaas, Rolf S., et al. "Estimating variation within the genes and inferring the phylogeny of 186 sequenced diverse Escherichia coli genomes." BMC genomics 13.1 (2012).

Supplementary Figure 2 k-mer identity vs. percent nucleotide identity.

We ran 1,000 simulations where on each iteration we introduced 1 more random SNP into a sequence of length 1,000 bp and calculated the k-mer similarity between the original sequence and the sequence with introduced SNPs. Here, we plot the mean k-mer similarity observed for each percent identity. The grey area shows 3X the standard deviation around this mean.

Supplementary Figure 3 BIGSI scores vs. megaBLAST scores.

megaBLAST scores for a search of 100 antimicrobial resistance genes in a BLAST database of RefSeq-81 vs. the equivalent BIGSI scores in a search of a BIGSI of RefSeq-81. Pearson correlation of the scores was r = 0.998.

Supplementary Figure 4 Speed–space tradeoffs for exact-match queries.

We show query time for 2,157 antimicrobial resistance genes with T = 100% vs. peak disk size when searching databases of sizes from 10–10,000 microbial datasets. BIGSI’s query time does not increase significantly with N, as in this regime the query time is dominated by the constant time row lookups, rather than the bit-wise AND calculations.

Supplementary Figure 5 Simulated scaling to 1 million datasets.

We simulated scaling to 1 million datasets of peak data-structure storage requirements of BIGSI and SBT-fast, comparing performance with high/low proportion of sharing of k-mers between datasets (note y axis is on log scale). In the high k-mer sharing regime only 100 new k-mers are introduced per dataset, whereas the low k-mer sharing regime introduces 10,000 new k-mers per dataset. Since BIGSI scales linearly with number of datasets and independently of the number of k-mers, it uses the same storage per dataset in each regime. However, SBT-fast scales super-linearly with N since its Bloom filter size depends on the total number of kmers. For 1 million genomes with low k-mer sharing (right), which is the case we care about for global indexing, BIGSI would use 3.1 Terabytes whereas SBT-fast would use 25 Petabytes. When we index the ENA, we find each dataset adds 100,000 new k-mers on average, 10x more than the low kmer-sharing regime simulated here, which would further penalize SBT.

Supplementary Figure 6 Counts of the most frequent bacterial genera in the SRA/ENA data set.

Over 90% of the datasets were isolates of these 20 genera and 65% from the top 5 most prevalent genera.

Supplementary Figure 7 Permutation test for difference in phylogenetic spread of plasmids with ≥3 resistance genes versus those with 0.

We took the set of plasmids from Fig. 5 with at least 3 resistance genes (abbreviation 3G) and those with zero (abbrev. 0G). We defined “phylogenetic spread” of a plasmid as the median of the pairwise distances along a large subunit rRNA tree (incorporating branch lengths) between all pairs of genera in which the plasmid is detected, and calculated the 95% quantile of this distribution (red line). We then permuted the assignment of each plasmid to the classes 3G and 0G one million times (maintaining the class counts), each time calculating the difference in 95% quantiles for the two categories (3G and 0G). We show here the histogram of that statistic (i.e. the difference). This corresponds to a permutation test P value of 0.0024.

Supplementary Figure 8 Distribution of MOB types among phyla.

We show the proportion of each MOB type associated with different phyla based on a search of all known MOB types from Guglielmini et al. in the all-microbial-index.

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Supplementary Figures 1–8 and Supplementary Tables 1–3

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Supplementary Data

Supplementary Data 10

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Bradley, P., den Bakker, H.C., Rocha, E.P.C. et al. Ultrafast search of all deposited bacterial and viral genomic data. Nat Biotechnol 37, 152–159 (2019).

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