Exploring bacterial diversity via a curated and searchable snapshot of archived DNA sequences

Abstract

The open sharing of genomic data provides an incredibly rich resource for the study of bacterial evolution and function and even anthropogenic activities such as the widespread use of antimicrobials. However, these data consist of genomes assembled with different tools and levels of quality checking, and of large volumes of completely unprocessed raw sequence data. In both cases, considerable computational effort is required before biological questions can be addressed. Here, we assembled and characterised 661,405 bacterial genomes retrieved from the European Nucleotide Archive (ENA) in November of 2018 using a uniform standardised approach. Of these, 311,006 did not previously have an assembly. We produced a searchable COmpact Bit-sliced Signature (COBS) index, facilitating the easy interrogation of the entire dataset for a specific sequence (e.g., gene, mutation, or plasmid). Additional MinHash and pp-sketch indices support genome-wide comparisons and estimations of genomic distance. Combined, this resource will allow data to be easily subset and searched, phylogenetic relationships between genomes to be quickly elucidated, and hypotheses rapidly generated and tested. We believe that this combination of uniform processing and variety of search/filter functionalities will make this a resource of very wide utility. In terms of diversity within the data, a breakdown of the 639,981 high-quality genomes emphasised the uneven species composition of the ENA/public databases, with just 20 of the total 2,336 species making up 90% of the genomes. The overrepresented species tend to be acute/common human pathogens, aligning with research priorities at different levels from individual interests to funding bodies and national and global public health agencies.

Fine-Scale Reconstruction of the Evolution of FII-33 Multidrug Resistance Plasmids Enables High-Resolution Genomic Surveillance

“…To demonstrate the utility of our approach, we searched for and subtyped FII-33 plasmids in a curated collection of 661,405 bacterial draft genome sequences ( 27 ). The FII-33 repA1 gene was detected in 423 genomes.…”

“…The sequences in Text S1 were used to query the COBS index ( 26 ) of 661,405 curated draft genomes ( 27 ) with a kmer similarity cutoff of 1.00 such that only identical sequences were detected. The estimated core genome distances of the 423 genomes that contained the FII-33 repA1 gene were extracted from the pp-sketch index of the 661,405-genome collection ( 27 ) using pp-sketchlib (version 1.5.1) ( 28 ).…”

“…The sequences in Text S1 were used to query the COBS index ( 26 ) of 661,405 curated draft genomes ( 27 ) with a kmer similarity cutoff of 1.00 such that only identical sequences were detected. The estimated core genome distances of the 423 genomes that contained the FII-33 repA1 gene were extracted from the pp-sketch index of the 661,405-genome collection ( 27 ) using pp-sketchlib (version 1.5.1) ( 28 ). A neighbor-joining tree of the core distances was produced using RapidNJ (version 2.3.2) ( 29 ), and the tree, metadata, and query sequence presence/absence data were visualized in R using the ggtree package ( 30 ).…”

Genomic neighbor typing for bacterial outbreak surveillance

“…For reference sketch construction, we used a collection of assemblies containing bacterial genomes from the entire European Nucleotide Archive (ENA) in 2018 (n = 660,333) (25). Metadata from precomputed assembly genotypes was used to subset assemblies with complete lineage designation for inclusion (MLST).…”

Rapid adaptation of a complex trait during experimental evolution ofMycobacterium tuberculosis

“…We searched for the presence of mutations identified in our study in publicly available sequencing data using a searchable COBS index of bacterial genomes curated from the European Nucleotide Archive (Blackwell et al, 2021). We used the Python interface to search the COBS index (Bingmann et al, 2019).…”