Real sequencing datasets for evaluating bacterial SNP calling pipelines

Name: Real sequencing datasets for evaluating bacterial SNP calling pipelines
Published: 2019-01-01T00:00:00Z
License: https://ora.ox.ac.uk/terms_of_use

Bush, S

Documentation:: This archive is provided as an online supplementary dataset for the Bush, et al. manuscript "Genomic diversity affects the accuracy of bacterial SNP calling pipelines". Collectively, this represents an ‘all-in-one’ archive of data to facilitate both the replication and expansion of this study using real sequencing data. It comprises 18 sets of paired-end reads and assemblies (sourced from the REHAB project), the associated indexed reference genomes, SNP call truth sets, VCFs, a suite of Perl scripts, and operating notes.

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APA Style

Bush, S. (2019). Real sequencing datasets for evaluating bacterial SNP calling pipelines. University of Oxford.

MLA Style

Bush, S. Real Sequencing Datasets for Evaluating Bacterial SNP Calling Pipelines. University of Oxford, 2019.

Chicago Style

Bush, S. 2019. Real Sequencing Datasets for Evaluating Bacterial SNP Calling Pipelines. University of Oxford.
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Files:: illumina_reads_for_nanopore_illumina_hybrid_assemblies_PART1.t...

(gz, 1.5GB, Terms of use)

illumina_reads_for_nanopore_illumina_hybrid_assemblies_PART2.t...

(gz, 1.7GB, Terms of use)

nanopore_illumina_hybrid_assemblies.tar.gz

(gz, 82.7MB, Terms of use)

representative_genomes_for_nanopore_illumina_hybrid_assemblies...

(gz, 1.4GB, Terms of use)

scripts_and_notes.tar.gz

(gz, 31.8MB, Terms of use)

Authors/Creators

+ Bush, S More by this author/creator

Institution:: University of Oxford
Division:: MSD
Department:: NDM
Role:: Creator, Researcher

+ NIHR More from this funder

Grant:: HPRU-2012-10041

Publisher:: University of Oxford
Publication date:: 2019
DOI:: 10.5287/bodleian:nrmv8k5r8

Pubs id:: 1480829
UUID:: uuid:8f902497-955e-4b84-9b85-693ee0e4433e
Local pid:: pubs:1480829
Deposit date:: 2019-11-28
ARK identifier:: ark:/29072/ora_8f902497955e4b849b85693ee0e4433e

Related item:: Genomic diversity affects the accuracy of bacterial SNP calling pipelines
Description:: Background: Accurately identifying SNPs from bacterial sequencing data is an essential requirement for using genomics to track transmission and predict important phenotypes such as antimicrobial resistance. However, most previous performance evaluations of SNP calling have been restricted to eukaryotic (human) data. Additionally, bacterial SNP calling requires choosing an appropriate reference genome to align reads to, which, together with the bioinformatic pipeline, affects the accuracy and completeness of a set of SNP calls obtained.This study evaluates the performance of 209 SNP calling pipelines using a combination of simulated data from 254 strains of 10 clinically common bacteria and real data from environmentally-sourced and genomically diverse isolates within the genera Citrobacter, Enterobacter, Escherichia and Klebsiella. Results: We evaluated the performance of 209 SNP calling pipelines, aligning reads to genomes of the same or a divergent strain. Irrespective of pipeline, a principal determinant of reliable SNP calling was reference genome selection. Across multiple taxa, there was a strong inverse relationship between pipeline sensitivity and precision, and the Mash distance (a proxy for average nucleotide divergence) between reads and reference genome. The effect was especially pronounced for diverse, recombinogenic, bacteria such as Escherichia coli, but less dominant for clonal species such as Mycobacterium tuberculosis. Conclusions: The accuracy of SNP calling for a given species is compromised by increasing intra-species diversity. When reads were aligned to the same genome from which they were sequenced, among the highest performing pipelines was Novoalign/GATK. By contrast, when reads were aligned to particularly divergent genomes, the highest-performing pipelines often employed the aligners NextGenMap or SMALT, and/or the variant callers LoFreq, mpileup or Strelka. However, across the full range of genomes, among the consistently highest-performing pipelines was Snippy.

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Real sequencing datasets for evaluating bacterial SNP calling pipelines

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