Nov 13, 2020
Version 4
GHRU (Genomic Surveillance of Antimicrobial Resistance) Retrospective 1 Bioinformatics Methods V.4
- 1Centre for Genomic Pathogen Surveillance
Protocol Citation: Anthony Underwood 2020. GHRU (Genomic Surveillance of Antimicrobial Resistance) Retrospective 1 Bioinformatics Methods. protocols.io https://dx.doi.org/10.17504/protocols.io.bp2l6b11kgqe/v4Version created by Anthony Underwood
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
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Created: November 13, 2020
Last Modified: November 13, 2020
Protocol Integer ID: 44478
Abstract
A description of the pipelines used for analysing genome data from the retrospective 1 project of the NIHR Global Health Research Unit (Genomic Surveillance of Antimicrobial Resistance)
All genome sequence data were processed using versioned Nextflow [1] workflows and associated Docker [2] containers covering the foundational analyses of de novo assembly,mapping-based SNP phylogeny, MLST assignation and AMR determinant detection (table 1). The exact steps performed can be derived from examination of the pipeline code but each workflow will be described in brief.
De novo assembly
reads trimming and adapter removal using trimmomatic (0.38) [3], read correction using lighter (1.1.1) [4], downsampling to 100x coverage using seqtk (1.3) [5], read merging using flash (1.2.11) [6], assembly using SPAdes (3.12.0) [7]. Quality control was performed using fastqc (0.11.8) [8], multiqc (1.7) [9] and qualifyr (1.4.4) [10]. Species identification was carried out by bactinspector (0.1.3) [11] and contamination checked using Confindr (0.7.2) [12].
Mapping based phylogeny
Reads were trimmed as described for de novo assembly and mapped to a reference with bwa mem (0.7.17) [13], variants called and filtered using bcf tools (1.9) [14] and the filtering conditions for a low quality position being '%QUAL<25 || FORMAT/DP<10 || MAX(FORMAT/ADF)<2 || MAX(FORMAT/ADR)<2 || MAX(FORMAT/AD)/SUM(FORMAT/DP)<0.9 || MQ<30 || MQ0F>0.1'. A pseudoalignment where each sample has a base relative to the reference sequence. Missing bases and low quality bases are encoded using the - and N characters. The alignment was used to generate a maximum likelihood tree using iqtree (1.6.8) [15,16] and ultrafast bootstraps (parameters -m GTR+G -alrt 1000 -bb 1000).
AMR determinant detection
The ARIBA software (2.14.4) [17] was used to detect acquired genes using the NCBI database [18] (downloaded 2019-10-30) and the pointfinder database (downloaded 2019-12-11) adapted for Ariba [19].
MLST detection
The ARIBA software (2.14.4) [17] was used to determine the 7-locus MLST type using the profile and alleles found in the pubmlst database [20,21] (downloaded 2019-12-20).
Table 1: Nextflow workflows
| Workflow name | Workflow link | Docker hub Container(s) used | Version at publication | |
| De novo assembly | https://gitlab.com/cgps/ghru/pipelines/assembly | bioinformant/ghru-assembly:version OR registry.gitlab.com/cgps/ghru/pipelines/assembly:version | 1.5.5 | |
| Mapping based SNP phylogeny | https://gitlab.com/cgps/ghru/pipelines/snp_phylogeny | bioinformant/ghru-snp-phylogeny:version OR registry.gitlab.com/cgps/ghru/pipelines/snp_phylogeny:version | 1.2.2 | |
| AMR determinant detection | https://gitlab.com/cgps/ghru/pipelines/dsl2/pipelines/amr_prediction | bioinformant/ghru-amr-prediction:version OR registry.gitlab.com/cgps/ghru/pipelines/dsl2/pipelines/amr_prediction | 1.0 | |
| MLST | https://gitlab.com/cgps/ghru/pipelines/dsl2/pipelines/mlst | bioinformant/ghru-mlst:version OR registry.gitlab.com/cgps/ghru/pipelines/dsl2/pipelines/mlst:version | 1.0 |
References
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18. Bacterial Antimicrobial Resistance Reference Gene. Available at: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA313047. Accessed 26 October 2020.
19. ariba_amr_databases. Available at: https://gitlab.com/cgps/ghru/pipelines/data_sources/ariba_amr_databases. Accessed 26 October 2020.
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