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What is ARETE?
-ARETE (Antmicrobial Resistance: Emergence, Transmission, and Ecology) is a bioinformatics best-practice analysis pipeline for profiling the genomic repertoire and evolutionary dynamics of microorganisms with a particular focus on pathogens. We use ARETE is to identify important genes (e.g., those that confer antimicrobial resistance or contribute to virulence) and mobile genetic elements such as plasmids and genomic islands, and infer important routes by which these are transmitted using evidence from recombination, coevolution, and phylogenetic tree comparisons.
-ARETE produces a range of useful outputs, including those generated by each tool integrated into the pipeline, as well as summaries across the entire dataset such as phylogenetic profiles. Outputs from ARETE can also be fed into packages such as Coeus and MicroReact. Although ARETE was primarily developed with pathogens in mind, inference of pan-genomes, mobilomes, and phylogenomic histories can be performed for any set of microbial genomes, with the proviso that reference databases are much more complete for some groups than others! The tools in ARETE work best at the species and genus level of relatedness.
-A key design principle of ARETE is finding the right choice of software packages and parameter settings to support datasets of different sizes, introducing heuristics and swapping out tools as necessary. ARETE has been benchmarked on datasets ranging in size from fewer than ten to over 10,000 genomes from a multitude of species and genera including Enterococcus faecium, Escherichia coli, Listeria, and Salmonella. Another key principle is letting the user choose which subsets of the pipeline they wish to run; you may already have assembled genomes, or you may not care about, say, recombination detection. There are also cases where it is useful to manually review the outputs from a particular step before moving on to the next one. ARETE makes this easy to do.
+ARETE (Antimicrobial Resistance: Emergence, Transmission, and Ecology) is a bioinformatics best-practice analysis pipeline for profiling the genomic repertoire and evolutionary dynamics of microorganisms with a particular focus on pathogens. We use ARETE is to identify important genes (e.g., those that confer antimicrobial resistance or contribute to virulence) and mobile genetic elements such as plasmids and genomic islands, and infer important routes by which these are transmitted using evidence from recombination, coevolution, and phylogenetic tree comparisons.
+ARETE produces a range of useful outputs (see outputs), including those generated by each tool integrated into the pipeline, as well as summaries across the entire dataset such as phylogenetic profiles. Outputs from ARETE can also be fed into packages such as Coeus and MicroReact. Although ARETE was primarily developed with pathogens in mind, inference of pan-genomes, mobilomes, and phylogenomic histories can be performed for any set of microbial genomes, with the proviso that reference databases are much more complete for some groups than others! The tools in ARETE work best at the species and genus level of relatedness.
+A key design principle of ARETE is finding the right choice of software packages and parameter settings to support datasets of different sizes, introducing heuristics and swapping out tools as necessary. ARETE has been benchmarked on datasets ranging in size from fewer than ten to over 10,000 genomes from a multitude of species and genera including Enterococcus faecium, Escherichia coli, Listeria, and Salmonella. Another key principle is letting the user choose which subsets of the pipeline they wish to run; you may already have assembled genomes, or you may not care about, say, recombination detection. There are also cases where it is useful to manually review the outputs from a particular step before moving on to the next one. ARETE makes this easy to do.
Table of Contents
- About the pipeline
- Quick start
- A couple of examples -
- Documentation
- Credits
- Contributing to ARETE
- Citing ARETE @@ -120,7 +124,7 @@
- Raw Read QC (
FastQC)
@@ -154,13 +158,19 @@ - Genome alignment using SKA2 (
ska2) - Recombination detection using Verticall (
verticall) and/or Gubbins (gubbins) - Identification of coordinated gain and loss of features using EvolCCM (to add) +
- Phylogenetic inference of LGT using rSPR (to add) +
- Comparison of genomic neighbourhoods using the Gene Order Workflow (to add) +
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A couple of examples
Assembly, annotation, and pan-genome inference from a modestly sized dataset (50 or so genomes) from paired-end reads
Annotation to evolutionary dynamics on 300-ish genomes
Annotation to evolutionary dynamics on 10,000 genomes
-Documentation
-Documentation about the pipeline: usage and output.
Credits
The ARETE software was originally written and developed by Finlay Maguire and Alex Manuele, and is currently developed by João Cavalcante.
Rob Beiko is the PI of the ARETE project. The project Co-PI is Fiona Brinkman. Other project leads include Andrew MacArthur, Cedric Chauve, Chris Whidden, Gary van Domselaar, John Nash, Rahat Zaheer, and Tim McAllister.
@@ -217,9 +225,11 @@Contributing to ARETE
Thank you for your interest in contributing to ARETE. We are currently in the process of formalizing contribution guidelines. In the meantime, please feel free to open an issue describing your suggested changes.
Citing ARETE
-Please cite the tools used in your ARETE run: a comprehensive list can be found in the
-CITATIONS.mdfile.An early version of ARETE was used for assembly and feature prediction in the following paper: -Sanderson H, Gray KL, Manuele A, Maguire F, Khan A, Liu C, Navanekere Rudrappa C, Nash JHE, Robertson J, Bessonov K, Oloni M, Alcock BP, Raphenya AR, McAllister TA, Peacock SJ, Raven KE, Gouliouris T, McArthur AG, Brinkman FSL, Fink RC, Zaheer R, Beiko RG. Exploring the mobilome and resistome of Enterococcus faecium in a One Health context across two continents. Microb Genom. 2022 Sep;8(9):mgen000880. doi: 10.1099/mgen.0.000880. PMID: 36129737; PMCID: PMC9676038.
+Please cite the tools used in your ARETE run: A comprehensive list can be found in the
+CITATIONS.mdfile.An early version of ARETE was used for assembly and feature prediction in the following paper:
++
Sanderson H, Gray KL, Manuele A, Maguire F, Khan A, Liu C, Navanekere Rudrappa C, Nash JHE, Robertson J, Bessonov K, Oloni M, Alcock BP, Raphenya AR, McAllister TA, Peacock SJ, Raven KE, Gouliouris T, McArthur AG, Brinkman FSL, Fink RC, Zaheer R, Beiko RG. Exploring the mobilome and resistome of Enterococcus faecium in a One Health context across two continents. Microb Genom. 2022 Sep;8(9):mgen000880. doi: 10.1099/mgen.0.000880. PMID: 36129737; PMCID: PMC9676038.
+This pipeline uses code and infrastructure developed and maintained by the nf-core initative, and reused here under the MIT license.
@@ -279,5 +289,5 @@Citing ARETE
diff --git a/output/index.html b/output/index.html index dd26d0e9..86a663b7 100644 --- a/output/index.html +++ b/output/index.html @@ -48,6 +48,10 @@ - Usage +
- FAQ + +
- Output
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Pipeline information
About the pipeline
The nf-core project provided overall project template, pre-written software modules when available, and general best practice recommendations.ARETE is organized as a series of subworkflows, each of which executes a different conceptual step of the pipeline. The subworkflow orgnaization provides suitable entry and exit points for users who want to run only a portion of the full pipeline.
Genome subsetting:
-The user can optionally subdivide their set of genomes into lineages as defined by PopPUNK (See documentation). PopPUNK quickly subdivides a set of genomes into 'lineages' based on core and accessory genome identity. If this option is selected, all genomes will still be annotated, but cross-genome comparisons (e.g., pan-genome inference and phylogenomics) will use only a single representative genome. The user can run PopPUNK with a spread of different thresholds and decide how to proceed based on the number of lineages produced. l
+The user can optionally subdivide their set of genomes into lineages as defined by PopPUNK (See documentation). PopPUNK quickly subdivides a set of genomes into 'lineages' based on core and accessory genome identity. If this option is selected, all genomes will still be annotated, but cross-genome comparisons (e.g., pan-genome inference and phylogenomics) will use only a single representative genome. The user can run PopPUNK with a spread of different thresholds and decide how to proceed based on the number of lineages produced.
Short-read processing and assembly:
About the pipeline
Coevolution: -- Identification of coordinated gain and loss of features using EvolCCM (to add)
-Lateral gene transfer: -- Phylogenetic inference of LGT using rSPR (to add)
-Gene order: -- Comparison of genomic neighbourhoods using the Gene Order Workflow (to add)
-See our roadmap for future development targets.
+Coevolution:
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Lateral gene transfer:
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Gene order:
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See our roadmap for a full list of future development targets.
Quick Start
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