The Gammaproteobacteria Epidemiologic Annotation Pipeline

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Dickey, John W. Schmidt, James L. Bono, Manita Guragain This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3894530/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Salmonella enterica and Escherichia coli are major food-borne human pathogens, and their genomes are routinely sequenced for clinical surveillance. Computational pipelines designed for analyzing pathogen genomes should both utilize the most current information from annotation databases and increase the coverage of these databases over time. We report the development of the Gammaproteobacteria Epidemiologic Annotation (GEA) pipeline to analyze large batches of E. coli and S. enterica genomes. The GEA pipeline takes as input paired Illumina raw reads files which are then assembled followed by annotation. Alternatively, assemblies can be provided as input and directly annotated. The pipeline provides predictive genome annotations for E. coli and S. enterica with a focus on the Center for Genomic Epidemiology tools. Annotation results are provided as a tab delimited text file. The GEA pipeline is designed for large-scale E. coli and S. enterica genome assembly and characterization using the Center for Genomic Epidemiology command-line tools and high-performance computing. Large scale annotation is demonstrated by an analysis of more than 14,000 Salmonella genome assemblies. Testing the GEA pipeline on E. coli raw reads demonstrates reproducibility across multiple compute environments and computational usage is optimized on high performance computers. Biological sciences/Computational biology and bioinformatics Biological sciences/Microbiology Biological sciences/Molecular biology Figures Figure 1 Introduction Salmonella enterica (hereafter Salmonella ) are estimated to cause at least 1 million illnesses in the United States each year [ 1 ] . Escherichia coli are ubiquitous in a wide variety of environments relevant to food safety including food animal gastrointestinal systems, animal production sites, human gastrointestinal systems, meats, and manure impacted soils. A small but clinically important sub-set of E. coli are pathogenic. The ubiquitous nature of E. coli contributes to their relevance beyond food safety. Due to their prominence and small genome size, Salmonella and E. coli are also two of the top organisms with available whole genome sequencing read archives ( https://www.ncbi.nlm.nih.gov/sra?term=(%22public%22%5BAccess%5D)%20AND%20%22genomic%22%5BSource%5D ) and assemblies ( https://www.ncbi.nlm.nih.gov/genome/browse#!/overview/ ). Such large datasets often rely on high-performance computing to accelerate computational tasks via increased RAM, threads, and parallelization [ 2 ] . Large datasets can benefit from data analysis pipelines, which process many input files with an initial set of user specifications and distill the results to a small number of organized outputs for interpretation [ 3 ] . Useful pipelines for epidemiologic annotation should take advantage of the most up-to-date reference information available. Actively curated reference databases meet this need by rapidly incorporating newly released genomic data. The interplay between these two dependencies can be thought of as a positive feedback loop wherein 1. Running the pipeline on new strains improves the database coverage and quality by exposing knowledge gaps and 2. The database improvement leads to more accurate search hits when running the pipeline on new strains. Here, we introduce the GEA pipeline. GEA stands for Gammaproteobacteria Epidemiologic Annotation. Analyses central to the GEA pipeline are those using Center for Genomic Epidemiology (CGE) developed tools [ 4 ] . The databases for these tools to search are updated frequently, facilitating the positive feedback loop between our pipeline and these databases. Several existing pipelines utilize CGE developed tools [ 5 , 6 , 7 , 8 , 9 ] . But we are unaware of any other published pipelines, which use FimTyper [ 10 ] , MLST [ 11 ] , PlasmidFinder [ 12 ] , ResFinder [ 13 ] , SerotypeFinder [ 14 ] , and VirulenceFinder [ 15 ] in tandem. Another important feature of the pipeline is the use of a container. Containers allow for compute mobility [ 16 ] and provide an increased level of reproducibility [ 17 ] . The container housing the software tools for running the GEA pipeline also takes advantage of the Scientific File System (SCIF) [ 18 ] , providing independent mount points to different apps in the container with incompatible environmental requirements. Prior versions of the GEA pipeline have been used in published research [ 19 , 20 , 21 ] in food safety, risk assessment, antimicrobial resistance gene transfer, and virulence research at the US Meat Animal Research Center. This demonstrates the utility of the pipeline and the benefit of the distilled annotation summary across hundreds of genomes. This paper describes the methods used for creating the pipeline and provides a kilo-scale demonstration of the pipeline on S. enterica assemblies. The pipeline is available from github.com/Phylloxera/GEA-dev. Results Computer Resource Usage Table 1 provides the usage summary for testing. The run time varied from 5.04 to 34.56 hours (3.15 to 21.6 minutes-per- E .- coli -library). Table 1 Computational resources used for GEA Pipeline testing. Computer Name CPUs RAM (GB) OS Job Scheduler HPC Apptainer Version Run Time (minutes-per- E. - coli -library) Moose 64 1986 Oracle Linux Server 8.8 NA Yes 1.2.3 3.16 Ceres 72 360 AlmaLinux 9.2 (Turquoise Kodkod) slurm Yes 1.2.2 3.23 Atlas 48 360 CentOS Linux 7 (Core) slurm Yes 1.1.6 3.15 Desktop 12 20 Ubuntu 22.04.3 LTS NA No 1.2.4 21.6 Output Table 2 provides partial output of the metadata.txt tab delimited file showing a portion of the summary. The data suggests a relationship between FIM type and Antimicrobial Resistance Gene (ARG) content for the plate of E . coli libraries. The complete summary output file (GEA_ecoli_test_Ceres_metadata.txt) from USDA Ceres [ 22 ] is in Supplementary Data S1. Table 2 A portion of the GEA Pipeline E. coli annotation output showing acquired antimicrobial resistance gene content by fim type. 79 libraries with FimH82 and 0 resistance genes are not shown. Library FimType AqResGeneCount AqResGeneList SAM128606 FimH36 4 aph(6)-Id,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,sul2,tet(B) SAM128608 FimH36 4 aph(6)-Id,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,sul2,tet(B) SAM128609 FimH36 4 aph(6)-Id,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,sul2,tet(B) SAM128625 FimH36 4 aph(6)-Id,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,sul2,tet(B) SAM128626 FimH36 4 aph(6)-Id,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,sul2,tet(B) SAM128645 FimH36 4 aph(6)-Id,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,sul2,tet(B) SAM128646 FimH36 4 aph(6)-Id,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,sul2,tet(B) SAM128676 FimH36 4 aph(6)-Id,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,sul2,tet(B) SAM128693 FimH36 4 aph(6)-Id,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,sul2,tet(B) SAM128613 FimH36 0 SAM128614 FimH54 3 aph(6)-Id,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,tet(B) SAM128631 FimH54 3 aph(6)-Id,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,tet(B) SAM128629 FimH54 1 tet(A) SAM128611 FimH552 4 aph(3'')-Ib,blaCMY-2,sul2,tet(A) SAM128642 FimH82 3 aph(3'')-Ib,sul2,tet(A) SAM128640 FimH82 1 tet(J) SAM128598 FimH82 0 Demonstration The pipeline ran on the 14,310 S. enterica assemblies in ~ 72 hours (~ 18 seconds-per- S .- enterica -assembly) on the USDA/Mississippi State University Atlas cluster [ 22 ] . ResFinder [ 13 ] ARG abundance positively correlated with PlasmidFinder [ 12 ] plasmid abundance (Fig. 1 ). The complete summary output file is in GEA_senterica_demo_Atlas_metadata.txt in Supplementary Data S1. Discussion A diverse set of bioinformatic tools has been developed for phenotypic prediction based on genomic data, especially for human pathogens. These tools often grow out of the requirements sought by a group of researchers. In the case of GEA, these included the desire to assemble and run CGE tools at the command-line on large numbers of strains with a single summary output and to have genome assemblies in a single directory ready for submission to NCBI. The pipeline has since been run independently across the labs of 2 Principal Investigators, in-house. Based on internal feedback, the Bioinformatician suspected that some non-reproducibility across runs with the same input data (sometimes with analyses conducted many months apart) was due to updates in the actively curated CGE databases. This was confirmed in certain instances, but other instances may have been due to bugs in the code. For this reason, the user has the option to update their local copy of the CGE databases and use the most up-to-date version, or to leave the databases static for reproducibility across independent computational runs. Reproducibility has long been an aspiration of scientific analysis, however database dependent analyses may demonstrably benefit from non-reproducibility as database coverage increases with the passage of time. In our testing phase, we sought to have reproducibility across multiple computational environments and this was largely achieved. The only difference in the outputs across high performance computers was due to the contig names assigned by shovill ( https://github.com/tseemann/shovill ). In all these cases, the length, coverage, and Pilon [ 23 ] name were identical whereas shovill assigned contig integers differed by 1. Furthermore, shovill contig names included the date assembled, an additional possible source of discrepancy for runs taking place on different days. E.g., contig0020 5 len = 509 cov = 31.1 corr = 0 origname = NODE_348_length_509_cov_31.119617_pilon sw = shovill-spades/1.1.0 date = 202311 07 on Ceres vs contig0020 4 len = 509 cov = 31.1 corr = 0 origname = NODE_348_length_509_cov_31.119617_pilon sw = shovill-spades/1.1.0 date = 202311 01 on Moose (discrepancies in bold; example from row 15, column 128 of GEA_ecoli_test_Ceres_metadata.txt in Supplementary Data S1). The identical coverage and Pilon designation indicates that, in all cases, these were identical contigs, but that the final integer contig name assignment in shovill may not be deterministic. The other discrepancy was caused by insufficient memory being available to Skesa [ 24 ] on the Desktop computer causing two libraries to not assemble resulting in missing Skesa plasmid annotations (rows 33 and 35, columns 21 and 22 of GEA_ecoli_test_Ceres_metadata.txt in Supplementary Data S1). Importantly, these assembly failures were documented by the GEA pipeline log, which alerted the Desktop user. Apart from these two discrepancy sources (shovill final contig naming and Skesa memory requirements not being met), we expect that GEA will provide reproducible results for a given version of the pipeline and databases. GEA has clear advantages and limitations relative to tools with similar goals. First, long available tools, such as nullarbor ( https://github.com/tseemann/nullarbor ) and TORMES [ 7 ] have the distinction of an active user base, citations, and more time under development. Bacannot [ 25 ] is a newer tool, which is container based like GEA. Software containerization increases reproducibility over OS specific source-compile-install-run and cross-platform package manager methodologies [ 17 ] . RSYD-BASIC [ 26 ] is also a newer tool which produces a tabular output somewhat like that produced by GEA. The lack of a web server option is a limitation of GEA. However, having a batch command-line implementation of CGE tools was a central functionality driving the development of GEA. GEA also lacks phylogenetic methods, except to the extent that typing predictions are phylogenetically informative. Currently, GEA is only indicated for E. coli and S. enterica . The strongest advantages of GEA are a single dependency (Apptainer [ 27 ] ), batch processing in an HPC environment proven by hundreds of successful analyses of illumina raw read libraries 21 , and the successful demonstration at the kilo-scale of a processing rate of ~ 18-seconds-per- S. - enterica -assembly as demonstrated in this report. Other tools, which could be incorporated into GEA in the future include long-read raw data inputs and identification of additional genotypes/phenotypes/pathogens as new CGE tools are released. GEA is now available for other users with institutional HPCs for rapid characterization of large batches of E. coli and Salmonella genomes from diverse sample sources. GEA is available for download from github.com/Phylloxera/GEA-dev. Methods Pipeline GEA is written in Bash. The current version has several new features relative to prior development iterations used in previous work. The user can specify the taxon. The user can specify whether to update their local copy of the databases. The input data can be raw gzipped paired reads (fastq) or genome assemblies (fasta). The following new tools have been added: Ezclermont [ 28 ] , FimTyper [ 10 ] , VirulenceFinder [ 15 ] , and the 5 loci databases( https://github.com/Phylloxera/5loci ). The container recipe utilizes SCIF [ 18 ] for software environment modularity inside the software container. The container recipe, container, and pipeline are made available via https://github.com/Phylloxera/GEA-dev . GEA also utilizes SeqSero2 [ 29 ] , BLAST+ [ 30 ] and stats.sh( https://jgi.doe.gov/data-and-tools/software-tools/bbtools/bb-tools-user-guide/statistics-guide/ ). BLAST is used as the search method for the CGE tools and to query the custom 5 loci databases created for the current version. When the input files to GEA are raw reads, assemblies are created with Shovill( https://github.com/tseemann/shovill ) and SKESA [ 26 ] and additional plasmid annotations are provided in the output where circularized by Skesa. The shovill pipeline utilizes SPAdes [ 31 ] , Velvet [ 32 ] , Lighter [ 33 ] , FLASh [ 34 ] , SAMtools [ 35 ] , BWA-MEM [ 36 ] , KMC [ 37 ] , seqtk( https://github.com/lh3/seqtk ), pigz( https://zlib.net/pigz/ ), Pilon [ 23 ] , Trimmomatic [ 38 ] and samclip( https://github.com/tseemann/samclip ). The container with the needed software was created using Apptainer [ 27 ] . Testing GEA was tested on 3 linux high performance computers and a desktop computer with Hyper-V enabled on Windows 10 Professional (Table 1 ). The data used in testing were a single plate of 96 illumina E. coli raw read libraries from a long-term evolutionary study. Testing utilized the -u F option to query identical versions of the CGE databases and evaluate reproducibility across the compute environments. Demonstration To demonstrate the pipeline at kilo-scale, GEA was run on 14,310 Salmonella enterica genome assemblies released during October, 2023. The assemblies were downloaded on November 6, 2023 using datasets( https://www.ncbi.nlm.nih.gov/datasets ) with download genome options: taxon 28901, --include genome, --exclude-atypical, --released-after 10/1/2023, --released-before 10/31/2023, --assembly-source GenBank, and --dehydrated. Fasta files were moved to a single folder to be used as input. GEA was run on the Atlas high performance computer system of Mississippi State University and the US Department of Agriculture [ 22 ] on November 11, 2023, with options: -t senterica, -u F, -r 336:00:00, -m 360G, and -c 48. Initial tests predicted a run time of 2–5 days. Declarations Acknowledgments: Stephanie Schmidt provided secretarial support. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA. The USDA is an equal opportunity provider and employer. Author Contributions: Conceptualization (JWS, MG); Data Curation (AMD); Formal Analysis (AMD); Investigation (AMD); Methodology (AMD, JWS, JLB, MG); Resources (AMD, JWS, JLB, MG); Software (AMD); Visualization (AMD); Writing (AMD, JWS, MG); Editing (JWS, JLB, MG) Data and Software Availability: Underlying data Test data: The test data can be made available upon request. Demonstration data: The datasets download genome command specifications are described in the Methods. Extended data USDA-NAL: Supplementary Data S1. This project contains the following extended data: -GEA_ecoli_test_Ceres_metadata.txt. GEA Pipeline summary output (metadata.txt) for 96 E. coli Illumina libraries used as test data. -GEA_senterica_demo_Atlas_metadata.txt GEA Pipeline summary output (metadata.txt) for 14,310 S. enterica assemblies used as demonstration data. Extended data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Software and Code The full pipeline (as an apptainer container) and files required for building the pipeline from scratch are available from: [https://github.com/Phylloxera/GEA-dev] The version used in all analyses herein is 0.0.1-alpha.1 GEA License: [https://github.com/Phylloxera/GEA-dev/blob/main/LICENSE] The five loci BLAST databases are available from: [https://github.com/Phylloxera/5loci] 5loci License: [https://github.com/Phylloxera/5loci/blob/main/LICENSE] Additional Information: Competing interests No competing interests were disclosed. Grant information Funding for this research was provided by the USDA. 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Supplementary Files SupplementaryDataS1.zip Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 02 May, 2024 Reviews received at journal 30 Apr, 2024 Reviewers agreed at journal 15 Apr, 2024 Reviews received at journal 06 Mar, 2024 Reviewers agreed at journal 23 Feb, 2024 Reviewers agreed at journal 12 Feb, 2024 Reviewers invited by journal 12 Feb, 2024 Editor assigned by journal 12 Feb, 2024 Editor invited by journal 05 Feb, 2024 Submission checks completed at journal 05 Feb, 2024 First submitted to journal 24 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Dickey","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYPACZh4GHiD1gYGBsYEkLYwzSNHCANLCzEOMFv72sw8fFzBYy/D3HD4mbdtWJ9vfwH7xMQ8eLRJn0o2NZzCk80icbUuTzm07bDzjAE+xMT4tDAfS2KR5GA7zGPDzmAG1HEhsOMCTJjkDjw75889gWvi/SVu21SXOJ6TF4AbMFt4eNmnGNubEDQfYj0l8wKPF8MYzZmMeA6Bfzhwztuw5d9h442EeZgN8WuTOpzE+5qmwtufvSX5440dZney84+0PHyTg8z7EeWCSRQJMMfMYENQAA8xQ57A/IFrLKBgFo2AUjAgAAGEgRbVksWf2AAAAAElFTkSuQmCC","orcid":"","institution":"United States Department of Agriculture","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Aaron","middleName":"M.","lastName":"Dickey","suffix":""},{"id":271356218,"identity":"e7780fe6-28ae-4b2a-a6c2-67f7cb75d425","order_by":1,"name":"John W. Schmidt","email":"","orcid":"","institution":"United States Department of Agriculture","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"John","middleName":"W.","lastName":"Schmidt","suffix":""},{"id":271356219,"identity":"705038b6-dea2-459a-8e92-ad8f2b819a9c","order_by":2,"name":"James L. Bono","email":"","orcid":"","institution":"United States Department of Agriculture","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"James","middleName":"L.","lastName":"Bono","suffix":""},{"id":271356220,"identity":"81ddcee2-576e-4896-ba9d-ef56b760fa7f","order_by":3,"name":"Manita Guragain","email":"","orcid":"","institution":"United States Department of Agriculture","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Manita","middleName":"","lastName":"Guragain","suffix":""}],"badges":[],"createdAt":"2024-01-24 15:32:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3894530/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3894530/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50808728,"identity":"eedb2c96-9a6c-4df6-930e-c004fb47bdf8","added_by":"auto","created_at":"2024-02-07 16:55:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":338287,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between ResFinder ARG abundance and PlasmidFinder plasmid abundance.\u003c/strong\u003e Data are GEA summary results from 14,310 \u003cem\u003eSalmonella enterica\u003c/em\u003e genome assemblies released by NCBI during October, 2023. Statistical tests were conducted and plot was rendered using ggbetweenstats function from the ggstatsplot package(https://indrajeetpatil.github.io/ggstatsplot/) version 0.12.1 in R version 4.3.1.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3894530/v1/6b0cb28baa00b0a2b01f335c.png"},{"id":50808990,"identity":"5fc8dbfa-9def-42ca-a6e6-4790c5740c6c","added_by":"auto","created_at":"2024-02-07 17:03:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":506566,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3894530/v1/f390d30e-9fbe-4159-84c9-d85db1d675b4.pdf"},{"id":50808730,"identity":"459646f6-72d0-4c77-8c20-b79ab4e3555c","added_by":"auto","created_at":"2024-02-07 16:55:20","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2969618,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryDataS1.zip","url":"https://assets-eu.researchsquare.com/files/rs-3894530/v1/97eb8a33ef2667d4334330e1.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Gammaproteobacteria Epidemiologic Annotation Pipeline","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cem\u003eSalmonella enterica\u003c/em\u003e (hereafter \u003cem\u003eSalmonella\u003c/em\u003e) are estimated to cause at least 1\u0026nbsp;million illnesses in the United States each year\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003eEscherichia coli\u003c/em\u003e are ubiquitous in a wide variety of environments relevant to food safety including food animal gastrointestinal systems, animal production sites, human gastrointestinal systems, meats, and manure impacted soils. A small but clinically important sub-set of \u003cem\u003eE. coli\u003c/em\u003e are pathogenic. The ubiquitous nature of \u003cem\u003eE. coli\u003c/em\u003e contributes to their relevance beyond food safety. Due to their prominence and small genome size, \u003cem\u003eSalmonella\u003c/em\u003e and \u003cem\u003eE. coli\u003c/em\u003e are also two of the top organisms with available whole genome sequencing read archives (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/sra?term=(%22public%22%5BAccess%5D)%20AND%20%22genomic%22%5BSource%5D\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/sra?term=(%22public%22%5BAccess%5D)%20AND%20%22genomic%22%5BSource%5D\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and assemblies (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/genome/browse#!/overview/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/genome/browse#!/overview/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Such large datasets often rely on high-performance computing to accelerate computational tasks via increased RAM, threads, and parallelization\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Large datasets can benefit from data analysis pipelines, which process many input files with an initial set of user specifications and distill the results to a small number of organized outputs for interpretation\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eUseful pipelines for epidemiologic annotation should take advantage of the most up-to-date reference information available. Actively curated reference databases meet this need by rapidly incorporating newly released genomic data. The interplay between these two dependencies can be thought of as a positive feedback loop wherein 1. Running the pipeline on new strains improves the database coverage and quality by exposing knowledge gaps and 2. The database improvement leads to more accurate search hits when running the pipeline on new strains.\u003c/p\u003e \u003cp\u003eHere, we introduce the GEA pipeline. GEA stands for Gammaproteobacteria Epidemiologic Annotation. Analyses central to the GEA pipeline are those using Center for Genomic Epidemiology (CGE) developed tools\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. The databases for these tools to search are updated frequently, facilitating the positive feedback loop between our pipeline and these databases. Several existing pipelines utilize CGE developed tools\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. But we are unaware of any other published pipelines, which use FimTyper\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, MLST\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, PlasmidFinder\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, ResFinder\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, SerotypeFinder\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, and VirulenceFinder\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e in tandem.\u003c/p\u003e \u003cp\u003eAnother important feature of the pipeline is the use of a container. Containers allow for compute mobility\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e and provide an increased level of reproducibility\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. The container housing the software tools for running the GEA pipeline also takes advantage of the Scientific File System (SCIF)\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, providing independent mount points to different apps in the container with incompatible environmental requirements.\u003c/p\u003e \u003cp\u003ePrior versions of the GEA pipeline have been used in published research\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e in food safety, risk assessment, antimicrobial resistance gene transfer, and virulence research at the US Meat Animal Research Center. This demonstrates the utility of the pipeline and the benefit of the distilled annotation summary across hundreds of genomes. This paper describes the methods used for creating the pipeline and provides a kilo-scale demonstration of the pipeline on \u003cem\u003eS. enterica\u003c/em\u003e assemblies. The pipeline is available from github.com/Phylloxera/GEA-dev.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eComputer Resource Usage\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides the usage summary for testing. The run time varied from 5.04 to 34.56 hours (3.15 to 21.6 minutes-per-\u003cem\u003eE\u003c/em\u003e.-\u003cem\u003ecoli\u003c/em\u003e-library).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComputational resources used for GEA Pipeline testing.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComputer Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCPUs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRAM (GB)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJob Scheduler\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHPC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eApptainer Version\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRun Time (minutes-per-\u003cem\u003eE.\u003c/em\u003e-\u003cem\u003ecoli\u003c/em\u003e-library)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOracle Linux Server 8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCeres\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAlmaLinux 9.2 (Turquoise Kodkod)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eslurm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtlas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCentOS Linux 7 (Core)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eslurm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDesktop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUbuntu 22.04.3 LTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e21.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eOutput\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides partial output of the metadata.txt tab delimited file showing a portion of the summary. The data suggests a relationship between FIM type and Antimicrobial Resistance Gene (ARG) content for the plate of \u003cem\u003eE\u003c/em\u003e. \u003cem\u003ecoli\u003c/em\u003e libraries. The complete summary output file (GEA_ecoli_test_Ceres_metadata.txt) from USDA Ceres\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e is in Supplementary Data S1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eA portion of the GEA Pipeline \u003cem\u003eE. coli\u003c/em\u003e annotation output showing acquired antimicrobial resistance gene content by \u003cem\u003efim\u003c/em\u003e type. 79 libraries with FimH82 and 0 resistance genes are not shown.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLibrary\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFimType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAqResGeneCount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAqResGeneList\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAM128606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFimH36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaph(6)-Id,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,sul2,tet(B)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAM128608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFimH36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaph(6)-Id,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,sul2,tet(B)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAM128609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFimH36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaph(6)-Id,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,sul2,tet(B)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAM128625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFimH36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaph(6)-Id,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,sul2,tet(B)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAM128626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFimH36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaph(6)-Id,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,sul2,tet(B)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAM128645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFimH36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaph(6)-Id,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,sul2,tet(B)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAM128646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFimH36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaph(6)-Id,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,sul2,tet(B)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAM128676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFimH36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaph(6)-Id,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,sul2,tet(B)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAM128693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFimH36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaph(6)-Id,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,sul2,tet(B)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAM128613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFimH36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAM128614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFimH54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaph(6)-Id,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,tet(B)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAM128631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFimH54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaph(6)-Id,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,aph(3'')-Ib,tet(B)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAM128629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFimH54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003etet(A)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAM128611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFimH552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaph(3'')-Ib,blaCMY-2,sul2,tet(A)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAM128642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFimH82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaph(3'')-Ib,sul2,tet(A)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAM128640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFimH82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003etet(J)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAM128598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFimH82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDemonstration\u003c/h2\u003e \u003cp\u003eThe pipeline ran on the 14,310 \u003cem\u003eS. enterica\u003c/em\u003e assemblies in ~\u0026thinsp;72 hours (~\u0026thinsp;18 seconds-per-\u003cem\u003eS\u003c/em\u003e.-\u003cem\u003eenterica\u003c/em\u003e-assembly) on the USDA/Mississippi State University Atlas cluster\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. ResFinder\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e ARG abundance positively correlated with PlasmidFinder\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e plasmid abundance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The complete summary output file is in GEA_senterica_demo_Atlas_metadata.txt in Supplementary Data S1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eA diverse set of bioinformatic tools has been developed for phenotypic prediction based on genomic data, especially for human pathogens. These tools often grow out of the requirements sought by a group of researchers. In the case of GEA, these included the desire to assemble and run CGE tools at the command-line on large numbers of strains with a single summary output and to have genome assemblies in a single directory ready for submission to NCBI.\u003c/p\u003e \u003cp\u003eThe pipeline has since been run independently across the labs of 2 Principal Investigators, in-house. Based on internal feedback, the Bioinformatician suspected that some non-reproducibility across runs with the same input data (sometimes with analyses conducted many months apart) was due to updates in the actively curated CGE databases. This was confirmed in certain instances, but other instances may have been due to bugs in the code. For this reason, the user has the option to update their local copy of the CGE databases and use the most up-to-date version, or to leave the databases static for reproducibility across independent computational runs. Reproducibility has long been an aspiration of scientific analysis, however database dependent analyses may demonstrably benefit from non-reproducibility as database coverage increases with the passage of time.\u003c/p\u003e \u003cp\u003eIn our testing phase, we sought to have reproducibility across multiple computational environments and this was largely achieved. The only difference in the outputs across high performance computers was due to the contig names assigned by shovill (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/tseemann/shovill\u003c/span\u003e\u003cspan address=\"https://github.com/tseemann/shovill\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In all these cases, the length, coverage, and Pilon\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e name were identical whereas shovill assigned contig integers differed by 1. Furthermore, shovill contig names included the date assembled, an additional possible source of discrepancy for runs taking place on different days. E.g., contig0020\u003cb\u003e5\u003c/b\u003e len\u0026thinsp;=\u0026thinsp;509 cov\u0026thinsp;=\u0026thinsp;31.1 corr\u0026thinsp;=\u0026thinsp;0 origname\u0026thinsp;=\u0026thinsp;NODE_348_length_509_cov_31.119617_pilon sw\u0026thinsp;=\u0026thinsp;shovill-spades/1.1.0 date\u0026thinsp;=\u0026thinsp;202311\u003cb\u003e07\u003c/b\u003e on Ceres vs contig0020\u003cb\u003e4\u003c/b\u003e len\u0026thinsp;=\u0026thinsp;509 cov\u0026thinsp;=\u0026thinsp;31.1 corr\u0026thinsp;=\u0026thinsp;0 origname\u0026thinsp;=\u0026thinsp;NODE_348_length_509_cov_31.119617_pilon sw\u0026thinsp;=\u0026thinsp;shovill-spades/1.1.0 date\u0026thinsp;=\u0026thinsp;202311\u003cb\u003e01\u003c/b\u003e on Moose (discrepancies in bold; example from row 15, column 128 of GEA_ecoli_test_Ceres_metadata.txt in Supplementary Data S1). The identical coverage and Pilon designation indicates that, in all cases, these were identical contigs, but that the final integer contig name assignment in shovill may not be deterministic. The other discrepancy was caused by insufficient memory being available to Skesa\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e on the Desktop computer causing two libraries to not assemble resulting in missing Skesa plasmid annotations (rows 33 and 35, columns 21 and 22 of GEA_ecoli_test_Ceres_metadata.txt in Supplementary Data S1). Importantly, these assembly failures were documented by the GEA pipeline log, which alerted the Desktop user. Apart from these two discrepancy sources (shovill final contig naming and Skesa memory requirements not being met), we expect that GEA will provide reproducible results for a given version of the pipeline and databases.\u003c/p\u003e \u003cp\u003eGEA has clear advantages and limitations relative to tools with similar goals. First, long available tools, such as nullarbor (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/tseemann/nullarbor\u003c/span\u003e\u003cspan address=\"https://github.com/tseemann/nullarbor\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and TORMES\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e have the distinction of an active user base, citations, and more time under development. Bacannot\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e is a newer tool, which is container based like GEA. Software containerization increases reproducibility over OS specific source-compile-install-run and cross-platform package manager methodologies\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. RSYD-BASIC\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e is also a newer tool which produces a tabular output somewhat like that produced by GEA. The lack of a web server option is a limitation of GEA. However, having a batch command-line implementation of CGE tools was a central functionality driving the development of GEA. GEA also lacks phylogenetic methods, except to the extent that typing predictions are phylogenetically informative. Currently, GEA is only indicated for \u003cem\u003eE. coli\u003c/em\u003e and \u003cem\u003eS. enterica\u003c/em\u003e. The strongest advantages of GEA are a single dependency (Apptainer\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e), batch processing in an HPC environment proven by hundreds of successful analyses of illumina raw read libraries\u003csup\u003e21\u003c/sup\u003e, and the successful demonstration at the kilo-scale of a processing rate of ~\u0026thinsp;18-seconds-per-\u003cem\u003eS.\u003c/em\u003e-\u003cem\u003eenterica\u003c/em\u003e-assembly as demonstrated in this report.\u003c/p\u003e \u003cp\u003eOther tools, which could be incorporated into GEA in the future include long-read raw data inputs and identification of additional genotypes/phenotypes/pathogens as new CGE tools are released. GEA is now available for other users with institutional HPCs for rapid characterization of large batches of \u003cem\u003eE. coli\u003c/em\u003e and \u003cem\u003eSalmonella\u003c/em\u003e genomes from diverse sample sources. GEA is available for download from github.com/Phylloxera/GEA-dev.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePipeline\u003c/h2\u003e \u003cp\u003eGEA is written in Bash. The current version has several new features relative to prior development iterations used in previous work.\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe user can specify the taxon.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe user can specify whether to update their local copy of the databases.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe input data can be raw gzipped paired reads (fastq) or genome assemblies (fasta).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe following new tools have been added: Ezclermont\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e, FimTyper\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, VirulenceFinder\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, and the 5 loci databases(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/Phylloxera/5loci\u003c/span\u003e\u003cspan address=\"https://github.com/Phylloxera/5loci\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe container recipe utilizes SCIF\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e for software environment modularity inside the software container.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe container recipe, container, and pipeline are made available via \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/Phylloxera/GEA-dev\u003c/span\u003e\u003cspan address=\"https://github.com/Phylloxera/GEA-dev\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eGEA also utilizes SeqSero2\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e, BLAST+\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e and stats.sh(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://jgi.doe.gov/data-and-tools/software-tools/bbtools/bb-tools-user-guide/statistics-guide/\u003c/span\u003e\u003cspan address=\"https://jgi.doe.gov/data-and-tools/software-tools/bbtools/bb-tools-user-guide/statistics-guide/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). BLAST is used as the search method for the CGE tools and to query the custom 5 loci databases created for the current version. When the input files to GEA are raw reads, assemblies are created with Shovill(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/tseemann/shovill\u003c/span\u003e\u003cspan address=\"https://github.com/tseemann/shovill\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and SKESA\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e and additional plasmid annotations are provided in the output where circularized by Skesa. The shovill pipeline utilizes SPAdes\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e, Velvet\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e, Lighter\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e, FLASh\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e, SAMtools\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e, BWA-MEM\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e, KMC\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e, seqtk(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/lh3/seqtk\u003c/span\u003e\u003cspan address=\"https://github.com/lh3/seqtk\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), pigz(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://zlib.net/pigz/\u003c/span\u003e\u003cspan address=\"https://zlib.net/pigz/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Pilon\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e, Trimmomatic\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e and samclip(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/tseemann/samclip\u003c/span\u003e\u003cspan address=\"https://github.com/tseemann/samclip\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The container with the needed software was created using Apptainer\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eTesting\u003c/h2\u003e \u003cp\u003eGEA was tested on 3 linux high performance computers and a desktop computer with Hyper-V enabled on Windows 10 Professional (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The data used in testing were a single plate of 96 illumina \u003cem\u003eE. coli\u003c/em\u003e raw read libraries from a long-term evolutionary study. Testing utilized the -u F option to query identical versions of the CGE databases and evaluate reproducibility across the compute environments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDemonstration\u003c/h2\u003e \u003cp\u003eTo demonstrate the pipeline at kilo-scale, GEA was run on 14,310 \u003cem\u003eSalmonella enterica\u003c/em\u003e genome assemblies released during October, 2023. The assemblies were downloaded on November 6, 2023 using datasets(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/datasets\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/datasets\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with download genome options: taxon 28901, --include genome, --exclude-atypical, --released-after 10/1/2023, --released-before 10/31/2023, --assembly-source GenBank, and --dehydrated. Fasta files were moved to a single folder to be used as input. GEA was run on the Atlas high performance computer system of Mississippi State University and the US Department of Agriculture\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e on November 11, 2023, with options: -t senterica, -u F, -r 336:00:00, -m 360G, and -c 48. Initial tests predicted a run time of 2\u0026ndash;5 days.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStephanie Schmidt provided secretarial support. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA. The USDA is an equal opportunity provider and employer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization (JWS, MG); Data Curation (AMD); Formal Analysis (AMD); Investigation (AMD); Methodology (AMD, JWS, JLB, MG); Resources (AMD, JWS, JLB, MG); Software (AMD); Visualization (AMD); Writing (AMD, JWS, MG); Editing (JWS, JLB, MG)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and Software Availability:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnderlying data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTest data: The test data can be made available upon request.\u003c/p\u003e\n\u003cp\u003eDemonstration data: The datasets download genome command specifications are described in the Methods.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtended data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUSDA-NAL: Supplementary Data S1.\u003c/p\u003e\n\u003cp\u003eThis project contains the following extended data:\u003c/p\u003e\n\u003cp\u003e-GEA_ecoli_test_Ceres_metadata.txt. GEA Pipeline summary output (metadata.txt) for 96 \u003cem\u003eE. coli\u003c/em\u003e Illumina libraries used as test data.\u003c/p\u003e\n\u003cp\u003e-GEA_senterica_demo_Atlas_metadata.txt GEA Pipeline summary output (metadata.txt) for 14,310 \u003cem\u003eS. enterica\u003c/em\u003e assemblies used as demonstration data.\u003c/p\u003e\n\u003cp\u003eExtended data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSoftware and Code\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe full pipeline (as an apptainer container) and files required for building the pipeline from scratch are available from: [https://github.com/Phylloxera/GEA-dev]\u003c/p\u003e\n\u003cp\u003eThe version used in all analyses herein is 0.0.1-alpha.1\u003c/p\u003e\n\u003cp\u003eGEA License: [https://github.com/Phylloxera/GEA-dev/blob/main/LICENSE]\u003c/p\u003e\n\u003cp\u003eThe five loci BLAST databases are available from: [https://github.com/Phylloxera/5loci]\u003c/p\u003e\n\u003cp\u003e5loci License: [https://github.com/Phylloxera/5loci/blob/main/LICENSE]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional Information:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo competing interests were disclosed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGrant information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding for this research was provided by the USDA. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eScallan, E. \u003cem\u003eet al.\u0026nbsp;\u003c/em\u003eFoodborne illness acquired in the United States--major pathogens\u003cem\u003e.\u003c/em\u003e \u003cem\u003eEmerg. Infect. Dis.\u003c/em\u003e \u003cstrong\u003e17,\u003c/strong\u003e 7-15. (2011).\u0026nbsp;doi.org/10.3201/eid1701.P11101\u003c/li\u003e\n \u003cli\u003eFjukstad, B. \u0026amp; Bongo, L.A. A review of scalable bioinformatics pipelines. \u003cem\u003eData Sci. Eng.\u003c/em\u003e \u003cstrong\u003e2,\u003c/strong\u003e 245-251. (2017). doi.org/10.1007/s41019-017-0047-z\u003c/li\u003e\n \u003cli\u003eLeipzig, J. A review of bioinformatic pipeline frameworks. \u003cem\u003eBrief. Bioinform.\u003c/em\u003e \u003cstrong\u003e18,\u0026nbsp;\u003c/strong\u003e530-536. 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(2014). doi.org/10.1093/bioinformatics/btu170\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3894530/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3894530/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003eSalmonella enterica\u003c/em\u003e and \u003cem\u003eEscherichia coli\u003c/em\u003e are major food-borne human pathogens, and their genomes are routinely sequenced for clinical surveillance. Computational pipelines designed for analyzing pathogen genomes should both utilize the most current information from annotation databases and increase the coverage of these databases over time. We report the development of the Gammaproteobacteria Epidemiologic Annotation (GEA) pipeline to analyze large batches of \u003cem\u003eE. coli\u003c/em\u003e and \u003cem\u003eS. enterica\u003c/em\u003e genomes. The GEA pipeline takes as input paired Illumina raw reads files which are then assembled followed by annotation. Alternatively, assemblies can be provided as input and directly annotated. The pipeline provides predictive genome annotations for \u003cem\u003eE. coli\u003c/em\u003e and \u003cem\u003eS. enterica\u003c/em\u003e with a focus on the Center for Genomic Epidemiology tools. Annotation results are provided as a tab delimited text file. The GEA pipeline is designed for large-scale \u003cem\u003eE. coli\u003c/em\u003e and \u003cem\u003eS. enterica\u003c/em\u003e genome assembly and characterization using the Center for Genomic Epidemiology command-line tools and high-performance computing. Large scale annotation is demonstrated by an analysis of more than 14,000 \u003cem\u003eSalmonella\u003c/em\u003e genome assemblies. Testing the GEA pipeline on \u003cem\u003eE. coli\u003c/em\u003e raw reads demonstrates reproducibility across multiple compute environments and computational usage is optimized on high performance computers.\u003c/p\u003e","manuscriptTitle":"The Gammaproteobacteria Epidemiologic Annotation Pipeline","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-07 16:55:15","doi":"10.21203/rs.3.rs-3894530/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-02T06:03:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-30T13:19:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"54ee26e6-6e0c-4f82-875b-2aad4a84fb82","date":"2024-04-15T07:17:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-06T07:27:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"0da8d666-2ae4-463e-a580-15639f97e14f","date":"2024-02-24T03:39:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4e5e9266-7a5b-4f29-9592-f6e258c12e58","date":"2024-02-12T21:14:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-12T15:31:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-12T12:44:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-02-05T16:18:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-05T16:12:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-01-24T15:30:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cf49e00a-1704-49f6-b14c-76dd1a1219a3","owner":[],"postedDate":"February 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":28605672,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":28605673,"name":"Biological sciences/Microbiology"},{"id":28605674,"name":"Biological sciences/Molecular biology"}],"tags":[],"updatedAt":"2024-06-03T05:44:15+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-07 16:55:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3894530","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3894530","identity":"rs-3894530","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}