diff --git a/joss.05397/10.21105.joss.05397.crossref.xml b/joss.05397/10.21105.joss.05397.crossref.xml new file mode 100644 index 0000000000..f56e791f38 --- /dev/null +++ b/joss.05397/10.21105.joss.05397.crossref.xml @@ -0,0 +1,391 @@ + + + + 20231019T100359-d2ff7b2afc7d410ff5b1b31b2c4d79ed578b8c85 + 20231019100359 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 10 + 2023 + + + 8 + + 90 + + + + Baargin: a Nextflow workflow for the automatic analysis +of bacterial genomics data with a focus on Antimicrobial +Resistance + + + + Juliette + Hayer + https://orcid.org/0000-0003-4899-9637 + + + Jacques + Dainat + https://orcid.org/0000-0002-6629-0173 + + + Ella + Marcy + https://orcid.org/0009-0002-6000-1665 + + + Anne-Laure + Bañuls + https://orcid.org/0000-0002-2106-8667 + + + + 10 + 19 + 2023 + + + 5397 + + + 10.21105/joss.05397 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.8386399 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/5397 + + + + 10.21105/joss.05397 + https://joss.theoj.org/papers/10.21105/joss.05397 + + + https://joss.theoj.org/papers/10.21105/joss.05397.pdf + + + + + + The staggering death toll of drug-resistant +bacteria + Thompson + Nature + 10.1038/D41586-022-00228-X + 0028-0836 + 2022 + Thompson, T. (2022). The staggering +death toll of drug-resistant bacteria. Nature. +https://doi.org/10.1038/D41586-022-00228-X + + + Nextflow enables reproducible computational +workflows + DI Tommaso + Nature Biotechnology 2017 +35:4 + 4 + 35 + 10.1038/nbt.3820 + 1546-1696 + 2017 + DI Tommaso, P., Chatzou, M., Floden, +E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow +enables reproducible computational workflows. Nature Biotechnology 2017 +35:4, 35(4), 316–319. +https://doi.org/10.1038/nbt.3820 + + + Bactopia: a flexible pipeline for complete +analysis of bacterial genomes + Petit III + Msystems + 10.1128/mSystems.00190-20 + 2379-5077 + 2020 + Petit III, &. R., R. A. (2020). +Bactopia: a flexible pipeline for complete analysis of bacterial +genomes. Msystems. +https://doi.org/10.1128/mSystems.00190-20 + + + fastp: an ultra-fast all-in-one FASTQ +preprocessor + Chen + Bioinformatics + 17 + 34 + 10.1093/BIOINFORMATICS/BTY560 + 1367-4803 + 2018 + Chen, S., Zhou, Y., Chen, Y., & +Gu, J. (2018). fastp: an ultra-fast all-in-one FASTQ preprocessor. +Bioinformatics, 34(17), i884–i890. +https://doi.org/10.1093/BIOINFORMATICS/BTY560 + + + Using SPAdes De Novo +Assembler + Prjibelski + Current Protocols in +Bioinformatics + 1 + 70 + 10.1002/CPBI.102 + 1934-340X + 2020 + Prjibelski, A., Antipov, D., +Meleshko, D., Lapidus, A., & Korobeynikov, A. (2020). Using SPAdes +De Novo Assembler. Current Protocols in Bioinformatics, 70(1), e102. +https://doi.org/10.1002/CPBI.102 + + + Unicycler: Resolving bacterial genome +assemblies from short and long sequencing reads + Wick + PLOS Computational Biology + 6 + 13 + 10.1371/JOURNAL.PCBI.1005595 + 1553-7358 + 2017 + Wick, R. R., Judd, L. M., Gorrie, C. +L., & Holt, K. E. (2017). Unicycler: Resolving bacterial genome +assemblies from short and long sequencing reads. PLOS Computational +Biology, 13(6), e1005595. +https://doi.org/10.1371/JOURNAL.PCBI.1005595 + + + Improved metagenomic analysis with Kraken +2 + Wood + Genome Biology + 1 + 20 + 10.1186/S13059-019-1891-0 + 2019 + Wood, D. E., Lu, J., & Langmead, +B. (2019). Improved metagenomic analysis with Kraken 2. Genome Biology, +20(1), 1–13. +https://doi.org/10.1186/S13059-019-1891-0 + + + Metagenome analysis using the Kraken software +suite + Lu + Nature protocols + 12 + 17 + 10.1038/S41596-022-00738-Y + 2022 + Lu, J., Rincon, N., Wood, D. E., +Breitwieser, F. P., Pockrandt, C., Langmead, B., Salzberg, S. L., & +Steinegger, M. (2022). Metagenome analysis using the Kraken software +suite. Nature Protocols, 17(12), 2815. +https://doi.org/10.1038/S41596-022-00738-Y + + + QUAST: quality assessment tool for genome +assemblies + Gurevich + Bioinformatics + 8 + 29 + 10.1093/BIOINFORMATICS/BTT086 + 1367-4803 + 2013 + Gurevich, A., Saveliev, V., Vyahhi, +N., & Tesler, G. (2013). QUAST: quality assessment tool for genome +assemblies. Bioinformatics, 29(8), 1072–1075. +https://doi.org/10.1093/BIOINFORMATICS/BTT086 + + + BUSCO Update: Novel and Streamlined Workflows +along with Broader and Deeper Phylogenetic Coverage for Scoring of +Eukaryotic, Prokaryotic, and Viral Genomes + Manni + Molecular Biology and +Evolution + 10 + 38 + 10.1093/MOLBEV/MSAB199 + 2021 + Manni, M., Berkeley, M. R., Seppey, +M., Simão, F. A., & Zdobnov, E. M. (2021). BUSCO Update: Novel and +Streamlined Workflows along with Broader and Deeper Phylogenetic +Coverage for Scoring of Eukaryotic, Prokaryotic, and Viral Genomes. +Molecular Biology and Evolution, 38(10), 4647–4654. +https://doi.org/10.1093/MOLBEV/MSAB199 + + + MLST + Seemann + 2022 + Seemann, T. (2022). MLST. +https://github.com/tseemann/mlst + + + In silico detection and typing of plasmids +using PlasmidFinder and plasmid multilocus sequence +typing + Carattoli + Antimicrobial agents and +chemotherapy + 7 + 58 + 10.1128/AAC.02412-14 + 1098-6596 + 2014 + Carattoli, A., Zankari, E., +Garciá-Fernández, A., Larsen, M. V., Lund, O., Villa, L., Aarestrup, F. +M., & Hasman, H. (2014). In silico detection and typing of plasmids +using PlasmidFinder and plasmid multilocus sequence typing. +Antimicrobial Agents and Chemotherapy, 58(7), 3895–3903. +https://doi.org/10.1128/AAC.02412-14 + + + Platon: identification and characterization +of bacterial plasmid contigs in short-read draft assemblies exploiting +protein sequence-based replicon distribution scores + Schwengers + Microbial genomics + 10 + 6 + 10.1099/MGEN.0.000398 + 2057-5858 + 2020 + Schwengers, O., Barth, P., +Falgenhauer, L., Hain, T., Chakraborty, T., & Goesmann, A. (2020). +Platon: identification and characterization of bacterial plasmid contigs +in short-read draft assemblies exploiting protein sequence-based +replicon distribution scores. Microbial Genomics, 6(10), 1–12. +https://doi.org/10.1099/MGEN.0.000398 + + + CARD 2023: expanded curation, support for +machine learning, and resistome prediction at the Comprehensive +Antibiotic Resistance Database + Alcock + Nucleic acids research + D1 + 51 + 10.1093/NAR/GKAC920 + 1362-4962 + 2023 + Alcock, B. P., Huynh, W., Chalil, R., +Smith, K. W., Raphenya, A. R., Wlodarski, M. A., Edalatmand, A., Petkau, +A., Syed, S. A., Tsang, K. K., Baker, S. J. C., Dave, M., McCarthy, M. +C., Mukiri, K. M., Nasir, J. A., Golbon, B., Imtiaz, H., Jiang, X., +Kaur, K., … McArthur, A. G. (2023). CARD 2023: expanded curation, +support for machine learning, and resistome prediction at the +Comprehensive Antibiotic Resistance Database. Nucleic Acids Research, +51(D1). https://doi.org/10.1093/NAR/GKAC920 + + + AMRFinderPlus and the Reference Gene Catalog +facilitate examination of the genomic links among antimicrobial +resistance, stress response, and virulence + Feldgarden + Scientific Reports 2021 11:1 + 1 + 11 + 10.1038/s41598-021-91456-0 + 2045-2322 + 0123456789 + 2021 + Feldgarden, M., Brover, V., +Gonzalez-Escalona, N., Frye, J. G., Haendiges, J., Haft, D. H., +Hoffmann, M., Pettengill, J. B., Prasad, A. B., Tillman, G. E., Tyson, +G. H., & Klimke, W. (2021). AMRFinderPlus and the Reference Gene +Catalog facilitate examination of the genomic links among antimicrobial +resistance, stress response, and virulence. Scientific Reports 2021 +11:1, 11(1), 1–9. +https://doi.org/10.1038/s41598-021-91456-0 + + + Prokka: rapid prokaryotic genome +annotation + Seemann + Bioinformatics + 14 + 30 + 10.1093/BIOINFORMATICS/BTU153 + 1367-4803 + 2014 + Seemann, T. (2014). Prokka: rapid +prokaryotic genome annotation. Bioinformatics, 30(14), 2068–2069. +https://doi.org/10.1093/BIOINFORMATICS/BTU153 + + + Bakta: rapid and standardized annotation of +bacterial genomes via alignment-free sequence +identification + Schwengers + Microbial Genomics + 11 + 7 + 10.1099/MGEN.0.000685 + 2021 + Schwengers, O., Jelonek, L., +Dieckmann, M. A., Beyvers, S., Blom, J., & Goesmann, A. (2021). +Bakta: rapid and standardized annotation of bacterial genomes via +alignment-free sequence identification. Microbial Genomics, 7(11), 685. +https://doi.org/10.1099/MGEN.0.000685 + + + Roary: rapid large-scale prokaryote pan +genome analysis + Page + Bioinformatics + 22 + 31 + 10.1093/BIOINFORMATICS/BTV421 + 1367-4803 + 2015 + Page, A. J., Cummins, C. A., Hunt, +M., Wong, V. K., Reuter, S., Holden, M. T. G., Fookes, M., Falush, D., +Keane, J. A., & Parkhill, J. (2015). Roary: rapid large-scale +prokaryote pan genome analysis. Bioinformatics, 31(22), 3691–3693. +https://doi.org/10.1093/BIOINFORMATICS/BTV421 + + + + + + diff --git a/joss.05397/10.21105.joss.05397.jats b/joss.05397/10.21105.joss.05397.jats new file mode 100644 index 0000000000..b0833e049b --- /dev/null +++ b/joss.05397/10.21105.joss.05397.jats @@ -0,0 +1,770 @@ + + +
+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +5397 +10.21105/joss.05397 + +Baargin: a Nextflow workflow for the automatic analysis +of bacterial genomics data with a focus on Antimicrobial +Resistance + + + +https://orcid.org/0000-0003-4899-9637 + +Hayer +Juliette + + + +* + + +https://orcid.org/0000-0002-6629-0173 + +Dainat +Jacques + + + + +https://orcid.org/0009-0002-6000-1665 + +Marcy +Ella + + + + + +https://orcid.org/0000-0002-2106-8667 + +Bañuls +Anne-Laure + + + + + + +MIVEGEC, University of Montpellier, IRD, CNRS, 34394, +Montpellier, France + + + + +Laboratoire Mixte International Drug Resistance in +Southeast Asia + + + + +Centre Hospitalier Universitaire (CHU) Lapeyronie, +Montpellier, France + + + + +* E-mail: + + +18 +2 +2023 + +8 +90 +5397 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Nextflow +Whole Genome Shotgun +Genomics +Long reads sequencing technology +Short reads sequencing technology +Antimicrobial Resistance +Pangenome + + + + + + Summary +

The emergence and development of Antimicrobial Resistance (AMR) is + a global health problem, that could cause about 10 million deaths + yearly by 2050 + (Thompson, + 2022). The study of the genomes of these (multi)resistant + bacterial strains is of high importance to understand emergence and + circulation of the resistance. In the past couple of decades, high + throughput sequencing technologies have seriously improved and it has + become more affordable to sequence the full genomes of hundreds of + bacterial strains at a time. As a counterpart, these experiments + produce large amount of data that needs to be analysed by various + bioinformatics methods and tools for reconstructing the genomes and + therefore identify their specific features and the genetic + determinants of the AMR. For automating the bioinformatics analysis of + multiple strains, we have developed a Nextflow + (DI + Tommaso et al., 2017) workflow called baargin + (Bacterial Assembly and Antimicrobial Resistance Genes detection In + Nextflow) + https://github.com/jhayer/baargin. + It enables to conduct sequencing reads quality control, genome + assembly and annotation, Multi-Locus Sequence Typing and plasmid + identification, as well as antimicrobial resistance determinants + detection, and pangenome analysis. The use of Nextflow, a workflow + management system, makes our workflow portable, flexible, and able to + conduct reproducible analyses.

+ + + Statement of need +

High Throughput Sequencing technologies produce a significant + amount of data and researchers are producing genomics data all over + the world on a daily basis. These technologies are notably used for + studying bacterial genomes in order to understand the spread of + bacterial pathogens and their resistance to antibiotics. In the + bacterial genomics field, it is possible to sequence the DNA from + multiple bacterial strains at the same time. The analysis of these + sequencing data requires the use of a wide range of bioinformatics + programs to be able to identify the genes and their functions, and + among those, the genes and mutations conferring resistance to + antimicrobial drugs. In order to make the results of these analyses + comparable, it is crucial to standardise, automate and parallelise all + the steps. The baargin workflow allows the user to + perform a complete in silico analysis of bacterial + genomes, from the quality control of the raw data, to the detection of + AMR genes and mutations, on multiple datasets of the same bacterial + species in parallel. It compiles and summarise the results from all + the analysis steps, allowing comparative studies. As a last step, + baargin performs a pangenome analysis of all the + strains provided, producing the basis for the construction of a + phylogenetic tree. The use of Nextflow and containers ensures the + reproducibility of the data analysis. Only few bacterial genomics + workflows are available, like Bactopia + (Petit + III, 2020), which is highly flexible and complete in term of + tools available. Therefore, we needed a lighter workflow, with only a + few tools and databases installed for our collaborators that have only + limited computing resources and storage. Also, + baargin is specifically designed for detecting AMR + genes and plasmid features, and include a decontamination step of the + assembly, allowing the downstream analyses to be performed especially + on the contigs belonging to the targeted species.

+ + + Materials and Methods + + Features +

Baargin is designed to automatically parallelise workflow steps. + It does not require manual intervention from the users between + steps. Each workflow step, called process, uses containers, via + Docker or Singularity, which also greatly improves traceability and + reproducibility. Additional processes can be easily added in the + future as the workflow is designed in modules, making it flexible + for adding or removing steps.

+ + + Workflow +

[fig:Figure1] + describes the workflow:

+ + +

Input can be either a folder containing paired-end short + reads (fastq format), a folder containing already assembled + contigs (fasta format files), or an index file containing the + paths to pair-end short reads (fastq files) and to long reads + (ONT, fastq file) for the same sample/strain in order to perform + hybrid assembly. If assembled contigs are provided, the analysis + will start at step 4.

+ + +

Quality check and adapters removal is performed on the short + reads using Fastp + (Chen + et al., 2018).

+ + +

De novo assembly is run using SPAdes + (Prjibelski + et al., 2020) if only short reads were provided, and with + Unicycler + (Wick + et al., 2017) for a hybrid assembly when short and long + reads are provided.

+ + +

Taxonomic assignment of the contigs is performed using + Kraken2 + (Wood + et al., 2019) and the contigs classified at the taxonomic + level provided by the user (with the taxid, and including the + children taxa) are retrieved and therefore named as + “deconta” for decontaminated contigs + (Lu + et al., 2022). The dataset containing all the contigs are + named as “raw”. From here all the steps except + the annotation (8) will be performed on both sets of contigs + “raw” and “deconta”.

+ + +

A quality check of the assembly is conducted using Quast + (Gurevich + et al., 2013) and BUSCO + (Manni + et al., 2021). For BUSCO, the users have the possibility + to specify the taxonomic lineage database to use for searching + the housekeeping genes (at the class level of the strain to + analyse for example: + enterobacterales_odb10)

+ + +

The contigs (raw and + deconta) are then screened to identify the + sequence type of the strain using the MLST tool (Multi-Locus + Sequence Typing) + (Seemann, + 2022).

+ + +

The contigs are subsequently submitted to plasmid + identification using PlasmidFinder + (Carattoli + et al., 2014) and additionally with Platon if the user + provides a database for it + (Schwengers + et al., 2020).

+ + +

Antimicrobial Resistance Genes (ARGs) are then searched in + the contigs using both CARD RGI + (Alcock + et al., 2023) and the NCBI AMRFinderPlus + (Feldgarden + et al., 2021). For certain species only, AMRFinderPlus + can also detect some mutations conferring resistance, if the + user provides that option.

+ + +

A genome annotation is performed on the + deconta contigs using Prokka by default + (Seemann, + 2014), or using Bakta + (Schwengers + et al., 2021) if the user provides a database for it.

+ + +

Once all the strains datasets provided are annotated a + pangenome analysis is done using Roary + (Page + et al., 2015).

+ + + + + Output +

The results are located in a nested folder architecture. For each + dataset, a folder with the sampleID is created, and contains 5 + subfolders: - qc - assembly - AMR - annotation - plasmids These + subfolders contain the main outputs from the concerned analyses. + Additionally, at the root of the results folder (indicated by the + user) 2 folders are created: pangenome, containing + the results for Roary, and compile_results that + contains summary files for each analysis where the results for all + datasets provided in input are compiled as presence/absence matrices + for further comparative analyses.

+ + + + Discussion and conclusions +

We presented here an easy-to-use workflow for Bacterial Assembly + and Antimicrobial Resistance Genes detection In Nextflow: + baargin. It allows the users to analyse genomic + datasets from short and long sequencing reads, of several bacterial + strains from the same species in one command line. The workflow will + automatically assemble the genomes, check for contamination and + specifically extract the sequences that belong to the expected taxon. + It will then identify their sequence type and screen the assemblies + for plasmids sequences and ARGs. The fact that + baargin is implemented in Nextflow and is based on + containers makes the analyses reproducible. Its modular design makes + it easy to customise and extend, by adding new modules for new + processes.

+ + + Figures + +

Flowchart of baargin + workflow.

+ + + + + Acknowledgements +

We acknowledge contributions from Son Thai Nguyen and Julio + Benavides during the development of this workflow.

+

The authors acknowledge the ISO 9001 certified IRD i-Trop HPC + (South Green Platform) at IRD Montpellier for providing HPC resources + to develop the workflow reported within this paper. + https://bioinfo.ird.fr + http://www.southgreen.fr

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