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"description": "<h1>\n <picture>\n <source media=\"(prefers-color-scheme: dark)\" srcset=\"docs/images/nf-core-pathogensurveillance_logo_dark.png\">\n <img alt=\"nf-core/pathogensurveillance\" src=\"docs/images/nf-core-pathogensurveillance_logo_light.png\">\n </picture>\n</h1>\n\n[](https://github.com/codespaces/new/nf-core/pathogensurveillance)\n[](https://github.com/nf-core/pathogensurveillance/actions/workflows/nf-test.yml)\n[](https://github.com/nf-core/pathogensurveillance/actions/workflows/linting.yml)[](https://nf-co.re/pathogensurveillance/results)[](https://doi.org/10.5281/zenodo.15757418)\n[](https://www.nf-test.com)\n\n[](https://www.nextflow.io/)\n[](https://github.com/nf-core/tools/releases/tag/3.5.1)\n[](https://docs.conda.io/en/latest/)\n[](https://www.docker.com/)\n[](https://sylabs.io/docs/)\n[](https://cloud.seqera.io/launch?pipeline=https://github.com/nf-core/pathogensurveillance)\n\n[](https://nfcore.slack.com/channels/pathogensurveillance)[](https://bsky.app/profile/nf-co.re)[](https://mstdn.science/@nf_core)[](https://www.youtube.com/c/nf-core)\n\n## Introduction\n\n**nf-core/pathogensurveillance** is a population genomics pipeline for pathogen identification, variant detection, and biosurveillance.\nThe pipeline accepts paths to raw reads for one or more organisms and creates reports in the form of an interactive HTML document.\nSignificant features include the ability to analyze unidentified eukaryotic and prokaryotic samples, creation of reports for multiple user-defined groupings of samples, automated discovery and downloading of reference assemblies from [NCBI RefSeq](https://www.ncbi.nlm.nih.gov/refseq/), and rapid initial identification based on k-mer sketches followed by a more robust multi gene phylogeny and SNP-based phylogeny.\n\nThe pipeline is built using [Nextflow](https://www.nextflow.io), a workflow tool to run tasks across multiple compute infrastructures in a very portable manner.\nIt uses Docker/Singularity/Conda to make installation trivial and results highly reproducible.\nThe [Nextflow DSL2](https://www.nextflow.io/docs/latest/dsl2.html) implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies.\nWhere appropriate, these processes have been submitted to and installed from [nf-core/modules](https://github.com/nf-core/modules) in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!\n\nOn release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure.\nThis ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world data sets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from the full-sized test can be viewed on the [nf-core website](https://nf-co.re/pathogensurveillance/results).\n\n## Pipeline summary\n\n\n\n**See [](docs/example_report.html) for an example report generated by the pipeline.**\n\n## Why use pathogensurveillance?\n\n**TL;DR:** **unknown gDNA FASTQ -\\> sample ID + phylogeny + publication-quality figures**\n\nMost genomic tools are designed to be used with a reference genome.\nYet this is at odds with the work of pathogen diagnosticians, who often deal with unknown samples.\nFinding the right reference manually may be cumbersome and require a suprising amount of technical skill.\n\n**Pathogensurveillance picks a good reference genome for you**. It does this using the program sourmash. In simple terms, this takes a sample's DNA \"fingerprint\" and finds the closest match in a \"DNA fingerprint library\" spanning the tree of life. In more technical terms, the pipeline generates k-mer sketches from your reads assembled into genomes, then uses the identified reference to do a boilerplate, but robust phylogenetic analysis of your submitted samples.\n\nIn our experience, the pipeline usually chooses the best possible reference genome.\nAt a minimum it will choose a reference that is good enough to build an informative phylogeny and allow you to see the contextual placement your samples.\n\nPathogensurveillance is designed to use as many types of genomic DNA input as possible.\nIt works for common shortread and longread sequencing technologies and for both prokaryotes and eukaryotes.\nThere is a good deal of emergent complexity required to work with such a broad sample range, but pathogensurveillance handles this automatically.\n\nWhile pathogensurveillance may be a useful tool for researchers of all levels, it was designed with those who may have limited bioinformatics training in mind.\n**pathogensurveillance is very simple to run**.\nAt a minimum, all that needs to be supplied is a .CSV file with a single column specifying the path to your sample's sequencing reads.\nOther information is optional, but if provided will used to customize the output report or conditionally use particular reference genomes.\n\n**pathogensurveillance is particularly good for:**\n\n- unknown sample identification\n- exploratory population analysis using minimal input parameters\n- inexperienced bioinformatics users\n- efficient parallelization of tasks\n- repeated analysis (given caching) where you would like to add new samples to a past run\n\n**Note that pathogensurveillance works for non pathogens too!**\n\n**pathogensurveillance is not designed for:**\n\n- viral sequence\n- non gDNA datasets (DNA assembly fasta files, RNA-seq, RAD-seq, ChIP-seq, etc.)\n- mixed/impure samples (this may change in future versions)\n- Highly specialized population genetic analysis, or researchers who would like to extensively test parameters at each stage\n\n## Installation\n\nThe pipeline is automatically installed when run by nextflow, but you will need to install the following dependencies:\n\n- [nextflow](https://nf-co.re/docs/usage/installation)\n- At least one of the following [container engines](https://www.nextflow.io/docs/latest/container.html): [`apptainer`](https://apptainer.org/), [`charliecloud`](https://charliecloud.io/), [`docker`](https://www.docker.com/), [`podman`](http://podman.io/), [`sarus`](https://sarus.readthedocs.io/en/stable/), [`shifter`](https://shifter.readthedocs.io/en/latest/), [`singularity`](https://singularityware.github.io/index.html), or [`conda`](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html) (only use `conda` if no other option is available)\n\n## Quick start guide\n\n> [!NOTE]\n> If you are new to Nextflow and nf-core, please refer to [this page](https://nf-co.re/docs/usage/installation) on how to set-up Nextflow. Make sure to [test your setup](https://nf-co.re/docs/usage/introduction#how-to-run-a-pipeline) with `-profile test` before running the workflow on actual data.\n\nNote that some form of configuration will be needed so that Nextflow knows how to fetch the required software.\nThis is usually done in the form of a config profile.\nYou can chain multiple config profiles in a comma-separated string.\nIn most cases you will include one profile that defines a tool to reproducibly install and use software needed by the pipeline.\nThis is typically one of `docker`, `singularity`, or `conda`.\nIdeally `conda` should not be used unless `docker` or `singularity` cannot be used.\n\nProfiles can also be used to store parameters for the pipeline, such as input data and pipeline options.\nBefore using you own data, consider trying out a small example dataset included with the pipeline as a profile.\nAvailable test dataset profiles include:\n\n- `test`: Test profile of 1 small genome used to run the pipeline as fast as possible for testing purposes.\n- `test_serratia`: Test profile of 10 serratia isolates from Williams et al. 2022 (https://doi.org/10.1038/s41467-022-32929-2)\n- `test_bordetella`: Test profile of 5 Bordetella pertussis isolates sequenced with with Illumina and Nanopore from Wagner et al. 2023\n- `test_salmonella`: Test profile of 5 salmonella isolates from Hawkey et al. 2024 (https://doi.org/10.1038/s41467-024-54418-4)\n- `test_boxwood_blight`: Test profile of 5 samples of the boxwood blight fungus Cylindrocladium buxicola from LeBlanc et al. 2020 (https://doi.org/10.1094/PHYTO-06-20-0219-FI)\n- `test_mycobacteroides`: Test profile of 5 Mycobacteroides abscessus samples from Bronson et al. 2021 (https://doi.org/10.1038/s41467-021-25484-9)\n- `test_bacteria`: Test profile of 10 mixed bacteria from various sources\n- `test_klebsiella`: Test profile of 10 K. pneumoniae and related species from Holt et al. 2015 (https://doi.org/10.1073/pnas.1501049112)\n- `test_small_genomes`: Test profile consisting of 6 samples from species with small genomes from various sources.\n\nAdding `_full` to the end of any of these profiles will run a larger (often much larger) version of these datasets.\n\nFor example, you can run the `test_bacteria` profile with the `docker` profile:\n\n```bash\nnextflow run nf-core/pathogensurveillance -r 1.1.0 -profile docker,test_bacteria -resume --outdir test_output\n```\n\nYou can see the samplesheets used in these profiles here:\n\nhttps://github.com/nf-core/test-datasets/tree/pathogensurveillance\n\nTo run your own input data, prepare a samplesheet as described in the [usage documentation](docs/usage/#samplesheet-input) section below and run the following command:\n\n```bash\nnextflow run nf-core/pathogensurveillance -r <REPLACE WITH VERSION> -profile <REPLACE WITH RUN TOOL> -resume --input <REPLACE WITH TSV/CSV> --outdir <REPLACE WITH OUTPUT PATH>\n```\n\nWhere:\n\n- `<REPLACE WITH RUN TOOL>` is one of `docker`, `singularity`, or `conda`\n- `<REPLACE WITH TSV/CSV>` is the path to the input samplesheet\n- `<REPLACE WITH OUTPUT PATH>` is the path to where to save the output\n- `<REPLACE WITH VERSION>` is the version of the pipeline to run\n\n## Documentation\n\nFor more details and further functionality, please refer to the [usage documentation](https://nf-co.re/pathogensurveillance/usage) and the [parameter documentation](https://nf-co.re/pathogensurveillance/parameters).\nTo see the results of an example test run with a full size dataset refer to the [results](https://nf-co.re/pathogensurveillance/results) tab on the nf-core website pipeline page.\nFor more details about the output files and reports, please refer to the [output documentation](https://nf-co.re/pathogensurveillance/output).\n\n## Credits\n\nThe following people contributed to the pipeline: Zachary S.L. Foster, Martha Sudermann, Camilo Parada-Rojas, Logan K. Blair, Fernanda I. Bocardo, Ricardo Alcal\u00e1-Brise\u00f1o, Hung Phan, Nicholas C. Cauldron, Alexandra J. Weisberg, Je\ufb00 H. Chang, and Niklaus J. Gr\u00fcnwald.\n\n## Funding\n\nThis work was supported by grants from USDA ARS (2072-22000-045-000-D) to NJG, USDA NIFA (2021-67021-34433; 2023-67013-39918) to JHC and NJG, as well as USDAR ARS NPDRS and FNRI and USDA APHIS to NJG.\n\n## Contributions and Support\n\nIf you would like to contribute to this pipeline, please see the [contributing guidelines](.github/CONTRIBUTING.md).\n\nFor further information or help, don't hesitate to get in touch on the [Slack `#pathogensurveillance` channel](https://nfcore.slack.com/channels/pathogensurveillance) (you can join with [this invite](https://nf-co.re/join/slack)).\n\n## Citations\n\nPlease cite use of nf-core/pathogensurveillance as follows:\n\n> Foster, ZSL, Sudermann, MA, Parada Rojas, CH, Blair, LK, Iruegas Bocardo, F, Dhakal, U, Weisberg, AJ, Phan, H, Chang, JH, Grunwald, NJ. **Pathogensurveillance: an automated pipeline for population genomic analyses and pathogen identification.** BioRxiv 2025 2025.10.31.685798. doi: https://doi.org/10.1101/2025.10.31.685798\n\nAn extensive list of references for the tools used by the pipeline can be found in the [`CITATIONS.md`](CITATIONS.md) file.\n\nYou can cite the `nf-core` publication as follows:\n\n> **The nf-core framework for community-curated bioinformatics pipelines.**\n>\n> Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.\n>\n> _Nat Biotechnol._ 2020 Feb 13. doi: [10.1038/s41587-020-0439-x](https://dx.doi.org/10.1038/s41587-020-0439-x).\n\n<picture>\n <source media=\"(prefers-color-scheme: dark)\" srcset=\"docs/images/combined_logos_dark.png\">\n <img alt=\"Logos of University of Oregon and USAD\" src=\"docs/images/combined_logos_light.png\">\n</picture>\n",
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