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            "description": "<h1>\n  <picture>\n    <source media=\"(prefers-color-scheme: dark)\" srcset=\"docs/images/nf-core-bacass_logo_dark.png\">\n    <img alt=\"nf-core/bacass\" src=\"docs/images/nf-core-bacass_logo_light.png\">\n  </picture>\n</h1>\n\n[![GitHub Actions CI Status](https://github.com/nf-core/bacass/actions/workflows/nf-test.yml/badge.svg)](https://github.com/nf-core/bacass/actions/workflows/nf-test.yml)\n[![GitHub Actions Linting Status](https://github.com/nf-core/bacass/actions/workflows/linting.yml/badge.svg)](https://github.com/nf-core/bacass/actions/workflows/linting.yml)[![AWS CI](https://img.shields.io/badge/CI%20tests-full%20size-FF9900?labelColor=000000&logo=Amazon%20AWS)](https://nf-co.re/bacass/results)[![Cite with Zenodo](http://img.shields.io/badge/DOI-10.5281/zenodo.XXXXXXX-1073c8?labelColor=000000)](https://doi.org/10.5281/zenodo.XXXXXXX)\n[![nf-test](https://img.shields.io/badge/unit_tests-nf--test-337ab7.svg)](https://www.nf-test.com)\n\n[![Nextflow](https://img.shields.io/badge/version-%E2%89%A524.10.5-green?style=flat&logo=nextflow&logoColor=white&color=%230DC09D&link=https%3A%2F%2Fnextflow.io)](https://www.nextflow.io/)\n[![nf-core template version](https://img.shields.io/badge/nf--core_template-3.3.2-green?style=flat&logo=nfcore&logoColor=white&color=%2324B064&link=https%3A%2F%2Fnf-co.re)](https://github.com/nf-core/tools/releases/tag/3.3.2)\n[![run with conda](http://img.shields.io/badge/run%20with-conda-3EB049?labelColor=000000&logo=anaconda)](https://docs.conda.io/en/latest/)\n[![run with docker](https://img.shields.io/badge/run%20with-docker-0db7ed?labelColor=000000&logo=docker)](https://www.docker.com/)\n[![run with singularity](https://img.shields.io/badge/run%20with-singularity-1d355c.svg?labelColor=000000)](https://sylabs.io/docs/)\n[![Launch on Seqera Platform](https://img.shields.io/badge/Launch%20%F0%9F%9A%80-Seqera%20Platform-%234256e7)](https://cloud.seqera.io/launch?pipeline=https://github.com/nf-core/bacass)\n\n[![Get help on Slack](http://img.shields.io/badge/slack-nf--core%20%23bacass-4A154B?labelColor=000000&logo=slack)](https://nfcore.slack.com/channels/bacass)[![Follow on Bluesky](https://img.shields.io/badge/bluesky-%40nf__core-1185fe?labelColor=000000&logo=bluesky)](https://bsky.app/profile/nf-co.re)[![Follow on Mastodon](https://img.shields.io/badge/mastodon-nf__core-6364ff?labelColor=FFFFFF&logo=mastodon)](https://mstdn.science/@nf_core)[![Watch on YouTube](http://img.shields.io/badge/youtube-nf--core-FF0000?labelColor=000000&logo=youtube)](https://www.youtube.com/c/nf-core)\n\n## Introduction\n\n**nf-core/bacass** is a bioinformatics best-practice analysis pipeline for simple bacterial assembly and annotation. The pipeline is able to assemble short reads, long reads, or a mixture of short and long reads (hybrid assembly).\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. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The [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. Where possible, 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. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, 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/bacass/results).\n\n## Pipeline summary\n\n### Short Read Assembly\n\nThis pipeline is primarily for bacterial assembly of next-generation sequencing reads. It can be used to quality trim your reads using [FastP](https://github.com/OpenGene/fastp) and performs basic sequencing QC using [FastQC](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Afterwards, the pipeline performs read assembly using [Unicycler](https://github.com/rrwick/Unicycler). Contamination of the assembly is checked using [Kraken2](https://ccb.jhu.edu/software/kraken2/) and [Kmerfinder](https://bitbucket.org/genomicepidemiology/kmerfinder/src/master/) to verify sample purity.\n\n### Long Read Assembly\n\nFor users that only have Nanopore data, the pipeline quality trims these using [PoreChop](https://github.com/rrwick/Porechop) or filter long reads by quality using [Filtlong](https://github.com/rrwick/Filtlong) and assesses basic sequencing QC utilizing [NanoPlot](https://github.com/wdecoster/NanoPlot) and [PycoQC](https://github.com/a-slide/pycoQC). Contamination of the assembly is checked using [Kraken2](https://ccb.jhu.edu/software/kraken2/) and [Kmerfinder](https://bitbucket.org/genomicepidemiology/kmerfinder/src/master/) to verify sample purity.\n\nThe pipeline can then perform long read assembly utilizing [Unicycler](https://github.com/rrwick/Unicycler), [Miniasm](https://github.com/lh3/miniasm) in combination with [Racon](https://github.com/isovic/racon), [Canu](https://github.com/marbl/canu) or [Flye](https://github.com/fenderglass/Flye) by using the [Dragonflye](https://github.com/rpetit3/dragonflye)(\\*) pipeline. Long reads assembly can be polished using [Medaka](https://github.com/nanoporetech/medaka) or [NanoPolish](https://github.com/jts/nanopolish) with Fast5 files.\n\n> [!NOTE]\n> Dragonflye is a comprehensive pipeline designed for genome assembly of Oxford Nanopore Reads. It facilitates the utilization of Flye (default), Miniasm, and Raven assemblers, along with Racon (default) and Medaka polishers. For more information, visit the [Dragonflye GitHub](https://github.com/rpetit3/dragonflye) repository.\n\n### Hybrid Assembly\n\nFor users specifying both short read and long read (NanoPore) data, the pipeline can perform a hybrid assembly approach utilizing [Unicycler](https://github.com/rrwick/Unicycler) (short read assembly followed by gap closing with long reads) or [Dragonflye](https://github.com/rpetit3/dragonflye) (long read assembly followed by polishing with short reads), taking the full set of information from short reads and long reads into account.\n\n### Assembly QC and annotation\n\nIn all cases, the assembly is assessed using [QUAST](http://bioinf.spbau.ru/quast) and [BUSCO](https://busco.ezlab.org/). The resulting bacterial assembly is furthermore annotated using [Prokka](https://github.com/tseemann/prokka), [Bakta](https://github.com/oschwengers/bakta) or [DFAST](https://github.com/nigyta/dfast_core).\n\nIf Kmerfinder is invoked, the pipeline will group samples according to the [Kmerfinder](https://bitbucket.org/genomicepidemiology/kmerfinder/src/master/)-estimated reference genomes. Afterwards, two QUAST steps will be carried out: an initial ('general') [QUAST](http://bioinf.spbau.ru/quast) of all samples without reference genomes, and subsequently, a 'by reference genome' [QUAST](http://bioinf.spbau.ru/quast) to aggregate samples with their reference genomes.\n\n> [!NOTE]\n> This scenario is supported when [Kmerfinder](https://bitbucket.org/genomicepidemiology/kmerfinder/src/master/) analysis is performed only.\n\n## Usage\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\nFirst, prepare a samplesheet with your input data that looks as follows:\n\n`samplesheet.tsv`:\n\n```tsv\nID      R1                            R2                            LongFastQ                    Fast5    GenomeSize\nshortreads      ./data/S1_R1.fastq.gz       ./data/S1_R2.fastq.gz       NA                            NA      NA\nlongreads       NA                          NA                          ./data/S1_long_fastq.gz      ./data/FAST5  2.8m\nshortNlong      ./data/S1_R1.fastq.gz       ./data/S1_R2.fastq.gz       ./data/S1_long_fastq.gz      ./data/FAST5  2.8m\n\n```\n\nEach row represents a fastq file (single-end) or a pair of fastq files (paired end).\n\nShort read assembly with Unicycler, `--kraken2db` can be any [compressed database (`.tar.gz`/`.tgz`)](https://benlangmead.github.io/aws-indexes/k2):\n\n```console\nnextflow run nf-core/bacass -profile <docker/singularity/podman/shifter/charliecloud/conda/institute> --input samplesheet.tsv --assembly_type 'short' --kraken2db \"https://genome-idx.s3.amazonaws.com/kraken/k2_standard_8gb_20210517.tar.gz\"\n```\n\nLong read assembly with Miniasm:\n\n```console\nnextflow run nf-core/bacass -profile <docker/singularity/podman/shifter/charliecloud/conda/institute> --input samplesheet.tsv --assembly_type 'long' --assembler 'miniasm' --kraken2db \"https://genome-idx.s3.amazonaws.com/kraken/k2_standard_8gb_20210517.tar.gz\"\n```\n\n```bash\nnextflow run nf-core/bacass \\\n  -profile <docker/singularity/.../institute> \\\n  --input samplesheet.tsv \\\n  --outdir <OUTDIR>\n```\n\n> [!WARNING]\n> Please provide pipeline parameters via the CLI or Nextflow `-params-file` option. Custom config files including those provided by the `-c` Nextflow option can be used to provide any configuration _**except for parameters**_; see [docs](https://nf-co.re/docs/usage/getting_started/configuration#custom-configuration-files).\n\nFor more details and further functionality, please refer to the [usage documentation](https://nf-co.re/bacass/usage) and the [parameter documentation](https://nf-co.re/bacass/parameters).\n\n## Pipeline output\n\nTo see the results of an example test run with a full size dataset refer to the [results](https://nf-co.re/bacass/results) tab on the nf-core website pipeline page.\nFor more details about the output files and reports, please refer to the\n[output documentation](https://nf-co.re/bacass/output).\n\n## Credits\n\nnf-core/bacass was initiated by [Andreas Wilm](https://github.com/andreas-wilm), originally written by [Alex Peltzer](https://github.com/apeltzer) (DSL1), rewritten by [Daniel Straub](https://github.com/d4straub) (DSL2) and maintained by [Daniel Valle-Millares](https://github.com/Daniel-VM).\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 `#bacass` channel](https://nfcore.slack.com/channels/bacass) (you can join with [this invite](https://nf-co.re/join/slack)).\n\n## Citations\n\nIf you use nf-core/bacass for your analysis, please cite it using the following doi: [10.5281/zenodo.2669428](https://doi.org/10.5281/zenodo.2669428)\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",
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