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## Introduction
**nf-core/metatdenovo** is a bioinformatics best-practice analysis pipeline for assembly and annotation of metatranscriptomic and metagenomic data from prokaryotes, eukaryotes or viruses.
On 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/metatdenovo/results).
## Usage
1. Read QC ([`FastQC`](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/))
2. Present QC for raw reads ([`MultiQC`](http://multiqc.info/))
3. Quality trimming and adapter removal for raw reads ([`Trim Galore!`](https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/))
4. Optional: Filter sequences with [`BBduk`](https://jgi.doe.gov/data-and-tools/software-tools/bbtools/bb-tools-user-guide/bbduk-guide/)
5. Optional: Normalize the sequencing depth with [`BBnorm`](https://jgi.doe.gov/data-and-tools/software-tools/bbtools/bb-tools-user-guide/bbnorm-guide/)
6. Merge trimmed, pair-end reads ([`Seqtk`](https://github.com/lh3/seqtk))
7. Choice of de novo assembly programs:
1. [`RNAspades`](https://cab.spbu.ru/software/rnaspades/) suggested for Eukaryote de novo assembly
2. [`Megahit`](https://github.com/voutcn/megahit) suggested for Prokaryote de novo assembly
8. Choice of orf caller:
1. [`TransDecoder`](https://github.com/TransDecoder/TransDecoder) suggested for Eukaryotes
2. [`Prokka`](https://github.com/tseemann/prokka) suggested for Prokaryotes
3. [`Prodigal`](https://github.com/hyattpd/Prodigal) suggested for Prokaryotes
9. Quantification of genes identified in assemblies:
1. Generate index of assembly ([`BBmap index`](https://sourceforge.net/projects/bbmap/))
2. Mapping cleaned reads to the assembly for quantification ([`BBmap`](https://sourceforge.net/projects/bbmap/))
3. Get raw counts per each gene present in the assembly ([`Featurecounts`](http://subread.sourceforge.net)) -> TSV table with collected featurecounts output
10. Functional annotation:
1. [`Eggnog`](https://github.com/eggnogdb/eggnog-mapper) -> Reformat TSV output "eggnog table"
2. [`KOfamscan`](https://github.com/takaram/kofam_scan)
3. [`HMMERsearch`](https://www.ebi.ac.uk/Tools/hmmer/search/hmmsearch) -> Ranking orfs based on HMMprofile with [`Hmmrank`](https://github.com/erikrikarddaniel/hmmrank)
11. Taxonomic annotation:
1. [`EUKulele`](https://github.com/AlexanderLabWHOI/EUKulele) -> Reformat TSV output "Reformat_tax.R"
2. [`CAT`](https://github.com/dutilh/CAT)
12. Summary statistics table. "Collect_stats.R"
## Usage
> [!NOTE]
> 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.
First, prepare a samplesheet with your input data that looks as follows:
`samplesheet.csv`:
```
| sample | fastq_1 | fastq_2
| -------- | ------------------------- | ------------------------- |
| sample1 | ./data/S1_R1_001.fastq.gz | ./data/S1_R2_001.fastq.gz |
| sample2 | ./data/S2_fw.fastq.gz | ./data/S2_rv.fastq.gz |
| sample3 | ./S4x.fastq.gz | ./S4y.fastq.gz |
| sample4 | ./a.fastq.gz | ./b.fastq.gz |
```
Each row represents a fastq file (single-end) or a pair of fastq files (paired-end).
Now, you can run the pipeline using:
```bash
nextflow run nf-core/metatdenovo \
-profile \
--input samplesheet.csv \
--outdir
```
> [!WARNING]
> 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).
For more details and further functionality, please refer to the [usage documentation](https://nf-co.re/metatdenovo/usage) and the [parameter documentation](https://nf-co.re/metatdenovo/parameters).
## Pipeline output
To see the results of an example test run with a full size dataset refer to the [results](https://nf-co.re/metatdenovo/results) tab on the nf-core website pipeline page.
For more details about the output files and reports, please refer to the
[output documentation](https://nf-co.re/metatdenovo/output).
> [!NOTE]
> Tables in `summary_tables` directory under the output directory are made especially for further analysis in tools like R or Python.
## Credits
nf-core/metatdenovo was originally written by Danilo Di Leo (@danilodileo), Emelie Nilsson (@emnilsson) & Daniel Lundin (@erikrikarddaniel).
## Contributions and Support
If you would like to contribute to this pipeline, please see the [contributing guidelines](.github/CONTRIBUTING.md).
For further information or help, don't hesitate to get in touch on the [Slack `#metatdenovo` channel](https://nfcore.slack.com/channels/metatdenovo) (you can join with [this invite](https://nf-co.re/join/slack)).
## Citations
If you use nf-core/metatdenovo for your analysis, please cite it using the following doi: [10.5281/zenodo.10666590](https://doi.org/10.5281/zenodo.10666590)
An extensive list of references for the tools used by the pipeline can be found in the [`CITATIONS.md`](CITATIONS.md) file.
You can cite the `nf-core` publication as follows:
> **The nf-core framework for community-curated bioinformatics pipelines.**
>
> Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.
>
> _Nat Biotechnol._ 2020 Feb 13. doi: [10.1038/s41587-020-0439-x](https://dx.doi.org/10.1038/s41587-020-0439-x).
