[](https://github.com/nf-core/metatdenovo/actions/workflows/ci.yml)
[](https://github.com/nf-core/metatdenovo/actions/workflows/linting.yml)[](https://nf-co.re/metatdenovo/results)[](https://doi.org/10.5281/zenodo.10666590)
[](https://www.nf-test.com)
[](https://www.nextflow.io/)
[](https://docs.conda.io/en/latest/)
[](https://www.docker.com/)
[](https://sylabs.io/docs/)
[](https://cloud.seqera.io/launch?pipeline=https://github.com/nf-core/metatdenovo)
[](https://nfcore.slack.com/channels/metatdenovo)[](https://twitter.com/nf_core)[](https://mstdn.science/@nf_core)[](https://www.youtube.com/c/nf-core)
## 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.
The 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!
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).
## Pipeline summary
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).
