metatdenovo

KO | EN

gitlite — search

#javascript #hacktoberfest #react #machine-learning #rust #linux #macos #nodejs #awesome #vue #awesome-list #ios #openai #open-source

(★ 35)

Assembly and annotation of metatranscriptomic or metagenomic data for prokaryotic, eukaryotic and viruses.

metatdenovo 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

[![Open in GitHub Codespaces](https://img.shields.io/badge/Open_In_GitHub_Codespaces-black?labelColor=grey&logo=github)](https://github.com/codespaces/new/nf-core/metatdenovo)
[![GitHub Actions CI Status](https://github.com/nf-core/metatdenovo/actions/workflows/nf-test.yml/badge.svg)](https://github.com/nf-core/metatdenovo/actions/workflows/nf-test.yml)
[![GitHub Actions Linting Status](https://github.com/nf-core/metatdenovo/actions/workflows/linting.yml/badge.svg)](https://github.com/nf-core/metatdenovo/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/metatdenovo/results)[![Cite with Zenodo](http://img.shields.io/badge/DOI-10.5281/zenodo.10666590-1073c8?labelColor=000000)](https://doi.org/10.5281/zenodo.10666590)
[![nf-test](https://img.shields.io/badge/unit_tests-nf--test-337ab7.svg)](https://www.nf-test.com)

[![Nextflow](https://img.shields.io/badge/version-%E2%89%A525.10.4-green?style=flat&logo=nextflow&logoColor=white&color=%230DC09D&link=https%3A%2F%2Fnextflow.io)](https://www.nextflow.io/)
[![nf-core template version](https://img.shields.io/badge/nf--core_template-4.0.2-green?style=flat&logo=nfcore&logoColor=white&color=%2324B064&link=https%3A%2F%2Fnf-co.re)](https://github.com/nf-core/tools/releases/tag/4.0.2)
[![run with conda](http://img.shields.io/badge/run%20with-conda-3EB049?labelColor=000000&logo=anaconda)](https://docs.conda.io/en/latest/)
[![run with docker](https://img.shields.io/badge/run%20with-docker-0db7ed?labelColor=000000&logo=docker)](https://www.docker.com/)
[![run with singularity](https://img.shields.io/badge/run%20with-singularity-1d355c.svg?labelColor=000000)](https://sylabs.io/docs/)
[![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/metatdenovo)

[![Get help on Slack](http://img.shields.io/badge/slack-nf--core%20%23metatdenovo-4A154B?labelColor=000000&logo=slack)](https://nfcore.slack.com/channels/metatdenovo)[![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)

## 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

![nf-core/metatdenovo metro map](https://raw.githubusercontent.com/nf-core/metatdenovo/master/docs/images/metat-metromap.png)

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 both prokaryote and eukaryote assembly
   2. [`Megahit`](https://github.com/voutcn/megahit) suggested for both prokaryote and eukaryote assembly; requires less resources
8. Choice of orf caller:
   1. [`TransDecoder`](https://github.com/TransDecoder/TransDecoder) suggested for eukaryotes; only ORFs
   2. [`Prokka`](https://github.com/tseemann/prokka) suggested for prokaryotes; ORFs and other features plus functional annotation
   3. [`Prodigal`](https://github.com/hyattpd/Prodigal) suggested for Prokaryotes; only ORFs
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. [`Prokka`](https://github.com/tseemann/prokka) feature identification and annotation for prokaryotes
    2. [`eggNOG-mapper`](https://github.com/eggnogdb/eggnog-mapper)
    3. [`KofamScan`](https://github.com/takaram/kofam_scan)
    4. [`HMMER`](https://www.ebi.ac.uk/Tools/hmmer/search/hmmsearch) search ORFs with a set of HMM profiles, and rank results
11. Taxonomic annotation:
    1. [`EUKulele`](https://github.com/AlexanderLabWHOI/EUKulele)
    2. [`Diamond`](https://github.com/bbuchfink/diamond)
12. Summary statistics.

## Usage

> [!NOTE]
> If you are new to Nextflow and nf-core, please refer to [this page](https://nf-co.re/docs/get_started/environment_setup/overview) on how to set-up Nextflow. Make sure to [test your setup](https://nf-co.re/docs/get_started/run-your-first-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
sample3,./a.fastq.gz,./b.fastq.gz
```

Each row represents a fastq file (single-end) or a pair of fastq files (paired-end).
The fastq files need to end with `.fq` or `.fastq`, followed by `.gz` if gzipped.
Read files from multiple rows with the same sample name will be concatenated and treated as a single sample.
A mix of single-end and paired-end files is allowed, but do not mix single-end and paired-end for the same sample name.

Now, you can run the pipeline using:

```bash
nextflow run nf-core/metatdenovo \
   -profile <docker/singularity/.../institute> \
   --input samplesheet.csv \
   --outdir <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/running/run-pipelines#using-parameter-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 the `summary_tables` directory under the output directory are made especially for further analysis in tools like R or Python.
> Their formats are standardized and column names consistent between tables.

## 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](docs/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 as follows:

> **The Nextflow Nf-Core/Metatdenovo Pipeline for Reproducible Annotation of Metatranscriptomes, and More.**
>
> Di Leo, Danilo, Emelie Nilsson, Arianna Krinos, Jarone Pinhassi, and Daniel Lundin.
>
> PeerJ 13 (December 2025): e20328. https://doi.org/10.7717/peerj.20328.

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).

metatdenovo

metatdenovo 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

metatdenovo

metatdenovo 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README