nf-core/metatdenovo is a bioinformatics best-practice analysis pipeline for Assembly and annotation of metatranscriptomic data, both prokaryotic and eukaryotic.

The pipeline is built using Nextflow, 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 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 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.

1. Read QC (FastQC)
2. Present QC for raw reads (MultiQC)
3. Quality trimming and adapters removal for raw reads Trimm Galore!
4. Filter sequences with BBduk
5. Normalize the sequences with khmer or BBnorm
6. Merge trimmed, pair-end reads ( Seqtk)
7. Choice of de novo assembly programs:
   1. RNAspade suggested for Eukaryotes de novo assembly
   2. Megahit suggested for Prokaryotes de novo assembly
8. Choice of orf caller:
   1. TransDecoder suggested for Eukaryotes
   2. Prokka suggested for Prokaryotes
   3. Prodigal suggested for Prokaryotes
9. Quantification of genes identified in assemblies:
   1. generate index of assembly BBmap index
   2. Mapping cleaned reads to the assembly for quantification BBmap
   3. Get raw counts per each gene present in the assembly Featurecounts -> TSV table with collected featurecounts output
10. Functional annotation:
    1. Eggnog -> Reformat TSV output "eggnog table"
    2. KOfamscan
    3. HMMERsearch -> Ranking orfs based on HMMprofile with Hmmrank
11. Taxonomical annotation:
    1. EUKulele -> Reformat TSV output "Reformat_tax.R"
    2. CAT
    3. Choice of functional annotation: 1. Eggnog-mapper 2. Run-DBcan 3. Hmmsearch. Besides searching the ORFs, each ORF's hits will be ranked.
12. Summary statistics table. Collect_stats.R

1. Install Nextflow (>=22.10.1)

2. Install any of Docker, Singularity (you can follow this tutorial), Podman, Shifter or Charliecloud for full pipeline reproducibility (you can use Conda both to install Nextflow itself and also to manage software within pipelines. Please only use it within pipelines as a last resort; see docs).

3. Download the pipeline and test it on a minimal dataset with a single command:

   nextflow run nf-core/metatdenovo -profile test,YOURPROFILE --outdir <OUTDIR>

   Note that some form of configuration will be needed so that Nextflow knows how to fetch the required software. This is usually done in the form of a config profile (YOURPROFILE in the example command above). You can chain multiple config profiles in a comma-separated string.

   • The pipeline comes with config profiles called docker, singularity, podman, shifter, charliecloud and conda which instruct the pipeline to use the named tool for software management. For example, -profile test,docker.
   • Please check nf-core/configs to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use -profile <institute> in your command. This will enable either docker or singularity and set the appropriate execution settings for your local compute environment.
   • If you are using singularity, please use the nf-core download command to download images first, before running the pipeline. Setting the NXF_SINGULARITY_CACHEDIR or singularity.cacheDir Nextflow options enables you to store and re-use the images from a central location for future pipeline runs.
   • If you are using conda, it is highly recommended to use the NXF_CONDA_CACHEDIR or conda.cacheDir settings to store the environments in a central location for future pipeline runs.

4. Start running your own analysis!

   nextflow run nf-core/metatdenovo --input samplesheet.csv --outdir <OUTDIR> -profile <docker/singularity/podman/shifter/charliecloud/conda/institute>

The nf-core/metatdenovo pipeline comes with documentation about the pipeline usage, parameters and output.

nf-core/metatdenovo was originally written by Danilo Di Leo (@danilodileo), Emelie Nilsson (@emnilsson) & Daniel Lundin (@erikrikarddaniel).

If you would like to contribute to this pipeline, please see the contributing guidelines.

For further information or help, don't hesitate to get in touch on the Slack #metatdenovo channel (you can join with this invite).

An extensive list of references for the tools used by the pipeline can be found in the 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.