Introduction

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.

Pipeline summary

1. Read QC (FastQC)
2. Present QC for raw reads (MultiQC)
3. Quality trimming and adapter removal for raw reads (Trim Galore!)
4. Optional: Filter sequences with BBduk
5. Optional: Normalize the sequencing depth with BBnorm
6. Merge trimmed, pair-end reads (Seqtk)
7. Choice of de novo assembly programs:
   1. RNAspades suggested for Eukaryote de novo assembly
   2. Megahit suggested for Prokaryote 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. Taxonomic annotation:
    1. EUKulele -> Reformat TSV output "Reformat_tax.R"
    2. CAT
12. Summary statistics table. "Collect_stats.R"

Usage

[!NOTE] If you are new to Nextflow and nf-core, please refer to this page on how to set-up Nextflow. Make sure to test your setup 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:

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.

For more details and further functionality, please refer to the usage documentation and the parameter documentation.

Pipeline output

To see the results of an example test run with a full size dataset refer to the results tab on the nf-core website pipeline page. For more details about the output files and reports, please refer to the output documentation.

[!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.

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

Citations

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.