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.
- Read QC (
FastQC) - Present QC for raw reads (
MultiQC) - Quality trimming and adapter removal for raw reads (
Trim Galore!) - Optional: Filter sequences with
BBduk - Optional: Normalize the sequencing depth with
BBnorm - Merge trimmed, pair-end reads (
Seqtk) - Choice of de novo assembly programs:
- Choice of orf caller:
TransDecodersuggested for EukaryotesProkkasuggested for ProkaryotesProdigalsuggested for Prokaryotes
- Quantification of genes identified in assemblies:
- Generate index of assembly (
BBmap index) - Mapping cleaned reads to the assembly for quantification (
BBmap) - Get raw counts per each gene present in the assembly (
Featurecounts) -> TSV table with collected featurecounts output
- Generate index of assembly (
- Functional annotation:
Eggnog-> Reformat TSV output "eggnog table"KOfamscanHMMERsearch-> Ranking orfs based on HMMprofile withHmmrank
- Taxonomic annotation:
- Summary statistics table. "Collect_stats.R"
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.
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.
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).
If you use nf-core/metatdenovo for your analysis, please cite it using the following doi: 10.5281/zenodo.10666590
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.