nf-core/metatdenovo

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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.

Usage

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 both prokaryote and eukaryote assembly
   2. Megahit suggested for both prokaryote and eukaryote assembly; requires less resources
8. Choice of orf caller:
   1. TransDecoder suggested for eukaryotes; only ORFs
   2. Prokka suggested for prokaryotes; ORFs and other features plus functional annotation
   3. Prodigal suggested for Prokaryotes; only ORFs
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. Prokka feature identification and annotation for prokaryotes
    2. eggNOG-mapper
    3. KofamScan
    4. HMMER search ORFs with a set of HMM profiles, and rank results
11. Taxonomic annotation:
    1. EUKulele
    2. Diamond
12. Summary statistics.

Usage

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). The fastq files need to be gzipped and their names end with .fq.gz or .fastq.gz.

Now, you can run the pipeline using:

nextflow run nf-core/metatdenovo \
   -profile <docker/singularity/.../institute> \
   --input samplesheet.csv \
   --outdir <OUTDIR>

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.

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

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.