.. toctree:: :hidden: setup howto uppmax
nbis-meta is a snakemake workflow for metagenomics projects
To start using the workflow either clone the latest version of the repo, run:
git clone https://github.com/NBISweden/nbis-meta.git
or download the latest release from the release page and extract the archive.
Then change directory into the nbis-meta
folder and create the core conda
environment:
cd nbis-meta
conda env create -f environment.yml
Note
mamba instead of conda
mamba
is a faster replacement for conda. Give it a try by installing it from
the conda-forge channel: conda install -c conda-forge mamba
.
You can then run mamba env create -f environment.yml
.
You may also pull the latest Docker image:
docker pull nbisweden/nbis-meta:latest
You are now ready to start using the workflow!
- for information on how to prepare necessary files see :doc:`Getting-started <setup>`
- then check out the :doc:`How-to page <howto>` for more info on how to run the workflow
This workflow can perform preprocessing of paired- and/or single-end whole-genome shotgun metagenomic data (in fastq-format) using e.g.:
- Trimmomatic (adapter/quality trimming)
- Cutadapt (adapter trimming)
- SortMeRNA (rRNA filtering)
- Fastuniq (de-duplication)
- FastQC and MultiQC (read QC and report generation)
Preprocessed reads can be used for taxonomic classification and profiling using tools such as:
- Kraken2
- Centrifuge
- MetaPhlAn3
producing taxonomic profiles of the samples, as well as interactive krona plots.
Preprocessed reads can also be assembled and analyzed further using tools such as:
- Megahit/MetaSPADES (for metagenomic assembly)
- prodigal (gene calling)
- pfam_scan, eggnog-mapper, Resistance Gene Identifier (protein level annotations)
- bowtie2 (mapping reads to contigs)
- featureCounts (assigning and counting mapped reads)
- edgeR + metagenomeSEQ (normalization of read counts for genes/features)
- contigtax + sourmash (taxonomic assignments)
- metabat2, CONCOCT, MaxBin2 (metagenomic binning)
- checkm (genome bin QC)
- GTDB-TK (genome bin phylogenetic assignments)
- fastANI (genome bin clustering)