Data processing workflow and supplementary data for Li et al. 2018 - The effect of filtration method on the efficiency of environmental DNA capture and quantification via metabarcoding. Molecular Ecology Resourses DOI: https://doi.org/10.1111/1755-0998.12899
Release 1.0 of this repository has been archived:
##Contents
- reference sequences (curated reference databases) used in analyses in Genbank format (here)
- adapter sequences used for 12S fragment (here)
- SRA accession numbers for raw Illumina data (here)
- Taxonomic assignment results (here)
- R scripts used to produce the figures in the paper (here)
- Appendix S1: Read counts of OTUs data was used for the R script (.csv) (here)
- Figure S3: Maximum likelihood phylogenetic tree of the all 12S sequences from the custom reference database (.png) (here)
##Instructions on how to set up all dependencies for data processing/analyses
To facilitate full reproducibility of our analyses we provide Jupyter notebooks illustrating our workflow and all necessary supplementary data in this repository.
Illumina data was processed (from raw reads to taxonomic assignments) using the metaBEAT pipeline. The pipeline relies on a range of open bioinformatics tools, which we have wrapped up in a self contained docker image which includes all necessary dependencies here.
##Setting up the environment
In order to retrieve supplementary data (reference sequences etc.) start by cloning this repository to your current directory:
git clone --recursive https://github.com/HullUni-bioinformatics/Li_et_al_2018_eDNA_filtration.git
In order to make use of our self contained analysis environment you will have to install Docker on your computer. Docker is compatible with all major operating systems. See the Docker documenation for details. On Ubuntu installing Docker should be as easy as:
sudo apt-get install docker.io
Once Docker is installed you can enter the environment by typing, e.g.:
sudo docker run -i -t --net=host --name metaBEAT -v $(pwd):/home/working chrishah/metabeat /bin/bash
This will download the metaBEAT image (if it's not yet present on your computer) and enter the 'container', i.e. the self contained environment (Note that sudo may be necessary in some cases). With the above command the container's directory /home/working will be mounted to your current working directory (as instructed by $(pwd)), in other words, anything you do in the container's /home/working directory will be synced with your current working directory on your local machine.
##Data processing workflow as Jupyter notebooks
Raw illumina data has been deposited with Genbank (BioProject: PRJNA414952; BioSample accession: SAMN07811461-SAMN07811580; Sequence Read Archive accessions: SRR6189420-SRR6189539) - see sample specific accessions here. Before following the workflow below, you'll need to download the raw reads from SRA. To download the raw read data you can follow the steps in this Jupyter notebook.
With the data in place you should be able to fully rerun/reproduce our analyses by following the steps outlined in the this Jupyter notebook.
The workflow illustrated in the notebooks assumes that the raw Illumina data is present in a directory raw_reads at the base of the repository structure and that the files are named according to the following convention:
'sampleID-marker', followed by '_R1' or '_R2' to identify the forward/reverse read file respectively. sampleID must corresponds to the first column in the file Sample_accessions.tsv here.