metagenomics_snakemake

Snakemake-based pipeline for metagenomics classification. Currently support short-reads Illumina Sequences and long-reads ONT Sequences.

https://github.com/pablorr24/metagenomics_snakemake

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Snakemake-based pipeline for metagenomics classification. Currently support short-reads Illumina Sequences and long-reads ONT Sequences.

README.html

Snakemake Metagenomics Workflow

Summary:
This Snakemake-based program allows to classify metagenomic samples
from NGS and ONT samples.


Brief Description
The program allows to clasify and analyze NGS sequences and long-read
ONT samples. The program consists of 3 workflows: short-reads
classification, long-reads classification and post-classification
workflow. The short-reads workflow and the long-reads workflow have a
QC-only mode, which runs FastQC and NanoPlot respectively. This method
is useful to evaluate sequence quality before classification. The
post-classification workflow works on the results of the classification
workflows and provides additional information using a metadata file and
an additional target variable.


Prerequisites
This installation requires git and conda/miniconda. If they are
already installed, skip these steps, otherwise install them they with
the following steps:
Git Installation:

sudo apt install git
Miniconda Installation:
mkdir -p ~/miniconda3
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
After installing, initialize your newly-installed Miniconda. The following commands initialize for bash and zsh shells:zrm -rf ~/miniconda3/miniconda.sh
After installing, initialize your newly-installed Miniconda. The
following commands initialize for bash and zsh shells:
~/miniconda3/bin/conda init bash
~/miniconda3/bin/conda init zsh


Snakemake Installation
git clone https://github.com/pablorr24/metagenomics_snakemake/
cd metagenomics/Snakemake
conda env create -f environment.yml -n snakemake_meta
conda activate snakemake_meta


Database Installation
If you already have a database such as Silva, Greengenes, RefSeq,
Kraken2, or a similar classification database, you can skip this step.
Otherwise, make sure you install a database. The following instructions
will download and install the Silva database
kraken2-build --special silva --db SilvaDB
kraken2-build --special greengenes --db greengenes

Running a workflow
To run a workflow, first modify the configuration file and adjust to
your parameters. Afterwards, run Snakemake


Short-reads
snakemake -s Snakefile_fastqc --cores all

snakemake -s Snakefile_full_workflow --cores all




Long-reads
snakemake -s Snakefile_nanoplot --cores all

snakemake -s Snakefile_long_read --cores all




Post Classification Workflow
snakemake -s Snakefile_post_analysis --cores all
Metadata File

The post-classification workflow requires a metadata file, with one row
per sample, and different columns specifying specific sample variables
(sample location, species, etc).


Output
After running the workflow, a timestamped folder is created in the
output folder. All your results will be inside this
folder.


References
Rules
The rule ‘create_otu_table’ uses a modified version of the
‘kraken2OTU.py’ script created by GitHub user sipost1, available in https://github.com/sipost1/kraken2OTUtable/blob/main/kraken2otu.py
The rules ‘calculate_alpha_diversity’ and ‘calculate_beta_diversity’
use a modified version of the ‘alpha_diversity.py’and
’beta_diversity_py’ script created by GitHub user jenniferlu717 in the
DiversityTools repository, available in https://github.com/jenniferlu717/KrakenTools/blob/master/DiversityTools/alpha_diversity.py
The rules ‘create_dendogram’ and ‘create_pcoa_plot’ use a
modified_version of the ‘dendro.R’ and ‘pca.R’ created by GitHub user
GATB in the simka repository, both available in https://github.com/GATB/simka/tree/master/scripts/visualization
External Software
Andrews, S. (2010). FastQC: A Quality Control Tool for High
Throughput Sequence Data [Online]. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/
Bolger, A. M., Lohse, M., & Usadel, B. (2014). Trimmomatic: A
flexible trimmer for Illumina Sequence Data. Bioinformatics, btu170.
Wood, D.E., Lu, J. & Langmead, B. Improved metagenomic analysis
with Kraken 2. Genome Biol 20, 257 (2019). https://doi.org/10.1186/s13059-019-1891-0
Ondov, B.D., Bergman, N.H. & Phillippy, A.M. Interactive
metagenomic visualization in a Web browser. BMC Bioinformatics 12, 385
(2011). https://doi.org/10.1186/1471-2105-12-385
Wouter De Coster, Svenn D’Hert, Darrin T Schultz, Marc Cruts,
Christine Van Broeckhoven, NanoPack: visualizing and processing
long-read sequencing data, Bioinformatics, Volume 34, Issue 15, August
2018, Pages 2666–2669, https://doi.org/10.1093/bioinformatics/bty149
Kim D, Song L, Breitwieser FP, Salzberg SL. Centrifuge: rapid and
sensitive classification of metagenomic sequences. Genome Res. 2016
Dec;26(12):1721-1729. doi: 10.1101/gr.210641.116. Epub 2016 Oct 17.
PMID: 27852649; PMCID: PMC5131823.

Owner

Citation (citations/fastqc_citation.txt)

Short-reads FastQC workflow

FastQC
Andrews, S. (2010). FastQC:  A Quality Control Tool for High Throughput Sequence Data [Online]. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/

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