Songjun's master degree project about functional metagenomic
This project is a workflow to process the raw metagenomic dataset for two annotation servers: MG-RAST and GhostKOALA, and integrate the final abundance number into KEGG pathwy.
The workflow include two parts:
The first part is a snakemake pipeline, which performs quality assessment and filtering of raw fastq files to generate filtered fasta files and amino acid files that are ready for annotation servers.
Example:
Second part consists of a python script and a R shiny app, which generates annotated results from GhostKOALA in KEGG website and visualizes them in KEGG pathways by using the shiny app based on PathView package.
usage:
./group.py -m metadata.txt -o output.csv
where metadata.txt contains the experimental setup (see example below). The result will be printed to output.csv
A metadata file is also necessary for the python script. It is used for grouping results from annotation server. A example file is attached in metadata.txt.
** Example metadata file:**
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Required software and version:
- Python/3.7.3, R/4.0.1, snakemake/5.26.1, Fastqc/0.11.8, Mulitqc/1.8, PEAR/0.9.1, seqtk/1.0, BLAST/2.5.0+, SPAdes/3.9.1, bwa/0.7.17