Create the input data for ribodiff.
Rscript createcounttable.R -i data_path -s singal_name -b background_name -c conbditions (comma separated, e.g.: Wildtype,Mutant) -r number_of_replicates -o output_path
Count data from htseq-count.
Ribodiff allows to call differentially translated regions across two different conditions, incorporating Control data.
- Ribodiff can be found here. All credits go for the developers; published in Zhong et al.
- Install ribodiff via bioconda in an virtual environment (ribodiff)
- activate env
- Clone Ribodiff from git
- Go into the scripts folder where TE.py is
python2 TE.py -e conditionsheet -c counttable -o outputfile
Data from createcounttable.R
Riborex allows to call differentially translated regions across two different conditions, incorporating Control data.
- Riborex was published in Li et al.
- Install all packages stated in the header of
riborex.R.
Rscript riborex -i data_path -s singal_name -b background_name -c conbditions (comma separated, e.g.: Wildtype,Mutant) -r number_of_replicates -o output_path
Count data from htseq-count.
Script filters results of ribodiff and riborex based on p-value threshold (<0.05) and compare the two lists of ribodiff and riborex to find regions metnioned in both lists.
python ribodiff_riborex_compare.py
Results of riborex.R and ribodiff.
Very small scripts to annotate the results of ribodiff_riborex_compare.py.
annotate_gene_lists.sh regions_file annoation_file output_file
Result of ribodiff_riborex_compare.py