A worklfow which allows quantification of identified and unidentified feature points across multiple runs. We use a basic identification workflow, followed by a feature detection algorithm and use the results to annotate quantitative data points with identification infos. These points are used as anchor points to align the retention times across multiple runs and summarizes the results in a large tsv-table.
This workflow was implemented to be used on DDA-data.
The workflow is containerized via docker. Please follow the installation guide for docker to have it installed in your system, if not already. The workflow can be run as is. Examples of some calls can be found further below.
You can clone this project via:
git clone --recursive https://github.com/mpc-bioinformatics/unbeQuant
All dependencies can be downloaded via compile_and_setup_dependencies.sh for a local execution. Make sure you are in a Python-Environment,
to prevent errors or dependency conflicts with the operating systems package manager.
The docker can be created via:
docker build -t unbeqonet:local . -f docker/Dockerfile
# PXD028605 Protein FASTA
nextflow run main_workflow_protein_fasta.nf \
--main_fasta_file < Path to the FASTA files >.fasta \
--main_raw_files_folder < Path to the .raw/.d of Thermo/Bruker files which should be analyzed > \
--main_comet_params < Path to the comet configuration parameters text >
# Run unbeQuant together with ProGFASTAGen (ProtGraph)
nextflow run main_workflow_precursor_specific_fasta.nf \
--main_sp_embl_file < Path to the SP-EMBL file >.txt \
--main_raw_files_folder < Path to the .raw/.d of Thermo/Bruker files which should be analyzed > \
--main_comet_params < Path to the comet configuration parameters text, here with digestion turned off >For a detailed list of possible parameters, check the scripts directly.