Proteomics data analysis is dominated by database-based search engines strategies. Perhaps the most common protocol today is to retrieve raw data from a mass spectrometry, convert the raw data from binary format to a text-based format and then process it using a database search algorithm. The resulting data need to be statistically filtered in order to converge to a final list of identified peptides and proteins.
Amount Search Engines, Comet (the youngest son of SEQUEST) is one of the most popular nowadays. Today we are going to show how to run a simple analysis protocol using the Comet database search engine followed by statistical analysis using PeptideProphet and ProteinProphet, two of the most known and robust processing algorithms for proteomics data.
This pipeline is available in TPP, however several users prefer to use the individual components rather than Trans-proteomics Pipeline. The big differential here is how we are going to do it. Instead of going through the step-by-step in how to install and configure Comet and TPP, we are going to run the pipeline using Docker containers from the BioDocker project (you can get more information on the project here).