ProReP-ProteinResultsParserv3.0AToolForHandling.pptVIP

Outline Background Data generation from Mass Spec Experiment Mascot Search Engine Why to parse Mascot results? Parser features Results Conclusions Acknowledgments Background High-throughput “shotgun” Proteomics Identify, characterize and quantify all expressed proteins simultaneously in a mixture. Mass Spectrometry Peptide mass fingerprinting Collision Induced Dissociation (CID) spectra from MS/MS analysis LC/MS/MS approach used to identify protein components in a complex mixture Tandem mass spectra helps in inferring amino acid sequences of peptides Database Searching Mascot Search Engine Uses mass spectrometry data to identify proteins from primary sequence databases MS/MS ion search Enzyme cleavage rules applied to sequences in the protein databases Experimental mass values compared with calculated fragment ion mass values Use scoring algorithm to identify the closest match or matches Probability based MOWSE scoring algorithm Databases MSDB – non-identical protein sequence DB NCBInr SwissProt dbEST – “single-pass” cDNA sequences or EST’s Motivation Mass spectrometry generates enormous amount of data Mascot returns on an average hundreds of proteins matching the mass spectral data Time consuming to analyze the mascot results manually Need different ways of looking at data Comparison of various data sets (experiments) No tools were available in public domain to analyze Mascot results Protein Results Parser v3.0 Features Single File parsing Sequence coverage - with single file parsing Two-file comparison Multiple files Compare Combine Tool was developed using Perl/Tk Windows application Screened Html Result (smaller file size) Results – Multiple file comparison (sequential display) Results – Multiple file comparison (tabular display) Combine – Merging of multiple experiments Results – combining multiple experiments Conclusion Decreased data analysis and processing time. Search results reduced using user specified criteria in an automated way. Removal