Tandem mass spectrometry (MS/MS) followed by database search is the method of choice for high throughput protein identification in modern proteomic studies. Database searching methods employ spectral matching algorithms and statistical models to identify and quantify proteins in a sample. The major focus of these statistical methods is to assign probability scores to the identifications to distinguish between high confidence, reliable identifications that may be accepted (typically corresponding to a false discovery rate, FDR, of 1% or 5%) and lower confidence, spurious identifications that are rejected. These identification probabilities are determined, in general, considering only evidence from the MS/MS data. However, considering the wealth of external (orthogonal) data available for most biological systems, integrating such orthogonal information into proteomics analysis pipelines can be a promising approach to improve the sensitivity of these analysis pipelines and rescue true positive identifications that were rejected for want of sufficient evidence supporting their presence.
In this dissertation, approaches based on naive bayes rescoring, search space restriction, and a hybrid approach that combines both are described for integrating orthogonal information in proteomic analysis pipelines. These methods have been applied for integrating transcript abundance data from RNA-seq and identification frequency data from the Global Proteome Machine database, GPMDB (one of the largest repositories of proteomic experiment results), into analysis pipelines, improving the number of peptide and protein identifications from MS/MS data. Further, estimation of false discovery rates in very large proteomic datasets was also investigated. In very large datasets, usually resulting from integrating data from multiple experiments, some assumptions used in typical target-decoy based FDR estimation in smaller datasets no longer hold true, resulting in artificially inflated error rates. Alternative approaches that would allow accurate FDR estimation in these large scale datasets have been described and benchmarked.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116717/1/avinashs_1.pd
