| Current practice in mass spectrometry-based proteomics is to identify peptides by comparison of experimental spectra with theoretical spectra derived from a reference protein database. However, this strategy necessarily fails to detect peptides and proteins whose amino acid sequence differs from the reference sequence, such as when there is a genetic difference between the sample and reference genome. Fortunately, next generation sequencing (NGS), specifically RNA-Seq, enables comprehensive determination of the coding transcript sequences present in a given sample. These transcript sequences can then be translated in silico to the corresponding proteins and used to build a customized proteomic database that captures all sample-specific (i.e. specific to an individual) protein variations including those resulting from alternative splicing, single amino acid polymorphisms (SAPs), insertions, deletions, translational frameshifts, fusion genes, and RNA editing events. In this dissertation, I show how customized proteomic databases derived from RNA-Seq data can be employed during MS-searching to both enhance proteomic analysis and discover novel peptides. Chapter 2 describes the discovery of novel splice-junction peptides. Chapter 3 describes the large-scale detection of SAP-containing peptides. Finally, Chapter 4 combines the bioinformatic pipelines from Chapter 2 and 3 and implements them within Galaxy-P, a web-based platform for the flexible construction of NGS and proteomic workflows. |
