Background: Current practice in mass spectrometry (MS)-based proteomics is to identify peptides by comparison of experimental mass spectra with theoretical mass spectra derived from a reference protein database; however, this strategy necessarily fails to detect peptide and protein sequences that are absent from the database. We and others have recently shown that customized proteomic databases derived from RNA-Seq data can be employed for MS-searching to both improve MS analysis and identify novel peptides. While this general strategy constitutes a significant advance for the discovery of novel protein variations, it has not been readily transferable to other laboratories due to the need for many specialized software tools. To address this problem, we have implemented readily accessible, modifiable, and extensible workflows within Galaxy-P, short for Galaxy for Proteomics, a web-based bioinformatic extension of the Galaxy framework for the analysis of multi-omics (e.g. genomics, transcriptomics, proteomics) data. Results: We present three bioinformatic workflows that allow the user to upload raw RNA sequencing reads and convert the data into high-quality customized proteomic databases suitable for MS searching. We show the utility of these workflows on human and mouse samples, identifying 544 peptides containing single amino acid polymorphisms (SAPs) and 187 peptides corresponding to unannotated splice junction peptides, correlating protein and transcript expression levels, and providing the option to incorporate transcript abundance measures within the MS database search process (reduced databases, incorporation of transcript abundance for protein identification score calculations, etc.). Conclusions: Using RNA-Seq data to enhance MS analysis is a promising strategy to discover novel peptides specific to a sample and, more generally, to improve proteomics results. The main bottleneck for widespread adoption of this strategy has been the lack of easily used and modifiable computational tools. We provide a solution to this problem by introducing a set of workflows within the Galaxy-P framework that converts raw RNA-Seq data into customized proteomic databases.
Bibliographical noteFunding Information:
This work was supported by NIH grants 1P01GM081629, U54DK093467, 1P50HG004952 to LMS and NSF grant 1147079 to TJG. RNA-Sequencing work was performed at the University of Wisconsin-Madison Biotechnology Center. We thank Donnie Stapleton, Mark Keller, and Alan Attie for supplying the mouse islet samples (National Institute of Diabetes and Digestive Kidney Diseases grants 58037 and 66369). Galaxy-P is maintained by the Minnesota Supercomputing Center at the University of Minnesota. GMS was supported by the NIH Genomic Sciences Training Program 5T32HG002760.