Using Galaxy-P to leverage RNA-Seq for the discovery of novel protein variations

Abstract

BackgroundCurrent 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.ResultsWe 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.).ConclusionsUsing 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.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-703) contains supplementary material, which is available to authorized users.

Human Proteomic Variation Revealed by Combining RNA-Seq Proteogenomics and Global Post-Translational Modification (G-PTM) Search Strategy

“…6,23−25 The ease of maintaining the same versions and settings of individual software makes bioinformatic analysis more user-friendly and reproducible. The Galaxy-P workflows used to produce SAV and NSJ peptide FASTA databases were described by Sheynkman et al 6,7,9 Each database employed in the present work is the union of three databases: the sample-specific SAV database, the sample-specific NSJ database, and the Homo sapiens (Human) UniProt reference proteome containing protein sequences with site-specific PTM annotations. The UniProt database format that includes these PTM annotations is called the UniProt extended markup language (UniProt-XML).…”

“…The SAV and NSJ databases were produced and appended to the reference proteome in UniProt XML format to create a sample-specific database for each cell line. For the present work, we adapted and combined scripts from the works by Sheynkman et al 6,7,9 to develop software named SampleSpecificDBGenerator used to perform this process. This program and source code can be obtained at .…”

“…A number of studies have tackled this problem by mining RNA-Seq data to include single amino acid variant (SAV) peptides 6−8 or novel splice junction (NSJ) peptides 6,8−11 in the custom protein sequence database to allow them to be detected. Studies have also focused on other RNA features, such as RNA-editing 8 or active translation by ribosomes using ribosome profiling data.…”

Testing and Validation of Computational Methods for Mass Spectrometry

“…The utilization of single, yet complex omics data sets for method comparison can be challenging, yet in a time where a broad set of technologies are available and multiomics studies are of paramount interest, the need for adequate reference data across high-throughput technologies, for example, 29 is crucial. There are genuine opportunities for the generation and dissemination of data from matching samples across omics domains, under controlled conditions, and as replicated measurements, and we expect a great potential of such analyses also for improving computational methods.…”

Proteogenomics: Integrating Next-Generation Sequencing and Mass Spectrometry to Characterize Human Proteomic Variation

“…Indeed, reduced databases have been shown to increase the peptide identification rate by 5% for moderate-coverage proteomic data sets (29). However, this improvement vanishes for deeper-coverage proteomic data (41). In fact, there may be danger of removing proteins with low RNA-protein abundance correlations or with transcripts that are undersampled in RNA-Seq, such as mRNAs without polyA tails (42).…”