Tools to Covisualize and Coanalyze Proteomic Data with Genomes and Transcriptomes: Validation of Genes and Alternative mRNA Splicing

Abstract

Direct links between proteomic and genomic/transcriptomic data are not frequently made, partly because of lack of appropriate bioinformatics tools. To help address this, we have developed the PG Nexus pipeline. The PG Nexus allows users to covisualize peptides in the context of genomes or genomic contigs, along with RNA-seq reads. This is done in the Integrated Genome Viewer (IGV). A Results Analyzer reports the precise base position where LC-MS/MS-derived peptides cover genes or gene isoforms, on the chromosomes or contigs where this occurs. In prokaryotes, the PG Nexus pipeline facilitates the validation of genes, where annotation or gene prediction is available, or the discovery of genes using a "virtual protein"-based unbiased approach. We illustrate this with a comprehensive proteogenomics analysis of two strains of Campylobacter concisus . For higher eukaryotes, the PG Nexus facilitates gene validation and supports the identification of mRNA splice junction boundaries and splice variants that are protein-coding. This is illustrated with an analysis of splice junctions covered by human phosphopeptides, and other examples of relevance to the Chromosome-Centric Human Proteome Project. The PG Nexus is open-source and available from https://github.com/IntersectAustralia/ap11_Samifier. It has been integrated into Galaxy and made available in the Galaxy tool shed.

An integrative strategy to identify the entire protein coding potential of prokaryotic genomes by proteogenomics

“…However, in both cases, DBs specific for the conditions studied are generated, requiring bioinformatics expertise and limiting the general applicability of the resource. The GFF file we provide can be very valuable for other proteogenomics software solutions like GenoSuite (Kumar et al 2013), PGP (Tovchigrechko et al 2014), and PG Nexus (Pang et al 2014), which allow users to search their data against a six-frame translation and later visualize identified peptides onto a genome sequence but lack integrated and consolidated annotations.…”

“…However, they do not integrate different annotations of the same genome. PG Nexus (Pang et al 2014) uses the NCBI RefSeq annotation, a Glimmer ab initio prediction (Delcher et al 2007), and a six-frame translation against which peptides are searched with Mascot (Perkins et al 1999) and later visualized onto the genome. However, the annotations are not integrated and consolidated; the boundaries of novel ORFs still have to be discovered based on peptide evidence, which requires substantial manual effort.…”

“…Defining DB complexity as the number of distinct tryptic peptides of 6-40aa in length, the complexity of the resulting iPtgxDB was ∼50% of that of a full six-frame translated genome that Mascot (Perkins et al 1999) or PG Nexus (Pang et al 2014) would rely on to identify proteogenomic evidence for novel peptides. Despite the relatively large number of entries, the DB complexity is only 70% of that of baker's yeast and below 20% of a human protein DB (Supplemental Table S2).…”

“…RNA-seq data have been used to limit the protein search DB size to achieve better statistical power (Wang et al 2012;Woo et al 2013;Zickmann and Renard 2015). Other MS-friendly DB solutions that integrate data from different species or strains include MScDB (Marx et al 2013), MSMSpdbb (de Souza et al 2010, and PG Nexus (Pang et al 2014). Even pipeline solutions were developed that allow the search of proteomics data against a sixframe translation-based DB, including Peppy (Risk et al 2013), GenoSuite (Kumar et al 2013), and PGP (Tovchigrechko et al 2014).…”

PGx: Putting Peptides to BED

“…It follows naturally that the ideal unified coordinate system for proteogenomics should remain genomic in nature. Indeed, effective tools that can map MS-based proteomics results onto genomic coordinates have recently become available (Peppy, 2 Proteogenomic Mapping Tool, 3 Pepline, 4 MS-Dictionary, 5 GappedDictionary, 6 IggyPep, 7 MSProGene, 8 ProteoAnnotator, 9 PGNexus, 10 and GalaxyP 11 ); however, these tools are usually couched in a relatively involved and comprehensive pipeline (e.g., the GalaxyP pipeline consists of up to 140 steps) and typically impose a specific mass-informatic 12 workflow on the practitioner, by, for example, requiring the generation of short peptide sequence tags (PSTs) or some complex form of de novo peptide sequencing followed by a lookup against the full six-frame translation of the genomic sequence. Our experience suggests that a more common scenario involves the production, by the genomic arm of the workflow, of a (liberally) predicted proteome (containing what is assumed to be a superset of the observable proteome) so as to leverage existing PSM search engines (such as Mascot, 13 Sequest, 14 X!Tandem 15 ) that require a straightforward representation of the predicted proteome (in the form of a FASTA file).…”