iPiG: Integrating Peptide Spectrum Matches into Genome Browser Visualizations

Kuhring, Mathias; Renard, Bernhard Y.

doi:10.1371/journal.pone.0050246

Cited by 25 publications

(30 citation statements)

References 16 publications

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“…are mostly designed to align peptide sequence (text input) to genomic sequences. Notable exceptions are tools like PGTools [30], PG Nexus [31], iPiG [32], proBAMsuite [33], and other pipelines (for example, the one described in [34]), which were designed not only to provide a genome browser-based visualization of MS-based peptide identifications, but in some cases also to provide scripts for data processing, peptide search, FDR calculations, among others, starting from complete datasets of raw MS files or PSM inputs, on a truly global proteomic approach. However, in our opinion some of such tools decreases the user freedom to apply search engines and approaches that they prefer rather than the one(s) employed at the referred tools (in addition, as we described above, PG Nexus, e.g.…”

Section: Discussionmentioning

confidence: 99%

A tool for integrating genetic and mass spectrometry‐based peptide data: Proteogenomics Viewer

et al. 2017

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In this manuscript we describe Proteogenomics Viewer, a web-based tool that collects MS peptide identification, indexes to genomic sequence and structure, assigns exon usage, reports the identified protein isoforms with genomic alignments and, most importantly, allows the inspection of MS2 information for proper peptide identification. It also provides all performed indexing to facilitate global analysis of the data. The relevance of such tool is that there has been an increase in the number of proteogenomic efforts to improve the annotation of both genomics and proteomics data, culminating with the release of the two human proteome drafts. It is now clear that mass spectrometry-based peptide identification of uncharacterized sequences, such as those resulting from unpredicted exon joints or non-coding regions, is still prone to a higher than expected false discovery rate. Therefore, proper visualization of the raw data and the corresponding genome alignments are fundamental for further data validation and interpretation. Also see the video abstract here: http://youtu.be/5NzyRvuk4Ac.

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Section: Discussionmentioning

confidence: 99%

A tool for integrating genetic and mass spectrometry‐based peptide data: Proteogenomics Viewer

et al. 2017

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“…For the human tissue and the ovarian cancer phosphoproteome data PoGo’s performance was compared against PGx 3 (downloaded from https://github.com/FenyoLab/PGx) and iPiG 4 (downloaded from https://sourceforge.net/projects/ipig/), two standalone tools available to map peptides to their corresponding genomic coordinates. Each dataset was formatted using in-house scripts in R and perl to fit the required input format for each tool.…”

Section: Methodsmentioning

confidence: 99%

“…Substantial advances in MS technologies enable more complete identification and quantification of proteomes, making these data more comparable to transcriptomics. Tools like PGx 3 and iPiG 4 to readily visualize proteomics with corresponding RNA-sequencing data on a reference genome are now increasingly indispensable. While iPiG heavily relies on the annotation format used for UCSC genes, PGx uses sample specific protein sequence databases derived from RNA-sequencing experiments and corresponding genomic coordinates.…”

Section: Main Textmentioning

confidence: 99%

PoGo: Jumping from Peptides to Genomic Loci

Pirklbauer

Bender

et al. 2016

Preprint

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11Current tools for visualization and integration of proteomics with other omics datasets are 12 inadequate for large-scale studies and capture only basic sequence identity information. We Main Text 19Mass spectrometry (MS) and Next-generation sequencing (NGS) Figure 38 All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/079772 doi: bioRxiv preprint first posted online Oct. 7, 2016; 1), thereby connecting the protein sequences to the genomic coordinate space. Database 39 search tools enable peptides to be identified from MS using a protein-DB.5 By using the to PGx (9.7 GB and 11.9 GB respectively). These data show a major improvement of speed 59 and memory usage in addition to application with a readily available reference annotation. (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/079772 doi: bioRxiv preprint first posted online Oct. 7, 2016; ( Figure S5). Unique mappings to single transcripts and single genes were reduced to 94.9% 83 and 84.1% while the number of peptides belonging to multiple genes increased 84 exponentially by 220.9% and 3,175.2% (Figures S5 and S6). The mapping of additional 85 repeats of the sequence 'VPEPGCTK' following application with mismatches were validated 86 through identified peptides in the sample ( Figure 1C) Figure S7). 98PoGo also integrates peptide quantitation with genomic loci through the GCT file format.

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“…The proteomics data sets also require complex workflows, including database searching and proper interpretation of the statistical results. Several groups have developed tools to aid in proteogenomic database construction (41, 97–102) and visualization of peptides on the genome or in comparison to gene models (103–107). Both NGS and proteomic technologies and tools are rapidly evolving, requiring constant parallel efforts to develop adaptable bioinformatic tools for their analysis.…”

Section: Other Proteogenomic Issuesmentioning

confidence: 99%

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

Sheynkman

Shortreed

Cesnik

et al. 2016

Annual Rev. Anal. Chem.

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Mass spectrometry–based proteomics has emerged as the leading method for detection, quantification, and characterization of proteins. Nearly all proteomic workflows rely on proteomic databases to identify peptides and proteins, but these databases typically contain a generic set of proteins that lack variations unique to a given sample, precluding their detection. Fortunately, proteogenomics enables the detection of such proteomic variations and can be defined, broadly, as the use of nucleotide sequences to generate candidate protein sequences for mass spectrometry database searching. Proteogenomics is experiencing heightened significance due to two developments: (a) advances in DNA sequencing technologies that have made complete sequencing of human genomes and transcriptomes routine, and (b) the unveiling of the tremendous complexity of the human proteome as expressed at the levels of genes, cells, tissues, individuals, and populations. We review here the field of human proteogenomics, with an emphasis on its history, current implementations, the types of proteomic variations it reveals, and several important applications.

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iPiG: Integrating Peptide Spectrum Matches into Genome Browser Visualizations

Cited by 25 publications

References 16 publications

A tool for integrating genetic and mass spectrometry‐based peptide data: Proteogenomics Viewer

A tool for integrating genetic and mass spectrometry‐based peptide data: Proteogenomics Viewer

PoGo: Jumping from Peptides to Genomic Loci

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

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