PROTEOFORMER 2.0: Further Developments in the Ribosome Profiling-assisted Proteogenomic Hunt for New Proteoforms

Verbruggen, Steven; Ndah, Elvis; Criekinge, Wim Van; Gessulat, Siegfried; Küster, Bernhard; Wilhelm, Mathias; Damme, Petra Van; Menschaert, Gerben

doi:10.1074/mcp.ra118.001218

Cited by 44 publications

(58 citation statements)

References 63 publications

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“…Notably, we used correlation to MS²PIP-predicted spectra before to discriminate high-confidence unannotated peptides in Arabidopsis thaliana (Willems et al 2017). In line with our previously reported findings when making use of the PROTEOFORMER pipeline (Verbruggen et al 2019), our proteogenomic pipeline , illustrates how Percolator rescoring increases the number and confidence of (un)annotated peptide identifications (Figure 2, 4).…”

Section: Discussionsupporting

confidence: 78%

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Lost and found: re-searching and re-scoring proteomics data aids the discovery of bacterial proteins and improves proteome coverage

Willems

Fijałkowski

Damme

2019

Preprint

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Prokaryotic genome annotation is heavily dependent on automated gene annotation pipelines that are prone to propagate errors and underestimate genome complexity. We describe an optimized proteogenomic workflow that uses ribo-seq and proteomic data of Salmonella Typhiumurium to identify unannotated proteins or alternative protein forms raised upon alternative translation initiation (i.e. N-terminal proteoforms). This data analysis encompasses the searching of cofragmenting peptides and post-processing with extended peptide-to-spectrum quality features including comparison to predicted fragment ion intensities. When applying this strategy, an enhanced proteome-depth is achieved as well as greater confidence for unannotated peptide hits.We demonstrate the general applicability of our pipeline by re-analyzing public Deinococcus radiodurans datasets. Taken together, systematic re-analysis using available prokaryotic (proteome) datasets holds great promise to assist in experimentally-based genome annotation.

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Section: Discussionsupporting

confidence: 78%

“…As such, these fragment intensity correlation features are especially useful to attain a higher confidence for novel (i.e. database unannotated) peptide identifications (Willems et al 2017;Verbruggen et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Lost and found: re-searching and re-scoring proteomics data aids the discovery of bacterial proteins and improves proteome coverage

Willems

Fijałkowski

Damme

2019

Preprint

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“…Verbruggen et al (14) present an updated version of Proteoformer, a pipeline for processing ribosome profiling data to generate protein sequence database, which can be used to improve MS/MS data interpretation. Major updates in Proteoformer 2.0 include improved Ribo-Seq data quality assessment, read preprocessing and alignment, transcript calling, and proteoform calling using multiple methods.…”

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confidence: 99%

Proteomics Is Not an Island: Multi-omics Integration Is the Key to Understanding Biological Systems

Zhang

Küster

2019

Molecular & Cellular Proteomics

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“…Galaxy [10] has proven a highly capable platform for meeting the requirements of multi-omic informatics, including proteogenomics, as described by us and others [11][12][13][14][15]. Its amenability to integration of disparate software in a unified, user-friendly environment, along with a variety of useful features including complex workflow creation, provenance tracking and reproducibility, address the challenges of proteogenomics.…”

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confidence: 99%

“…As part of our work developing Galaxy for proteomics (Galaxy-P[16]), we have focused on putting in place a number of tools for the various steps necessary for proteogenomics --from raw data processing and sequence database generation [9,11,12,17], to tools for interpreting the potential impact of identified sequence variants [18] and mechanisms of regulation indicated by RNA-protein response [19]. Others have also contributed to this growing community of proteogenomic researchers utilizing Galaxy to address their data analysis and informatics needs [11][12][13][14][15].…”

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confidence: 99%

Multi-omics Visualization Platform: An extensible Galaxy plug-in for multi-omics data visualization and exploration

McGowan

Johnson

Kumar

et al. 2019

Preprint

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BackgroundProteogenomics integrates genomics, transcriptomics and mass spectrometry (MS)-based proteomics data to identify novel protein sequences arising from gene and transcript sequence variants. Proteogenomic data analysis requires integration of disparate 'omic software tools, as well as customized tools to view and interpret results. The flexible Galaxy platform has proven valuable for proteogenomic data analysis. Here, we describe a novel Multi-omics Visualization Platform (MVP) for organizing, visualizing and exploring proteogenomic results, adding a critically needed tool for data exploration and interpretation. Findings MVP is built as an HTML Galaxy plugin, primarily based on Javascript. Via the Galaxy API, MVP uses SQlite databases as input --a custom datatype (mzSQlite) containing MS-based peptide identification information, a variant annotation table, and a coding sequence table. Users can interactively filter identified peptides based on sequence and data quality metrics, view annotated peptide MS data, visualize protein-level information, along with genomic coordinates. Peptides that pass the user-defined thresholds can be sent back to Galaxy via the API for further analysis; processed data and visualizations can also be saved and shared. MVP leverages the Integrated Genomics Viewer JavaScript (IGVjs) framework, enabling interactive visualization of peptides and corresponding transcript and genomic coding information within the MVP interface.Conclusions MVP provides a powerful, extensible platform for automated, interactive visualization of proteogenomics results within the Galaxy environment, adding a unique and critically needed tool for empowering exploration and interpretation of results. The platform is extensible, providing a basis for further development of new functionalities for proteogenomic data visualization. FindingsProteogenomics has emerged as a powerful approach to characterizing expressed protein products within a wide-variety of studies [1][2][3][4][5]. Proteogenomics, a multi-omic approach, involves the integration of genomic and/or transcriptomic data with mass spectrometry (MS)-based proteomics data. Typically, a proteogenomics-based study starts with a sample (e.g. cells grown in culture, tissue sample etc.) which are analyzed using both next generation sequencing technologies (usually RNA-Seq) and MS-based proteomics. Once assembled from RNA-Seq data, the transcriptome sequence is translated in-silico to generate a database of potentially expressed proteins encoded by the RNA. This protein sequence database contains both proteins of known sequences contained in reference databases, as well as novel protein sequences which are derived from the transcriptome sequence via comparison to reference genome sequence. These novel sequences may include variants arising from single-amino acid substitutions, short insertions/deletions, RNA processing events (truncations, splice variants) or even translation from unexpected genomic regions [2].Parallel to the RNA-Seq analysis, tandem mas...

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PROTEOFORMER 2.0: Further Developments in the Ribosome Profiling-assisted Proteogenomic Hunt for New Proteoforms

Cited by 44 publications

References 63 publications

Lost and found: re-searching and re-scoring proteomics data aids the discovery of bacterial proteins and improves proteome coverage

Lost and found: re-searching and re-scoring proteomics data aids the discovery of bacterial proteins and improves proteome coverage

Proteomics Is Not an Island: Multi-omics Integration Is the Key to Understanding Biological Systems

Multi-omics Visualization Platform: An extensible Galaxy plug-in for multi-omics data visualization and exploration

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