HiRIEF LC-MS enables deep proteome coverage and unbiased proteogenomics

Branca, Rui M.; Orre, Lukas M.; Johansson, Henrik J.; Granholm, Viktor; Huss, Mikael; Pérez-Bercoff, Åsa; Forshed, Jenny; Käll, Lukas; Lehtiö, Janne

doi:10.1038/nmeth.2732

Cited by 229 publications

(294 citation statements)

References 31 publications

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“…It was developed a new strategy of analysis which allowed the extension of the number of identified proteins to 3429 (1724 were of new description here). The list of identified proteins is reported in Supplementary Raw MS files as processed with the Thermo Scientific Proteome Discoverer software: peak were searched by the MASCOT and SEQUEST against Uniprot human database, filtered for a maximum 1% FDR using Percolator; the Peptide Mass Deviation was set to 10 ppm and a minimum of six 6 amino acids per identified peptide were required [2,3]. The Database search parameters were mass tolerance precursor 20 ppm [4], mass tolerance fragment CID 0.8 Da with dynamic modification of deamidation (N, Q), oxidation (M) and static modification of alkylation with IAM (C).…”

Section: Subject Area Biologymentioning

confidence: 99%

From hundreds to thousands: Widening the normal human Urinome (1)

Santucci,

Candiano,

Petretto

et al. 2015

Journal of Proteomics

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Section: Subject Area Biologymentioning

confidence: 99%

From hundreds to thousands: Widening the normal human Urinome (1)

Santucci,

Candiano,

Petretto

et al. 2015

Journal of Proteomics

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“…To accurately identify novel peptides, it is necessary to filter the search results to control the FDR. Recently, more stringent filtering strategies, such as posterror probability (89) or separate FDRs for annotated and novel peptides (46,90) were employed in proteomic analysis. In this work, we used a more stringent strategy (novel FDR) to increase the accuracy of identified novel peptides (76,77).…”

Section: Resultsmentioning

confidence: 99%

“…Over the last few years, computational proteomics has become a dramatically growing field, and a handful of tools have been developed to execute complete proteogenomic analyses (11,46,47). Two excellent reviews have described a comprehensive overview of the various problems commonly encountered and their current solutions for this growing research area (8,11).…”

mentioning

confidence: 99%

GAPP: A Proteogenomic Software for Genome Annotation and Global Profiling of Post-translational Modifications in Prokaryotes

Zhang

Yang

Zeng

et al. 2016

Molecular & Cellular Proteomics

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Although the number of sequenced prokaryotic genomes is growing rapidly, experimentally verified annotation of prokaryotic genome remains patchy and challenging. To facilitate genome annotation efforts for prokaryotes, we developed an open source software called GAPP for genome annotation and global profiling of post-translational modifications (PTMs) in prokaryotes. With a single command, it provides a standard workflow to validate and refine predicted genetic models and discover diverse PTM events. We demonstrated the utility of GAPP using proteomic data from Helicobacter pylori, one of the major human pathogens that is responsible for many gastric diseases. Our results confirmed 84.9% of the existing predicted H. pylori proteins, identified 20 novel protein coding genes, and corrected four existing gene models with regard to translation initiation sites. In particular, GAPP revealed a large repertoire of PTMs using the same proteomic data and provided a rich resource that can be used to examine the functions of reversible modifications in this human pathogen. This software is a powerful tool for genome annotation and global discovery of PTMs and is applicable to any sequenced prokaryotic organism; we expect that it will become an integral part of ongoing genome annotation efforts for prokaryotes. GAPP is freely available at https://sourceforge.net/ projects/gappproteogenomic/. Molecular & Cellular

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“…The sample preparation and mass spectrometry experiment are described in Ref. 19. Briefly, 24 h after seeding, A431 cell cultures (in duplicate) were treated with gefitinib and harvested 2 h, 6 h, and 24 h after treatment.…”

Section: A431 Human Cell Line Proteomics Datamentioning

confidence: 99%

SpliceVista, a Tool for Splice Variant Identification and Visualization in Shotgun Proteomics Data

Zhu

Hultin‐Rosenberg

Forshed

et al. 2014

Molecular & Cellular Proteomics

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Alternative splicing is a pervasive process in eukaryotic organisms. More than 90% of human genes have alternatively spliced products, and aberrant splicing has been shown to be associated with many diseases. Current methods employed in the detection of splice variants include prediction by clustering of expressed sequence tags, exon microarray, and mRNA sequencing, all methods focusing on RNA-level information. There is a lack of tools for analyzing splice variants at the protein level. Here, we present SpliceVista, a tool for splice variant identification and visualization based on mass spectrometry proteomics data. SpliceVista retrieves gene structure and translated sequences from alternative splicing databases and maps MS-identified peptides to splice variants. Eukaryotic genes are composed of exonic (protein-coding) and intronic (non-coding) regions. Alternative splicing is a process in which pre-mRNA is cut at junction sites and the resulting exonic sequences are reconnected in different ways to form different versions of mature mRNA. It has been shown that 92% to 94% of human genes can undergo alternative splicing (1, 2). This process plays an essential role in increasing the proteome diversity in eukaryotic organisms. For multiexon mRNAs, different splicing patterns can occur, such as exon skipping (an exon is either included or excluded from the mature mRNAs), alternative 5Ј or 3Ј splicing (exons are spliced in different lengths), or mutually exclusive splicing (exons are selectively spliced to be exclusively present in different splice forms). Alternative splicing is carried out by the spliceosome, which consists of five small nuclear ribonucleoprotein particles, U1, U2, U4, U5, and U6, and more than 150 other proteins (3). Mutations in splicing sites or in the main components of the splicing machinery will affect the genes' splicing patterns and potentially give rise to alternative protein products that might have different conformations, functions, or subcellular locations. Disruption of the splicing machinery has been shown to be associated with many human diseases such as cystic fibrosis, Alzheimer disease, and cancer (4 -6).Large efforts have been put into the identification of gene products generated by alternative splicing. This is a challenging task, as alternative splice forms are often temporal, tissue specific, and low abundant (7). So far most work has been done starting at the mRNA level, making use of the vast amount of public-domain expressed sequence tag data as well as RNA sequencing data (8 -11). Expressed sequence tags or RNA sequencing reads that belong to one gene are clustered together and then aligned with the genomic sequence in order to identify alternative splicing events. These efforts have resulted in many publically available alternative splicing databases. Most of them are generated by mining data from GenBank, UniGene and Swiss-Prot. The Evidence

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HiRIEF LC-MS enables deep proteome coverage and unbiased proteogenomics

Cited by 229 publications

References 31 publications

From hundreds to thousands: Widening the normal human Urinome (1)

From hundreds to thousands: Widening the normal human Urinome (1)

GAPP: A Proteogenomic Software for Genome Annotation and Global Profiling of Post-translational Modifications in Prokaryotes

SpliceVista, a Tool for Splice Variant Identification and Visualization in Shotgun Proteomics Data

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