Open Source System for Analyzing, Validating, and Storing Protein Identification Data

Craig, Robertson; Cortens, John P.; Beavis, Ronald C.

doi:10.1021/pr049882h

Cited by 644 publications

(510 citation statements)

References 23 publications

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“…In 2004, Hillman et al [52] analyzed 1,363 human protein sequences deposited into the SwissProt database and found 107 entries (7.9% of those analyzed) that were derived from transcripts that are apparently subject to NMD. More recently an analysis of mass spectrometry data from the Global Proteome Machine [53] and PeptideAtlas [54] repositories reached the same conclusion: 3UI-containing transcripts can indeed express protein [55]. These results suggest that either the peptides detected in the above proteomics studies are the products of a single round of translation [37], or some endogenous human NMD substrates can undergo multiple rounds of translation prior to being decayed.…”

Section: Splicing Directs Mrnp Formationmentioning

confidence: 85%

Introns in UTRs: Why we should stop ignoring them

et al. 2012

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Although introns in 5 0 -and 3 0 -untranslated regions (UTRs) are found in many protein coding genes, rarely are they considered distinctive entities with specific functions. Indeed, mammalian transcripts with 3 0 -UTR introns are often assumed nonfunctional because they are subject to elimination by nonsense-mediated decay (NMD). Nonetheless, recent findings indicate that 5 0 -and 3 0 -UTR intron status is of significant functional consequence for the regulation of mammalian genes. Therefore these features should be ignored no longer.

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Section: Splicing Directs Mrnp Formationmentioning

confidence: 85%

Introns in UTRs: Why we should stop ignoring them

et al. 2012

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“…With the explosion of proteomics data in recent years, the time is ripe to revisit the idea, with some preliminary demonstration of success being reported in two recent publications [21,22]. The availability of online data repositories developed in recent years [23][24][25][26][27], as well as emerging unified standards for representing shotgun proteomics data, such as mzXML [28] and mzData (http://psi dev.sourceforge.net/ms/#mzdata), have made it possible to collect and catalog an adequate set of peptide CID spectra to create a high-quality spectral library. For the spectral library to be comprehensive (containing sufficient entries to cover a high proportion of the observed proteome) and accurate (containing high-quality and truly characteristic MS/MS spectra that can be confidently mapped to peptides), it is important to gather raw MS/MS spectra from a wide variety of sources, to identify them as accurately as possible, to filter out the inevitable false positives, and to process the spectra to reduce noise and other experimental artifacts.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a spectral library searching method for peptide identification from MS/MS

et al. 2007

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A notable inefficiency of shotgun proteomics experiments is the repeated rediscovery of the same identifiable peptides by sequence database searching methods, which often are time-consuming and error-prone. A more precise and efficient method, in which previously observed and identified peptide MS/MS spectra are catalogued and condensed into searchable spectral libraries to allow new identifications by spectral matching, is seen as a promising alternative. To that end, an open-source, functionally complete, high-throughput and readily extensible MS/MS spectral searching tool, SpectraST, was developed. A high-quality spectral library was constructed by combining the high-confidence identifications of millions of spectra taken from various data repositories and searched using four sequence search engines. The resulting library consists of over 30 000 spectra for Saccharomyces cerevisiae. Using this library, SpectraST vastly outperforms the sequence search engine SEQUEST in terms of speed and the ability to discriminate good and bad hits. A unique advantage of SpectraST is its full integration into the popular Trans Proteomic Pipeline suite of software, which facilitates user adoption and provides important functionalities such as peptide and protein probability assignment, quantification, and data visualization. This method of spectral library searching is especially suited for targeted proteomics applications, offering superior performance to traditional sequence searching.

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“…The first of these challenges was the need for central and long‐term public repositories to store the generated data. Several such generic repositories are now available, for example PRIDE 4, GPMDB 5, PeptideAtlas 6, and MassIVE (http://massive.ucsd.edu/ProteoSAFe) for shotgun results; and PASSEL 7, SRMAtlas (http://www.srmatlas.org), and Panorama 8 for targeted proteomics quantification data. More specific databases have also been established, related to: diseases, for example TBDB for tuberculosis 9; organisms, for example ProteomicsDB 10 and the Human Proteome Map 11 for the human proteome, and pep2pro for Arabidopsis 12; or subproteomes, for example CSF‐PR 13 for cerebrospinal fluid or TOPPR 14 and TopFIND 15 for in vivo cleaved proteins.…”

Section: Introductionmentioning

confidence: 99%

“…It should be noted that some of the existing proteomics databases, most notably GPMDB 5 and PeptideAtlas 6, routinely reprocess their data using dedicated bioinformatics tools and pipelines. GPMDB makes use of the X!Tandem search engine 61, whereas PeptideAtlas employs the Trans Proteomic Pipeline 62.…”

Section: Introductionmentioning

confidence: 99%

Exploring the potential of public proteomics data

et al. 2015

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In a global effort for scientific transparency, it has become feasible and good practice to share experimental data supporting novel findings. Consequently, the amount of publicly available MS‐based proteomics data has grown substantially in recent years. With some notable exceptions, this extensive material has however largely been left untouched. The time has now come for the proteomics community to utilize this potential gold mine for new discoveries, and uncover its untapped potential. In this review, we provide a brief history of the sharing of proteomics data, showing ways in which publicly available proteomics data are already being (re‐)used, and outline potential future opportunities based on four different usage types: use, reuse, reprocess, and repurpose. We thus aim to assist the proteomics community in stepping up to the challenge, and to make the most of the rapidly increasing amount of public proteomics data.

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Open Source System for Analyzing, Validating, and Storing Protein Identification Data

Cited by 644 publications

References 23 publications

Introns in UTRs: Why we should stop ignoring them

Introns in UTRs: Why we should stop ignoring them

Development and validation of a spectral library searching method for peptide identification from MS/MS

Exploring the potential of public proteomics data

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