X!!Tandem, an Improved Method for Running X!Tandem in Parallel on Collections of Commodity Computers

Bjornson, Robert; Carriero, Nicholas; Colangelo, Christopher M.; Shifman, Mark A.; Cheung, Kei‐Hoi; Miller, Perry L.; Williams, K. R.

doi:10.1021/pr0701198

Cited by 131 publications

(94 citation statements)

References 10 publications

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“…We have started this effort by developing an optimized parallel version of X!Tandem to take advantage of multiple cluster nodes with dual CPUs to substantially speed up the massive database searches. 35 Such HPC solutions are key to the successful utilization of high-throughput protein profiling technologies.…”

Section: Discussion Status and Further Plansmentioning

confidence: 99%

YPED: A Web-Accessible Database System for Protein Expression Analysis

Shifman

Colangelo

et al. 2007

J. Proteome Res.

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We have developed an integrated web-accessible software system called the Yale Protein Expression Database (YPED) to address the need for storage, retrieval, and integrated analysis of large amounts of data from high throughput proteomic technologies. YPED is an open source system which integrates gel analysis results with protein identifications from DIGE experiments. The system associates the DIGE gel spots and image, analyzed with DeCyder, with mass spectrometric protein identifications from selected gel spots. Following in gel trypsin digestion, proteins in spots of interest are analyzed using MALDI-TOF/TOF on an AB 4700 or, more recently, on an AB 4800 with protein identifications performed by Mascot in conjunction with the AB GPS Explorer system. In addition to DIGE, YPED currently handles protein identifications from MudPIT, iTRAQ, and ICAT experiments. Sample descriptions are compatible with the evolving MIAPE standards. Tandem MS/MS results from MudPIT, and ICAT analyses are validated with the Trans-Proteomic Pipeline and then stored in the database for viewing and linking to the identified proteins. Researchers can view, subset, and download their data through a secure Web interface that includes a table containing proteins identified, a sample summary, the sample description, and a clickable gel image for DIGE samples. Tools are available to facilitate sample comparison and the viewing of phosphoproteins. A summary report with PANTHER Classification System annotations is also available to aid in biological interpretation of the results. The source code is open-source and is available from http://yped.med.yale.edu/yped_dist.

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Section: Discussion Status and Further Plansmentioning

confidence: 99%

YPED: A Web-Accessible Database System for Protein Expression Analysis

Shifman

Colangelo

et al. 2007

J. Proteome Res.

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“…Expression heterogeneity (4) can be improved by joining forces with biology/disease driven research groups for example by enhancing collaboration between C-HPP and B/D HPP teams. Proteogenomic approach integrating genome, transcriptome with proteome data helps in general to (5) identify protein forms originating from genetic variability and (3) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 Expression of αvβ6 integrin enhances both plasminogen and latent-transforming growth factor-β1 dependent proliferation, invasion and ERK1/2 signaling in colorectal cancer cells 7 Proteomics technology: application 18 Overexpression of αvβ6 integrin alters the colorectal cancer cell proteome in favor of elevated proliferation and a switching in cellular adhesion which increases invasion 7 Proteomics technology: application 19 Approaching the organellar brain proteome to understand the molecular basis of Schizophrenia …”

Section: Acs Paragon Plus Environmentmentioning

confidence: 99%

Quest for Missing Proteins: Update 2015 on Chromosome-Centric Human Proteome Project

Horvatovich¹,

Lundberg²,

Chen³

et al. 2015

J. Proteome Res.

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This paper summarizes the recent activities of the Chromosome-Centric Human Proteome Project (C-HPP) consortium, which develops new technologies to identify yet-to-be annotated proteins (termed "missing proteins") in biological samples that lack sufficient experimental evidence at the protein level for confident protein identification. The C-HPP also aims to identify new protein forms that may be caused by genetic variability, post-translational modifications, and alternative splicing. Proteogenomic data integration forms the basis of the C-HPP's activities; therefore, we have summarized some of the key approaches and their roles in the project. We present new analytical technologies that improve the chemical space and lower detection limits coupled to bioinformatics tools and some publicly available resources that can be used to improve data analysis or support the development of analytical assays. Most of this paper's content has been compiled from posters, slides, and discussions presented in the series of C-HPP workshops held during 2014. All data (posters, presentations) used are available at the C-HPP Wiki (http://c-hpp.webhosting.rug.nl/) and in the Supporting Information.

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“…Over time, a number of open source software packages have been written for the creation of grids (20,21). Within the field of proteomics, several efforts have been made in grid-based proteomics in data sharing (22) and mass-parallelism in protein identification (23).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Using parallelism to speed up the X!Tandem search can thus save a considerate amount of overall processing time, as was shown earlier by Bjornson et. al (23). When a cluster of machines is available, the parallel X!…”

Section: Appendixmentioning

confidence: 99%