Computational Proteomics Analysis System (CPAS):  An Extensible, Open-Source Analytic System for Evaluating and Publishing Proteomic Data and High Throughput Biological Experiments

Rauch, Adam; Bellew, Matthew; Eng, Jimmy K.; Fitzgibbon, Matthew; Holzman, Ted; Hussey, Peter S.; Igra, Mark; MacLean, Brendan; Lin, Chen Wei; Detter, Andrea; Fang, Ruihua; Faça, Vitor M.; Gafken, Phil; Zhang, Heidi; Whitaker, Jeffrey; States, David J.; Hanash, Sam; Paulovich, and Amanda; McIntosh, Martin W.

doi:10.1021/pr0503533

Cited by 200 publications

(173 citation statements)

References 16 publications

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“…Tandem database search engine 20 with a previously described score plugin, 21 PeptideProphet analysis, 22 and results were uploaded to CPAS (Computational Proteomics Analysis System). 23 Peptide identifications were required to have Figure 1. Percent of features with intensities less than a certain value.…”

Section: Methodsmentioning

confidence: 99%

Head-to-Head Comparison of Serum Fractionation Techniques

et al. 2006

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Multiple approaches for simplifying the serum proteome have been described. These techniques are generally developed across different laboratories, samples, mass spectrometry platforms, and analysis tools. Hence, comparing the available schemes is impossible from the existing literature because of confounding variables. We describe a head-to-head comparison of several serum fractionation schemes, including N-linked glycopeptide enrichment, cysteinyl-peptide enrichment, magnetic bead separation (C3, C8, and WCX), size fractionation, protein A/G depletion, and immunoaffinity column depletion of abundant serum proteins. Each technique was compared to results obtained from unfractionated human serum. The results show immunoaffinity subtraction is the most effective means for simplifying the serum proteome while maintaining reasonable sample throughput. The reported dataset is publicly available and provides a standard against which emergent technologies can be compared and evaluated for their contribution to serum-based biomarker discovery.

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Section: Methodsmentioning

confidence: 99%

Head-to-Head Comparison of Serum Fractionation Techniques

et al. 2006

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“…In contrast to existing web-based tools such as MASPECTRAS [4] or CPAS [5], ms_lims embraces a client-server architecture, which allows for more dynamic interaction. Additionally, ms_lims also differs from libraries Abbreviations: LIMS, laboratory information management system; MGF, Mascot generic file; SQL, Structured Query Language Ã These authors contributed equally to this work.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast to existing web-based tools such as MASPECTRAS [4] or CPAS [5], ms_lims embraces a client-server architecture, which allows for more dynamic interaction. Additionally, ms_lims also differs from libraries such as OpenMS [6] in that the main focus lies on automation and data storage, rather than on data processing algorithms.…”

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confidence: 99%

ms_lims, a simple yet powerful open source laboratory information management system for MS‐driven proteomics

et al. 2010

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MS-based proteomics produces large amounts of mass spectra that require processing, identification and possibly quantification before interpretation can be undertaken. Highthroughput studies require automation of these various steps, and management of the data in association with the results obtained. We here present ms_lims (http://genesis.UGent.be/ ms_lims), a freely available, open-source system based on a central database to automate data management and processing in MS-driven proteomics analyses. Keywords:Bioinformatics / Data management / Laboratory information management system / Mascot / MS Proteomics labs nowadays often acquire hundreds of thousands to millions of MS/MS spectra per proteome analysis to make large-scale (comprehensive) proteome maps [1]. They rely on contemporary mass spectrometers with rapid duty cycles that increase the amount of produced data by a full order of magnitude compared to older instruments [2]. Automating the processing of these data, and managing their provenance has correspondingly become an important postanalysis task. The automation of these tasks requires the implementation of a start-to-end workflow around a central database management system that is designed for proteomics experiments, with some of the most prominent commercial and academic systems recently reviewed in [3]. Typical actions include collecting and warehousing MS/MS peak lists (often acquired on multiple, different instruments), assigning the accumulated MS/MS data to peptide identifications, quantifying peptides and proteins from MS or MS/MS data, and organizing both data and analysis results in a navigable project structure. In most high-throughput environments, these diverse actions are typically undertaken by different individuals, which further necessitates a role-based implementation of the software interfaces [4].To tackle and manage these problems associated with MSdriven proteomics, we developed ms_lims, an open source and instrument vendor-independent system for proteomics data management. In contrast to existing web-based tools such as MASPECTRAS [4] or CPAS [5], ms_lims embraces a client-server architecture, which allows for more dynamic interaction. Additionally, ms_lims also differs from libraries Abbreviations: LIMS, laboratory information management system; MGF, Mascot generic file; SQL, Structured Query Language Ã These authors contributed equally to this work.

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“…LabKey Software (Seattle, WA, USA) describes CPAS [14] as providing ''a comprehensive proteomics system for processing, analyzing, storing and sharing high volumes of tandem MS data''. CPAS is a component of LabKey Server and is maintained by the LabKey Software Foundation (https://www.labkey.org/project/home/begin.view).…”

Section: General Remarksmentioning

confidence: 99%

Using Laboratory Information Management Systems as central part of a proteomics data workflow

et al. 2010

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The organization and storage of proteomics data are challenging issues today and even more for the rising amount of information in the future. This review article describes the advantages of using Laboratory Information Management Systems (LIMS) in proteomics laboratories. Seven typical LIMS are explored in detail to describe their role in an even bigger interrelation. They are a central part of the proteomics data workflow, starting with data generation and ending with the publication in journals and repositories. Therefore, they enable community‐wide data utilization and further Systems Biology discoveries.

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Computational Proteomics Analysis System (CPAS): An Extensible, Open-Source Analytic System for Evaluating and Publishing Proteomic Data and High Throughput Biological Experiments

Cited by 200 publications

References 16 publications

Head-to-Head Comparison of Serum Fractionation Techniques

Head-to-Head Comparison of Serum Fractionation Techniques

ms_lims, a simple yet powerful open source laboratory information management system for MS‐driven proteomics

Using Laboratory Information Management Systems as central part of a proteomics data workflow

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