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Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics.

Brusniak MY, Bodenmiller B, Campbell D, Cooke K, Eddes J, Garbutt A, Lau H, Letarte S, Mueller LN, Sharma V, Vitek O, Zhang N, Aebersold R, Watts JD - BMC Bioinformatics (2008)

Bottom Line: However, they are generally not comparable to each other in terms of functionality, user interfaces, information input/output, and do not readily facilitate appropriate statistical data analysis.The Corra computational framework leverages computational innovation to enable biologists or other researchers to process, analyze and visualize LC-MS data with what would otherwise be a complex and not user-friendly suite of tools.For the user not trained in bioinformatics, Corra represents a complete, customizable, free and open source computational platform enabling LC-MS-based proteomic workflows, and as such, addresses an unmet need in the LC-MS proteomics field.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA. mbrusnia@systemsbiology.org

ABSTRACT

Background: Quantitative proteomics holds great promise for identifying proteins that are differentially abundant between populations representing different physiological or disease states. A range of computational tools is now available for both isotopically labeled and label-free liquid chromatography mass spectrometry (LC-MS) based quantitative proteomics. However, they are generally not comparable to each other in terms of functionality, user interfaces, information input/output, and do not readily facilitate appropriate statistical data analysis. These limitations, along with the array of choices, present a daunting prospect for biologists, and other researchers not trained in bioinformatics, who wish to use LC-MS-based quantitative proteomics.

Results: We have developed Corra, a computational framework and tools for discovery-based LC-MS proteomics. Corra extends and adapts existing algorithms used for LC-MS-based proteomics, and statistical algorithms, originally developed for microarray data analyses, appropriate for LC-MS data analysis. Corra also adapts software engineering technologies (e.g. Google Web Toolkit, distributed processing) so that computationally intense data processing and statistical analyses can run on a remote server, while the user controls and manages the process from their own computer via a simple web interface. Corra also allows the user to output significantly differentially abundant LC-MS-detected peptide features in a form compatible with subsequent sequence identification via tandem mass spectrometry (MS/MS). We present two case studies to illustrate the application of Corra to commonly performed LC-MS-based biological workflows: a pilot biomarker discovery study of glycoproteins isolated from human plasma samples relevant to type 2 diabetes, and a study in yeast to identify in vivo targets of the protein kinase Ark1 via phosphopeptide profiling.

Conclusion: The Corra computational framework leverages computational innovation to enable biologists or other researchers to process, analyze and visualize LC-MS data with what would otherwise be a complex and not user-friendly suite of tools. Corra enables appropriate statistical analyses, with controlled false-discovery rates, ultimately to inform subsequent targeted identification of differentially abundant peptides by MS/MS. For the user not trained in bioinformatics, Corra represents a complete, customizable, free and open source computational platform enabling LC-MS-based proteomic workflows, and as such, addresses an unmet need in the LC-MS proteomics field.

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Related in: MedlinePlus

Corra graphical user interface (GUI). Example screenshots of the Corra GUI, provided as a web client using Google Web Toolkit. The GUI guides users, step by step, through the Corra pipeline, and also to serves to organize data by project, in a user-friendly way, not requiring extensive knowledge of computational biology. A) Project setup GUI panel guides project organization and status. B) Analysis GUI panel displays figures from analyses.
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Figure 5: Corra graphical user interface (GUI). Example screenshots of the Corra GUI, provided as a web client using Google Web Toolkit. The GUI guides users, step by step, through the Corra pipeline, and also to serves to organize data by project, in a user-friendly way, not requiring extensive knowledge of computational biology. A) Project setup GUI panel guides project organization and status. B) Analysis GUI panel displays figures from analyses.

Mentions: Figure 5A shows the Corra project setup page, where the user can create new, as well as retrieve existing projects. In addition to guiding the user through the Corra workflow, the graphical interface also allows the user to monitor the processing status of a project, as well as visualize the analysis results when they are available. The Project setup page also captures meta-data information, which can also be used during statistical analysis. The user must indicate "Sample ID" (same as individual ID), "Condition", "MS Replicate" and "Time Point" information for each LC-MS run (more than two conditions can be used for a statistical contrast study). Figure 5B shows the analysis panel, in which users can view APML outputs in a plotted graphical format, or outputs from CorraStatistics.R. A tab delimited file, which can be used as an inclusion list for follow-up targeted MS/MS analyses, is also available for download to the client computer.


Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics.

Brusniak MY, Bodenmiller B, Campbell D, Cooke K, Eddes J, Garbutt A, Lau H, Letarte S, Mueller LN, Sharma V, Vitek O, Zhang N, Aebersold R, Watts JD - BMC Bioinformatics (2008)

Corra graphical user interface (GUI). Example screenshots of the Corra GUI, provided as a web client using Google Web Toolkit. The GUI guides users, step by step, through the Corra pipeline, and also to serves to organize data by project, in a user-friendly way, not requiring extensive knowledge of computational biology. A) Project setup GUI panel guides project organization and status. B) Analysis GUI panel displays figures from analyses.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2651178&req=5

Figure 5: Corra graphical user interface (GUI). Example screenshots of the Corra GUI, provided as a web client using Google Web Toolkit. The GUI guides users, step by step, through the Corra pipeline, and also to serves to organize data by project, in a user-friendly way, not requiring extensive knowledge of computational biology. A) Project setup GUI panel guides project organization and status. B) Analysis GUI panel displays figures from analyses.
Mentions: Figure 5A shows the Corra project setup page, where the user can create new, as well as retrieve existing projects. In addition to guiding the user through the Corra workflow, the graphical interface also allows the user to monitor the processing status of a project, as well as visualize the analysis results when they are available. The Project setup page also captures meta-data information, which can also be used during statistical analysis. The user must indicate "Sample ID" (same as individual ID), "Condition", "MS Replicate" and "Time Point" information for each LC-MS run (more than two conditions can be used for a statistical contrast study). Figure 5B shows the analysis panel, in which users can view APML outputs in a plotted graphical format, or outputs from CorraStatistics.R. A tab delimited file, which can be used as an inclusion list for follow-up targeted MS/MS analyses, is also available for download to the client computer.

Bottom Line: However, they are generally not comparable to each other in terms of functionality, user interfaces, information input/output, and do not readily facilitate appropriate statistical data analysis.The Corra computational framework leverages computational innovation to enable biologists or other researchers to process, analyze and visualize LC-MS data with what would otherwise be a complex and not user-friendly suite of tools.For the user not trained in bioinformatics, Corra represents a complete, customizable, free and open source computational platform enabling LC-MS-based proteomic workflows, and as such, addresses an unmet need in the LC-MS proteomics field.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA. mbrusnia@systemsbiology.org

ABSTRACT

Background: Quantitative proteomics holds great promise for identifying proteins that are differentially abundant between populations representing different physiological or disease states. A range of computational tools is now available for both isotopically labeled and label-free liquid chromatography mass spectrometry (LC-MS) based quantitative proteomics. However, they are generally not comparable to each other in terms of functionality, user interfaces, information input/output, and do not readily facilitate appropriate statistical data analysis. These limitations, along with the array of choices, present a daunting prospect for biologists, and other researchers not trained in bioinformatics, who wish to use LC-MS-based quantitative proteomics.

Results: We have developed Corra, a computational framework and tools for discovery-based LC-MS proteomics. Corra extends and adapts existing algorithms used for LC-MS-based proteomics, and statistical algorithms, originally developed for microarray data analyses, appropriate for LC-MS data analysis. Corra also adapts software engineering technologies (e.g. Google Web Toolkit, distributed processing) so that computationally intense data processing and statistical analyses can run on a remote server, while the user controls and manages the process from their own computer via a simple web interface. Corra also allows the user to output significantly differentially abundant LC-MS-detected peptide features in a form compatible with subsequent sequence identification via tandem mass spectrometry (MS/MS). We present two case studies to illustrate the application of Corra to commonly performed LC-MS-based biological workflows: a pilot biomarker discovery study of glycoproteins isolated from human plasma samples relevant to type 2 diabetes, and a study in yeast to identify in vivo targets of the protein kinase Ark1 via phosphopeptide profiling.

Conclusion: The Corra computational framework leverages computational innovation to enable biologists or other researchers to process, analyze and visualize LC-MS data with what would otherwise be a complex and not user-friendly suite of tools. Corra enables appropriate statistical analyses, with controlled false-discovery rates, ultimately to inform subsequent targeted identification of differentially abundant peptides by MS/MS. For the user not trained in bioinformatics, Corra represents a complete, customizable, free and open source computational platform enabling LC-MS-based proteomic workflows, and as such, addresses an unmet need in the LC-MS proteomics field.

Show MeSH
Related in: MedlinePlus