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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

APML parser documentation. Corra software provides an APML parser package written in java. This is to facilitate Corra customization via the adaptation of existing software or analytical components, or importing of new software or analytical components, as required by users with specific workflow needs. This figure shows an example screenshot of the parser package documentation.
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Figure 3: APML parser documentation. Corra software provides an APML parser package written in java. This is to facilitate Corra customization via the adaptation of existing software or analytical components, or importing of new software or analytical components, as required by users with specific workflow needs. This figure shows an example screenshot of the parser package documentation.

Mentions: To enable Corra to launch multiple and disparate tools, as well as to facilitate the integration of other new and yet to be developed LC-MS tools into Corra, we needed to implement APML parsers and enable the use of APML across the whole Corra platform. We thus implemented a generic APML parser library package using Java Standard Edition 6. To ensure efficient memory and parser use, Simple API for XML and Stream API for XML were used in the library package. The APML parser package is also included with Corra, along with the APML schema documentation, to the quantitative proteomics tool development community, to enable the integration of other pre-existing and newly developed tools into the Corra framework, and thus enable other workflows and applications of Corra on an 'as needed' basis. The APML schema and implementation are also readily extensible, and thus customizable, via the addition of new optional elements and by extending the provided java classes. The Javadoc, org.systemsbiology.libs.apmlparser, is thus provided to assist any developer who wishes to plug the current parser into another analytical tool implementation, for his or her own specific needs (see Figure 3). APML peaklist and alignment viewers are also implemented within Corra to provide the user with a visual 2D display of LC-MS intensity data, within m/z and LC retention time dimensions (see Figure 4).


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)

APML parser documentation. Corra software provides an APML parser package written in java. This is to facilitate Corra customization via the adaptation of existing software or analytical components, or importing of new software or analytical components, as required by users with specific workflow needs. This figure shows an example screenshot of the parser package documentation.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: APML parser documentation. Corra software provides an APML parser package written in java. This is to facilitate Corra customization via the adaptation of existing software or analytical components, or importing of new software or analytical components, as required by users with specific workflow needs. This figure shows an example screenshot of the parser package documentation.
Mentions: To enable Corra to launch multiple and disparate tools, as well as to facilitate the integration of other new and yet to be developed LC-MS tools into Corra, we needed to implement APML parsers and enable the use of APML across the whole Corra platform. We thus implemented a generic APML parser library package using Java Standard Edition 6. To ensure efficient memory and parser use, Simple API for XML and Stream API for XML were used in the library package. The APML parser package is also included with Corra, along with the APML schema documentation, to the quantitative proteomics tool development community, to enable the integration of other pre-existing and newly developed tools into the Corra framework, and thus enable other workflows and applications of Corra on an 'as needed' basis. The APML schema and implementation are also readily extensible, and thus customizable, via the addition of new optional elements and by extending the provided java classes. The Javadoc, org.systemsbiology.libs.apmlparser, is thus provided to assist any developer who wishes to plug the current parser into another analytical tool implementation, for his or her own specific needs (see Figure 3). APML peaklist and alignment viewers are also implemented within Corra to provide the user with a visual 2D display of LC-MS intensity data, within m/z and LC retention time dimensions (see Figure 4).

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