Progress 09/13/05 to 01/15/10
Outputs
Progress Report Objectives (from AD-416) Design software to process proteomics data and provide statistical solutions for assembly of mass spectra data from soybean rust proteins. Approach (from AD-416) Software will be developed or adapted to process and interpret mass spectrometry data derived from soybean rust infected plants. Tasks will include producing output in a format that facilitates further analysis, adapting or developing new software for parsing peptides and re-assembly into proteins, and performing the statistical analysis required for results confirmation. Research topics include monitoring false positive rates; designing algorithms for the statistical analysis of assembling proteins from peptides; designing a work-flow for software and analysis. The goal of the research was to enable the bioinformatics component of the soybean and soybean rust proteomics efforts. No research was performed on this project this year. This project was only kept open for the purpose of closeout. This process has been completed so we are now terminating.
Impacts
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Progress 10/01/08 to 09/30/09
Outputs
Progress Report Objectives (from AD-416) Design software to process proteomics data and provide statistical solutions for assembly of mass spectra data from soybean rust proteins. Approach (from AD-416) Software will be developed or adapted to process and interpret mass spectrometry data derived from soybean rust infected plants. Tasks will include producing output in a format that facilitates further analysis, adapting or developing new software for parsing peptides and re-assembly into proteins, and performing the statistical analysis required for results confirmation. Research topics include monitoring false positive rates; designing algorithms for the statistical analysis of assembling proteins from peptides; designing a work-flow for software and analysis. Significant Activities that Support Special Target Populations To enable the bioinformatics component of the soybean and soybean rust proteomics efforts, a software platform was developed to enable data analysis. Included in the software was a probability-based model that linked standard statistical analysis to data confidence. This software has been released to the public and the data analysis of soybean research projects described in project 1275-21220-221-00D is moving forward. Quarterly meetings, annual budget reports, and an annual written progress report are used to monitor project progress.
Impacts
(N/A)
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Progress 10/01/07 to 09/30/08
Outputs
Progress Report Objectives (from AD-416) Design software to process proteomics data and provide statistical solutions for assembly of mass spectra data from soybean rust proteins. Approach (from AD-416) Software will be developed or adapted to process and interpret mass spectrometry data derived from soybean rust infected plants. Tasks will include producing output in a format that facilitates further analysis, adapting or developing new software for parsing peptides and re-assembly into proteins, and performing the statistical analysis required for results confirmation. Research topics include monitoring false positive rates; designing algorithms for the statistical analysis of assembling proteins from peptides; designing a work-flow for software; and analysis. Significant Activities that Support Special Target Populations To enable the bioinformatics component of the soybean and soybean rust proteomics efforts, a software platform was developed to enable data analysis. Included in the software was a probability-based model that linked standard statistical analysis to data confidence. This software has been released to the public and the data analysis of soybean research projects described in 1275-21220-221-00D is moving forward. A novel data structure enabling improved performance for the software was published and released to the public. Quarterly meetings, annual budget reports, and an annual written progress report are used to monitor project progress.
Impacts
(N/A)
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Progress 10/01/06 to 09/30/07
Outputs
Progress Report Objectives (from AD-416) Design software to process proteomics data and provide statistical solutions for assembly of mass spectra data from soybean rust proteins. Approach (from AD-416) Software will be developed or adapted to process and interpret mass spectrometry data derived from soybean rust infected plants. Tasks will include producing output in a format that facilitates further analysis, adapting or developing new software for parsing peptides and re-assembly into proteins, and performing the statistical analysis required for results confirmation. Research topics include monitoring false positive rates; designing algorithms for the statistical analysis of assembling proteins from peptides; designing a work-flow for software and analysis. Significant Activities that Support Special Target Populations This report serves to document research conducted under a specific cooperative agreement between ARS and the Department of Applied Mathematics and Statistics, Johns Hopkins University. This project supports objectives of parent project 1275-21220-221-00D, �Genomics and Proteomics Approaches to Broadening Resistance of Soybean to Pests and Pathogens� and additional details of research can be found in the report for the parent project. To enable the bioinformatics component of the soybean and soybean rust proteomics efforts, a software platform was developed to enable data analysis. Included in the software was a probability-based model that linked standard statistical analysis to data confidence. This software has been released to the public and the data analysis of soybean research projects described in 1275-21220-221-00D is moving forward. Software has been modified to enhance performance and additional modeling has been performed to assess the accuracy of results. Quarterly meetings, annual budget reports, and an annual written progress reports are used to monitor project progress.
Impacts
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Progress 10/01/05 to 09/30/06
Outputs
Progress Report 4d Progress report. This report serves to document research conducted under a specific cooperative agreement between ARS and the Department of Applied Mathematics and Statistics, Johns Hopkins University. This project supports objectives of parent project 1275-21220-221-00D, Genomics and Proteomics Approaches to Broadening Resistance of Soybean to Pests and Pathogens and additional details of research can be found in the report for the parent project. To enable the bioinformatics component of the soybean and soybean rust proteomics efforts, a software platform was developed to enable data analysis. Included in the software was a probability-based model that linked standard statistical analysis to data confidence. This software has been released to the public and the data analysis of soybean research projects described in 1275-21220-221-00D is moving forward.
Impacts
(N/A)
Publications
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