Location: Plant, Soil and Nutrition Research2013 Annual Report
1a. Objectives (from AD-416):
1. To improve software directed at analyzing mass spectrometry profiling of small molecules. 2. To enhance analysis of ARS generated datasets beyond current capacity.
1b. Approach (from AD-416):
Analysis of mass spectrometry datasets is highly challenging and often represents a rate limiting step in metabolomics. Nonlinear Dynamics, a leading software provider for the proteomics community, approached ARS regarding the development of a new product for metabolomics analysis. ARS staff provided pre-publication data to Nonlinear Dynamics, to assist with the development of software and training of customers. Nonlinear Dynamics will assist ARS scientists in the annotation of this dataset, such that the number of identified compounds found in the tomato fruit metabolome will rise. This partnership will greatly enhance ARS efforts to analyze the composition of conventionally improved and transgenic tomato varieties. This donation to ARS of time and expertise by Nonlinear Dynamics is worth thousands of dollars. Additionally, Nonlinear Dynamics and ARS scientists will cooperate to write a technical note describing new bioinformatics methods for the metabolomics community.
3. Progress Report:
The goal of this project is two-fold: to both better understand the chemical composition of tomato and to develop better methods for the analysis of such datasets. Deliverables are intended as peer-reviewed publications that answer both biological and bioinformatics-related questions. Data were provided to corporate partners for analysis following consultation by phone and email. A revised manuscript describing the tomato fruit metabolome was submitted. Collaborators at Colorado State were inspired by the clustering analysis that is intrinsic to our bioinformatics pipeline and have revised their workflow to take advantage of data simplification techniques to improve accuracy. Collaborators at Cornell University have agreed to assist in the analysis of the existing tomato fruit dataset and invest in collecting new data. We are excited at the potential for comparing bioinformatics methodologies (ARS/Colorado State versus Colorado State vs. Nonlinear Dynamics).