Research

1. Computational Metabolomics

Metabolomics is a rapidly developing field of "omics" research concerned with the high-throughput identification and quantification of small molecule metabolites in the metabolome. The metabolome constitutes a wide array of compound classes that are crucial for the normal functioning of a biological system. As a result, the metabolomics approach promises to offer new insights in many areas of biological investigation.

Metabolomics research benefited greatly from advances in mass spectrometry (MS), liquid chromatography (LC), and gas chromatography (GC). These advances allow researchers to detect many metabolites that could not be detected previously. On the other hand, the high complexity of LC- and GC-MS data generated from biological samples makes data preprocessing non-trivial (Figure 1). In particular, there is an urgent need to reduce the high false positive and high negative rate of compound identifications caused by errors in steps 1 to 5 of the data preprocessing workflow.

Toward this end, the Du-Lab Team has developed a software package named ADAP (Automated Data Analysis Pipeline) for both LC- and GC-MS data preprocessing. ADAP is written in Java and has been incorporated into the MZmine 2 framework to take advantage of the latter's existing strength including modular design and rich visualization capabilities.
LC- and GC-MS data preprocessing workflow
Figure 1. Computational workflow for preprocessing LC- and GC-MS metabolomics data.

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2. Computational Proteomics

Chemical cross-linking combined with mass spectrometry provides a powerful method for identifying protein-protein interactions and probing the structure of protein complexes. A number of strategies have been reported that take advantage of the high sensitivity and high resolution of modern mass spectrometers. Approaches typically include synthesis of novel cross-linking compounds, and/or isotopic labeling of the cross-linking reagent and/or protein, and label-free methods.

The Du-Lab Team has developed Xlink-Identifier, a comprehensive data analysis platform to support label-free analyses. It can identify interpeptide, intrapeptide, and deadend cross-links as well as underivatized peptides. The software streamlines data preprocessing, peptide scoring, and visualization and provides an overall data analysis strategy for studying protein-protein interactions and protein structure using mass spectrometry.

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Funding