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.
Aleksandr Smirnov, Wei Jia, Douglas I. Walker, Dean P. Jones, Xiuxia Du: ADAP-GC 3.2: Graphical Software Tool for Efficient Spectral Deconvolution of Gas Chromatography-High Resolution Mass Spectrometry Metabolomics Data. Journal of Proteome Research 2018, 17 (1):470-478.
Owen Myers, Susan Sumner, Shuzhao Li, Stephen Barnes, Xiuxia Du: One step forward for reducing false positive and false negative compound identifications from mass spectrometry metabolomics data: new algorithms for constructing extracted ion chromatograms and detecting chromatographic peaks. Analytical Chemistry 2017, 89(17):8696-8703.
Owen Myers, Susan Sumner, Shuzhao Li, Stephen Barnes, Xiuxia Du: A detailed investigation and comparison of the XCMS and MZmine 2 chromatogram construction and chromatographic peak detection methods for preprocessing mass spectrometry metabolomics data. Analytical Chemistry 2017, 89(17):8689-8695.
Yan Ni, Mingming Su, Yunping Qiu, Wei Jia, Xiuxia Du: ADAP-GC 3.0: Improved Peak Detection and Deconvolution of Co-eluting Metabolites from GC/TOF-MS Data for Metabolomics Studies, Analytical Chemistry 2016, 88 (17):8802-8811.
Xiuxia Du,; Steve Zeisel: Spectral deconvolution for gas chromatography mass spectrometry-based metabolomics: current status and future perspectives.Computational and structural biotechnology journal 2013, 4, e201301013.
Yan Ni, Yunping Qiu, Wenxin Jiang, Kyle Suttlemyre, Mingming Su, Wenchao Zhang, Wei Jia, Xiuxia Du: ADAP-GC 2.0: deconvolution of coeluting metabolites from GC/TOF-MS data for metabolomics studies.Analytical chemistry 2012, 84 (15):6619-6629.
Wenxin Jiang, Yunping Qiu, Yan Ni, Mingming Su, Wei Jia, Xiuxia Du: An automated data analysis pipeline for GC-TOF-MS metabonomics studies.Journal of proteome research 2010, 9 (11):5974-5981.
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.
Severine Clavier, Xiuxia Du, Sandrine Sagan, Gerard Bolbach, Emmanuelle Sachon: An integrated cross-linking-MS approach to investigate cell penetrating peptides interacting partners.EuPA OPEN PROTEOMICS 2014; 3:229-238.
Aleksandra A. Watson, Pravin Mahajan, Haydyn D. T. Mertens, Michael J. Deery, Wenchao Zhang, Peter Pham, Xiuxia Du, Till Bartke, Wei Zhang, Christian Edlich, Georgina Berridge, Yun Chen, Nicola A. Burgess-Brown, Tony Kouzarides,
Nicola Wiechens, Tom Owen-Hughes, Dmitri I. Svergun, Opher Gileadi and Ernest D. Laue: The PHD and chromo domains regulate the ATPase activity of the human chromatin remodeler CHD4.Journal of molecular biology 2012, 422 (1), 3-17.
Xiuxia Du, Saiful M. Chowdhury, Nathan P. Manes, Si Wu, M. Uljana Mayer, Joshua N. Adkins, Gordon A. Anderson, and Richard D. Smith: Xlink-identifier: an automated data analysis platform for confident identifications of chemically cross-linked peptides using tandem mass spectrometry.Journal of proteome research 2011, 10 (3), 923-931.
Saiful M. Chowdhury, Xiuxia Du, Nikola Tolic, Si Wu, Ronald J. Moore, M. Uljana Mayer, Richard D. Smith, and Joshua N. Adkins: Identification of cross-linked peptides after click-based enrichment using sequential collision-induced dissociation and electron transfer dissociation tandem mass spectrometry.Analytical chemistry 2009, 81 (13), 5524-5532.