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研究員
宋定懿 Ting-Yi Sung

Research Fellow
Ph.D., Operations Research, New York University
Tel: +886-2-2788-3799 ext. 1711 Fax: +886-2-2782-4814
Email: tsung@iis.sinica.edu.tw
http://www.iis.sinica.edu.tw/pp./tsung/eindex.html

Research Description ● Research Fellow, IIS, Academia
Sinica (2000~)
The ultimate objective of our research is to identify biomarkers for disease diagnosis. Most ● Associate Research Fellow, IIS,
FDA-approved biomarkers for cancer diagnosis are membrane proteins and post-transla- Academia Sinica (1989~2000)

tionally modi ed proteins. Currently, mass spectrometry (MS) is the predominant technol- ● Ten Outstanding Young Women
ogy for large-scale proteomics research. Therefore, our research is mainly focused on bio- Award (1998)
informatics for proteomics, including MS-based proteomics, post-translationally modi ed ● Ph.D., Operations Research, New

proteomics, and membrane proteomics, and structure prediction for membrane proteins. York University (1989)
For MS-based proteomics, we have developed an automated system to identify intact gly- ● MBA, State University of New York
copeptides, which, to the best of our knowledge, is the rst system capable of large-scale at Buffalo (1983)

analysis on complex biological samples. In recent years, SWATH-MS has been proposed as a ● BS, Management Science, Nation-
new data-independent acquisition experiment technique for targeted proteomics studies, al Chiao Tung University (1980)
and has attracted much attention. We have developed computational methods to generate
in silico tandem mass spectra from SWATH-MS data to perform protein identi cation. Com-

bining the search results from SWATH-MS data and from conventional data-dependent acquisition data will enable the identi cation of
additional proteins.

In the sub- eld of membrane proteomics, we have been working on structure prediction of membrane proteins. Speci cally, we have

developed methods for predicting transmembrane helices and topology, helix-helix interactions and contacts, and lipid exposure. In
addition, since signal peptides are essential for protein secretion and can be easily confused with transmembrane helices by prediction
tools, we have proposed a prediction method for signal peptides. We have also constructed a knowledge base for all known helix-helix
interactions in currently available structures. Furthermore, we have developed a human proteome database, with an emphasis on the
membrane proteome, which contains useful and comprehensive protein information.

Publications

1. Jhih-Siang Lai, Cheng-Wei Cheng, Allan Lo*, Ting-Yi Sung*, 6. Allan Lo, Cheng-Wei Cheng, Yi-Yuan Chiu, Ting-Yi Sung*,
and Wen-Lian Hsu, Lipid exposure prediction enhances the Wen-Lian Hsu*, TMPad: an integrated structural database for
inference of rotational angles of transmembrane helices, BMC helix-packing folds in transmembrane proteins, Nucleic Acids
Bioinformatics, vol. 14, pages 304, October 2013. Research, vol.. 39, Suppl. 1 (Database issue), pp. D347-355,
2011.
2. Chia-Ying Cheng, Chia-Feng Tsai, Yu-Ju Chen, Ting-Yi Sung*
and Wen-Lian Hsu, Spectrum-based method to generate good 7. Chih-Chiang Tsou, Chia-Feng Tasi, Ying-Hao Tsui, Putty-
decoy libraries for spectral library searching in peptide identi- Reddy Sudhir, Yi-Ting Wang, Yu-Ju Chen, Jeou-Yuan Chen,

fications, Journal of Proteome Research, vol. 12, pages 2305- Ting-Yi Sung*, Wen-Lian Hsu*, IDEAL-Q: An automated
2310, April 2013. tool for label-free quantitation analysis using an efficient pep-

tide alignment approach and spectral data validation, Molecu-
3. Lien-Chin Chen, Mei-Ying Liu, Yung-Chin Hsiao, Wai-Kok lar and Cellular Proteomics, vol.. 9, pages 131-144, 2010.
Chong, Hsin-Yi Wu, Wen-Lian Hsu, Pao-Chi Liao*, Ting-Yi
Sung*, Shih-Feng Tsai*, Jau-Song Yu*, Yu-Ju Chen*, De- 8. Hsin-Nan Lin, Ching-Tai Chen, Ting-Yi Sung, Shinn-Ying Ho,
coding the disease-associated proteins encoded in the human and Wen-Lian Hsu, Protein subcellular localization prediction
chromosome 4, Journal of Proteome Research, vol. 12, num- of eukaryotes using a knowledge-based approach, BMC Bio-
ber 1, pages 33-44, January 2013. informatics, vol. 10, Suppl. S15, pages S8, December 2009.
4. Jhih-Siang Lai, Cheng-Wei Cheng, Ting-Yi Sung*, Wen-Lian 9. Allan Lo, Yi-Yuan Chiu, Einar Andreas Rødland, Ping-Chiang
Hsu*, Computational comparative study of tuberculosis pro- Lyu, Ting-Yi Sung*, and Wen-Lian Hsu*, Predicting helix-he-
teomes using a model learned from signal peptide structures, lix interactions from residue contacts in membrane proteins,
PLoS ONE, vol. 7, number 4, pages e35018, April 2012. Bioinformatics, vol. 25(8), pages 996-1003, February 2009.
5. Hsin-Nan Lin, Cédric Notredame, Jia-Ming Chang, Ting-Yi 10. Chih-Chiang Tsou, Yin-Hao Tsui, Yi-Hwa Yian, Yi-Ju Chen,
Sung*, Wen-Lian Hsu*, Improving the alignment quality of Han-Yin Yang, Chuan-Yih Yu, Ke-Shiuan Lynn, Yu-Ju Chen,
consistency based aligners with an evaluation function us- Ting-Yi Sung*, and Wen-Lian Hsu*, MaXIC-Q Web: A Fully
ing synonymous protein words, PLoS ONE, vol. 6(12), pages Automated Web Service Using Statistical and Computational
e27872, December 2011. Methods for Protein Quantitation Based on Stable Isotope
Labeling and LC-MS, Nucleic Acids Research, vol. 37 (Web
Server Issue), pages W661-W669, 2009.

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