Page 67 - 2017 Brochure
P. 67
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/pages/tsung/
• Research Fellow, Institute of Information Science, Academia Sinica (2000-present)
• Associate Research Fellow, Institute of Information Science, Academia Sinica (1989-2000)
• Ten Outstanding Young Women Award, Taiwan (1998)
• Ph.D., Operations Research, New York University (1989)
• M.B.A., State University of New York at Buffalo (1983)
• B.S., Management Science, National Chiao Tung University (1980)
Research Description which performs label-free quantitation analysis.
In top-down proteomics, we have recently developed a tool, called
The ultimate objective of our research is to facilitate precision iTop-Q, which can automatically detect proteoforms and perform
medicine for cancers. It has been reported that proteomics data quantitation analysis.
can distinguish more subtypes of cancers than genomics data, It has been reported that variations in individual proteins play
and variant peptides in particular proteins may explain why some important roles in cancer development. Currently, most variations
patients do not respond to some drugs. Thus our research is mainly are detected from the genomic level and are inferred to the
focused on bioinformatics for proteomics and proteogenomics. protein level by translation. Although it is challenging, it is crucial
to investigate the proteogenomic characterization of cancers, i.e.,
Mass spectrometry (MS) has become the predominant technology to identify variant peptides from high-throughput MS data. We
for large-scale proteomics research. We have developed participate in the Taiwan Cancer Moonshot Project and focus on
computational methods and tools for protein identification and studying lung cancer. In this project, we perform data analysis on
quantitation. In recent years, we have developed a tool, called MS data to identify and quantify proteins from patient samples. At
MAGIC, to identify intact glycoproteins, which, to the best of our the same time, we are developing computational methods to find
knowledge, is the first system capable of large-scale analysis variant peptides from the MS data, with an objective to facilitate the
without requiring prior protein or glycan information. To keep development of precision medicine for cancers.
pace with rapid improvements in MS technology and the ever-
increasing size of MS data sets, we are developing a new version Yu-Ju Chen*, and Ting-Yi Sung*, “MAGIC: an automated N-linked
of Multi-Q, Multi-Q 2, which will update our tool for isobaric-labelling glycoprotein identification tool using a Y1-ion pattern matching
quantitation analysis that was published ten years ago. We also algorithm and in silico MS2 approach,” Analytical Chemistry, v. 87,
plan to develop IDEAL-Q 2, a new version of our tool, IDEAL-Q, no. 4, p. 2466-2473, 2015.
7. Ke-Shiuan Lynn, Mei-Ling Cheng, Yet-Ran Chen, Chin Hsu, Ann
Publications Chen, T. Mamie Lih, Hui-Yin Chang, Ching-jang Huang, Ming-
Shi Shiao, Wen-Harn Pan*, Ting-Yi Sung*, and Wen-Lian Hsu*,
1. Hui-Yin Chang, Ching-Tai Chen, Chiun-Gung Juo, Wen-Lian Hsu, “Metabolite identification for mass spectrometry-based metabolomics
and Ting-Yi Sung, “iTop-Q: an intelligent top-down proteomics using multiple types of correlated ion information,” Analytical
quantification tool using the DYAMOND algorithm for charge state Chemistry, v. 87, no. 4, p. 2143–2151, 2015.
deconvolution,” 15th HUPO World Congress, September 2016. 8. Jhih-Siang Lai, Cheng-Wei Cheng, Allan Lo*, Ting-Yi Sung*, and
Wen-Lian Hsu, “Lipid exposure prediction enhances the inference of
2. T. Mamie Lih, Wai-Kok Choong, Chen-Chun Chen, Cheng-Wei rotational angles of transmembrane helices,” BMC Bioinformatics, v.
Cheng, Hsin-Nan Lin, Ching-Tai Chen, Hui-Yin Chang, Wen-Lian 14, p. 304, 2013.
Hsu and Ting-Yi Sung*, “MAGIC-web: a platform for untargeted and 9. Chia-Ying Cheng, Chia-Feng Tsai, Yu-Ju Chen, Ting-Yi Sung*, and
targeted N-linked glycoprotein identification,” Nucleic Acids Research Wen-Lian Hsu, “Spectrum-based method to generate good decoy
44, Web Server Issue, p. W575-580, 2016. libraries for spectral library searching in peptide identifications,”
Journal of Proteome Research, v. 12, no. 5, p. 2305-2310, 2013.
3. Hui-Yin Chang, Ching-Tai Chen, T. Mamie Lih, Ke-Shiuan Lynn, 10. Lien-Chin Chen, Mei-Ying Liu, Yung-Chin Hsiao, Wai-Kok Chong,
Chiun-Gung Juo, Wen-Lian Hsu, Ting-Yi Sung*, “iMet-Q: a user- Hsin-Yi Wu, Wen-Lian Hsu, Pao-Chi Liao*, Ting-Yi Sung*, Shih-
friendly tool for metabolomics quantitation using dynamic peak-width Feng Tsai*, Jau-Song Yu*, and Yu-Ju Chen*, “Decoding the disease-
determination,” PLoS ONE, v. l. 11, no. 1, p. e0146112, 2016. associated proteins encoded in the human chromosome 4,” Journal of
Proteome Research, v. 12, no. 1, p. 33-44, 2013.
4. Wai-Kok Choong, Hui-Yin Chang, Ching-Tai Chen, Chia-Feng
Tsai, Wen-Lian Hsu, Yu-Ju Chen*, and Ting-Yi Sung*, Informatics 65
view on the challenges of identifying missing proteins from shotgun
proteomics, Journal of Proteome Research, v. 14, no. 12, pp. 5396-
5407, 2015.
5. P. Horvatovich, EK Lunberg, Yu-Ju Chen, Ting-Yi Sung, F. He, et al.,
“Quest for missing proteins: Update 2015 on Chromosome-Centric
Human Proteome Project,” Journal of Proteome Research, v. 14, no. 9,
p. 3415–3431, 2015.
6. Ke-Shiuan Lynn, Chen-Chun Chen, T. Mamie Lih, Cheng-Wei Cheng,
Wan-Chih Su, Chun-Hao Chang, Chia-Ying Cheng, Wen-Lian Hsu,
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/pages/tsung/
• Research Fellow, Institute of Information Science, Academia Sinica (2000-present)
• Associate Research Fellow, Institute of Information Science, Academia Sinica (1989-2000)
• Ten Outstanding Young Women Award, Taiwan (1998)
• Ph.D., Operations Research, New York University (1989)
• M.B.A., State University of New York at Buffalo (1983)
• B.S., Management Science, National Chiao Tung University (1980)
Research Description which performs label-free quantitation analysis.
In top-down proteomics, we have recently developed a tool, called
The ultimate objective of our research is to facilitate precision iTop-Q, which can automatically detect proteoforms and perform
medicine for cancers. It has been reported that proteomics data quantitation analysis.
can distinguish more subtypes of cancers than genomics data, It has been reported that variations in individual proteins play
and variant peptides in particular proteins may explain why some important roles in cancer development. Currently, most variations
patients do not respond to some drugs. Thus our research is mainly are detected from the genomic level and are inferred to the
focused on bioinformatics for proteomics and proteogenomics. protein level by translation. Although it is challenging, it is crucial
to investigate the proteogenomic characterization of cancers, i.e.,
Mass spectrometry (MS) has become the predominant technology to identify variant peptides from high-throughput MS data. We
for large-scale proteomics research. We have developed participate in the Taiwan Cancer Moonshot Project and focus on
computational methods and tools for protein identification and studying lung cancer. In this project, we perform data analysis on
quantitation. In recent years, we have developed a tool, called MS data to identify and quantify proteins from patient samples. At
MAGIC, to identify intact glycoproteins, which, to the best of our the same time, we are developing computational methods to find
knowledge, is the first system capable of large-scale analysis variant peptides from the MS data, with an objective to facilitate the
without requiring prior protein or glycan information. To keep development of precision medicine for cancers.
pace with rapid improvements in MS technology and the ever-
increasing size of MS data sets, we are developing a new version Yu-Ju Chen*, and Ting-Yi Sung*, “MAGIC: an automated N-linked
of Multi-Q, Multi-Q 2, which will update our tool for isobaric-labelling glycoprotein identification tool using a Y1-ion pattern matching
quantitation analysis that was published ten years ago. We also algorithm and in silico MS2 approach,” Analytical Chemistry, v. 87,
plan to develop IDEAL-Q 2, a new version of our tool, IDEAL-Q, no. 4, p. 2466-2473, 2015.
7. Ke-Shiuan Lynn, Mei-Ling Cheng, Yet-Ran Chen, Chin Hsu, Ann
Publications Chen, T. Mamie Lih, Hui-Yin Chang, Ching-jang Huang, Ming-
Shi Shiao, Wen-Harn Pan*, Ting-Yi Sung*, and Wen-Lian Hsu*,
1. Hui-Yin Chang, Ching-Tai Chen, Chiun-Gung Juo, Wen-Lian Hsu, “Metabolite identification for mass spectrometry-based metabolomics
and Ting-Yi Sung, “iTop-Q: an intelligent top-down proteomics using multiple types of correlated ion information,” Analytical
quantification tool using the DYAMOND algorithm for charge state Chemistry, v. 87, no. 4, p. 2143–2151, 2015.
deconvolution,” 15th HUPO World Congress, September 2016. 8. Jhih-Siang Lai, Cheng-Wei Cheng, Allan Lo*, Ting-Yi Sung*, and
Wen-Lian Hsu, “Lipid exposure prediction enhances the inference of
2. T. Mamie Lih, Wai-Kok Choong, Chen-Chun Chen, Cheng-Wei rotational angles of transmembrane helices,” BMC Bioinformatics, v.
Cheng, Hsin-Nan Lin, Ching-Tai Chen, Hui-Yin Chang, Wen-Lian 14, p. 304, 2013.
Hsu and Ting-Yi Sung*, “MAGIC-web: a platform for untargeted and 9. Chia-Ying Cheng, Chia-Feng Tsai, Yu-Ju Chen, Ting-Yi Sung*, and
targeted N-linked glycoprotein identification,” Nucleic Acids Research Wen-Lian Hsu, “Spectrum-based method to generate good decoy
44, Web Server Issue, p. W575-580, 2016. libraries for spectral library searching in peptide identifications,”
Journal of Proteome Research, v. 12, no. 5, p. 2305-2310, 2013.
3. Hui-Yin Chang, Ching-Tai Chen, T. Mamie Lih, Ke-Shiuan Lynn, 10. Lien-Chin Chen, Mei-Ying Liu, Yung-Chin Hsiao, Wai-Kok Chong,
Chiun-Gung Juo, Wen-Lian Hsu, Ting-Yi Sung*, “iMet-Q: a user- Hsin-Yi Wu, Wen-Lian Hsu, Pao-Chi Liao*, Ting-Yi Sung*, Shih-
friendly tool for metabolomics quantitation using dynamic peak-width Feng Tsai*, Jau-Song Yu*, and Yu-Ju Chen*, “Decoding the disease-
determination,” PLoS ONE, v. l. 11, no. 1, p. e0146112, 2016. associated proteins encoded in the human chromosome 4,” Journal of
Proteome Research, v. 12, no. 1, p. 33-44, 2013.
4. Wai-Kok Choong, Hui-Yin Chang, Ching-Tai Chen, Chia-Feng
Tsai, Wen-Lian Hsu, Yu-Ju Chen*, and Ting-Yi Sung*, Informatics 65
view on the challenges of identifying missing proteins from shotgun
proteomics, Journal of Proteome Research, v. 14, no. 12, pp. 5396-
5407, 2015.
5. P. Horvatovich, EK Lunberg, Yu-Ju Chen, Ting-Yi Sung, F. He, et al.,
“Quest for missing proteins: Update 2015 on Chromosome-Centric
Human Proteome Project,” Journal of Proteome Research, v. 14, no. 9,
p. 3415–3431, 2015.
6. Ke-Shiuan Lynn, Chen-Chun Chen, T. Mamie Lih, Cheng-Wei Cheng,
Wan-Chih Su, Chun-Hao Chang, Chia-Ying Cheng, Wen-Lian Hsu,
