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Research Fellow 宋定懿 研
究
Ting-Yi Sung 人
員
Ph.D., Operations Research, New York University, United States Faculty
T +886-2-2788-3799 ext. 1711 E tsung@iis.sinica.edu.tw
F +886-2-2782-4814 W www.iis.sinica.edu.tw/pages/tsung/
・ Associate Research Fellow, Institute of Information Science, Academia Sinica (1989-
2000)
・ Deputy Director, Institute of Information Science, Academia Sinica (1996-1999)
・ Ten Outstanding Young Women Award (1998)
Research Description
My research interest is bioinformatics for proteomics and proteogenomics. Studying proteomics is crucial for biomedical research because
proteins perform cellular functions and are also drug targets. Mass spectrometry (MS) has become a predominant technology for large-scale
proteomics research in the past years. Thus we started to work on bioinformatics for MS-based proteomics in late 2003. When we investigated
the challenges of identifying missing proteins which lack experiment evidence at the protein level, we observed a great portion of missing
proteins with evidence at transcript level have sequence variants in their protein sequences. Furthermore, it is reported in the literature that
single amino acid variations (SAVs) can lead to or are related to cancers; for example, the SAV L858R in the epidermal growth factor receptor
has been observed at genomic level in lung cancer patients of Taiwan. However, most of variant peptides containing cancer-related SAVs
have not been identi ed from MS experiments, i.e., not being detected at the protein level. Thus we are also interested in bioinformatics for
proteogenomics to validate genetic variation events at the protein level.
The main tasks of MS data analysis include protein identi cation and quantitation for proteomics studies and variant peptide identi cation
for proteogenomics studies. We have developed computational methods and software tools for protein identification and quantitation.
In addition, we also design algorithms that facilitate variant peptide identi cation and propose rigorous analytical procedures for further
con rming variant peptides identi ed from MS data. Currently, we are participating in Taiwan Cancer Moonshot Project to study lung cancer
and the Chromosome-centric Human Proteome Project to study missing proteins.
Publications
1. Wai-Kok Choong, Ching-Tai Chen, Jen-Hung Wang, and Ting- using DYAMOND algorithm," Analytical Chemistry, vol. 89, no. Brochure 2020
Yi Sung, "iHPDM: in silico human proteome digestion map with 24, pp. 13128-13136, Dec. 2017.
proteolytic peptide analysis and graphical visualizations," Journal
of Proteome Research, vol. 18, no. 12, pp. 4124-4132, Dec. 2019. 7. T. Mamie Lih, Wai-Kok Choong, Chen-Chun Chen, Cheng-
Wei Cheng, Hsin-Nan Lin, Ching-Tai Chen, Hui-Yin Chang,
2. Thejkiran Pitti, Ching-Tai Chen, Hsin-Nan Lin, Wai-Kok Wen-Lian Hsu, and Ting-Yi Sung, "MAGIC-web: a platform for
Choong, Wen-Lian Hsu, and Ting-Yi Sung, "N-GlyDE: a two- untargeted and targeted N-linked glycoprotein identification,"
stage N-linked glycosylation site prediction incorporating gapped Nucleic Acids Research , vol. 44, Web Server Issue, pp. W575-
dipeptides and pattern-based encoding," Scientific Reports, vol. 9, 580, 2016.
pp. 15975, Nov. 2019.
8. Hui-Yin Chang, Ching-Tai Chen, T. Mamie Lih, Ke-Shiuan Lynn,
3. Ching-Tai Chen, Chu-Ling Ko, Wai-Kok Choong, Jen-Hung Chiun-Gung Juo, Wen-Lian Hsu, and Ting-Yi Sung, "iMet-Q: a
Wang, Wen-Lian Hsu, and Ting-Yi Sung, "WinProphet: a user- user-friendly tool for metabolomics quantitation using dynamic
friendly pipeline management system for proteomics data analysis peak-width determination," PLoS ONE , vol. 11, no. 1, pp.
based on Trans-Proteomic Pipeline," Analytical Chemistry, vol. e0146112, Jan. 2016.
91, no. 15, pp. 9403-9406, Aug. 2019.
9. Wai-Kok Choong, Hui-Yin Chang, Ching-Tai Chen, Chia-Feng
4. T. Mamie Lih, Wai-Kok Choong, Yu-Ju Chen, and Ting-Yi Tsai, Wen-Lian Hsu, Yu-Ju Chen, and Ting-Yi Sung, "Informatics
Sung, "Evaluating the possibility of detecting variants in shotgun view on the challenges of identifying missing proteins from
proteomics via LeTE-fusion analysis pipeline," Journal of shotgun proteomics," Journal of Proteome Research, vol. 14, no.
Proteome Research, vol. 17, no. 9, pp. 2937-2952, Sep. 2018. 12, pp. 5396-5407, Dec. 2015.
5. Wai-Kok Choong, T. Mamie Lih, Yu-Ju Chen, and Ting-Yi Sung, 10. Ke-Shiuan Lynn, Chen-Chun Chen, T. Mamie Lih, Cheng-Wei
"Decoding the effect of isobaric substitutions on identifying Cheng, Wan-Chih Su, Chun-Hao Chang, Chia-Ying Cheng,
missing proteins and variant peptides in human proteome," Wen-Lian Hsu, Yu-Ju Chen, and Ting-Yi Sung, "MAGIC: an
Journal of Proteome Research, vol. 16, no. 12, pp. 4415-4424, automated N-linked glycoprotein identification tool using a Y1-
Dec. 2017 ion pattern matching algorithm and in silico MS2 approach,"
Analytical Chemistry, Vol. 87, No. 4, pp. 2466-2473, Feb. 2015.
6. Hui-Yin Chang, Ching-Tai Chen, Chu-Ling Ko, Yi-Ju Chen, Yu-
Ju Chen, Wen-Lian Hsu, Chiun-Gung Juo, and Ting-Yi Sung,
"iTop-Q: an intelligent tool for top-down proteomics quantitation
159
究
Ting-Yi Sung 人
員
Ph.D., Operations Research, New York University, United States Faculty
T +886-2-2788-3799 ext. 1711 E tsung@iis.sinica.edu.tw
F +886-2-2782-4814 W www.iis.sinica.edu.tw/pages/tsung/
・ Associate Research Fellow, Institute of Information Science, Academia Sinica (1989-
2000)
・ Deputy Director, Institute of Information Science, Academia Sinica (1996-1999)
・ Ten Outstanding Young Women Award (1998)
Research Description
My research interest is bioinformatics for proteomics and proteogenomics. Studying proteomics is crucial for biomedical research because
proteins perform cellular functions and are also drug targets. Mass spectrometry (MS) has become a predominant technology for large-scale
proteomics research in the past years. Thus we started to work on bioinformatics for MS-based proteomics in late 2003. When we investigated
the challenges of identifying missing proteins which lack experiment evidence at the protein level, we observed a great portion of missing
proteins with evidence at transcript level have sequence variants in their protein sequences. Furthermore, it is reported in the literature that
single amino acid variations (SAVs) can lead to or are related to cancers; for example, the SAV L858R in the epidermal growth factor receptor
has been observed at genomic level in lung cancer patients of Taiwan. However, most of variant peptides containing cancer-related SAVs
have not been identi ed from MS experiments, i.e., not being detected at the protein level. Thus we are also interested in bioinformatics for
proteogenomics to validate genetic variation events at the protein level.
The main tasks of MS data analysis include protein identi cation and quantitation for proteomics studies and variant peptide identi cation
for proteogenomics studies. We have developed computational methods and software tools for protein identification and quantitation.
In addition, we also design algorithms that facilitate variant peptide identi cation and propose rigorous analytical procedures for further
con rming variant peptides identi ed from MS data. Currently, we are participating in Taiwan Cancer Moonshot Project to study lung cancer
and the Chromosome-centric Human Proteome Project to study missing proteins.
Publications
1. Wai-Kok Choong, Ching-Tai Chen, Jen-Hung Wang, and Ting- using DYAMOND algorithm," Analytical Chemistry, vol. 89, no. Brochure 2020
Yi Sung, "iHPDM: in silico human proteome digestion map with 24, pp. 13128-13136, Dec. 2017.
proteolytic peptide analysis and graphical visualizations," Journal
of Proteome Research, vol. 18, no. 12, pp. 4124-4132, Dec. 2019. 7. T. Mamie Lih, Wai-Kok Choong, Chen-Chun Chen, Cheng-
Wei Cheng, Hsin-Nan Lin, Ching-Tai Chen, Hui-Yin Chang,
2. Thejkiran Pitti, Ching-Tai Chen, Hsin-Nan Lin, Wai-Kok Wen-Lian Hsu, and Ting-Yi Sung, "MAGIC-web: a platform for
Choong, Wen-Lian Hsu, and Ting-Yi Sung, "N-GlyDE: a two- untargeted and targeted N-linked glycoprotein identification,"
stage N-linked glycosylation site prediction incorporating gapped Nucleic Acids Research , vol. 44, Web Server Issue, pp. W575-
dipeptides and pattern-based encoding," Scientific Reports, vol. 9, 580, 2016.
pp. 15975, Nov. 2019.
8. Hui-Yin Chang, Ching-Tai Chen, T. Mamie Lih, Ke-Shiuan Lynn,
3. Ching-Tai Chen, Chu-Ling Ko, Wai-Kok Choong, Jen-Hung Chiun-Gung Juo, Wen-Lian Hsu, and Ting-Yi Sung, "iMet-Q: a
Wang, Wen-Lian Hsu, and Ting-Yi Sung, "WinProphet: a user- user-friendly tool for metabolomics quantitation using dynamic
friendly pipeline management system for proteomics data analysis peak-width determination," PLoS ONE , vol. 11, no. 1, pp.
based on Trans-Proteomic Pipeline," Analytical Chemistry, vol. e0146112, Jan. 2016.
91, no. 15, pp. 9403-9406, Aug. 2019.
9. Wai-Kok Choong, Hui-Yin Chang, Ching-Tai Chen, Chia-Feng
4. T. Mamie Lih, Wai-Kok Choong, Yu-Ju Chen, and Ting-Yi Tsai, Wen-Lian Hsu, Yu-Ju Chen, and Ting-Yi Sung, "Informatics
Sung, "Evaluating the possibility of detecting variants in shotgun view on the challenges of identifying missing proteins from
proteomics via LeTE-fusion analysis pipeline," Journal of shotgun proteomics," Journal of Proteome Research, vol. 14, no.
Proteome Research, vol. 17, no. 9, pp. 2937-2952, Sep. 2018. 12, pp. 5396-5407, Dec. 2015.
5. Wai-Kok Choong, T. Mamie Lih, Yu-Ju Chen, and Ting-Yi Sung, 10. Ke-Shiuan Lynn, Chen-Chun Chen, T. Mamie Lih, Cheng-Wei
"Decoding the effect of isobaric substitutions on identifying Cheng, Wan-Chih Su, Chun-Hao Chang, Chia-Ying Cheng,
missing proteins and variant peptides in human proteome," Wen-Lian Hsu, Yu-Ju Chen, and Ting-Yi Sung, "MAGIC: an
Journal of Proteome Research, vol. 16, no. 12, pp. 4415-4424, automated N-linked glycoprotein identification tool using a Y1-
Dec. 2017 ion pattern matching algorithm and in silico MS2 approach,"
Analytical Chemistry, Vol. 87, No. 4, pp. 2466-2473, Feb. 2015.
6. Hui-Yin Chang, Ching-Tai Chen, Chu-Ling Ko, Yi-Ju Chen, Yu-
Ju Chen, Wen-Lian Hsu, Chiun-Gung Juo, and Ting-Yi Sung,
"iTop-Q: an intelligent tool for top-down proteomics quantitation
159
