Page 15 - 2017 Brochure
P. 15
currently developing a new version of our previously published tool, Multi-Q, for Research Description
isobaric-labeling quantitation analysis of TMT 10-plex-labeled samples. Top-down
proteomics analysis has been receiving attention of late, because it allows for the Arthur Chun-Chieh Shih
characterization and identification of post-translational modifications. However, the
data analysis for top-down proteomics is challenging. Much of the difficulty lies in Research Fellow
deconvoluting charge state peaks, in order to detect a proteoform. We have recently
proposed a method, called DYAMOND, to solve this problem and have developed Ting-Yi Sung
a tool, called iTop-Q, which implements DYAMOND for top-down proteomics
quantitation. Research Fellow
Bioinformatics for Glycan Synthesis and MS-based Metabolomics. Jan-Ming Ho
Although the synthesis of oligosaccharides is a mature methodology, the practice
is still limited to specialized laboratories. To reduce the complexity of intermediate Research Fellow
separation and protecting group manipulation, we have developed an automated
method for programmable one-pot oligosaccharide synthesis. In MS-based Chung-Yen Lin
metabolomics, we have developed an automated metabolite quantitation tool,
called iMet-Q, which provides highly accurate values. In addition, we have proposed Associate Research Fellow
a computational method for metabolite identification, which includes an effective
clustering step to group a metabolite and its fragments, followed by searches Wen-Lian Hsu
against different metabolite databases. Currently, we are developing an automated
tool to implement our methods. Distinguished Research Fellow
Taiwan Cancer Moonshot Project. Huai-Kuang Tsai
In August 2016, Taiwan was invited by the U.S. National Cancer Institute (NCI) to
join the international Cancer Moonshot Project. A major goal that we are working Research Fellow
toward is proteogenomic characterization of cancers. We are especially focused
on early onset and early stage lung cancer, for which many mutations are similar. Postdocs
One major challenge is to use MS data to detect variant peptides (peptides arising
from mutations). In order to do so, we are designing computational methods that Yu-Jung Chang
are specific for identification of variants as well as developing tools for researchers Ching-Tai Chen
to choose appropriate proteases for protein digestion. Proper digestion prior to MS Shu-Hwa Chen
experimentation will help to render variant peptides identifiable. Additionally, our Jia-Hsin Huang
new Multi-Q 2 tool will be applied for quantitation of TMT 10-plex labeled samples. Hsin-Nam Lin
We are currently analyzing MS big data acquired from lung cancer patient tissues. Yu-Wei Tsay
According to NCI criteria, we must identify at least 10,000 proteins from each sample
to produce a sufficient profile. Along with the basic profiling, we also aim to find 13
variant peptides.
Collaborators: Since bioinformatics is an interdisciplinary research area, we have
many collaborations with principal investigators from Agriculture Biotechnology
Research Center (ABRC), Biodiversity Research Center (BRC), the Genomics
Research Center (GRC), Institute of Biomedical Science (IBMS), Institute of Chemistry
(IC), Institute of Cellular and Organismic Biology (ICOB), Institute of Molecular
Biology (IMB), Institute of Plant Science and Microbiology (IPSM), and Institute of
Statistical Sciences (ISS) at Academia Sinica; National Health Research Institute,
Taiwan; and College of Bioresources and Agriculture, College of Life Science,
and College of Medicine at National Taiwan University; College of Life Science at
National Cheng Kung University; Department of Aquaculture at National Taiwan
Ocean University; Fisheries Research Institute; and physicians from National Taiwan
University Hospital. Furthermore, we also have international collaborative research
projects with the Department of Plant Biology and Medical School at Michigan State
University, David Geffen School of Medicine at UCLA, Institute for Protein Research
at Osaka University (JAPAN), and National Institute of Advanced Industrial Science
and Technology (AIST) in Japan.
isobaric-labeling quantitation analysis of TMT 10-plex-labeled samples. Top-down
proteomics analysis has been receiving attention of late, because it allows for the Arthur Chun-Chieh Shih
characterization and identification of post-translational modifications. However, the
data analysis for top-down proteomics is challenging. Much of the difficulty lies in Research Fellow
deconvoluting charge state peaks, in order to detect a proteoform. We have recently
proposed a method, called DYAMOND, to solve this problem and have developed Ting-Yi Sung
a tool, called iTop-Q, which implements DYAMOND for top-down proteomics
quantitation. Research Fellow
Bioinformatics for Glycan Synthesis and MS-based Metabolomics. Jan-Ming Ho
Although the synthesis of oligosaccharides is a mature methodology, the practice
is still limited to specialized laboratories. To reduce the complexity of intermediate Research Fellow
separation and protecting group manipulation, we have developed an automated
method for programmable one-pot oligosaccharide synthesis. In MS-based Chung-Yen Lin
metabolomics, we have developed an automated metabolite quantitation tool,
called iMet-Q, which provides highly accurate values. In addition, we have proposed Associate Research Fellow
a computational method for metabolite identification, which includes an effective
clustering step to group a metabolite and its fragments, followed by searches Wen-Lian Hsu
against different metabolite databases. Currently, we are developing an automated
tool to implement our methods. Distinguished Research Fellow
Taiwan Cancer Moonshot Project. Huai-Kuang Tsai
In August 2016, Taiwan was invited by the U.S. National Cancer Institute (NCI) to
join the international Cancer Moonshot Project. A major goal that we are working Research Fellow
toward is proteogenomic characterization of cancers. We are especially focused
on early onset and early stage lung cancer, for which many mutations are similar. Postdocs
One major challenge is to use MS data to detect variant peptides (peptides arising
from mutations). In order to do so, we are designing computational methods that Yu-Jung Chang
are specific for identification of variants as well as developing tools for researchers Ching-Tai Chen
to choose appropriate proteases for protein digestion. Proper digestion prior to MS Shu-Hwa Chen
experimentation will help to render variant peptides identifiable. Additionally, our Jia-Hsin Huang
new Multi-Q 2 tool will be applied for quantitation of TMT 10-plex labeled samples. Hsin-Nam Lin
We are currently analyzing MS big data acquired from lung cancer patient tissues. Yu-Wei Tsay
According to NCI criteria, we must identify at least 10,000 proteins from each sample
to produce a sufficient profile. Along with the basic profiling, we also aim to find 13
variant peptides.
Collaborators: Since bioinformatics is an interdisciplinary research area, we have
many collaborations with principal investigators from Agriculture Biotechnology
Research Center (ABRC), Biodiversity Research Center (BRC), the Genomics
Research Center (GRC), Institute of Biomedical Science (IBMS), Institute of Chemistry
(IC), Institute of Cellular and Organismic Biology (ICOB), Institute of Molecular
Biology (IMB), Institute of Plant Science and Microbiology (IPSM), and Institute of
Statistical Sciences (ISS) at Academia Sinica; National Health Research Institute,
Taiwan; and College of Bioresources and Agriculture, College of Life Science,
and College of Medicine at National Taiwan University; College of Life Science at
National Cheng Kung University; Department of Aquaculture at National Taiwan
Ocean University; Fisheries Research Institute; and physicians from National Taiwan
University Hospital. Furthermore, we also have international collaborative research
projects with the Department of Plant Biology and Medical School at Michigan State
University, David Geffen School of Medicine at UCLA, Institute for Protein Research
at Osaka University (JAPAN), and National Institute of Advanced Industrial Science
and Technology (AIST) in Japan.
