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Research Laboratories 研究群

生物資訊實驗室

Bioinformatics Laboratory

Research Faculty
Research Faculty

Ting-Yi Sung Jan-Ming Ho Chun-Nan Hsu Wen-Lian Hsu Chung-Yen Lin Arthur Chun-Chieh Shih Huai-Kuang Tsai
Research Fellow Research Fellow Research Fellow Distinguished Research Fellow Associate Research Fellow Associate Research Fellow Associate Research Fellow

Group Profile
Postdoctoral
Bioinformatics Ph.D. Program at Taiwan International Gradu- Regulatory mechanism and network. Transcription factors
ate Program (TIGP), Academia Sinica was inaugurated in 2003. (TFs) and their binding sites (TFBSs) play important roles in Yu-Jung Chang
Bioinformatics Lab plays an crucial role in the program. As of gene transcription. In past years, we developed two TFBS iden- Lien-Chin Chen
Spring 2012, seven students have received their Ph.D. degrees tification methods that were shown highly accurate in identify-
and 39 students are currently enrolled, including local students ing motifs, outperforming major existing methods. In addition, Yi-Ching Chen
and foreign students from Canada, Germany, India, Malaysia, we constructed a user-friendly interactive platform (MYBS) for Chia-Ying Cheng
Nigeria, the Philippines, Slovakia, the United States, and Viet- dynamic binding site mapping. Based on MYBS, we further
nam. investigated the impact of DNA binding position variants on Ke-Shiuan Lynn
yeast gene expression. Our analysis supports the importance
Our current research is focused on bioinformatics for “omics” of nucleotide variants at variable positions of TFBSs in gene metabolomics research. However, since mass spectral data acquired from
studies, classified into two main areas: genomics and transcrip- regulation. We now further study the regulatory mechanisms metabolomics experiments are very different from those acquired from pro-
tomics, and proteomics and metabolomics, as described below. in yeast and higher organisms (e.g., humans), including iden- teomics experiments, very few quantitation tools are available and no tool
is available for identifying metabolites. We will develop automated tools for
tifying TFBSs and discussing the functionality of degenerate
1. Genomics and Transcriptomics. positions in TFBSs and the regulatory rules of adjacent genes. MS-based metabolomics studies.
Genomics and transcriptomics studies based on next genera-
tion sequencing (NGS). Using the next-generation sequenc- 2. Proteomics and Metabolomics Protein structure and function predictions. We work on structure prediction
ing technology, we study the genomics and transcriptomics Mass Spectrometry (MS)-based proteomics and metabo- for transmembrane (TM) proteins. We have developed methods for topology Bioinformatics is a cross-
of microorganisms and human related to diseases. In the as- lomics. MS has become a predominant technology for pro- and helix-helix interaction/contact predictions and a knowledge base for all
pect of metagenomics, comparative investigation of microbial teomics research. Based on acquired high-throughput mass known helix-helix interactions in currently available structures. Currently, we disciplinary research area
communities across diverse environments is important and spectral data, researchers are interested in identifying and are working on predicting signal peptides and solvent accessibility of TM
challenging in metagenomics that enables the study of un- quantifying proteins involved in the samples so that differen- proteins. Toward tertiary structure prediction, we will develop methods to that aims to facilitate bio-
culturable microorganisms in their original environments. We tially expressed proteins between different cell states, e.g., tu- predict TM helix type and various angles of TM helix. Furthermore, we will
propose a series of computational methods to discriminate mor cells and normal cells, can be identified to facilitate further work on protein function prediction. logical research to explore
the differences among distinct microbial communities and to research, e.g., biomarker discovery. We have developed three Topological analysis of complex protein network. Recent research point- the nature and improve the
enhance the accuracy in estimation of the taxonomic com- automated quantitation tools, including MaXIC-Q, MaXIC-Q, ed out that oncogenic potential of EBV and KSHV is directly linked to latent
positions of metagenomes. We also plan to develop an inte- and IDEAL-Q, for various quantitation strategies. Currently, we infection. Hence, we try to decode complex host-pathogen interaction to quality of life.
grated platform including various databases, gene expression finished an integrated tool, called IDEAL-Q+, to support pro- identify key roles and important sub-networks as drug targets in our own
analysis, proteomic results and phylogenetic reconstruction to tein quantitation analyses. Furthermore, we have developed algorithms according to various topological features. Our aims here are try-
achieve a comprehensive view of microbial. Furthermore, we methods to improve protein identification, particularly, iden- ing to identify those protein complexes hijacked by pathogen proteins/ small
also investigate corals in Kenting to discover the interaction tification of proteins with some post-translational modifica- molecules and providing hints to block the mechanism of infection and stop
between corals and their symbiotic algae under the changes of tions (PTMs) since many PTMs are related to human diseases. possible carcinogenesis.
environmental factors. Gene regulation and evolution of Kranz In recent years, MS has been increasingly used for large-scale
anatomy in C4 plant photosynthesis development are other in- Cancer-centric membrane proteome portal. Membrane proteins represent
teresting topics to explore. over 50% of drug targets because of their location, abundance, and various
In regard of biomedical research, we investigate genome functions. Therefore, we are developing a cancer-centric human membrane
structural variations of the autism families. We also analyze protein portal to facilitate biomedical research.
transcriptomics data of different types of breast cancer in an
attempt to detect carcinogens. Furthermore, we study microR- Finally, we would like to acknowledge our collaborators as bioinformatics is
NAs in diseases and B cell differentiation. a cross-disciplinary research. We collaborate with researchers from Institutes
Furthermore, we plan to develop a short read sequence as- of Biomedical Sciences and Chemistry, and Genomics Research Center, Ag-
sembler and related analysis tools. ricultural Biotechnology Research Center, and Biodiversity Research Center
of Academia Sinica; National Taiwan University Hospital; National Health Re-
search Institute; College of Life Science, National Tsing Hua University; The
National Institute of Advanced Industrial Science and Technology, Japan;
School of Medicine, University of California, Los Angeles.

研究群
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