Institute of Information Science
Bioinformatics Laboratory
Principal Investigators:
:::Ting-Yi Sung(Chair) :::Jan-Ming Ho :::Chun-Nan Hsu
:::Wen-Lian Hsu :::Chung-Yen Lin :::Arthur Chun-Chieh Shih
:::Huai-Kuang Tsai

Postdoctoral Fellow:
Yu-Jung Chang Lien-Chin Chen Yi-Ching Chen
Chia-Ying Cheng Ke-Shiuan Lynn

[ Group Profile ]
Bioinformatics Ph.D. Program at Taiwan International Graduate Program (TIGP), Academia Sinica was inaugurated in 2003. Bioinformatics Lab plays an crucial role in the program. As of Spring 2012, seven students have received their Ph.D. degrees and 39 students are currently enrolled, including local students and foreign students from Canada, Germany, India, Malaysia, Nigeria, the Philippines, Slovakia, the United States, and Vietnam. Our current research is focused on bioinformatics for"omics" studies, classified into two main areas: genomics and transcriptomics, and proteomics and metabolomics, as described below.
1. Genomics and Transcriptomics.
Genomics and transcriptomics studies based on next generation sequencing (NGS). Using the next-generation sequencing technology, we study the genomics and transcriptomics of microorganisms and human related to diseases. In the aspect of metagenomics, comparative investigation of microbial communities across diverse environments is important and challenging in metagenomics that enables the study of unculturable microorganisms in their original environments. We propose a series of computational methods to discriminate the differences among distinct microbial communities and to enhance the accuracy in estimation of the taxonomic compositions of metagenomes. We also plan to develop an integrated platform including various databases, gene expression analysis, proteomic results and phylogenetic reconstruction to achieve a comprehensive view of microbial. Furthermore, we also investigate corals in Kenting to discover the interaction between corals and their symbiotic algae under the changes of environmental factors. Gene regulation and evolution of Kranz anatomy in C4 plant photosynthesis development are other interesting topics to explore.
In regard of biomedical research, we investigate genome structural variations of the autism families. We also analyze transcriptomics data of different types of breast cancer in an attempt to detect carcinogens. Furthermore, we study microRNAs in diseases and B cell differentiation.
Furthermore, we plan to develop a short read sequence assembler and related analysis tools.±Æª©´¡¹Ï

Regulatory mechanism and network. Transcription factors (TFs) and their binding sites (TFBSs) play important roles in gene transcription. In past years, we developed two TFBS identification methods that were shown highly accurate in identifying motifs, outperforming major existing methods. In addition, we constructed a user-friendly interactive platform (MYBS) for dynamic binding site mapping. Based on MYBS, we further investigated the impact of DNA binding position variants on yeast gene expression. Our analysis supports the importance of nucleotide variants at variable positions of TFBSs in gene regulation. We now further study the regulatory mechanisms in yeast and higher organisms (e.g., humans), including identifying TFBSs and discussing the functionality of degenerate positions in TFBSs and the regulatory rules of adjacent genes.

2. Proteomics and Metabolomics
Mass Spectrometry (MS)-based proteomics and metabolomics. MS has become a predominant technology for proteomics research. Based on acquired high-throughput mass spectral data, researchers are interested in identifying and quantifying proteins involved in the samples so that differentially expressed proteins between different cell states, e.g., tumor cells and normal cells, can be identified to facilitate further research, e.g., biomarker discovery. We have developed three automated quantitation tools, including MaXIC-Q, MaXIC-Q, and IDEAL-Q, for various quantitation strategies. Currently, we finished an integrated tool, called IDEAL-Q+, to support protein quantitation analyses. Furthermore, we have developed methods to improve protein identification, particularly, identification of proteins with some post-translational modifications (PTMs) since many PTMs are related to human diseases. In recent years, MS has been increasingly used for large-scale metabolomics research. However, since mass spectral data acquired from metabolomics experiments are very different from those acquired from proteomics experiments, very few quantitation tools are available and no tool is available for identifying metabolites. We will develop automated tools for MS-based metabolomics studies.

Protein structure and function predictions. We work on structure prediction for transmembrane (TM) proteins. We have developed methods for topology and helix-helix interaction/contact predictions and a knowledge base for all known helix-helix interactions in currently available structures. Currently, we are working on predicting signal peptides and solvent accessibility of TM proteins. Toward tertiary structure prediction, we will develop methods to predict TM helix type and various angles of TM helix. Furthermore, we will work on protein function prediction.

Topological analysis of complex protein network. Recent research pointed out that oncogenic potential of EBV and KSHV is directly linked to latent infection. Hence, we try to decode complex host-pathogen interaction to identify key roles and important sub-networks as drug targets in our own algorithms according to various topological features. Our aims here are trying to identify those protein complexes hijacked by pathogen proteins/ small molecules and providing hints to block the mechanism of infection and stop possible carcinogenesis.

Cancer-centric membrane proteome portal. Membrane proteins represent over 50% of drug targets because of their location, abundance, and various functions. Therefore, we are developing a cancer-centric human membrane protein portal to facilitate biomedical research.

Finally, we would like to acknowledge our collaborators as bioinformatics is a cross-disciplinary research. We collaborate with researchers from Institutes of Biomedical Sciences and Chemistry, and Genomics Research Center, Agricultural Biotechnology Research Center, and Biodiversity Research Center of Academia Sinica; National Taiwan University Hospital; National Health Research 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.


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