Institute of Biomedical Sciences (IBMS),
Academia Sinica,
Room N121,
128 Sec.2,
Yen-Chiu-Yuan Rd.,
Nangkang District,
Taipei, Taiwan 115
Tel: +886-2-27899033
Fax:+886-2-27887641
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Dr. Ming-Jing Hwang's Lab
Bioinformatics and Biomolecule Modeling
| Research Description﹕ |
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The main focus of our research has been to develop fast computational algorithms for large-scale analysis of biological sequences and structures. The UniMarker (UM) method allowed us to compare the human genome with the mouse genome, to cross-reference all the human SNP and EST sequences in the public domain, and to locate their genomic position, all in just hours or few days of the CPU time of one Pentium IV personal computer. More recently, we have extended the UM method into a generalized marker-based method to map the genome DNA sequences of evolutionarily distant species, such as human and puffer fish, and to find genome-wide repeat sequences, particularly those that are very long and highly degenerate. For structural bioinformatics, we have developed a fast protein structure comparison method called FLASH or OPAAS. The method allowed us to systematically characterize the entire protein structure database, hence to ask questions such as how protein structures might have evolved and what might have been the functional and physical constraints that have contributed to shape the protein universe. In addition to algorithm development, more recently we have also applied systems biology approaches, such as network analysis and mathematical modeling, to study complex biological systems.
Positions Available: Postdoc & Research Assistants.
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| 研究目標﹕ |
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本實驗室以研發新穎快速的演算法為主軸，處理分析大量的基因序列及蛋白質結構資料。兩個我們已研發出的演算法分別為一可以在個人電腦上快速處理大量的人類單核甘酸突變(SNP)及表現基因片斷(EST)序列，並找出這些DNA片斷在基因體上的位置，或比較兩個哺乳類如人類與小鼠的基因體，這一個方法我們稱之為獨特標的法(UniMarker Method). 最近我們更將這個方法延展為一泛標的法，可用來比對演化上遠如人類與河豚之基因體序列及找尋重覆序列，尤其是那些很長且演化久遠的。在結構生物資訊學方面，我們的演算法，稱為FLASH (亦稱OPAAS)，可快速找出所有已知蛋白質結構其互相重疊的最佳及絕大多數生物學上有意義的解。利用這個演算法，我們將可有系統地分析蛋白質結構資料庫，並從中探討蛋白質結構演化的機制及其功能與物理因素的限制。除了演算法的研發之外，近年來我們也開始使用系統生物學的方法如系統網路分析及數學模擬探討複雜的生物系統。
歡迎有志生物資訊研究者(科系學歷不拘)，加入我們的團隊。
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