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研究群   |   Research Laboratories











 Bioinformatics Laboratory









 Research Faculty  Group Profile


 Ting-Yi	Sung  Our	current	research	is	classified	into	two	main	areas:	systems	biology	and	proteomics.	In	addition	  novel	targets	to	study	their	involvement	in	viral	pathogenesis.  proach	to	predict	various	properties	of	proteins.
 Research	Fellow  to	research,	we	inaugurated	the	Bioinformatics	Ph.D.	Program	in	Taiwan	International	Graduate
 Operations	Research	,	New	York	University  Program,	Academia	Sinica.	As	of	Fall	2009,	two	students	received	their	Ph.D.	degrees	and	34	stu-  ●  Metagenomics  (1)	Membrane	protein	structure	prediction:	We	plan	to	develop	novel
                                                                      methods	 to	 address	 membrane	 protein	 structure	 prediction	 by
 Jan-Ming	Ho  dents	enrolled,	including	local	students	and	foreign	students	from	Canada,	Germany,	India,	Malay-  Based	on	whole	genome	shotgun	sequencing	data,	we	develop	an	in-  dissecting	the	problem	into	several	components	in	tertiary	struc-
 sia,	the	Philippines,	Slovakia,	the	United	States,	and	Vietnam.	Our	research	and	major	collaborative
 Research	Fellow  tegrated	platform	including	various	databases,	gene	expression	analy-  ture	 model	 construction.	 Building	 upon	 our	 previous	 works	 in
 Electrical	Engineering	and	Computer	Science	,	  projects	are	described	below.  sis,	 proteomic	 results	 and	 phylogenetic	 reconstruction	 to	 achieve	 a	  topologing	and	helix-helix	interaction/contact	prediction,	we	are
 Northwestern	University  1. Systems Biology  comprehensive	view	of	microbials.  developing	a	highly	accurate	predictor	for	the	solvent	accessibility
 Chun-Nan	Hsu  ●  Post-transcriptional Gene Regulation  ●  Transcriptome Analysis  of	TM	proteins,	which	in	turn	provides	valuable	information	in	the
 Research	Fellow	                                                     rotational	and	exposure	preferences	of	individual	helices.	In	parallel,
 Computer	Science	,	University	of	Southern	  MicroRNAs	play	an	important	role	in	the	posttranscriptional	regulation	of	genes	and	diseases.	  The	integrative	bioinformatic	platforms	for	high-throughput	sequenc-  we	closely	examine	the	physical	constraints	governing	helix-pack-
 California  We	have	developed	a	reverse	approach	and	predicted	dozens	of	new	human	miRNA	genes.	  ing	of	non-model	species	aims	at	supporting	transcriptome	studies	of	  ing	(i.e.,	crossing	angles,	closest	point	of	contact,	etc),	and	construct
 Wen-Lian	Hsu  Some	of	these	and	their	target	genes	have	been	validated	through	experiments,	and	the	results	  specific	domestic	species,	including	A.	hallerissp.gemmifera,	Formosan	  a	knowledge	base	for	all	known	helix-helix	interactions	in	currently
               black	bear,	Formosan	cypress,	silvergrass,	pteridophyte,	and	coral.	Bi-
 Distinguished	Research	Fellow  provided	strong	evidence	that	our	predicted	microRNAs	and	their	targets	are	indeed	the	direct	  ologists	 will	 analyze	 the	 transcriptome	 of	 these	 non-model	 species,	  available	structures	so	as	to	facilitate	3D	structure	modeling.
 Operations	Research	,	Cornell	University  targets.	In	addition,	we	have	been	working	on	developing	a	systemic	approach	for	integrating	  (2)	 Protein	 function	 prediction,	 subcellular	 localization	 prediction,
 high-throughput	next	generation	sequencing	and	other	experimental	data,	in	order	to	uncover	  though	 their	 genomes	 have	 not	 yet	 been	 fully	 assembled.	 Overall,
 Chung-Yen	Lin  microRNA	 regulatory	 pathways	 in	 breast	 cancer	 metastasis,	 B-cell	 differentiation,	 and	T-type	  designing	effective	and	efficient	algorithms	and	software	to	deal	with	  and	remote	homology	detection:	Understanding	protein	function
 Assistant	Research	Fellow	  Ca2+	channels	involved	in	cardiac	hypertrophy.	In	the	study	of	microRNA	evolution,	we	are	go-  these	and	related	problems	is	a	challenge.  is	one	of	the	most	basic	problems	in	proteomics	research.	We	are
 Institute	of	Zoology	,	National	Taiwan	University                    particularly	 interested	 in	 predicting	 the	 functions	 of	 membrane
 ing	to	develop	a	computational	method	to	search	and	identify	homologous	miRNAs	in	distant	  2. Proteomics  proteins.	Since	the	function	of	a	protein	is	similar	to	its	homologous
 Arthur	Chun-Chieh	Shih  genomes.	We	expect	that	newly	generated	data	can	provide	us	with	more	details	on	how	the	  proteins,	we	will	also	work	on	remote	homology	detection	to	assist
 Associate	Research	Fellow  role	of	miRNAs	in	gene	regulation	has	been	expanded	in	evolution,	especially	in	the	lineage	  ●  Mass Spectrometry-based Proteomics	  function	prediction.	Furthermore,	since	the	function	of	a	protein	is
 CSIE,	National	Central	University  leading	to	human.  Mass	 spectrometry	 (MS)	 -based	 proteomic	 analysis	 involves	 protein	  related	to	its	subcellular	localization,	we	will	also	work	on	subcellu-
 Huai-Kuang	Tsai  ●  Regulatory Mechanism  identification	and	protein	quantitation	so	that	differentially	expressed	  lar	localization	prediction	for	various	species	and	organelles.
 Assistant	Research	Fellow	  proteins	between	different	cell	states,	e.g.,	tumor	cells	and	normal	cells,	  ●  Disease-centric Membrane Proteome Portal
 Computer	Science	and	Information	Engineering	,	  The	transcription	of	genes	is	controlled	by	interactions	between	transcription	factors	(TFs)	and	  can	be	identified	to	facilitate	biomarker	discovery.	In	this	area,	we	will
 National	Taiwan	University  their	binding	sites	(TFBSs)	(or	cis-regulatory	elements).	Inferring	the	function	of	a	TF	and	identify-  study	the	following	two	topics:  Biological	membranes	are	essential	components	of	life	and	the	struc-
 ing	its	binding	sites	is	helpful	for	understanding	the	mechanism	of	transcriptional	regulation.	In	  turing	elements	of	living	cells.	They	form	a	physical	barrier	between
 past	years,	we	developed	two	TFBS	identification	methods,	TFBSfinder	and	MAGIIC,	which	utilize	  (1)	 Protein	 quantitation:	 We	 have	 previously	 developed	 automated	  the	cells	and	their	external	environments,	as	well	as	with	different	in-
 several	data	sources,	including	DNA	sequences,	phylogenetic	information,	microarray	data	and	  quantitation	tools,	including	MaXIC-Q	for	iTRAQ-lableing	(JPR	2006,	  tracellular	organelles	within	eukaryotic	cells.	The	basic	structure	and
 Postdoctoral  ChIP-chip	data.	Empirical	tests	on	known	TFBSs	show	that	our	methods	are	highly	accurate	in	  NAR	2007),	MaXIC-Q	(NAR	2009)	for	ICAT-	and	SILAC-labeling,	and	  function	of	biomembranes	are	provided	by	a	lipid	bilayer.	However,
                 IDEAL-Q	 (MCP	 2010)	 for	 label-free	 approach.	 We	 will	 further	 en-
 identifying	motifs,	outperforming	current	methods	and	achieving	high	sensitivity	and	specificity
 for	predicting	experimentally	verified	TFBSs.	In	addition,	we	constructed	a	user-friendly	interac-  hance	these	tools.	First,	we	will	work	on	de-convoluting	overlap-  the	proteins	on	the	membrane	are	linked	to	many	unique	functions
                                                                    and	play	a	critical	role	in	communication	between	separated	compart-
 Yao-Lin	Chang  tive	platform	(MYBS)	for	dynamic	binding	site	mapping	using	ChIP-chip	data	and	phylogenetic	  ping	peptide	peaks	in	spectra.	Second,	we	will	specifically	tackle	the	  ments,	such	as	the	signal	transduction	through	surface	receptors	and
 footprinting	as	the	two	filters.	Based	on	MYBS,	we	further	investigated	the	impact	of	DNA	bind-  issue	of	chromatographic	shift,	by	providing	an	accurate	retention	  the	solutes	exchanged	by	protein	channels.	Because	of	their	location,
 Yu-Jung	Chang  ing	position	variants	on	yeast	gene	expression.	Our	analysis	shows	that	nucleotide	variations,	in	  time	prediction	method.	Finally,	we	will	integrate	the	above	tools	to	  abundance,	and	various	functions,	membrane	proteins	represent	over
 more	than	one-third	of	variable	positions	and	in	20%	of	dependent	position	pairs,	are	highly	cor-  form	an	integrated	platform	for	providing	comprehensive	quantita-
 Lien-Chin	Chen  related	to	gene	expression.	We	define	such	positions	as	functional.	However,	some	positions	are	  tive	analysis	for	label-free	and	stable	isotope	labeling	experiments.	  50%	 of	 pharmaceutical	 drug	 targets.	Therefore,	 we	 plan	 to	 develop
                                                                    a	 disease-centric	 human	 membrane	 protein	 portal	 to	 facilitate	 bio-
 Shu-Hwa	Chen  only	functional	as	dependent	pairs,	but	not	individually.	Our	analysis	supports	the	importance	  Friendly	interfaces	to	visualize	spectral	data	and	to	query	proteomic	  medical	research.	This	portal	will	integrate	various	existing	databases
                 information,	e.g.,	Gene	Ontology	and	pathway	annotations,	will	be
 of	nucleotide	variants	at	variable	positions	of	TFBSs	in	gene	regulation.	We	now	further	study	the
 Allan	Lo	  regulatory	mechanisms	in	yeast	and	higher	organisms	(e.g.,	humans),	including	identifying	tran-  provided.  and	prediction	tools,	including	our	predictors	on	membrane	protein
                                                                    structure,	in	order	to	provide	comprehensive	proteomic	information,
 scription	factor	binding	sites	and	discussing	the	functionality	of	degenerate	positions	in	TFBSs	  (2)	Protein	identification	and	post-translational	analysis:	Protein	iden-  attained	by	prediction	and	databases,	regarding		structure,	function,
 Ke-Shiuan	Lynn  and	the	regulatory	rule	of	adjacent	genes.  tification	 is	 a	 cornerstone	 of	 MS-based	 proteomics	 studies.	 Our	  sub-cellular	 localization,	 classification,	 interaction,	 and	 relationship
 Tse-Yi	Wang  ●  Network Biology  quantitation	tools	use	spectral	raw	data	and	protein	identification	  with	diseases.
                 results	from	existing	identification	tools	as	input.	Though	several
 We	will	decipher	the	cellular	interactomes	of	viruses	(e.g.,	herpes	viruses	and	the	hepatitis	C	vi-  prestigious	commercial	tools	have	been	available,	e.g.,	Mascot	and	  3. Major Collaborative Projects
 rus)	and	the	infected	hosts,	with	the	aim	of	identifying	the	protein	complexes	hijacked	by	patho-  SEQUEST,	they	suffer	several	limitations.	First,	these	search	tools	are
 gen	proteins	and	finding	ways	to	block	the	mechanism	of	infection.	Furthermore,	key	proteins	  usually	inconsistent,	and	remain	a	problem	for	proteomic	profiling.	  We	are	currently	participating	in	various	large-scale	thematic	projects,
 and	motifs	in	the	virus-host	network	will	be	identified	and	hopefully	provide	virologists	with	  In	the	case	of	post-translational	modifications	(PTMs),	the	identifi-  including	the	Bioethanol	from	Cellulosics	project	(NSC	project	hosted	by
                                                                  Dr.	Chi-Huey	Wong,	President	of	Academia	Sinica),	the	C4	Rice	project
                 cation	problem	becomes	substantially	more	difficult,	since	not	only	  (Academia	Sinica	project	hosted	by	Dr.	Wen-Hsiung	Li,	Director	of	Bio-
                 the	sequence	but	also	the	modification	sites	of	a	modified	protein	  diversity	Research	Center,	Academia	Sinica),	the	Bioinformatics	Core	for
                 need	 to	 be	 determined.	 Existing	 search	 tools	 can	 only	 identify	 a	  Genomic	Medicine	and	Biotechnology	Development	project	(National
                 few	types	of	PTMs	and	may	not	correctly	identify	their	modification	  Research	Program	for	Genomic	Medicine	(NRPGM)	hosted	by	Dr.	I-Shou
                 sites.	Moreover,	many	MS/MS	spectra	can	not	be	identified.	There-  Chang),	the	Construction	and	Integration	of	Biological	Resources	project
                 fore,	we	started	to	work	on	protein	identification.	Furthermore,	we	  (NSC	project	led	by	Director	General	Min-Liang	Kuo,	Department	of	Life
                 will	study	the	identification	of	proteins	with	PTMs,	including	phos-  Sciences,	 NSC,	 and	 Vice	 President	 Ching-Fong	 Chang,	 Nation	 Taiwan
                 phorylation,	nitrosylation	and	glycosylation.	   Ocean	 University),	 and	 the	 Networks	 of	 the	Two	 Component	 Systems
              ●  Protein Prediction Problems                      project	(NSC	project	hosted	by	Professor	Wen-Ching	Wang,	Director	of
                                                                  Molecular	and	Cellular	Biology	Institute,	National	Tsing	Hua	University).
               We	will	use	a	machine	learning	approach	and	a	knowledge-based	ap-
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