Workshop - Revision history

Grau at 11:41, 31 May 2011

2011-05-31T11:41:14Z

Keilwagen at 07:24, 25 May 2011

2011-05-25T07:24:57Z

← Older revision		Revision as of 07:24, 25 May 2011
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	__NOTOC__		__NOTOC__

	The first Jstacs workshop ~~will be~~ held on May, 19th at Martin Luther University. The aim of the workshop is to increase the visibility of Jstacs and the implemented projects, to bring developers and users together, as well as to identify further needs.		The first Jstacs workshop has been held on May, 19th at Martin Luther University. The aim of the workshop is to increase the visibility of Jstacs and the implemented projects, to bring developers and users together, as well as to identify further needs.

	''Depending on the audience the workshop will be held in English or German.''		''Depending on the audience the workshop will be held in English or German.''
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	== Location ==		== Location ==

	The workshop ~~will be~~ held on May, 19th in room 5.09 at the [http://www.informatik.uni-halle.de/?lang=en Institute of Computer Science] at Martin Luther University Halle ([http://maps.google.de/maps?f=q&source=s_q&hl=de&geocode=&q=Halle+Von-Seckendorff-Platz+1&aq=&sll=51.490443,12.035787&sspn=0.282602,0.281181&gl=de&ie=UTF8&hq=&hnear=Von-Seckendorff-Platz+1,+West+06120+Halle+(Saale),+Sachsen-Anhalt&ll=51.496922,11.936367&spn=0.01766,0.017574&z=16 Von-Seckendorff-Platz 1]).		The workshop has been held on May, 19th in room 5.09 at the [http://www.informatik.uni-halle.de/?lang=en Institute of Computer Science] at Martin Luther University Halle ([http://maps.google.de/maps?f=q&source=s_q&hl=de&geocode=&q=Halle+Von-Seckendorff-Platz+1&aq=&sll=51.490443,12.035787&sspn=0.282602,0.281181&gl=de&ie=UTF8&hq=&hnear=Von-Seckendorff-Platz+1,+West+06120+Halle+(Saale),+Sachsen-Anhalt&ll=51.496922,11.936367&spn=0.01766,0.017574&z=16 Von-Seckendorff-Platz 1]).

	== Contact ==		== Contact ==

	If you have any questions or comments, please do not hesitate to contact us by mail [mailto:jstacs@informatik.uni-halle.de jstacs@informatik.uni-halle.de].		If you have any questions or comments, please do not hesitate to contact us by mail [mailto:jstacs@informatik.uni-halle.de jstacs@informatik.uni-halle.de].

Grau at 05:11, 17 May 2011

2011-05-17T05:11:05Z

← Older revision		Revision as of 05:11, 17 May 2011
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	\| 11:00 - 11:30 \|\| Lasse Feldhahn \|\| Talk		\| 11:00 - 11:30 \|\| Lasse Feldhahn \|\| Talk
	\|align="left"\| '''''Discriminative Metropolis sampler for motif discovery'''''		\|align="left"\| '''''Discriminative Metropolis sampler for motif discovery'''''
			''Abstract'': The Metropolis sampler is an instance of MCMC learning, which approximates the distribution of parameters of a model given a learning principle and training data. In this talk, we consider a discriminative Metropolis sampler approximating the supervised posterior. For the evaluation of the objective function, we use existing components of Dispom, a discriminative motif discovery approach implemented in Jstacs. Besides the standard ZOOPS model, these components include a position distribution of motif occurrences.
			This talk presents the steps necessary for using the model of Dispom for discriminative motif discovery using the Metropolis sampler instead of the previous maximization of the supervised posterior, and presents first results on artificial as well as biological data.
	\|-		\|-
	\| 11:30 - 13:00		\| 11:30 - 13:00

Keilwagen: /* Program */

2011-05-16T05:15:32Z

Program

← Older revision		Revision as of 05:15, 16 May 2011
Line 58:		Line 58:
	\| 14:45 - 15:15 \|\| Martin Gleditzsch \|\| Talk		\| 14:45 - 15:15 \|\| Martin Gleditzsch \|\| Talk
	\|align="left"\| '''''PhyloBayes: An approach to model simultaneously evolutionary dependencies and statistical dependencies between neighbored DNA-Positions with Bayesian Networks'''''		\|align="left"\| '''''PhyloBayes: An approach to model simultaneously evolutionary dependencies and statistical dependencies between neighbored DNA-Positions with Bayesian Networks'''''
	''Abstact'': In the field of predicting regulatory elements in a set of sequences~~, there exist mainly two approaches~~. The first ~~one primary~~ uses information about dependencies ~~between neighbored DNA-positions and neglect~~ information about evolutionary ~~connections~~. The second approach uses ~~generally knowledge~~ about evolutionary dependencies ~~between the given sequences and tries to find highly conserved regions without considering the above mentioned statistical dependencies~~. Here we present the fusion of these two approaches using Bayesian networks and its integration in Jstacs.		''Abstact'': Two approaches exist in the field of predicting regulatory elements in a set of sequences. The first approach called de-novo motif discovery uses information about dependencies within binding sites but neglects information about evolutionary conservation. The second approach called phylogenetic footprinting uses information about evolutionary conservation but neglects information about dependencies within binding sites. Here, we present the fusion of these two complementary approaches using Bayesian networks and its integration in Jstacs.
	\|-		\|-
	\| 15:15- 15:30		\| 15:15- 15:30

194.94.136.71: /* Program */

2011-05-11T11:23:25Z

Program

← Older revision		Revision as of 11:23, 11 May 2011
Line 58:		Line 58:
	\| 14:45 - 15:15 \|\| Martin Gleditzsch \|\| Talk		\| 14:45 - 15:15 \|\| Martin Gleditzsch \|\| Talk
	\|align="left"\| '''''PhyloBayes: An approach to model simultaneously evolutionary dependencies and statistical dependencies between neighbored DNA-Positions with Bayesian Networks'''''		\|align="left"\| '''''PhyloBayes: An approach to model simultaneously evolutionary dependencies and statistical dependencies between neighbored DNA-Positions with Bayesian Networks'''''
	''Abstact'': In the field of predicting regulatory elements in a set of sequences, there exist mainly two approaches. The first one primary uses information about dependencies between neighbored DNA-positions and neglect information about evolutionary connections. The second approach uses generally knowledge about evolutionary dependencies between the given sequences and tries to find highly conserved regions without considering the above mentioned statistical dependencies. Here we present the fusion of these two approaches using Bayesian networks and its integration in ~~JStacs~~.		''Abstact'': In the field of predicting regulatory elements in a set of sequences, there exist mainly two approaches. The first one primary uses information about dependencies between neighbored DNA-positions and neglect information about evolutionary connections. The second approach uses generally knowledge about evolutionary dependencies between the given sequences and tries to find highly conserved regions without considering the above mentioned statistical dependencies. Here we present the fusion of these two approaches using Bayesian networks and its integration in Jstacs.
	\|-		\|-
	\| 15:15- 15:30		\| 15:15- 15:30

84.183.54.197: /* Program */

2011-05-11T11:19:46Z

Program

← Older revision		Revision as of 11:19, 11 May 2011
Line 58:		Line 58:
	\| 14:45 - 15:15 \|\| Martin Gleditzsch \|\| Talk		\| 14:45 - 15:15 \|\| Martin Gleditzsch \|\| Talk
	\|align="left"\| '''''PhyloBayes: An approach to model simultaneously evolutionary dependencies and statistical dependencies between neighbored DNA-Positions with Bayesian Networks'''''		\|align="left"\| '''''PhyloBayes: An approach to model simultaneously evolutionary dependencies and statistical dependencies between neighbored DNA-Positions with Bayesian Networks'''''
			''Abstact'': In the field of predicting regulatory elements in a set of sequences, there exist mainly two approaches. The first one primary uses information about dependencies between neighbored DNA-positions and neglect information about evolutionary connections. The second approach uses generally knowledge about evolutionary dependencies between the given sequences and tries to find highly conserved regions without considering the above mentioned statistical dependencies. Here we present the fusion of these two approaches using Bayesian networks and its integration in JStacs.
	\|-		\|-
	\| 15:15- 15:30		\| 15:15- 15:30

Keilwagen: /* Program */

2011-05-08T20:03:16Z

Program

← Older revision		Revision as of 20:03, 8 May 2011
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	! Title & Infos		! Title & Infos
	\|-		\|-
	\| 09:00 - 10:00 \|\| Jan Grau & Jens Keilwagen \|\| Tutorial		\| 09:00 - 09:45 \|\| Jan Grau & Jens Keilwagen \|\| Tutorial
	\|align="left"\| '''''Introduction to Jstacs'''''		\|align="left"\| '''''Introduction to Jstacs'''''
	''Information about'':		''Information about'':
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	[http://www.jstacs.de/api/de/jstacs/parameters/ParameterSet.html ParameterSet],		[http://www.jstacs.de/api/de/jstacs/parameters/ParameterSet.html ParameterSet],
	[http://www.jstacs.de/api/de/jstacs/models/mixture/StrandModel.html StrandModel]		[http://www.jstacs.de/api/de/jstacs/models/mixture/StrandModel.html StrandModel]
			\|-
			\| 09:45 - 10:00
			\|colspan="3" align="left" style="background-color:#eeeeee;"\| '''Break'''
	\|-		\|-
	\| 10:00 - 10:45 \|\| Jan Grau & Jens Keilwagen \|\| Tutorial		\| 10:00 - 10:45 \|\| Jan Grau & Jens Keilwagen \|\| Tutorial
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	''Information about'': Alignment, HMM package, Discriminative Sampling		''Information about'': Alignment, HMM package, Discriminative Sampling
	\|-		\|-
	\| 10:45 - 11:15 \|\| Lasse Feldhahn \|\| Talk		\| 11:00 - 11:30 \|\| Lasse Feldhahn \|\| Talk
	\|align="left"\| '''''Discriminative Metropolis sampler for motif discovery'''''		\|align="left"\| '''''Discriminative Metropolis sampler for motif discovery'''''
	\|-		\|-
	\| 11:15 - 13:00		\| 11:30 - 13:00
	\|colspan="3" align="left" style="background-color:#eeeeee;"\| '''Lunch'''		\|colspan="3" align="left" style="background-color:#eeeeee;"\| '''Lunch'''
	\|-		\|-
	\| 13:00 - 13:30 \|\| Ralf Eggeling \|\| Talk		\| 13:00 - 13:30 \|\| Ralf Eggeling \|\| Talk
	\|align="left"\| '''''Maximum Tsallis Entropy Models'''''		\|align="left"\| '''''Maximum Tsallis Entropy Models'''''
	''Abstract'': Classifying short sequence signals is a standard task in genome analysis. One important application is to distinguish splice donor and acceptor sites from decoy. A standard solution of the problem utilizes the maximum entropy principle. Here, we discuss a ~~generalizion~~ of this approach based on the Tsallis entropy and its implementation in Jstacs.		''Abstract'': Classifying short sequence signals is a standard task in genome analysis. One important application is to distinguish splice donor and acceptor sites from decoy. A standard solution of the problem utilizes the maximum entropy principle. Here, we discuss a generalization of this approach based on the Tsallis entropy and its implementation in Jstacs.
	\|-		\|-
	\| 13:30 - 14:00 \|\| Michael Seifert \|\| Talk		\| 13:30 - 14:00 \|\| Michael Seifert \|\| Talk
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	''Abstract'': Changes in gene expression levels play a central role in tumors. Additional information about the distribution of gene expression levels and distances between adjacent genes on chromosomes should be integrated into the analysis of tumor expression profiles. We use a Hidden Markov Model with distance-scaled transition matrices (DSHMM) to incorporate chromosomal distances of adjacent genes on chromosomes into the identification of differentially expressed genes in breast cancer.		''Abstract'': Changes in gene expression levels play a central role in tumors. Additional information about the distribution of gene expression levels and distances between adjacent genes on chromosomes should be integrated into the analysis of tumor expression profiles. We use a Hidden Markov Model with distance-scaled transition matrices (DSHMM) to incorporate chromosomal distances of adjacent genes on chromosomes into the identification of differentially expressed genes in breast cancer.
	\|-		\|-
	\| 14:00 - 14:30 \|\| Michael Scharfe \|\| Talk		\| 14:00 - 14:15
			\|colspan="3" align="left" style="background-color:#eeeeee;"\| '''Break'''
			\|-
			\| 14:15 - 14:45 \|\| Michael Scharfe \|\| Talk
	\|align="left"\| '''''A Hidden Markov Model for Phylogenetic Footprinting'''''		\|align="left"\| '''''A Hidden Markov Model for Phylogenetic Footprinting'''''
	''Abstract'': Phylogenetic footprinting is an important method to discover overrepresented and cross-species conserved sequence motifs (e.g. TFBSs). An extension of the classical Hidden Markov Model that implements phylogenetic emissions was developed using the HMM-Package of the current Jstacs version.		''Abstract'': Phylogenetic footprinting is an important method to discover overrepresented and cross-species conserved sequence motifs (e.g. TFBSs). An extension of the classical Hidden Markov Model that implements phylogenetic emissions was developed using the HMM-Package of the current Jstacs version.
	\|-		\|-
	\| 14:30 - 15:00 \|\| Martin Gleditzsch \|\| Talk		\| 14:45 - 15:15 \|\| Martin Gleditzsch \|\| Talk
	\|align="left"\| '''''PhyloBayes: An approach to model simultaneously evolutionary dependencies and statistical dependencies between neighbored DNA-Positions with Bayesian Networks'''''		\|align="left"\| '''''PhyloBayes: An approach to model simultaneously evolutionary dependencies and statistical dependencies between neighbored DNA-Positions with Bayesian Networks'''''
	\|-		\|-
	\| 15:00 - 16:00 \|\| \|\| Discussion		\| 15:15- 15:30
			\|colspan="3" align="left" style="background-color:#eeeeee;"\| '''Break'''

			\|-
			\| 15:30 - 16:30 \|\| \|\| Discussion
	\|align="left"\| '''''Discussion on future directions and improvements of Jstacs'''''		\|align="left"\| '''''Discussion on future directions and improvements of Jstacs'''''
	\|}		\|}

Keilwagen at 10:37, 6 May 2011

2011-05-06T10:37:13Z

← Older revision		Revision as of 10:37, 6 May 2011
Line 32:		Line 32:
	''Information about'': Alignment, HMM package, Discriminative Sampling		''Information about'': Alignment, HMM package, Discriminative Sampling
	\|-		\|-
	\| 10:45 - 11:15 \|\| ~~Michael Scharfe~~ \|\| Talk		\| 10:45 - 11:15 \|\| Lasse Feldhahn \|\| Talk
	\|align="left"\| '''''~~A Hidden Markov Model~~ for ~~Phylogenetic Footprinting~~'''''		\|align="left"\| '''''Discriminative Metropolis sampler for motif discovery'''''
	''Abstract'': Phylogenetic footprinting is an important method to discover overrepresented and cross-species conserved sequence motifs (e.g. TFBSs). An extension of the classical Hidden Markov Model that implements phylogenetic emissions was developed using the HMM-Package of the current Jstacs version.
	\|-		\|-
	\| 11:15 - 13:00		\| 11:15 - 13:00
	\|colspan="3" align="left" style="background-color:#eeeeee;"\| '''Lunch'''		\|colspan="3" align="left" style="background-color:#eeeeee;"\| '''Lunch'''
	\|-		\|-
	\|align="~~center~~"\| 13:00 - 13:30 \|\| Michael Seifert \|\| Talk		\| 13:00 - 13:30 \|\| Ralf Eggeling \|\| Talk
			\|align="left"\| '''''Maximum Tsallis Entropy Models'''''
			''Abstract'': Classifying short sequence signals is a standard task in genome analysis. One important application is to distinguish splice donor and acceptor sites from decoy. A standard solution of the problem utilizes the maximum entropy principle. Here, we discuss a generalizion of this approach based on the Tsallis entropy and its implementation in Jstacs.
			\|-
			\| 13:30 - 14:00 \|\| Michael Seifert \|\| Talk
	\|align="left"\| '''''Exploiting prior knowledge and gene distances in the analysis of tumor expression profiles with extended Hidden Markov Models'''''		\|align="left"\| '''''Exploiting prior knowledge and gene distances in the analysis of tumor expression profiles with extended Hidden Markov Models'''''
	''Abstract'': Changes in gene expression levels play a central role in tumors. Additional information about the distribution of gene expression levels and distances between adjacent genes on chromosomes should be integrated into the analysis of tumor expression profiles. We use a Hidden Markov Model with distance-scaled transition matrices (DSHMM) to incorporate chromosomal distances of adjacent genes on chromosomes into the identification of differentially expressed genes in breast cancer.		''Abstract'': Changes in gene expression levels play a central role in tumors. Additional information about the distribution of gene expression levels and distances between adjacent genes on chromosomes should be integrated into the analysis of tumor expression profiles. We use a Hidden Markov Model with distance-scaled transition matrices (DSHMM) to incorporate chromosomal distances of adjacent genes on chromosomes into the identification of differentially expressed genes in breast cancer.
	\|-		\|-
	\| 13:30 - 14:00 \|\| ~~Ralf Eggeling~~ \|\| Talk		\| 14:00 - 14:30 \|\| Michael Scharfe \|\| Talk
	\|align="left"\| '''''~~Maximum Tsallis Entropy Models~~'''''		\|align="left"\| '''''A Hidden Markov Model for Phylogenetic Footprinting'''''
	''Abstract'': ~~Classifying short sequence signals~~ is ~~a standard task in genome analysis. One~~ important ~~application is~~ to ~~distinguish splice donor~~ and ~~acceptor sites from decoy~~. ~~A standard solution~~ of the ~~problem utilizes~~ the ~~maximum entropy principle. Here, we discuss a generalizion~~ of ~~this approach based on~~ the ~~Tsallis entropy and its implementation in~~ Jstacs.		''Abstract'': Phylogenetic footprinting is an important method to discover overrepresented and cross-species conserved sequence motifs (e.g. TFBSs). An extension of the classical Hidden Markov Model that implements phylogenetic emissions was developed using the HMM-Package of the current Jstacs version.
	\|-		\|-
	\| 14:00 - 14:30 \|\| Martin Gleditzsch \|\| Talk		\| 14:30 - 15:00 \|\| Martin Gleditzsch \|\| Talk
	\|align="left"\| '''''PhyloBayes: An approach to model simultaneously evolutionary dependencies and statistical dependencies between neighbored DNA-Positions with Bayesian Networks'''''		\|align="left"\| '''''PhyloBayes: An approach to model simultaneously evolutionary dependencies and statistical dependencies between neighbored DNA-Positions with Bayesian Networks'''''
	\|-		\|-
	\| 14:30 - 15:15 \|\| \|\| Discussion		\| 15:00 - 16:00 \|\| \|\| Discussion
	\|align="left"\| '''''Discussion on future directions and improvements of Jstacs'''''		\|align="left"\| '''''Discussion on future directions and improvements of Jstacs'''''
	\|}		\|}

Keilwagen at 06:12, 5 May 2011

2011-05-05T06:12:44Z

← Older revision		Revision as of 06:12, 5 May 2011
Line 48:		Line 48:
	\|-		\|-
	\| 14:00 - 14:30 \|\| Martin Gleditzsch \|\| Talk		\| 14:00 - 14:30 \|\| Martin Gleditzsch \|\| Talk
	\|align="left"\| '''''~~tba~~'''''		\|align="left"\| '''''PhyloBayes: An approach to model simultaneously evolutionary dependencies and statistical dependencies between neighbored DNA-Positions with Bayesian Networks'''''
	\|-		\|-
	\| 14:30 - 15:15 \|\| \|\| Discussion		\| 14:30 - 15:15 \|\| \|\| Discussion

Keilwagen: /* Program */

2011-05-03T12:25:17Z

Program

← Older revision		Revision as of 12:25, 3 May 2011
Line 37:		Line 37:
	\|-		\|-
	\| 11:15 - 13:00		\| 11:15 - 13:00
	\|colspan="3" align="left"\| '''Lunch'''		\|colspan="3" align="left" style="background-color:#eeeeee;"\| '''Lunch'''
	\|-		\|-
	\|align="center"\| 13:00 - 13:30 \|\| Michael Seifert \|\| Talk		\|align="center"\| 13:00 - 13:30 \|\| Michael Seifert \|\| Talk

← Older revision		Revision as of 11:41, 31 May 2011
Line 14:		Line 14:
	\|-		\|-
	\| 09:00 - 09:45 \|\| Jan Grau & Jens Keilwagen \|\| Tutorial		\| 09:00 - 09:45 \|\| Jan Grau & Jens Keilwagen \|\| Tutorial
	\|align="left"\| '''''Introduction to Jstacs'''''		\|align="left"\| [http://www.jstacs.de/downloads/workshop/jstacs-intro.pdf '''''Introduction to Jstacs''''']
	''Information about'':		''Information about'':
	[http://www.jstacs.de/api/de/jstacs/data/Alphabet.html Alphabet],		[http://www.jstacs.de/api/de/jstacs/data/Alphabet.html Alphabet],
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	\|-		\|-
	\| 10:00 - 10:45 \|\| Jan Grau & Jens Keilwagen \|\| Tutorial		\| 10:00 - 10:45 \|\| Jan Grau & Jens Keilwagen \|\| Tutorial
	\|align="left"\| '''''New Features of Jstacs 1.5'''''		\|align="left"\| [http://www.jstacs.de/downloads/workshop/jstacs-1.5.pdf '''''New Features of Jstacs 1.5''''']
	''Information about'': Alignment, HMM package, Discriminative Sampling		''Information about'': Alignment, HMM package, Discriminative Sampling
	\|-		\|-
Line 48:		Line 48:
	\|-		\|-
	\| 13:30 - 14:00 \|\| Michael Seifert \|\| Talk		\| 13:30 - 14:00 \|\| Michael Seifert \|\| Talk
	\|align="left"\| '''''Exploiting prior knowledge and gene distances in the analysis of tumor expression profiles with extended Hidden Markov Models'''''		\|align="left"\| [http://www.jstacs.de/downloads/workshop/seifert_JstacsWorkshop_19_05_11.pdf '''''Exploiting prior knowledge and gene distances in the analysis of tumor expression profiles with extended Hidden Markov Models''''']
	''Abstract'': Changes in gene expression levels play a central role in tumors. Additional information about the distribution of gene expression levels and distances between adjacent genes on chromosomes should be integrated into the analysis of tumor expression profiles. We use a Hidden Markov Model with distance-scaled transition matrices (DSHMM) to incorporate chromosomal distances of adjacent genes on chromosomes into the identification of differentially expressed genes in breast cancer.		''Abstract'': Changes in gene expression levels play a central role in tumors. Additional information about the distribution of gene expression levels and distances between adjacent genes on chromosomes should be integrated into the analysis of tumor expression profiles. We use a Hidden Markov Model with distance-scaled transition matrices (DSHMM) to incorporate chromosomal distances of adjacent genes on chromosomes into the identification of differentially expressed genes in breast cancer.
	\|-		\|-
Line 55:		Line 55:
	\|-		\|-
	\| 14:15 - 14:45 \|\| Michael Scharfe \|\| Talk		\| 14:15 - 14:45 \|\| Michael Scharfe \|\| Talk
	\|align="left"\| '''''A Hidden Markov Model for Phylogenetic Footprinting'''''		\|align="left"\| [http://www.jstacs.de/downloads/workshop/jstacs_workshop_scharfe.pdf '''''A Hidden Markov Model for Phylogenetic Footprinting''''']
	''Abstract'': Phylogenetic footprinting is an important method to discover overrepresented and cross-species conserved sequence motifs (e.g. TFBSs). An extension of the classical Hidden Markov Model that implements phylogenetic emissions was developed using the HMM-Package of the current Jstacs version.		''Abstract'': Phylogenetic footprinting is an important method to discover overrepresented and cross-species conserved sequence motifs (e.g. TFBSs). An extension of the classical Hidden Markov Model that implements phylogenetic emissions was developed using the HMM-Package of the current Jstacs version.
	\|-		\|-
	\| 14:45 - 15:15 \|\| Martin Gleditzsch \|\| Talk		\| 14:45 - 15:15 \|\| Martin Gleditzsch \|\| Talk
	\|align="left"\| '''''PhyloBayes: An approach to model simultaneously evolutionary dependencies and statistical dependencies between neighbored DNA-Positions with Bayesian Networks'''''		\|align="left"\| [http://www.jstacs.de/downloads/workshop/jstacs_gleditzsch.pdf '''''PhyloBayes: An approach to model simultaneously evolutionary dependencies and statistical dependencies between neighbored DNA-Positions with Bayesian Networks''''']
	''Abstact'': Two approaches exist in the field of predicting regulatory elements in a set of sequences. The first approach called de-novo motif discovery uses information about dependencies within binding sites but neglects information about evolutionary conservation. The second approach called phylogenetic footprinting uses information about evolutionary conservation but neglects information about dependencies within binding sites. Here, we present the fusion of these two complementary approaches using Bayesian networks and its integration in Jstacs.		''Abstact'': Two approaches exist in the field of predicting regulatory elements in a set of sequences. The first approach called de-novo motif discovery uses information about dependencies within binding sites but neglects information about evolutionary conservation. The second approach called phylogenetic footprinting uses information about evolutionary conservation but neglects information about dependencies within binding sites. Here, we present the fusion of these two complementary approaches using Bayesian networks and its integration in Jstacs.
	\|-		\|-