reconstruction of regulatory modules based on heterogeneous data sources karen lemmens phd defence...

Reconstruction of regulatory modules based on heterogeneous data sources

Karen Lemmens

PhD DefenceSeptember 29th 2008

29 September 2008 Karen LemmensPhD defence

Outline

1. Introduction & objectives

2. Strategy– Data integration– Association rule mining algorithms

3. Main achievements– ReMoDiscovery: Unraveling the yeast transcriptional

network– DISTILLER: Condition-dependent combinatorial regulation

in E. coli

4. Conclusions and perspectives

1. Introduction 2. Strategy 3. Achievements 4. Conclusions


DNA1. Introduction 2. Strategy 3. Achievements 4. Conclusions


DNA & genes

TATCCCTCCCTGTTTATCATTAATTTCTAATTATCAGCGTTTTTGGCTGGCGGCGTAGCGATGCGCTGGTTACTCTGAAAACGTCTATGCAAATTAACAAAAGAGAATAGCTATGCATGATGCAAACATCCGCGTTGCCATCGCGGGAGCCGGGGGGCGTAGGGCCGCCAGTTGATTCAGGCGGCGCTGGCATTAGAGGGCGTGCAGTTGGGCGCTGCGCTGGAGCGTGAAGGATCTTCTGAGATCACCCATAAGGCGTCCAGCCGTATGACATTTGCTAACGGCGCGGTAAGATCGGCTTTGTGGTTGAGTGGTAAGGAAAGCGGTCTTTTTGATATGCGAGATGTACTTGATCTCAATAATTTGTAACCACAAAATATTTGTTATGGTGCAAAAATAACACATTTAATTTATTGATTATAAAGGGCTTTAATTTTTGGCCCTTTTATTTTTGGTGTTATGTTTTTAAATTGTCTATAAGTGCCAAATCGTCGGTAAGCAGATTTGCATTGATTTACGTCATCATTGTGAATTAATATGCAAATAAAGTGAGTGAATATTCTCTGGAGGGTGTTTTGATTAAGTCAGCGCTATTGGTTCTGGAAGACGGAACCCAGTTTCACGGTCGGGCCATAGGGGCAACAGGTTCGCCTGACCATCGTTCCGGCGCAAACTTCTGCGGAAGATGTGCTGAAAATGAATCCAGACGGCATCTTCCTCTCCAACGGTCCTGGCGACCCGGCCCCGTGCGATTACGCCATTACCGCCATCCAGAAATTCCTCGAAACCGATATTCCGAATTACATGTTTTG

DNA

mRNA

protein

GENE 1

GENE 2



DNA & genes


DNA

mRNA

protein

GENE 1

GENE 2

GENE 1 GENE 2



DNA & genes


DNA

mRNA

protein

GENE 1

GENE 2

GENE 1 GENE 2


TRANSCRIPTION


DNA & genes


DNA

mRNA

protein

GENE 1

GENE 2

GENE 1 GENE 2


TRANSCRIPTION

TRANSLATION


Condition-dependent transcription

DNA

mRNA

protein

GENE 1




DNA

mRNA

protein

GENE 1

TRANSCRIPTION

TRANSLATION




DNA

mRNA

protein

GENE 1 GENE 1

TRANSCRIPTION

TRANSLATION



Transcriptional regulation

GENE 1




Regulatory motifs

GENE 1




Regulatory motifs

GENE 1

GENE 1

Regulators




Regulatory motifs

GENE 1

GENE 1 GENE 1

Regulators



Transcriptional network1. Introduction 2. Strategy 3. Achievements 4. Conclusions


Transcriptional modules1. Introduction 2. Strategy 3. Achievements 4. Conclusions


Outline





in E. coli




Data integration

GENE 1



Data integration

GENE 1

Library of strains, eachwith a tagged regulator

Chromatin IP toenrich promoters

bound by regulatorin vivo

Microarray to identifypromoters bound by

regulator in vivo

Regulator Tag





regulator in vivo

Regulator Tag





regulator in vivo

Regulator Tag





regulator in vivo

Regulator Tag

ChIP-chip data



Data integration

GENE 1





regulator in vivo

Regulator Tag





regulator in vivo

Regulator Tag





regulator in vivo

Regulator Tag





regulator in vivo

Regulator Tag

Regulatory motifs

ChIP-chip data



Data integration

GENE 1





regulator in vivo

Regulator Tag





regulator in vivo

Regulator Tag





regulator in vivo

Regulator Tag





regulator in vivo

Regulator Tag

Regulatory motifs

ChIP-chip data

Microarray data



Network reconstruction

• Several methods for reconstruction of the transcriptional network exist

Not all aspects of transcription taken into account by these methods

** Van den Bulcke T., Lemmens K., Van de Peer Y., Marchal K. (2006) Inferring Transcriptional Networks by Mining Omics Data. Current Bioinformatics, vol. 1, no. 3, pp. 301-313. ** Dhollander T., Sheng Q., Lemmens K., De Moor B., Marchal K., Moreau Y. (2007) Query-driven module discovery in microarray data. Bioinformatics, vol. 23, no. 19, pp. 2573-2580.

BooleanODEBayesianAssociation (CLR, ARACNE)

ClusteringBiclustering Query-driven biclusteringMethod of Segal et al.LeMoNe

BayesianSEREND

GRAM MA-NetworkerSAMBA InferelatorCOGRIM

Expression data Data integration

Individual interactions

Transcriptional modules



Association rule mining

• Association rule mining algorithms

– Advantages:• Enable exhaustive search• Elegant and concurrent data integration• No co-expression assumption between regulator and target• Overlapping modules

– Problems• Binary or discretized data • Filtering method necessary



Outline





in E. coli




ReMoDiscovery: Unraveling the yeast transcriptional network




Represent data in a mathematical way





regulator in vivo

Regulator Tag





regulator in vivo

Regulator Tag





regulator in vivo

Regulator Tag





regulator in vivo

Regulator Tag




• Transcriptional module– Genes are regulated by a minimum number of regulators– Genes share minimum number of common regulatory

motifs– Genes are co-expressed




• Regulatory program:

Regulators: Motifs:

MBP1

SWI4

SWI6

STB1

• Co-expressed genes:

YDL003W YER001W YGR109C YGR189CYGR221C YHR149C YER070W YPL256CYNL300W YPL163C YPL267W YPR120CYMR199W YMR199W YMR179W YML027WYKL113C




• ReMoDiscovery outperforms related methods for module detection– GRAM– SAMBA

• Conclusions– Meaningful biological results– Better performance than related methods

association rule mining algorithms are well suited for identification of regulatory modules through data integration

Lemmens K., Dhollander T., De Bie T., Monsieurs P., Engelen K., Smets B., Winderickx J., De Moor B., Marchal K. (2006) Inferring transcriptional module networks from ChIP-chip-, motif- and microarray data. Genome Biology, vol. 7, no. 5, pp. R37.




• Many aspects of transcription into account:– Regulatory motifs– Regulators– Co-expression of genes

Condition dependency of the interactions is missing



Outline





in E. coli




DISTILLER: Condition-dependent combinatorial regulation in E. coli

• ReMoDiscovery:

– Co-expression in all conditions by correlation

– Apriori algorithm

– No filtering procedure

• DISTILLER:

– Condition dependency: bandwidth concept

– CHARM algorithm

– Filtering procedure to identify the most interesting modules




Pastor D., Cortes-Calabuig A., Lemmens K., De Moor B., Marchal K., Denecker M. (2007) GeneReg: Integration of Experimental Data on the DNA Transcription Process. Proceedings of BNAIC 2007.




• Example: FNR module




• FNR = one of the most extensively studied regulators

• Experimental validation of novel FNR targets

– High confidence: • ydhY (b1674) Partridge et al, 2008• yfgG (b2504)• hscC (b0650)• treF (b3519)

– Medium confidence:• yjhB (b4279)• ydjX (b1750)• yjtD (b4403)• ydaT (b1358)• yehD (b2111)• yhjA (b3518) Partridge et al, 2007• ftnB (b1902)




• Condition dependency

– Arrays were grouped into conditional categories

– Colors show to what extent the conditions of the modules of a particular regulator are enriched for a specific category




• Combinatorial regulation

– Static

– Highly combinatorial:• 42 regulons one regulator• 66 complex regulons two regulators• 70 complex regulons three or more regulators (maximum

of 8)




• Combinatorial regulation at the module level

• Lower combinatorial complexity• 25/150 modules at least two regulators (maximum of 3)• 24 modules involve at least one global regulator such as CRP,

FNR or ArcA




• Combinatorial regulation at connector gene level

One regulator may be sufficient to alter the expression of a connector gene upon a specific environmental cue




• Conclusions

– Reliable predictions

– Dynamic view on the network

– Combinatorial regulation

** Lemmens K., De Bie T., Dhollander T., Monsieurs P., De Moor B., Collado-Vides J., Engelen K., Marchal K. (2008) The condition-dependent transcriptional network in Escherichia coli. Accepted for publication in Annals of NYAS, DREAM2.

** Lemmens K., De Bie T., Dhollander T., De Keersmaecker S., Thijs I., Schoofs G., De Weerdt A., De Moor B., Vanderleyden J., Collado-Vides J., Engelen K., Marchal K. (2008) DISTILLER: a data integration framework to reveal condition dependency of complex regulons in Escherichia coli. Submitted to Genome Biology.



Outline





in E. coli




Conclusions

• Main contributions of this thesis:– Automated collection of data– ReMoDiscovery– DISTILLER

• Goals obtained via:– Data integration – Association rule mining algorithms well suited for

data integration and reconstruction of transcriptional network

• Several algorithmic problems were solved

• Novel biological findings



Perspectives

• Conceptual extensions:– Inclusion of other data sources

• Additional motifs from de novo motif detection• Small RNAs

– Comparison of networks

• Implementation-related and algorithmic improvements:– User-friendly interface– Microarray compendium– Filtering step– Motif detection algorithms



Acknowledgements

• CMPG - BioI– Prof. Dr. K. Marchal– BioI group

• ESAT/SCD – BioI– Prof. Dr. B. De Moor– Prof. Dr. Y. Moreau– BioI group

– Prof. Dr. T. De Bie

• CMPG– Prof. Dr. J. Vanderleyden– Dr. S. De Keersmaecker

• Computer Sciences– Prof. Dr. M. Denecker– A. Cortés Calabuig

– Prof. Dr. J. Collado-Vides


reconstruction of regulatory modules based on heterogeneous data sources karen lemmens phd defence...

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introduction objectives