1. Computational Discovery of miR-TF Regulatory Modules in Human Genome
Dang Hung Tran1,3, Kenji Satou1,2, Tu Bao Ho1, and Tho Hoan Pham1,3
1 Japan Advanced Institute of Science and Technology
2 Kanazawa University
3 Hanoi National University of Education
Singapore  September 9-11, 2009

2. Outline Background Motivation Discover miR-TF modules
Results and discussion
Conclusion
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3. Gene regulation Conventional view by: Recent view by:
Transcription factors (TF)
Activate gene expression
Depress gene expression
Recent view by:
Transcription factors
Small RNAs (e.g. microRNAs)
Depress gene expression
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4. What are microRNAs ? RNA world Recent concerning microRNAs Coding RNA
Non-coding RNA
Recent concerning
Small non-coding RNAs
microRNAs
Class of small non-coding RNAs
Bind to messenger RNAs
Repress gene expression
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5. microRNA biogenesis
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6. Chen and Rajewsky Nature Reviews Genetics (2007)
Research motivation
Gene regulation
Transcriptional level: by transcription factors (TF)
Post-transcriptional level: by microRNAs (miRNA)
How TFs and miRNAs function together?
Investigate coordinated gene regulation by TFs and miRNAs may elucidate their functions.
Chen and Rajewsky Nature Reviews Genetics (2007)
We need to Identify regulatory modules that consist of miRNAs, Transcription Factor, and regulated genes (miR-TF module)
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7. Related works Detection of miRNA regulatory modules
Tran et al. 2008, Joung et al. 2009, Liu et al. 2009
Concentrated on finding the relationship between miRNAs and their target genes
Finding the relationships between TFs and miRNAs
Cui et al. 2007, Shalgi et al. 2007, Zhou et al. 2008
Showed that miRNA mediated regulatory circuits are prevalent in the human genome
But the combined roles of miRNAs and other factors in gene regulatory network still remains unanswered
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8. Problem Given Find TFs – genes binding information
miRNAs – genes binding information
Find
miR-TF modules with biological meaning
Each module consist of three components: TFs, miRNAs, and genes regulated by them
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9. Algorithm Going over all genes i
For each i, search all subsets of miRNAs M’
For each M’, search all subsets of TFs T’
Find gene set G, bind by M’ and T’
If |G|>1 report modules and mark all the subsets of M’ and T’
Generally, the binding matrixes are relatively sparse and hence the computational complexity is reduced, therefore we are able to use an exhaustive searching algorithm to discover the optimal solutions within the given space
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10. Schematic description of the data set construction
Experiment
Databases
miRNAs and TFs regulatory signatures from CRSD database
CRSD integrates six well-known databases (e.g. UniGene, TRANSFAC, miRBASE)
Dataset contains
267 miRNAs, 483 TFs, and 1253 regulated genes
Performing algorithm
Written in C++
Run on Linux operating system
Schematic description of the data set construction
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11. Evaluation and findings (1/4)
Tested binding data at a number of different confidence level (0.05, 0.01, 0.005, 0.001)
Each module consists of three components
The number modules found by our algorithm is as follows
For further analysis, we selected the values of p1 and p2 equal to (182 modules)
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12. Evaluation and findings (2/4)
Some of miR-TF modules share a subset of miRNAs or TFs on regulation of the target genes
For example: modules 66, 67, 68
Three modules share a common miRNA hsa-miR-125b
Two of them share two miRNAs hsa-miR-125a and hsa-miR-125b
Two of them have the same TF Zic3
etc.
The coordinated regulation of target genes by miRNAs and TFs is more complicated
Illustration of the three miR-TF modules (66, 67, 68)
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13. Evaluation and findings (3/4)
It is hard to directly validate the miR-TF modules
Using GO to validate modules with respect to biological processes
The set of regulated genes and host genes of TFs in modules related to some specific GO terms
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14. Evaluation and findings (4/4)
Found that some miRNAs and TFs in miR-TF modules have associations with cancer developments
Several miRNAs related to lung and other human cancer
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15. Conclusion
Proposed a comprehensive search method for discovering functional miR-TF modules
Detected miR-TF modules
Involved in specific biological processes (validation with GO)
Contain miRNAs and TFs related to several types of cancer diseases
Moreover, provided a view of combinatorial regulation of TFs and miRNAs
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16. Future work
Algorithm does not consider the active behavioral characteristics of TFs
Integrate some datasets of gene expression may help us to discover more coherent functional miR-TF modules
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17. THANK YOU !
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