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Reconstruction of Gene Regulatory Networks from RNA-Seq Data Jianlin Jack Cheng Computer Science Department University of Missouri, Columbia ACM-BCB, 2014.

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Presentation on theme: "Reconstruction of Gene Regulatory Networks from RNA-Seq Data Jianlin Jack Cheng Computer Science Department University of Missouri, Columbia ACM-BCB, 2014."— Presentation transcript:

1 Reconstruction of Gene Regulatory Networks from RNA-Seq Data Jianlin Jack Cheng Computer Science Department University of Missouri, Columbia ACM-BCB, 2014

2 Big Data Challenge in Genomic Era DNA/RNA Sequencing Mass Spectrometry Biological Experiments Biological Experiments Genomics Transcriptomics Proteomics Metabolomics … Genomics Transcriptomics Proteomics Metabolomics … Biological System Analysis Knowledge Omics Data

3 Expression Profiles of Genes under Multiple Conditions / Time Points Con 1Con 2Con 3Con 4Con 5Con 6Con 7Con 8…. Gene 11030403520100560… Gene 2 Gene 3 Gene 4 ….

4 Gene Regulatory Networks (GRN) Bar-Joseph et al., 2003 GRN of yeast in rich medium Transcription factor (TF) regulates a gene TF1 TF2 TF3 Gene regulatory module

5 Bayesian Probabilistic Modeling Assign genes into co-regulated modules Construct regulatory relations of each module PosteriorLikelihood Prior

6 Gene Regulatory Network Modeling Zhu et al., 2013 Join

7 Gene Regulatory Logic of a Gene Module as a Decision Tree One Gene Module gene 1 gene 2 gene 3 …. gene n Biological Conditions (Treatments) in Columns Transcription factors and binary regulatory tree Low Expression High Expression

8 Regulatory Tree Construction Zhu et al., 2013 g1g2.gi..gng1g2.gi..gn μ 1, σ 1 μ 2, σ 2 Gaussian Mixture Pick a TF Divide conditions into two subsets based expression states Calculate probability

9 Regulatory Tree Construction Zhu et al., 2013 g1g2.gi..gng1g2.gi..gn Gaussian Mixture Repeat at next level

10 Regulatory Tree Construction Pick a TF Divide conditions based on TF states Calculate likelihood Select TF maximizing likelihood Repeat Zhu et al., 2013 g1 g2. gi. gn Gaussian Mixture Algorithm

11 Gene Re-Assignment μ1σ1μ1σ1 μ2σ2μ2σ2......... 0.30.21.5........ gigi Regulatory Tree of a Module

12 RNA-Seq Data of Soybean Nodulation An important source of protein and oil Nitrogen fixation enabled by soybean-rhizobia symbiotic interactions Nodule

13 Gene Regulatory Modules of Differentially Expressed Genes A TF functioning in nodulation according to literature. NSP, whose homologous protein is a nodulation signaling in rice. One out of 10 modules Zhu et al., 2013

14 Application to Other Species Arabidopsis Drosophila Mouse Human … Soybean proteins affect TWIST2 – a novel protein related to Kidney disease? Helix-loop-helix transcription factor 2

15 Acknowledgements Students Deb Bhattacharya Renzhi Cao Jie Hou Jilong Li Matt Spencer Trieu Tuan Mingzhu Zhu Collaborators Jim Birchler, Bill Folk, Kevin Fritsche, Michael Greenlief, Zezong Gu, Mark Hannink, Trupti Joshi, Dennis Lubahn, Valeri Mossine, Alan Parrish, Frank Schmidt, Gary Stacey, Grace Sun, John Walker, Dong Xu

16 Binding Site Analysis MEME + TomTom to identify two binding sites: BetabetaAlphazinc, finger and Leucine Zipper TFs in GRAS family contain proteins binding to the motifs.

17 Function Enrichment Validation Function predicted by MULTICOM-PDCN P-value calculated by hypergeometric distribution. Some functions are related to formation of nodule organ. Zhu et al., 2013

18 I: TF-TF interactions by STRING, L: Literature Function Support Protein Interaction and Literature Validation Zhu et al., 2013

19 Computational Model Evaluation

20 GRN of Human Prostate Cancer Under Botanical Treatments Lu et al., submitted

21 Li et al., submitted.


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