Modeling gene expression using five histone modifications
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1 Modeling gene expression using five histone modifications Fifth Annual Primes MIT Conference Lalita Devadas Mentor: Angela Yen May 17, 2015 Lalita Devadas Modeling gene expression using five histone modifications 1/31
2 Outline 1 Biological Background 2 Method 3 Results 4 Moving Forward Lalita Devadas Modeling gene expression using five histone modifications 2/31
3 Outline 1 Biological Background 2 Method 3 Results 4 Moving Forward Lalita Devadas Modeling gene expression using five histone modifications 3/31
4 Gene Expression Central dogma of molecular biology Lalita Devadas Modeling gene expression using five histone modifications 4/31
5 Gene Expression Relevance Important to understanding biological activity Crucial to advances in medicine Detection, prevention, and treatment of disease Lalita Devadas Modeling gene expression using five histone modifications 5/31
6 Gene Expression Regulation Genetic Sequences of nucleotides (ACTG) Lalita Devadas Modeling gene expression using five histone modifications 6/31
7 Gene Expression Regulation Epigenetic Changes to environment surrounding DNA Lalita Devadas Modeling gene expression using five histone modifications 7/31
8 Epigenetics Histone modifications Chemical changes to histone protein core or protruding tail Lalita Devadas Modeling gene expression using five histone modifications 8/31
9 Epigenomes Roadmap Project Lalita Devadas Modeling gene expression using five histone modifications 9/31
10 Outline 1 Biological Background 2 Method 3 Results 4 Moving Forward Lalita Devadas Modeling gene expression using five histone modifications 10/31
11 Data Pipeline Objective Density of Histone Modifications Prediction Level of Gene Expression Lalita Devadas Modeling gene expression using five histone modifications 11/31
12 Data Pipeline Overview All Histone Data Best-binning Relevant Histone Data Classification Off genes (Unexpressed) Predict at 0 Classification On genes (Expressed) Regression Predicted Expression Lalita Devadas Modeling gene expression using five histone modifications 12/31
13 Data Pipeline Best-bin approach All Histone Data Best-binning Relevant Histone Data Classification Off genes (Unexpressed) Predict at 0 Classification On genes (Expressed) Regression Predicted Expression Lalita Devadas Modeling gene expression using five histone modifications 13/31
14 Best-bin approach Dividing genes Lalita Devadas Modeling gene expression using five histone modifications 14/31
15 Best-bin approach Choosing best bin epigenome X, histone mark Y gene a bin 1 bin 2 bin 3 bin 4.. bin p... bin 81 expression gene b gene c..... p = best bin strongest correlation Lalita Devadas Modeling gene expression using five histone modifications 15/31
16 Data Pipeline Classification All Histone Data Best-binning Relevant Histone Data Classification Off genes (Unexpressed) Predict at 0 Classification On genes (Expressed) Regression Predicted Expression Lalita Devadas Modeling gene expression using five histone modifications 16/31
17 Types of Models Random Forest Random Forest model Returns majority vote of classification determined by a group of decision trees Lalita Devadas Modeling gene expression using five histone modifications 17/31
18 Types of Models Random Forest Random Forest model Returns majority vote of classification determined by a group of decision trees Lalita Devadas Modeling gene expression using five histone modifications 18/31
19 Data Pipeline Regression All Histone Data Best-binning Relevant Histone Data Classification Off genes (Unexpressed) Predict at 0 Classification On genes (Expressed) Regression Predicted Expression Lalita Devadas Modeling gene expression using five histone modifications 19/31
20 Types of Models Linear Model Linear model Finds a linear correlation between predictors and response Lalita Devadas Modeling gene expression using five histone modifications 20/31
21 Data Pipeline Overview All Histone Data Best-binning Relevant Histone Data Classification Off genes (Unexpressed) Predict at 0 Classification On genes (Expressed) Regression Predicted Expression Lalita Devadas Modeling gene expression using five histone modifications 21/31
22 Outline 1 Biological Background 2 Method 3 Results 4 Moving Forward Lalita Devadas Modeling gene expression using five histone modifications 22/31
23 Epigenomes Roadmap Project Lalita Devadas Modeling gene expression using five histone modifications 23/31
24 Data Pipeline Objective Density of Histone Modifications Prediction Level of Gene Expression H3K9me3 H3K4me3 H3K4me1 H3K36me3 H3K27me3 Lalita Devadas Modeling gene expression using five histone modifications 24/31
25 Results of Pipeline Conclusions Models created for cultured epigenomes have a much higher predictive power than those created for tissue samples H3K36me3 is the most important histone mark used for prediction Lalita Devadas Modeling gene expression using five histone modifications 25/31
26 Results of Pipeline Graph Lalita Devadas Modeling gene expression using five histone modifications 26/31
27 Specifics of Best Model Classification Accuracy Lalita Devadas Modeling gene expression using five histone modifications 27/31
28 Specifics of Best Model Regression Accuracy every data point represents one gene r 2 value: Lalita Devadas Modeling gene expression using five histone modifications 28/31
29 Outline 1 Biological Background 2 Method 3 Results 4 Moving Forward Lalita Devadas Modeling gene expression using five histone modifications 29/31
30 Next Steps Improve predictive power Broaden scope of predictors and response Further analysis of current results Apply procedure to different data Release code as a tool for other researchers Lalita Devadas Modeling gene expression using five histone modifications 30/31
31 Acknowledgements I would like to thank: My mentor, Angela Yen Prof. Manolis Kellis Roadmap Project PRIMES program My family Lalita Devadas Modeling gene expression using five histone modifications 31/31
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