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  • 1. Projects Wenbo Mu M.S, Bioinformatics M.S, Statistics B.S, Compute Sciences
  • 2. •Analysis of Gene Expression •Analysis of GeneticVariants •Analysis of DNA Methylation •Development of Data Mining Methods
  • 3. Analysis Of Gene Expression • Identified uniquely dys-regulated miRNAs in two types of colorectal cancer (MSI/MSS). • Discovered fenfluramine-induced gene dys-regulation pattern in human pulmonary artery smooth muscle and endothelial cells. • Explored distinct temporal involvement of microRNAs and pathways in lipopolysaccharide-induced acute lung injury in mice
  • 4. Analysis Of Gene Expression ---Quality Control Box plot Clustering PCA S2 S5 U3 U5 U6 C3 C6 T10 T2 T5 T8 U9 U12 24681012 Boxplot of Intensity C3 C5 C4 C1 C6 C2 C7 U8 U10 U3 U6 T6 S5 U1 T11 S6 T2 T4 U11 T12 T1 T8 U5 S4 U2 S2 S1 S3 S7 T3 T7 T10 T9 U9 U12 U4 T5 T13 U13 0.000.050.100.150.200.250.300.35 Sample Clustering (ward)Height ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● −60 −40 −20 0 20 40 60 −150−100−500 Dimension 1 Dimension2 MSS MSI Healthy
  • 5. Analysis Of Gene Expression ---Analysis of mRNA Expression A B 0 500 1000 1500 2000 2500 3000 0 500 1000 1500 2000 PASMC PAEC Total changed All upregulated Upregulated (>2) All downregulated Downregulated (>2) NumberofGenesNumberofGenes >2 fold 1.5-2 fold <1.5 fold PASMC↑ PAEC↑ PASMC↑ PAEC↓ PASMC↓ PAEC↑ PASMC↓ PAEC↓ PAEC PASMC2 PASMC2 PASM32 C 0 2 4 6 8 10 12 FoldChange1.5 Cytokine-cytokinereceptorinteraction Pathwaysincancer Focaladhesion MAKPsignalingtransduction Calciumsignalingpathway Axonguidance Regulationofactincytoskeleton Celladhesionmolecules(CAMs) ECM-receptorinteraction TGF-βsignalingpathway Vascularsmoothmusclecontraction Wntsignalingpathway Neuroactiveligand-receptorinteraction Cellsurfacereceptorlinkedsignaltransduction Chemokinesignalingpathway PASMC enriched GO Terms and Pathways Yao W, Mu W*, Zeifman A, Lofti M, Remillard CV, Makino A, Garcia JGN, Yuan JX, Zhang W. Fenfluramine-induced gene dysregulation in normal human pulmonary artery smooth muscle and endothelial cells. Pulmonary Circulation. 2011; 1(3): 405-418. PMID: 22140631 GO term and Pathway AnalysisOverview of Differential Genes
  • 6. Analysis Of Gene Expression ---Functional Analysis Network Analysis Yao W, Mu W*, Zeifman A, Lofti M, Remillard CV, Makino A, Garcia JGN, Yuan JX, Zhang W. Fenfluramine-induced gene dysregulation in normal human pulmonary artery smooth muscle and endothelial cells. Pulmonary Circulation. 2011; 1(3): 405-418. PMID: 22140631 STK35 BMPR1B ZFYVE16 TLL2 AMHR2 GDF10 Cytoscape
  • 7. Analysis Of Gene Expression ---Integrative Analysis of mRNA and microRNA Integrated analysis to identify biologically-relevant miRNA/mRNA Differential expression in CRC vs. normal 20#MSS# CRCs# 12#MSI# CRCs# 7#normal# mucosa# miRNA/mRNA expression profiling GeneChip® Human Gene 1.0 ST (29k transcripts) TaqMan® Human MicroRNA ‘A’ (377 microRNAs) Samples Identification of miRNAs and mRNAs differentially expressed between CRC and normal mucosa Prediction of mRNA targets according to miRNA expression Identification of MSI-specific, MSS- specific, and pairs common to both phenotypes (Table'1) Selection of 10 highest ranked pairs (Figure'1): •  miRNA absolute fold change > 2.0 •  target mRNA greatest absolute fold change Identification of inversely correlated mRNA/miRNA pairs within each CRC phenotype (MSI and MSS) 41# 0# 1# 2# 3# 4# Expression* Tumor# Normal# miRNA#*# *# 42# 41# 0# 1# 2#Expression* *# *# mRNA#*# Xicola R, Mu W, Rawson J.B, Huang L, Sapoznik V.R, Doyle B.J, Jover R, Carracedo A, Andreu M, Bessa X, Castells A, Boland C.R, Goel A, Investigators E, Dai Y, Llor X. Identification of miRNAs and their gene targets differentially expressed in microsatellite stable and unstable colorectal cancers through an integrated analysis. Digestive Disease Week. 2011
  • 8. Analysis Of GeneticVariants • Preprocess Raw Data and Quality Control • GWAS • eQTL Identification • Meta-analysis • Admixture Mapping • Genotype Imputation
  • 9. Analysis Of GeneticVariants ---Quality Control
  • 10. Analysis Of GeneticVariants ---GWAS
  • 11. Analysis Of GeneticVariants ---Imputation
  • 12. Analysis Of GeneticVariants ---Admixture Mapping −5051015 1 2 3 4 5 6 7 8 9 11 12 14 16 18 21 − log10(P) Score
  • 13. Analysis Of DNA Methylation • DNA methylation differentiation • mQTL Identification
  • 14. Analysis Of DNA Methylation ---Quality Control 60#CEU#and#73#YRI#samples# 485,578&CpG#sites# Illumina#450K#methyla=on#array# 1.#Remove#probes#whose#call#rate#<#0.95# 2.#Remove#probes#that#map#ambiguously#to#the#genome# (~140,000)# 3.#Remove#probes#that#contain#common#SNPs# [MAF>0.01]#(~55,000)# 4.#Remove#probes#on#sex#chromosomes#(~7,000)# 5.#Control#for#batch#effect#using#COMBAT# 283,540&highly#informa=ve##CpG#sites# Extract#CpG#sites#by#gene#symbol#annota=on# Iden=fy#differen=al#methylated#probes#between#YRI#and# CEU#samples# using#Wilcoxon#Rank_sum#test# Evaluate#significance#of#methyla=on#enrichment#for#VIPs# using#fisher#exact#test## 135(24)&differen=ally#methylated#CpG#sites# 402&CpG#sites#within#43#VIPs#
  • 15. Analysis Of DNA Methylation ---mQTL Identification
  • 16. Analysis Of DNA Methylation ---mQTL Identification
  • 17. Sample Codes ---eQTL Identification
  • 18. Development Of Data Mining Method
  • 19. Development Of Data Mining Method mRNA expression miRNA expression miRNA target prediction Correlation coefficient matrices TF binding similarity score matrix miRNA binding score matrix Score matrix Module Identification algorithm Transcription factor binding profile Module miRNA set TF set Gene set Mu W, Roqueiro D, Yang D. A Local Genetic Algorithm for the Identification of Condition-Specific microRNA-Gene Modules. Special Issue on Computational Systems Biology
  • 20. Development Of Data Mining Method Histogram of Permutated Modules p value: 0.0034 Frequency 0.55 0.60 0.65 0.70 0.75 0.80 0.85 02004006008001000 ●
  • 21. Development Of Data Mining Method
  • 22. SKILLS

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