hi, neo…! 

Bioinformatics and Genomic Medicine

Ju Han Kim, M.D., Ph.D., M.S. SNUBiomedical Informatics Agent Smith: Good bye, Mr. Anderson! Seoul Nat’t Univ. School of Medicine http://www.snubi.org/ Neo : My name IS Neo!

Bioinformatics & Genomic Medicine Bioinformatics & Genomic Medicine

• Who are the drivers • Who are the drivers • Problems in Genomics are the classical • Problems in Genomics are the classical problems of Informatics problems of Informatics • Biochip & Functional Genomics • Biochip & Functional Genomics • My contribution • My contribution • Integrative Biochip Informatics • Integrative Biochip Informatics • Emergence of New Medicine • Emergence of New Medicine

Who are the drivers? Paradigm Shift Who are the drivers? - Clinical Knowledge Management - • Sequences • Linkage map Clinician-directed Resource-directed • Physical map • Polys/gene (1-2/kb?)

• Expression profiles Dr. Elson • Structural info. Dr. Faughnan • How would you begin to estimate?

Dr. Abraham • Clinical data explosion Dr. Abraham Dr. Dandy • Genomic data explosion • Databases, algorithms, and HPC Informatics • The Internet Clinical Information Dr. Belsky Dr. connelly

1 The Central Dogma of Life Building blocks of life are digital!

• DNA Î 4-digit {A,G,T,C} string • RNA Î #of mRNA as the activity of the gene DNA • Protein Î 20-digit {amino acids} string functional machinery 3-D structure matters RNA • Metabolites • Cells, Tissues, Organ-Systems Protein • Individual or Population

Interactions Networks System

Problems in genomics are Bioinformatics & Genomic Medicine the classical problems in informatics

• Who are the drivers • Sequence alignment / homology / polys: • Problems in Genomics are the classical String search (ex. BLAST/FASTA, HMM) problems of Informatics • Combinatorial data explosion: • Biochip & Functional Genomics Optimization (ex. TSP), MCMC • My contribution • Predictive model building & function • Integrative Biochip Informatics Supervised machine learning • Emergence of New Medicine • High throughputs Exploratory data analysis, clustering

Clinical relevance of Biochip informatics Bioinformatics & Genomic Medicine Dx. Knowledge discovery • Who are the drivers • Problems in Genomics are the classical problems of Informatics • Clinical Relevance of Biochip Informatics Tx. • My contribution Px. • Integrative Biochip Informatics • Emergence of New Medicine

2 Biochip basics A Functional Genomics Strategy InterestingInteresting InterestingInteresting InterestingInteresting MakeMake PatientsPatients AnimalsAnimals CellCell Lines Lines BiochipBiochip

AppropriateAppropriate AppropriateAppropriate HybridizeHybridize ExtractExtract RNA RNA TissueTissue ConditionsConditions BiochipBiochip Bioinformatics pipeline AccessAccess FunctionalFunctional DataData Pre- Pre- ScanScan SignificanceSignificance ClusteringClustering processingprocessing BiochipBiochip

Post-clusterPost-cluster AnalysisAnalysis & & Biological Informatical Biological IntegrationIntegration Informatical ValidationValidation Validation?Validation? ??

Astronomer’s Learning

Babylonians created the map of starry sky. Organizing complex data and Astronomy started then… into a meaningful structure!

Biochip informatics: clustering Biochip informatics: clustering

A11 A12 A13 A14 A15 A16 A21 A22 A23 A24 A25 A26 A31 A32 A33 A34 A35 A36 A41 A42 A43 A44 A45 A46 A51 A52 A53 A54 A55 A56 A61 A62 A63 A64 A65 A66 A71 A72 A73 A74 A75 A76 A81 A82 A83 A84 A85 A86 A91 A92 A93 A94 A95 A96 clustering time

3 Hierarchical clustering in Genomics Hierarchical & Partitional Clustering

• single-linkage (nearest neighbor) • complete-linkage (farthest neighbor) • weighed pair-group average • unweighed pair-group average • weighted pair-group centroid • unweighted pair-group centroid Hierarchical Partitional • Ward’s method: min. sum of squares

K-means Algorithm (K=2) K-means Algorithm (K=2)

K-means Algorithm (K=2) K-means Algorithm (K=2)

4 K-means Algorithm (K=2) K-means Algorithm (K=2)

K-means Algorithm (K=2) K-means Algorithm (K=2)

K-means Algorithm (K=2) Bioinformatics & Genomic Medicine

• Who are the drivers • Problems in Genomics are the classical problems of Informatics • Biochip & Functional Genomics • My contribution • Integrative Biochip Informatics • Emergence of New Medicine Convergence!!

5 Xperanto: Expressionist’s Esperanto in XML

MAGE-ML (MicroArray Gene Expression) They must TALK!

BioCANDI: integrative analsysis

GRIP: Genome Research Informatics Pipeline GRIP: gene / protein information

6 ChromoViz ArrayXPath

Integrated biochip informatics Bioinformatics & Genomic Medicine

Clinical Public & Private • Who are the drivers Information Databases Miniaturization & streamlining • Problems in Genomics are the classical System Miniaturization & streamlining Image analysis Cluster analysis Pathway/network problems of Informatics Array fabrication Data mining analysis • Biochip & Functional Genomics Data management layer

• My contribution Systematic perturbation clone data Cell data Exp. data Inhouse data Slide data Hyb. data Outside data • Integrative Biochip Informatics Both Observational scan data and Experimental • Emergence of New Medicine Communication Standards & Ontology

IBM/Mayo Clinic Collaboration Applied Genomics Data Analysis

Genomic data (DNA) – GeneChip array data (RNA) Protein data

Databases Clinical Data Genome Signs Proteome Symptoms Disease Laboratory Tumors Radiology Drugs Etc. Phase I Optimized, individualized healthcare

7 http://cardiogenomics.med.harvard.edu http://cardiogenomics.med.harvard.edu

Bioinformatics & Genomic Medicine

• Who are the drivers • Problems in Genomics are the classical Emergence of New Medicine problems of Informatics • Biochip & Functional Genomics • My contribution • Integrative Biochip Informatics • Emergence of New Medicine

My view of ‘informatics’ revolution My view of the ‘Omic’ revolution My view of ‘informatics’ revolutiond ere ed pow inform -em ularly- ion Health Science Informatics ally n Molec tegrat atic atio ntal in orm egr orizo Clinical Informatics Inf l int H tica Gene Genome ver Chemoinformatics phramacogenomics mRNA Transcriptome drug design Protein Proteome Structural Informatics digital anatomy neuroinformatics Metabolite Metabolome Computational Physiology cardiovascular sim. Physiological dyn Physiome E-cell Computational Cell Biology in silico biology Biological org. Biome structural biology Biomolecular Informatics functional genomics

8 Emergence of New Medicine Weaving the revolutions!

The new medicine will be both Molecularly-informed & ThankThank you!you! Informatically-impowered Biomedical Informatics & Genomic Medicine http://www.snubi.org/

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