1. Disease Diagnosis by DNAC MEC seminar 25 May 04

2. DNA chip Blood Biopsy Sample rRNA/mRNA/ tRNA RNA RNA with cDNA Hybridization Mixture of cell-lines Reference Labeled cDNAs cDNA Bioinformatics tools Processing scanning Analyze Training samples Data

3. DNAC chip Blood Biopsy Sample rRNA mRNA tRNA RNA RNA with cDNA Hybridization Offline calculation Analyze Weight information Probe Training samples Data

4. Bioinformatics part Relationship between gene (mRNA) and disease Relationship between gene (mRNA) and disease Decision algorithm Decision algorithm Weighted sum (weight: t-value) Weighted sum (weight: t-value) T-test ? T-test ? The t-test assesses whether the means of two groups are statistically different from each other. The t-test assesses whether the means of two groups are statistically different from each other. This analysis is appropriate whenever you want to compare the means of two groups. This analysis is appropriate whenever you want to compare the means of two groups.

5. How t-values are determined? Gene selection using cDNA microarray data Gene selection using cDNA microarray data Log 2 (P atient /R eference ) Log 2 (P atient /R eference ) M****, A*****, C***** M****, A*****, C***** t-value calculation from clinical data t-value calculation from clinical data T-value determination T-value determination Remove logarithm Remove logarithm

6. How implement the weighted sum? x: expressed amount (concentration of transcribed mRNA) x: expressed amount (concentration of transcribed mRNA) t: weight factor (acquired from training data) t: weight factor (acquired from training data) Basic idea Basic idea Probe Probe Partial sequence of mRNA (complementary sequence) Partial sequence of mRNA (complementary sequence) Show fluorescence intensity proportional to weight Show fluorescence intensity proportional to weight

7. Three different methods mRNA

8. How make decision? From reference Experimental procedures are not same and there are many factors of variation Experimental procedures are not same and there are many factors of variation  direct comparison is impossible! From sample

9. Solution..?! Quantification of three genes in reference 1 Conversion of microarray data of two cell-lines 2 Plot of expression amount data on 3D space 3 Measurement of vertical distance to hyper-plane 4 Hybridization experiment of each gene 5 Measurement of fluorescence signal and calculation of the output 6 Determination of experimental threshold 7

10. x1x1 x2x2 x3x3 (+) (-) d1d1 d2d2 Cell-line1 Cell-line2 Calculated data (obtained from microarray data) (+) (-) d1’d1’ d2’d2’ Cell-line1 Cell-line2 Experimental Data (hybridization/detection)

11. Way to go… Probe construction Probe construction Length Length Fluorophore amount Fluorophore amount Quantification Quantification Template preparation (done) Template preparation (done) Just do it! Just do it! FRET probe design FRET probe design 1 st method probe re-design 1 st method probe re-design Consideration of mRNA secondary structure Consideration of mRNA secondary structure

12. Why RNA? Cell DNA mRNA Protein transcription translation Genetic Aging DiseaseDrug Environment Structural Genomics Functional Genomics Proteomics

13. Solution..?! Quantification of three genes in reference mRNA Theoretical hyperplane Distance between hyperplane and cell-line data Hybridization experiment for two cell-lines Absolute expression level of sample data Determine Experimental Threshold Fluorescence Signal, Output Theoretical Data Experimental Data