1. Modeling of Spliceosome
김동민
이경준
임종윤

2. Gene Finding Transcription: multi-step process
Long sequence in one Que (X)
Several steps like many enzymes (O)
promoter, 3’-processing, splice site, coding exon

3. Splicing Site
GT-AG : 99.24%, GC-AG : 0.69%, AT-AC : 0.05% (Burset et al., 2000)
Site recognition (Chiara et al., 1996)
25-base upstream of GT splice
GT, AG splice site
branchpoint sequence

4. Problem Discription
GT 또는 AG sequence site를 중심으로 특정 window size의 binary incoding된 sequence를 입력 받아 이 사이트가 exon-intron splicing site인지를 판별
Modeling of spliceosome

5. Training Data UCSC data GT, AG 앞 뒤 40 염기 Correct False Doner 1149 3813
Acceptor

6. Neural Network

7. Parameter Values input node : 328 hidden node : 70 output node : 1
learning rate : 10
slope parameter: 0.02
(activation function은 sigmoid 사용)

8. Prediction Ratio
Doner :96.33%
Acceptor site : 95.25%

9. HMM architecture S E di ii mi

10. HMM architecture(2) The number of states
The number of distinct observation symbols per state
The state transition probability distribution
The observation symbol probability distribution in state
The initial state distribution