1 / 10

Modeling of Spliceosome

Modeling of Spliceosome. 김동민 이경준 임종윤. 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. Splicing Site. GT-AG : 99.24%, GC-AG : 0.69%, AT-AC : 0.05% (Burset et al. , 2000)

siobhan-crofton
Download Presentation

Modeling of Spliceosome

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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 또는AG sequence site를중심으로특정window size의binary incoding된sequence를입력받아이사이트가exon-intron splicing site인지를판별 • Modeling of spliceosome

  5. Training Data • UCSC data • GT, AG 앞 뒤 40 염기 Correct False Doner 1149 3813 Acceptor 1143 6021

  6. Neural Network

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

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

  9. di S mi E ii HMM architecture

  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

More Related