精品PPT课件----Using works to Analyze Expression Data.ppt

A Probabilistic Learning Approach to Whole Genome Operon Prediction
Mark Craven, David Page, Jude Shavlik
Joseph Bockhorst, Jeremy Glasner
B' 00 
Talker: Jinsan Yang
Center for Bioinformation Technology (CBIT)
Abstract
Present putational approach to predict operons in the genomes of anisms.
Machine learning methods to induce predictive models from sequence data, gene expression data, functional annotations of genes.
Use of multiple models to predict promoters, terminators, operons.
Use of dynamic programming method to map every known and putative genes to the most probable operon.
Data analysed: E. Coli K-12 genome.
2
Center for Bioinformation Technology (CBIT)
Introduction
Approach: combining following two steps to predict an operon map for an entire genome.
First step: model to estimate the probability that an arbitrary sequence of genes constitute an operon.
Second step: dynamic programming method to assign every gene in the given genome to its most probable operon.
Multilevel-learning approach
3
Center for Bioinformation Technology (CBIT)
Problem Domain (1)
Primary task: predict operons in the E. coli genome.
E. coli genome: sequenced at the U. of Wisconsin (Blattner et al. 1997), consists of a single circular chromosome of double-stranded DNA, 4,639,221 base pairs, 4,400 genes.
Operon: a sequence of one or more genes that are transcribed as a unit.
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Center for Bioinformation Technology (CBIT)
Problem Domain (2)
Available data:
Complete DNA sequence of the gene
Beginning and ending positions of 3,033 genes and 1,372 putative genes.
Positions and sequences of 438 known promoters, 289 terminators.
Functional annotation codes characterizing 1,668 genes. (3 level, 123-leaf hierarchy)
Gene expression data for the activity levels of 4,097 genes /putative genes for 39 experiments.
365 known operons
It is estimated that there are several hundred undiscovered operons in E. coli.
Generation of negative examples (non-operons) by the fact that most oper