Stats254_Ch3_Sequence_1

# Stats254_Ch3_Sequenc - Stats M254 Statistical Methods in Computational Biology Chapter 3 Statistical Sequence Analysis 1 Outline of this chapter

This preview shows pages 1–4. Sign up to view the full content.

1 1 Chapter 3 Statistical Sequence Analysis Stats M254 Statistical Methods in Computational Biology 2 Outline of this chapter 3.1 Motif discovery PWM and Generalizations, Biophysical motif model, Gibbs motif sampler, EM, sequence segmentation; 3.2 Cis-regulatory modules Hierarchical mixture modeling, HMM for modules; 3.3 Sequence alignment Pairwise alignment, HMM for multiple alignment; 3.4 Motif finding in multiple species Motif model on phylogentic tree, phylogenetic motif finding, coupled HMM;

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
2 3 3.1 Motif discovery The problem of motif finding: 4 1. Motif model 1) Position-specific Weight Matrix (PWM) Motif (PWM) Estimate Modeling
3 5 • Estimation of PWM given binding sites: a) Calculate the count matrix: b) Two ways to estimate: Maximum likelihood estimate (MLE); Bayesian estimate (posterior mean). • Predict binding sites given PWM: Posterior odds calculation: Motif model versus background model. • Compared with

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

## This note was uploaded on 11/24/2010 for the course STAT 201a taught by Professor Wu during the Spring '10 term at Pasadena City College.

### Page1 / 6

Stats254_Ch3_Sequenc - Stats M254 Statistical Methods in Computational Biology Chapter 3 Statistical Sequence Analysis 1 Outline of this chapter

This preview shows document pages 1 - 4. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online