Learning PWN from Sequence and Expression Data

CREATED: 200712310832 Speaker: Chen Xin

** Motif discovery from expression data

micro array data -> clustering -> motif discovery

micro array data -> clustering <-> motif discovery

micro array data <-> motif discovery (better explain the data)

** PWM

• $\Theta = (\theta_1, \theta_2, \ldots , \theta_j)$, assume positions are independent
• $\theta_j = (\theta_{a,j}, \theta_{c,j}, \theta_{g,j}, \theta_{t,j})^T$
• $\theta_j$ is product of Dirichlet distributions
• $\pi_j(\theta_j)$ is Dir(1,1,1,1)
• $\Theta_{\max}$ = arg max $\pi(R | \Theta)$ where R is the set of binding sites
• Expression E = $(E_1, \ldots , E_n)$ where $E_i$ is associated with $R_i$
• observable effect of binding
• find $\Theta$ which maximizes $\pi(R, E | \Theta)$
• assumption $\log E_i \propto \pi(R_i|\Theta)$
• find $\Theta$ such that R fits expression log E best by linear correlation, solve using EM-like methods (slow)

** Contributions

• W-AlignACE, based on Gibbs sampling
• http://www1.spms.ntu.edu.sg/~chenxin/W-AlignACE/

** Validation

• look at IUPAC ambiguity codes
• core ~ most conserved positions in concensus pattern (Gasch et al 2000)
• ChIP-ChIP (Lee et al 2002)
• relation between ChIP-ChIP score and #binding sites in various promoter regions