Table 1 Definition of terms used in describing the MEME algorithm
From: The value of position-specific priors in motif discovery using MEME
n | number of input sequences |
|---|---|
L | length of input sequences |
X = {X1, ..., X n } | the set of n input sequences |
w | width of a MEME motif |
m = L - w + 1 | number of positions for a site |
γ | probability of a site in any sequence |
θ | PSPM model of motif; |
P = {Pi,j} | position-specific prior (PSP) |
w 0 | width for which input PSP is defined |
Z = {Zi,j} | missing information variables for i ∈ [1, n],j ∈ [-L, L] |
Z (t) | expectation of Z at EM iteration t |
| prior probability given PSP & model |
ϕ (t) | model parameters at EM iteration t |
ϕ = {θ, γ, P} | all sequence model parameters |
= Pr(Zi,j= 1|ϕ(t))