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Transcript
Regulatory element detection using
correlation with expression (REDUCE)
Literature search
WANG Chao
Sept 14, 2004
Harmen J. Bussemaker, Hao Li & Eric D. Siggia,
Regulatory Element Detection Using Correlation
with Expression, Nature Genetics vol 27, 167
(2001)
Some points
• A new method for discovering cis-regulatory
elements
• A new method for discovering cis-regulatory
elements
• A single genome-wide set of expression ratios,
The upstream sequence for each gene,
Outputs statistically significant motifs.
Extract biologically meaningful information
●
• One method for discovering them groups genes
into disjoint clusters based on similarity in
expression profile over a large number of
different conditions.
• The new method selects the most statisticaly
significant motifs from the set of all oligomers up
to a sepecified length, dimers(two oligomers with
a fixed spacing) and groups thereof based on
sequence alignment.
Result
four aspects of this algorithm:
• the iterative selection of motifs to optimize their
independence
• the construction of weight matrices from groups
of significant dimers
• following the fitting parameters as a function of
time to infer function
• analyzing the modulation of a consensus motif
by variable positions and flanking bases
Definitions and Method
Finding motifs relevant to cell cycle
Time courses for cell cycle and sporulation motifs
Modulation of the MSE motif in sporulation
Some points to discussion
• reduces experimental noise and is well suited for
uncovering groups of genes
• a quantitative expression of the widespread notion18
that transcription initiation occurs through the recruitment
of the polymerase by reversible binding to transcription
factors and hence to the regulatory sequences
• reconfirm almost all motifs found by clustering methods,
at least to the extent of finding a related sequence motif
that captures the same experimental signal
• the importance of simultaneously fitting as many genes
as possible
The end