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\centerline{\bf STANFORD UNIVERSITY}
\centerline{\bf DEPT OF STATISTICS}
\centerline{\big DEPARTMENTAL SEMINAR}
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\centerline{4:15 p.m., Tuesday, October 9, 2001}
\centerline{Sequoia Hall Rm. 200}
\centerline{(Cookies at 3:45 in 1st Floor Lounge)}

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\centerline{\sl Xiaole Liu}
\centerline{\sl Stanford Medical Informatics}
\centerline{\sl Stanford University}
\centerline{\sl Stanford,  CA 94305}
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\centerline{\bf Computational statistical algorithms for finding transcription factor binding sites}

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        The rapid development of sequencing technology enabled human genome and
many other genomes to be sequenced and publicly available. The microarray
technology has also become considerably more robust and sensitive. The
combination of the two allows biologists to study gene expression and
transcription regulation at a genome level. Given a set of upstream DNA
sequences whose downstream genes are clustered together base on similarity in
gene expression profile, or a set of DNA sequences enriched in chromatin
immunoprecipitation followed by microarray experiments (ChIP-array), it is
desirable to conduct computational analysis to find common sequence motifs that
are the potential transcription factor binding sites regulating transcription. 
        I will review the established algorithms for discovering common DNA
motifs in a set of sequences, and propose two computational statistics
approaches, BioProspector and MDscan. 
BioProspector searches for common sequence motifs from any general cluster of
DNA sequences, especially potential transcription factor binding sites from
upstream sequences of genes clustered by expression profile similarity.
BioProspector adopts a Gibbs sampling motif discovery strategy, but provides
many improvements. Motifs can have one-block, two-block, or two-block
palindromic patterns. BioProspector allows variable copies of a motif per
sequence, and uses background model with Markov dependency to improve the
specificity of motifs. The statistical significance of a discovered motif can
be calculated by Monte Carlo simulation. Preliminary results for testing each
BioProspector feature have been very encouraging. A BioProspector web site is
setup for biologists to load their sequences on the server for motif discovery.
Another program, MatrixScan, is developed to search the genome for more
potential sites using a discovered motif matrix. 
MDscan is a fast and novel algorithm that looks for motifs from a set of
sequences when one has confidence that a subgroup of the sequences contains the
motif more abundantly. It can be used to find protein-DNA interaction sites
from sequences selected by ChIP-array experiments because the sequences highly
enriched by ChIP-array are very likely to contain the real protein-DNA
interaction sites, and with multiple copies per sequence. The comparison of
MDscan with several other motif-finding programs shows the advantage of MDscan
in both speed and accuracy. It also succeeds in identifying the correct motifs
from all published ChIP-array experiments.
My research is designed to use computational statistics to find transcription
factor binding sites and aid in studying the underlying mechanism of gene
expression and transcription regulation. A better knowledge of transcription
regulation can provide insights into understanding biological systems, and
ultimately promote human health.

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