? We will continue the development of computer methods for analyzing gene regulation. We will further enhance methods to identify regulatory sites from the promoter regions of co-regulated genes with special emphasis on taking advantage of orthologous promoter regions from additional species. Included in the improvements will be better ways to identify multiple transcription factors that act coordinately to regulate gene expression. ? ? We will develop computational methods to help determine which transcription factors within a genome interact with which regulatory sites. The methods will be developed initially using bacterial genomes where a large number of genome sequences, from a wide range of phylogenetic distances, already exist. We will test the ability of different types of information, including genomic location, phylogenetic correlation and recognition code predictions, to aid in the identification of the associations between factors and sites. ? ? We will continue the development and enhancement of methods to predict RNA motifs composed of both sequence and structure constraints. We will go beyond the capabilities of programs like FOLDALIGN to detect conserved structures that are complex, including pseudoknots. Two approaches will be tested, one general one that should work on any collection of common RNA motifs and the other designed specifically to take advantage of phylogenetically conserved motifs in orthologous regions of multiple species. ? ? Each of these projects will be enhanced through collaborations with other groups, primarily experimentalists, who are interested in the application of our methods to their biological problems. ? ?

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
2R01HG000249-15
Application #
6774554
Study Section
Genome Study Section (GNM)
Program Officer
Good, Peter J
Project Start
1989-04-01
Project End
2008-04-30
Budget Start
2004-05-01
Budget End
2005-04-30
Support Year
15
Fiscal Year
2004
Total Cost
$306,000
Indirect Cost
Name
Washington University
Department
Biochemistry
Type
Schools of Medicine
DUNS #
068552207
City
Saint Louis
State
MO
Country
United States
Zip Code
63130
Ruan, Shuxiang; Stormo, Gary D (2018) Comparison of discriminative motif optimization using matrix and DNA shape-based models. BMC Bioinformatics 19:86
Chang, Yiming K; Zuo, Zheng; Stormo, Gary D (2018) Quantitative profiling of BATF family proteins/JUNB/IRF hetero-trimers using Spec-seq. BMC Mol Biol 19:5
Ruan, Shuxiang; Swamidass, S Joshua; Stormo, Gary D (2017) BEESEM: estimation of binding energy models using HT-SELEX data. Bioinformatics 33:2288-2295
Chang, Yiming K; Srivastava, Yogesh; Hu, Caizhen et al. (2017) Quantitative profiling of selective Sox/POU pairing on hundreds of sequences in parallel by Coop-seq. Nucleic Acids Res 45:832-845
Hu, Caizhen; Malik, Vikas; Chang, Yiming Kenny et al. (2017) Coop-Seq Analysis Demonstrates that Sox2 Evokes Latent Specificities in the DNA Recognition by Pax6. J Mol Biol 429:3626-3634
Roy, Basab; Zuo, Zheng; Stormo, Gary D (2017) Quantitative specificity of STAT1 and several variants. Nucleic Acids Res 45:8199-8207
Xiao, Shu; Lu, Jia; Sridhar, Bharat et al. (2017) SMARCAD1 Contributes to the Regulation of Naive Pluripotency by Interacting with Histone Citrullination. Cell Rep 18:3117-3128
Zuo, Zheng; Roy, Basab; Chang, Yiming Kenny et al. (2017) Measuring quantitative effects of methylation on transcription factor-DNA binding affinity. Sci Adv 3:eaao1799
Ruan, Shuxiang; Stormo, Gary D (2017) Inherent limitations of probabilistic models for protein-DNA binding specificity. PLoS Comput Biol 13:e1005638
Stormo, Gary D; Roy, Basab (2016) DNA Structure Helps Predict Protein Binding. Cell Syst 3:216-218

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