Predicting Genetic Regulatory Response Using Classification: Yeast Stress Response.- Detecting Functional Modules of Tranillegalscription Factor Binding Sites in the Human Genome.- Fishing for Proteins in the Pacific Northwest.- PhyloGibbs: A Gibbs Sampler Incorporating Phylogenetic Information.- Application of Kernel Method to Reveal Subtypes of TF Binding Motifs.- Learning Regulatory Network Models that Represent Regulator States and Roles.- Using Expression Data to Discover RNA and DNA Regulatory Sequence Motifs.- Parameter Landscape Analysis for Common Motif Discovery Programs.- Inferring Cis-region Hierarchies from Patterns in Time-Course Gene Expression Data.- Modeling and Analysis of Heterogeneous Regulation in Biological Networks.
Research in the ?eld of gene regulation is evolving rapidly in an ever-changing s- enti?c environment. Microarray techniques and comparative genomics have enabled more comprehensive studies of regulatory genomics and are proving to be powerful tools of discovery. The application of chromatin immunoprecipitation and microarrays (chIP-on-chip) to directly study the genomic binding locations of tranillegalscription factors has enabled more comprehensive modeling of regulatory networks. In addition, c- plete genome sequences and the comparison of numerous related species has dem- strated that conservation in non-coding DNA sequences often provides evidence for cis-regulatory binding sites. That said, much is still to be learned about the regulatory networks of these sequenced genomes. Systematic methods to decipher the regulatory mechanism are also crucial for c- roboratingthese regulatorynetworks.Thecoreof thesemethodsarethe motifdiscovery algorithms that can help predict cis-regulatory elements. These DNA-motif discovery programsarebecomingmoresophisticatedandare beginningto leverageevidencefrom comparative genomics (phylogenetic footprinting) and chIP-on-chip studies. How to use these new sources of evidence is an active area of research.
Inhaltsverzeichnis
Predicting Genetic Regulatory Response Using Classification: Yeast Stress Response.- Detecting Functional Modules of Tranillegalscription Factor Binding Sites in the Human Genome.- Fishing for Proteins in the Pacific Northwest.- PhyloGibbs: A Gibbs Sampler Incorporating Phylogenetic Information.- Application of Kernel Method to Reveal Subtypes of TF Binding Motifs.- Learning Regulatory Network Models that Represent Regulator States and Roles.- Using Expression Data to Discover RNA and DNA Regulatory Sequence Motifs.- Parameter Landscape Analysis for Common Motif Discovery Programs.- Inferring Cis-region Hierarchies from Patterns in Time-Course Gene Expression Data.- Modeling and Analysis of Heterogeneous Regulation in Biological Networks.
Klappentext
Research in the ?eld of gene regulation is evolving rapidly in an ever-changing s- enti?c environment. Microarray techniques and comparative genomics have enabled more comprehensive studies of regulatory genomics and are proving to be powerful tools of discovery. The application of chromatin immunoprecipitation and microarrays (chIP-on-chip) to directly study the genomic binding locations of tranillegalscription factors has enabled more comprehensive modeling of regulatory networks. In addition, c- plete genome sequences and the comparison of numerous related species has dem- strated that conservation in non-coding DNA sequences often provides evidence for cis-regulatory binding sites. That said, much is still to be learned about the regulatory networks of these sequenced genomes. Systematic methods to decipher the regulatory mechanism are also crucial for c- roboratingthese regulatorynetworks.Thecoreof thesemethodsarethe motifdiscovery algorithms that can help predict cis-regulatory elements. These DNA-motif discovery programsarebecomingmoresophisticatedandare beginningto leverageevidencefrom comparative genomics (phylogenetic footprinting) and chIP-on-chip studies. How to use these new sources of evidence is an active area of research.
Includes supplementary material: sn.pub/extras
