Reconstructing Regulation Networks of Meiosis in Silico
Reconstructing Regulation Networks of Meiosis in Silico
批准号:
7903674
负责人:
Wei Wang
金额:
$7.82万
依托单位国家:
美国
项目类别:
财政年份:
2009
资助国家:
美国
项目状态:
已结题
起止时间:
2009-08-20 至 2010-12-30
关键词:
AffectAlgorithmsBindingBinding SitesBioinformaticsBiologicalBiological ProcessBlast CellCellsComputational algorithmComputer SimulationDNA SequenceDataDatabasesDevelopmentEvolutionFungal GenomeGene ExpressionGene ProteinsGene TargetingGenesGenetic TranscriptionGenomeGenomicsGoalsHomologous GeneJointsLightLinkLocationLogicMeiosisMetabolismMethodsModelingMorphogenesisOrganismPhysical ChemistryProbabilityPromoter RegionsRegulationReproduction sporesResearchResearch PersonnelSaccharomyces cerevisiaeSaccharomycetalesSensitivity and SpecificitySourceStagingStatistical MethodsTimeTranscriptional RegulationYeastsbasecombinatorialgenome sequencinghuman dataresearch studytooltranscription factor
中文摘要
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英文摘要
DESCRIPTION (provided by applicant): In the post-genome era, it is critical to organize genes and proteins into functional biological networks. Our long-term goal is to dissect the transcriptional regulatory networks that regulate every biological process in the cell, understand the regulatory mechanisms, and discover fundamental principles that govern the evolution of these networks. The focus of this proposal is to develop new bioinformatics methods for reconstructing the transcriptional network regulating yeast sporulation, which consists of meiosis and spore morphogenesis, by integrating genomic data from different sources. These methods are uniquely appropriate for identifying combinatorial regulation of transcription factors particularly those transient ones and thus revealing the dynamic realization of the underlying network. The proposed research will contribute significantly to our understanding of yeast sporulation and may shed light on understanding meiosis in higher organisms. The bioinformatics methods developed and validated in the proposed study can also serve as a good starting point to develop equivalent tools for higher organisms.
The specific hypothesis behind the proposed research is that not all important transcriptional regulators of yeast sporulation have been identified, and many network links that determine regulatory logic are still missing. The specific aims are: 1. develop a new bioinformatics method to accurately construct transcription modules (defined to consist of a TF, its binding site and its target genes) using gene expression, transcription factor binding and sequence motif information; 2. construct transcription modules of budding yeast sporulation using the above algorithm, discover combinatorial regulation and determine transcriptional network of yeast sporulation by assembling these modules; 3. predict transcription modules and network topologies of sporulation in the 6 newly sequenced yeast genomes based on the network of budding yeast.
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DOI:
10.1186/1471-2105-9-395
发表时间:
2008-09-24
期刊:
BMC bioinformatics
影响因子:
3
作者:
[Shen L, Liu J, Wang W]
通讯作者:
Wang W
DOI:
10.1186/1752-0509-4-128
发表时间:
2010-09-10
期刊:
BMC systems biology
影响因子:
--
作者:
[Shen L, Chepelev I, Liu J, Wang W]
通讯作者:
Wang W
DOI:
10.1109/tcbb.2011.18
发表时间:
2011-09
期刊:
IEEE/ACM transactions on computational biology and bioinformatics
影响因子:
--
作者:
[Chang R, Shoemaker R, Wang W]
通讯作者:
Wang W
DOI:
10.1186/gb-2010-11-1-r7
发表时间:
2010-01-22
期刊:
Genome biology
影响因子:
12.3
作者:
[Won KJ, Ren B, Wang W]
通讯作者:
Wang W
DOI:
10.1371/journal.pcbi.1002300
发表时间:
2011-12
期刊:
PLoS computational biology
影响因子:
4.3
作者:
[Chang R, Shoemaker R, Wang W]
通讯作者:
Wang W
共 6 条
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资助金额:$70.03万
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依托单位:
Integrated analysis of genetic variation and epigenomic data
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资助金额:$52.04万
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Identification and analysis of non-coding loci critical for cell viability and chromatin organization
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财政年份:2015
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Engineering the binding specificity of modular domains
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资助金额:$31.27万
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财政年份:2015
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依托单位:
Fast and Robust Methods for Large Scale Genotype Phenotype Association Study
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资助金额:$8.54万
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财政年份:2015
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Fast and Robust Methods for Large Scale Genotype Phenotype Association Study
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Fast and Robust Methods for Large Scale Genotype Phenotype Association Study
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PROJECT A
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Protein recognition for modular domains
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海外基金