ABI Innovation: Computational Analysis of microRNA Binding

ABI Innovation:microRNA 结合的计算分析

基本信息

项目摘要

The project aims to develop novel computational methods and tools to study microRNA binding interactions and microRNAs' role in gene regulation. Small (~22 nucleotide), non-coding RNAs called microRNAs have been known to regulate genes involved in key aspects of animal development and physiology through binding-interactions with their mRNA targets. Since the first discovery of microRNAs in C. elegans in 1993, a large number of microRNAs have been discovered in metazoan, plants and viruses. Today, microRNAs are known to express ubiquitously in almost all cell types, evolutionarily conserved in most of metazoan and plant species, and potentially regulate more than 30% of mammalian gene products. Understanding of microRNAs' regulatory functions in the fundamental biological processes is thus essential towards gaining a global view of gene regulation, but still at its early stages despite the rapid advances in microRNA biology. The project to study microRNA gene regulation and phenotype development seeks to generate many computational algorithms, which will be converted into software tools. These tools will be subsequently released as open-source and freely available software packages to the scientific community. The research is expected to have great impact on education at all levels. The research will be incorporated into graduate, undergraduate and K-12 education. The research will also be disseminated to the research community, informal science education, and the public to enhance scientific understanding through freely distributed computational tools and web dissemination. In addition, mentoring and outreach for women and girls is planned to help attract more women into interdisciplinary science.RNA is emerging as an important part of gene regulatory mechanisms under various phenotypic conditions. With the current unprecedented availability of genome-scale RNA genomics and transcriptomics data, the project seeks to create a set of computational algorithms and statistical methods to model microRNA binding interactions and discover microRNA interaction patterns that will help elucidate many functional roles of microRNAs in gene regulation. The advanced probabilistic model of microRNA binding activities promises to greatly benefit mRNA target recognition under specific phenotypic conditions, lay the foundation for further study of inter-microRNA interactions, and provide insight into microRNAs' functional mechanisms in gene regulation and phenotype formulation. The research is expected to not only advance scientific understanding of microRNAs' role in global gene regulation and phenotype development, but also stimulate interest in developing and advancing efficient computational modeling and data integration methods in the informatics research field. The research information and products will be made available through the project website (http://hulab.ucf.edu/research/projects/miRNA/).
该项目旨在开发新的计算方法和工具来研究microRNA结合相互作用和microRNA在基因调控中的作用。已知称为microRNA的小(~22个核苷酸)非编码RNA通过与其mRNA靶标的结合相互作用来调节涉及动物发育和生理学的关键方面的基因。自从在C.自1993年elegans发现microRNA以来,在后生动物、植物和病毒中发现了大量的microRNA。如今,已知microRNA在几乎所有细胞类型中普遍表达,在大多数后生动物和植物物种中进化保守,并可能调节超过30%的哺乳动物基因产物。因此,了解microRNA在基本生物过程中的调控功能对于获得基因调控的全局观点至关重要,但尽管microRNA生物学取得了快速进展,但仍处于早期阶段。研究microRNA基因调控和表型发展的项目旨在产生许多计算算法,这些算法将被转换为软件工具。这些工具随后将作为开放源码软件包向科学界免费提供。预计这项研究将对各级教育产生重大影响。该研究将纳入研究生,本科和K-12教育。这项研究还将通过免费分发的计算工具和网络传播,向研究界、非正式科学教育和公众传播,以提高科学认识。此外,还计划对妇女和女孩进行辅导和外联,以帮助吸引更多妇女进入跨学科科学领域。RNA正在成为各种表型条件下基因调控机制的重要组成部分。随着目前前所未有的基因组规模的RNA基因组学和转录组学数据的可用性,该项目旨在创建一套计算算法和统计方法来模拟microRNA结合相互作用,并发现microRNA相互作用模式,这将有助于阐明microRNA在基因调控中的许多功能作用。microRNA结合活性的概率模型有望极大地有利于特定表型条件下mRNA靶点的识别,为进一步研究microRNA间的相互作用奠定基础,并为深入了解microRNA在基因调控和表型形成中的功能机制提供依据。该研究不仅有望促进对microRNA在全球基因调控和表型发展中作用的科学理解,还将激发人们对开发和推进信息学研究领域有效的计算建模和数据集成方法的兴趣。研究信息和产品将通过项目网站(http://hulab.ucf.edu/research/projects/miRNA/)提供。

项目成果

期刊论文数量(0)
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会议论文数量(0)
专利数量(0)

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Haiyan Hu其他文献

Geochemistry and sedimentology of the Lower Silurian Longmaxi mudstone in southwestern China: Implications for depositional controls on organic matter ccumulation
中国西南地区下志留统龙马溪泥岩的地球化学和沉积学:沉积控制对有机质堆积的意义
  • DOI:
  • 发表时间:
    2016
  • 期刊:
  • 影响因子:
    4.2
  • 作者:
    Yiquan Ma;Majie Fan;Yongchao Lu;Xusheng Guo;Haiyan Hu;Lei Chen;Chao Wang;Xiaochen Liu
  • 通讯作者:
    Xiaochen Liu
Simulation complexities in the dynamics of a continuously piecewise-linear oscillator
  • DOI:
    10.1016/0960-0779(95)00005-o
  • 发表时间:
    1995-11
  • 期刊:
  • 影响因子:
    7.8
  • 作者:
    Haiyan Hu
  • 通讯作者:
    Haiyan Hu
On-orbit assembly of a team of flexible spacecraft using potential field based method
使用基于势场的方法在轨组装一组柔性航天器
  • DOI:
    10.1016/j.actaastro.2017.01.021
  • 发表时间:
    2017-04
  • 期刊:
  • 影响因子:
    3.5
  • 作者:
    Ti Chen;Hao Wen;Haiyan Hu;Dongping Jin
  • 通讯作者:
    Dongping Jin
Hierarchical order of gene expression levels
基因表达水平的层次顺序
rRNAFilter: A Fast Approach for Ribosomal RNA Read Removal Without a Reference Database
rRNAFilter:无需参考数据库即可快速去除核糖体 RNA 片段的方法
  • DOI:
  • 发表时间:
    2017
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Y. Wang;Haiyan Hu;X. Li
  • 通讯作者:
    X. Li

Haiyan Hu的其他文献

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{{ truncateString('Haiyan Hu', 18)}}的其他基金

MCA: A Computational Framework to Study microRNAs in Cell-Cell Interactions
MCA:研究细胞间相互作用中 microRNA 的计算框架
  • 批准号:
    2120907
  • 财政年份:
    2021
  • 资助金额:
    $ 41.65万
  • 项目类别:
    Standard Grant
ABI Innovation: Computational Methods to Study Gene Transcription Initiation Patterns
ABI Innovation:研究基因转录起始模式的计算方法
  • 批准号:
    1661414
  • 财政年份:
    2017
  • 资助金额:
    $ 41.65万
  • 项目类别:
    Standard Grant
CAREER: A Computational Framework to Study Epigenetic Regulation
职业:研究表观遗传调控的计算框架
  • 批准号:
    1149955
  • 财政年份:
    2012
  • 资助金额:
    $ 41.65万
  • 项目类别:
    Continuing Grant
BRIGE: Computational Identification of Gene Regulatory Networks in Microalgae
BRIGE:微藻基因调控网络的计算识别
  • 批准号:
    1125676
  • 财政年份:
    2011
  • 资助金额:
    $ 41.65万
  • 项目类别:
    Standard Grant

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合作研究:ABI 创新:大规模神经形态数据集的计算探索
  • 批准号:
    2028361
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Collaborative Research: ABI Innovation: Towards Computational Exploration of Large-Scale Neuro-Morphological Datasets
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  • 批准号:
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Collaborative Project: ABI Innovation: Computational Identification & Screening for Deleterious Mutants
合作项目:ABI 创新:计算识别
  • 批准号:
    1661391
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    2017
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    $ 41.65万
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    Standard Grant
ABI Innovation: Computational Methods to Study Gene Transcription Initiation Patterns
ABI Innovation:研究基因转录起始模式的计算方法
  • 批准号:
    1661414
  • 财政年份:
    2017
  • 资助金额:
    $ 41.65万
  • 项目类别:
    Standard Grant
Collaborative Research: ABI Innovation: Towards Computational Exploration of Large-Scale Neuro-Morphological Datasets
合作研究:ABI 创新:大规模神经形态数据集的计算探索
  • 批准号:
    1661289
  • 财政年份:
    2017
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    $ 41.65万
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    Standard Grant
ABI Innovation: Collaborative Research: Computational framework for inference of metabolic pathway activity from RNA-seq data
ABI Innovation:协作研究:从 RNA-seq 数据推断代谢途径活性的计算框架
  • 批准号:
    1564559
  • 财政年份:
    2016
  • 资助金额:
    $ 41.65万
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    Standard Grant
ABI Innovation: A computational framework for integrating image informatics with transcriptomics for discovering spatiotemporally resolved regulatory gene networks in plants
ABI Innovation:将图像信息学与转录组学相结合的计算框架,用于发现植物中时空解析的调控基因网络
  • 批准号:
    1564621
  • 财政年份:
    2016
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    $ 41.65万
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ABI Innovation: Collaborative Research: Computational framework for inference of metabolic pathway activity from RNA-seq data
ABI Innovation:协作研究:从 RNA-seq 数据推断代谢途径活性的计算框架
  • 批准号:
    1564899
  • 财政年份:
    2016
  • 资助金额:
    $ 41.65万
  • 项目类别:
    Standard Grant
ABI Innovation: Collaborative Research: Computational framework for inference of metabolic pathway activity from RNA-seq data
ABI Innovation:协作研究:从 RNA-seq 数据推断代谢途径活性的计算框架
  • 批准号:
    1564936
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    2016
  • 资助金额:
    $ 41.65万
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Collaborative Research: ABI Innovation: Computational population-genetic analysis for detection of soft selective sweeps
合作研究:ABI 创新:用于检测软选择性扫描的计算群体遗传分析
  • 批准号:
    1458059
  • 财政年份:
    2015
  • 资助金额:
    $ 41.65万
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