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
• 负责人: Haiyan Hu
• 金额: $ 57.77万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1661414&HistoricalAwards=false
• 关键词: ABI; Innovation; Computational; Methods; Study

This project aims to develop computational methods and tools to discover how gene transcription initiation mechanisms vary, and their resulting functional consequences on gene transcriptional regulation. Gene transcriptional regulation refers to any process by which a cell regulates its genes expression. Properly regulated expression of genes is crucial for ensuring that biological processes are accurately carried out, for genes contributing to development, proliferation, programmed cell death (apoptosis), aging, and differentiation. Gene expression begins when mRNA molecules start to be synthesized, at the point on the gene where they initiate. To understand the regulation of gene expression, it is essential to discover the transcription initiation mechanisms under various conditions, and how these varied mechanisms lead to different outcomes, or phenotypes. High throughput sequencing of complete RNA sets synthesized in cells has produced large datasets, but matching large-scale computational studies, to understand phenotype-relevant transcription initiation mechanisms are still at its early stage. This project will study transcription initiation and gene regulation, with the goal of generating computational algorithms that capture the rules and conditions for selection of the transcription initiation site on a gene; the algorithms will then be converted into software tools. These tools will be released as open-source and freely available software packages to the scientific community and interested public. The research will be communicated in ways expected to have a great impact on education at many levels: it will be incorporated into lectures, labs, and research opportunities geared towards graduate, undergraduate and K-12 education. The research will also be disseminated to the research community and the public to enhance scientific understanding, through freely distributed computational tools and various modes of web dissemination. In addition, mentoring and outreach efforts targeted towards women and girls will help attract more women into engaging in research experiences in interdisciplinary science. Understanding the underlying mechanisms and functional consequences of gene transcription initiation is important to understand gene regulation. The project seeks to create a set of computational algorithms and statistical methods to discover the associations between transcription initiation and gene regulation mechanisms towards advancing our understanding of gene transcriptional regulation. The advanced graph theory-based algorithms and probabilistic models of gene transcription initiation and regulation through large-scale high-throughput transcriptomics, genomics and epigenomics data integration have the promise to unveil various transcription initiation mechanisms and their functional roles in gene transcriptional regulation and phenotype formulation. The research is expected to not only advance scientific understanding of 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/TransInitiation/).

该项目旨在开发计算方法和工具，以发现基因转录启动机制如何变化，以及它们对基因转录调控的功能影响。基因转录调控是指细胞调节其基因表达的任何过程。对于促进发育、增殖、细胞程序性死亡(细胞凋亡)、衰老和分化的基因，适当调控基因的表达对于确保生物过程的准确进行至关重要。当信使核糖核酸分子开始合成时，基因表达就开始了，在基因的起始点。为了了解基因表达的调控，有必要发现在不同条件下的转录启动机制，以及这些不同的机制如何导致不同的结果或表型。对细胞中合成的完整RNA集进行高通量测序已经产生了大量的数据集，但与大规模计算研究相匹配，了解表型相关的转录启动机制仍处于早期阶段。这个项目将研究转录启动和基因调控，目标是生成计算算法，以捕获选择基因上转录起始点的规则和条件；然后这些算法将被转换为软件工具。这些工具将作为开放源码和免费提供的软件包向科学界和感兴趣的公众发布。这项研究的交流方式预计将对多个层次的教育产生重大影响：它将被纳入面向研究生、本科生和K-12教育的讲座、实验室和研究机会。这项研究还将通过免费分发的计算工具和各种网络传播方式向研究界和公众传播，以增进科学了解。此外，针对妇女和女童的辅导和外联工作将有助于吸引更多的妇女参与跨学科科学的研究经验。了解基因转录启动的潜在机制和功能后果对于理解基因调控非常重要。该项目旨在创建一套计算算法和统计方法来发现转录启动和基因调控机制之间的关联，以促进我们对基因转录调控的理解。通过大规模的高通量转录组学、基因组学和表观基因组学数据集成，先进的基于图论的基因转录启动和调控的算法和概率模型有望揭示各种转录启动机制及其在基因转录调控和表型形成中的功能作用。这项研究不仅有望促进对全球基因调控和表型发育的科学理解，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/TransInitiation/).提供

项目成果

Deep Learning to Identify Transcription Start Sites from CAGE Data

• DOI: 10.1109/bibm49941.2020.9313267
• 发表时间: 2020-12
• 期刊: 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
• 作者: Hansi Zheng;X. Li;Haiyan Hu

FlexSLiM: a Novel Approach for Short Linear Motif Discovery in Protein Sequences

FlexSLiM：蛋白质序列中短线性基序发现的新方法

• DOI: 10.1145/3194480.3194501
• 发表时间: 2018
• 期刊: ICBCB 2018 Proceedings of the 2018 6th International Conference on Bioinformatics and Computational Biology
• 作者: Li, Xiaoman;Ge, Ping;Hu, Haiyan
• 通讯作者: Hu, Haiyan

Application of Deep Learning Models to MicroRNA Transcription Start Site Identification

• DOI: 10.1109/icbcb.2019.8854645
• 发表时间: 2019-03
• 期刊: 2019 IEEE 7th International Conference on Bioinformatics and Computational Biology ( ICBCB)
• 作者: Clayton Barham;Mingyu Cha;X. Li;Haiyan Hu

Shared distal regulatory regions may contribute to the coordinated expression of human ribosomal protein genes

• DOI: 10.1016/j.ygeno.2020.03.028
• 发表时间: 2020-07-01
• 期刊: GENOMICS
• 影响因子: 4.4
• 作者: Wang，Saidi;Hu，Haiyan;Li，Xiaoman
• 通讯作者: Li，Xiaoman