The MEME suite of motif-based sequence analysis tools

基于基序的序列分析工具 MEME 套件

• 批准号: 9251869
• 负责人: Timothy L Bailey
• 金额: $ 35.66万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-28 至 2018-09-09
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9251869
• 关键词: Active Sites; Algorithms; Amino Acid Motifs; Amino Acid Sequence; Base Sequence; Binding Proteins; Binding Sites; Biological; Biological Models; Cell physiology; Cells; Characteristics; Collection; Complex; Computer software; DNA; DNA Sequence; Data; Data Set; Databases; Disease; Disease model; Ensure; Enzymes; Evolution; Gene Expression; Genes; Genome; Genomic DNA; Genomics; Goals; Individual; Knowledge; Linux; Mass Spectrum Analysis; Mediating; MicroRNAs; Modeling; Molecular; Molecular Biology; Molecular Evolution; Nature; Output; Paper; Pattern; Peptide Signal Sequences; Phosphorylation; Phosphotransferases; Positioning Attribute; Post Translational Modification Analysis; Post-Translational Protein Processing; Procedures; Process; Proteins; Proteomics; RNA; RNA Sequences; RNA Splicing; Research Infrastructure; Running; Scanning; Scientist; Sequence Analysis; Series; Software Engineering; Statistical Models; System; Testing; Time; Work; cell type; chromatin immunoprecipitation; computerized tools; design; experience; human disease; improved; insight; interoperability; model building; molecular scale; protein folding; protein structure; public health relevance; software development; tool; usability; web portal

DESCRIPTION (provided by applicant): The broad goal of this project is to develop and apply computational tools for detecting, modeling and understanding biologically important sequence patterns, called motifs, encoded in the genome, in RNA and in proteins. Sequence motifs carry much of the information essential to the correct functioning of cells. For ex- ample, motifs in genomic DNA contain information that helps to regulate gene expression. Sequence motifs in RNA encode splice junctions and regulatory information such as microRNA binding sites. At the protein level, sequence motifs may participate in enzymatic binding sites, provide anchors for a protein structure or mediate posttranslational modifications such as phosphorylation by kinases. We model biological sequence patterns using statistical models that capture local sequence patterns while allowing for naturally occurring variability. Since 2011 more than 33,000 unique users have accessed the MEME Suite web portal, and the number of users has been steadily growing. As of June 28, 2013, the papers describing the MEME Suite have been cited 6827 times, according to Google scholar. In the proposed project, we aim to improve the core algorithms in the MEME Suite, add significant new functionality to the Suite, and improve the robustness, reliability and usability of software. In particular, we will significantly enhance the core motif discovery algorithm to scale to larger data sets, to identify new types of motifs, and t provide more accurate statistical confidence estimates. We will add functionality to the Suite to allow users to identify and characterize motifs associated with post-translational protein modifications. We will also carry out a series of software engineering and usability improvements that will greatly enhance the overall user experience. Our software can be locally installed or run remotely through our web portal to perform a diverse set of analyses on large, complex genomic and proteomic data sets. It is in widespread use by scientists around the world. We aim to continue to maintain and develop this software, facilitating scientific discovery and leading to insights into a wide spectrum of fundamental processes in molecular biology and human disease.

描述（由申请人提供）：该项目的广泛目标是开发和应用计算工具，用于检测、建模和理解生物学上重要的序列模式，称为基序，编码在基因组、RNA和蛋白质中。序列基序携带了细胞正常运作所必需的大部分信息。例如，基因组DNA中的基序包含有助于调节基因表达的信息。RNA中的序列基序编码剪接点和调控信息，如microRNA结合位点。在蛋白质水平，序列基序可以参与酶结合位点，为蛋白质结构提供锚定或介导翻译后修饰，如激酶磷酸化。我们使用统计模型来模拟生物序列模式，该模型捕获局部序列模式，同时允许自然发生的变异性。自2011年以来，超过33，000名独立用户访问了MEME Suite门户网站，用户数量一直在稳步增长。截至2013年6月28日，描述MEME套件的论文已经被引用了6827次，根据谷歌学者。 在拟议的项目中，我们的目标是改进MEME套件中的核心算法，为套件添加重要的新功能，并提高软件的鲁棒性，可靠性和可用性。特别是，我们将大大加强 核心基序发现算法可以扩展到更大的数据集，识别新类型的基序，并提供更准确的统计置信度估计。我们将向套件中添加功能，以允许用户识别和表征与翻译后蛋白质修饰相关的基序。我们还将进行一系列软件工程和可用性改进，这将大大提高整体用户体验。 我们的软件可以在本地安装或通过我们的门户网站远程运行，对大型复杂的基因组和蛋白质组数据集进行各种分析。它被世界各地的科学家广泛使用。我们的目标是继续维护和开发该软件，促进科学发现，并导致深入了解分子生物学和人类疾病的广泛基础过程。