SEQUENCE MOTIF DISCOVERY & SEARCH

序列基序发现

• 批准号: 8169340
• 负责人: TIMOTHY L BAILEY
• 金额: $ 2.27万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-05-01 至 2011-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8169340
• 关键词: Algorithms; Biological; Cells; Computer Retrieval of Information on Scientific Projects Database; Distant; Drosophila chb protein; Funding; Gene Proteins; Genes; Grant; Institution; Machine Learning; Methods; Molecular Models; Pattern; Proteins; Research; Research Personnel; Resources; Source; Structure; United States National Institutes of Health; base; biomedical scientist; molecular modeling; tool

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The MEME/MAST project allows biomedical scientists to model molecular sequences and identify relationships and patterns among them. This aids in understanding the structure and function of genes and proteins in the cell. MEME is a pattern discov¿ery tool, based on statistical learning algorithms, that finds sequence patterns in groups of proteins or genes. These patterns are useful for understanding the important biological features of the sequences. Furthermore, they can be used by the MAST algorithm to identify genes and proteins that share the patterns found by MEME, and are therefore likely to be func¿tionally or evolutionarily related to the original group of sequences. MEME and MAST comprise an extremely powerful method for identifying distant but important relationships among biological molecules.

该子项目是利用该技术的众多研究子项目之一 资源由 NIH/NCRR 资助的中心拨款提供。子项目和 研究者 (PI) 可能已从 NIH 的另一个来源获得主要资金， 因此可以在其他 CRISP 条目中表示。列出的机构是 对于中心来说，它不一定是研究者的机构。 MEME/MAST 项目允许生物医学科学家对分子序列进行建模并 识别关系和模式 他们之中。这有助于理解基因和蛋白质的结构和功能 细胞。 MEME是一种模式 发现工具，基于统计学习算法，可以发现序列模式 蛋白质或基因组。这些 模式对于理解序列的重要生物学特征很有用。 此外，它们还可以用于 通过 MAST 算法来识别具有相同模式的基因和蛋白质 MEME，因此很可能 与原始序列组在功能或进化上相关。 MEME 和 MAST 构成了一种极其强大的方法，用于识别遥远但 之间的重要关系 生物分子。