SEQUENCE MOTIF DISCOVERY & SEARCH

序列基序发现

• 批准号: 7955226
• 负责人: TIMOTHY L BAILEY
• 金额: $ 2.39万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-05-01 至 2010-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7955226
• 关键词: Algorithms; Biological; Biomedical Computing; 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.

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 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 其中。这有助于了解细胞中基因和蛋白质的结构和功能。MEME是一种模式 发现工具，基于统计学习算法，发现蛋白质或基因组中的序列模式。这些 模式对于理解序列的重要生物学特征是有用的。此外，它们可以用于 通过MAST算法来识别与MEME发现的模式相同的基因和蛋白质，因此可能 在功能上或进化上与原始序列组相关。 MEME和MAST构成了一种非常强大的方法，用于识别 生物分子