SEQUENCE MOTIF DISCOVERY & SEARCH

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 Meme/Mast项目允许生物医学科学家对分子序列进行建模，并确定关系和模式 其中就有。这有助于理解细胞中基因和蛋白质的结构和功能。表情包是一种模式 基于统计学习算法的发现工具，在蛋白质或基因组中寻找序列模式。这些 模式对于理解序列的重要生物学特征是有用的。此外，它们还可以用于 通过MAST算法来识别与Meme发现的模式相同的基因和蛋白质，因此很可能 在功能上或进化上与原始的一组序列相关。 Meme和Mast构成了一种极其强大的方法，用于识别彼此之间遥远但重要的关系 生物分子。

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.