SEQUENCE MOTIF DISCOVERY & SEARCH

MEME/MAST项目允许生物医学科学家模拟 分子序列，并确定之间的关系和模式 他们 这有助于理解基因的结构和功能 和蛋白质。 MEME是一个模式发现工具，基于 统计学习算法，发现序列模式， 蛋白质或基因组。 这些模式对以下方面很有用： 理解序列的重要生物学特征。 此外，它们可以被MAST算法用于识别基因 和蛋白质，共享模式发现的MEME，因此， 可能在功能上或进化上与原始 序列组。 MEME和MAST构成了一个非常强大的 一种用于识别在以下对象之间的遥远但重要的关系的方法 生物分子 NBCR通过“透明”提供MEME和MAST 超级计算”的生物研究界，通过一个 易于使用的万维网界面。 我们继续改进 MEME和MAST。 MAST已经增强了彩色，图形 输出，使遥远的关系易于可视化， 能够直接搜索DNA（基因）中发现的模式， 使用MEME的蛋白质序列。 我们为MEME添加了新功能 包括补偿基因序列的能力， 在训练数据中过度表现，并找到 更好地区分密切相关的分子组。 在 此外，我们还改进了MEME的输出，使其能够与 更好地与其他序列分析工具。 例如，MEME基序 可以看作是LOGOS，用来搜索蛋白质的BLOCKS数据库 基序，并用于通过提供的工具建立系统发育树， BLOCKS网站 (http：//www.blocks.fhcrc.org/blocks/process_blocks.html）。科学家 提交了大约300个MEME运行和180个MAST运行， SDSC的超级计算机（图1）。 增强型能力 和改进的用户界面的程序无疑将吸引 当我们推出新版本（MEME 2.2; MAST 2.2）时， 月 大约有1000个用户通过SDSC服务器运行MEME查询， 大约有20个用户运行了超过20个MEME查询。 约 500名用户通过SDSC服务器运行MAST查询。 图1：MEME Cray T3 E飞机的使用和MAST飞机在SDSC的DEC Alpha“农场”上的使用 通过SDSC MEME/MAST网站。

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 discovery 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 functionally 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. NBCR provides MEME and MAST via "transparent supercomputing" to the biological research community, via an easy-to-use world wide web interface. We have continued to improve both MEME and MAST. MAST has been enhanced with colored, graphical output to make distant relationships easy to visualize, and the ability to search DNA (genes) directly for patterns discovered in protein sequences using MEME. We have added new capabilities to MEME including the ability to compensate for genetic sequences that are over-represented in the training data, and to find patterns that better discriminate between closely related groups of molecules. In addition, we have improved MEME's output to allow it to interface better with other sequence analysis tools. For example, MEME motifs can be viewed as LOGOS, used to search the BLOCKS database of protein motifs, and used to build phylogenetic trees via the tools provided at the BLOCKS website (http://www.blocks.fhcrc.org/blocks/process_blocks.html). Scientists are submitting approximately 300 MEME runs and 180 MAST runs to the supercomputers at SDSC each month (Fig.1). The enhanced capabilities and improved user interfaces of the programs will doubtless attract more users when we introduce new versions (MEME 2.2; MAST 2.2) this month. About 1000 users have run MEME queries via the SDSC server, and about 20 users have run more than 20 MEME queries. Approximately 500 users have run MAST queries via the SDSC server. Figure 1: MEME usage on the Cray T3E and MAST usage on the DEC Alpha ``farm'' at SDSC via the SDSC MEME/MAST web site.