SEQUENCE MOTIF DISCOVERY & SEARCH

Meme/Mast项目允许生物医学科学家建立模型 分子序列，并识别它们之间的关系和模式 他们。这有助于理解基因的结构和功能。 和细胞中的蛋白质。Meme是一种模式发现工具，基于 统计学习算法，发现序列模式在 一组蛋白质或基因。这些模式对以下方面非常有用 了解序列的重要生物学特征。 此外，它们还可以被MAST算法用来识别基因 以及与模因相同的模式的蛋白质，因此 可能在功能上或进化上与原始的 一组序列。迷因和桅杆组成了一个极其强大的 一种识别远距离但重要的关系的方法 生物分子。NBCR通过“透明”提供表情包和母版 “超级计算”，通过一个 易于使用的万维网界面。我们一直在不断改进 迷因和桅杆都是如此。桅杆已增强为彩色、图形化 使远程关系更易于可视化的输出，以及 能够直接搜索DNA(基因)中发现的模式 使用模因的蛋白质序列。我们为Meme添加了新的功能 包括能够补偿基因序列 在训练数据中被过度表示，并找到 更好地区分密切相关的分子基团。在……里面 此外，我们改进了表情包的输出，使其能够与界面连接 与其他序列分析工具配合使用效果更好。例如，表情包主题 可以被视为LOGOS，用于搜索蛋白质块数据库 Motives，并用于通过 The BLOCK网站 (http://www.blocks.fhcrc.org/blocks/process_blocks.html).科学家 正在将大约300个迷因运行和180个主运行提交给 每个月在SDSC的超级计算机(图1)。增强的功能 而程序改进的用户界面无疑将吸引 当我们推出新版本(Meme 2.2；Mast 2.2)时会有更多用户 月份。大约有1000名用户通过SDSC服务器运行了Meme查询， 约有20名用户运行了20多个表情包查询。大致 已有500个用户通过SDSC服务器运行主查询。图1：表情包 SDSC在Cray T3E上的使用和在DEC Alpha“农场”上的桅杆使用 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.