Statistics of Sequence Comparison

序列比较统计

• 批准号: 8149590
• 负责人: STEPHEN F ALTSCHUL
• 金额: $ 39.17万
• 依托单位: NATIONAL LIBRARY OF MEDICINE
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8149590
• 关键词: statistics

Work this year focused on scoring systems for multiple alignment. Most pairwise and multiple sequence alignment programs seek alignments with optimal scores. Central to defining such scores is selecting a set of substitution scores for aligned amino acids or nucleotides. Substitution scores for local pairwise alignment are implicitly of log-odds form, comparing the probabilities of aligning two letters under models of relatedness and non-relatedness, and the best pairwise substitution scores are explicitly so constructed. We have developed ideas, based on the minimum description length principle, for extending this formalism to multiple alignments. Most simply, Bayesian methods can be used to derive "BILD" substitution scores from prior distributions describing columns of related letters. This approach has been used previously only to define scores for aligning individual sequences to sequence profiles, but it has much broader applicability. We have employed BILD scores in Gibbs sampling optimization procedures, and shown that they yield improved performance in constructing biologically accurate alignments. We have developed pilot programs for constructing gapped multiple alignments using BILD scores. Using artificial sequences, we have shown these programs to have superior performance to earlier programs at detecting domain boundaries. We have also appled them to the recognition and annotation of DNA-binding domains in Apicomplexan proteins. In related work, we have studied Dirichlet mixture models in the context of non-standard sequence composition. A Dirichlet mixture with M components over an alphabet of L letters has M*(L+1)-1 free parameters. If M = L/2, this is exactly as many as a symmetric pairwise substitution matrix. While each Dirichlet mixture implies a unique such matrix, we have shown that multiple mixtures can map to the same matrix, and some substitution matrices may not correspond to any Dirichlet mixture. A Dirichlet mixture for protein sequence analysis generally is constructed from a particular set of proteins, implying a particular background amino acid composition. The mixture should be non-optimal for the comparison of proteins with significantly different composition. We have described a sensible and efficient method for adjusting the parameters of a Dirichlet mixture so that they are consistent with any specified composition.

今年的工作重点是多个评分系统 结盟。 大多数成对和多序列比对 程序寻求与最佳分数的一致性。 中央至 定义这样的分数是选择一组替换 比对的氨基酸或核苷酸的分数。 替代 局部成对比对的分数隐式为 对数赔率形式，比较对齐的概率 相关性和非相关性模型下的两个字母， 最好的成对替换分数是明确的 如此构建。 我们开发了一些想法，基于 最小描述长度原则，用于扩展此 形式主义到多重对齐。 最简单的是，贝叶斯 方法可用于导出“BILD”替代分数 来自描述相关列的先验分布 字母。 这种方法以前只用过 定义将各个序列对齐的分数 序列概况，但它具有更广泛的适用性。 我们在吉布斯抽样中采用了 BILD 分数 优化程序，并表明它们产生 提高生物构建的性能 准确的对齐。 我们制定了试点计划 使用 BILD 构建缺口多重比对 分数。 使用人工序列，我们已经展示了这些 程序具有比早期程序更优越的性能 在检测域边界时。 我们也应用了它们 DNA 结合域的识别和注释 存在于顶复门蛋白中。 在相关工作中，我们研究了狄利克雷混合模型 在非标准序列组成的背景下。 一个 在字母表上具有 M 个分量的狄利克雷混合物 L 字母有 M*(L+1)-1 个自由参数。 如果 M = L/2，则 与对称成对替换一样多 矩阵。 虽然每个狄利克雷混合物都意味着一个独特的 这样的矩阵，我们已经证明多种混合物可以映射 到同一个矩阵，并且某些替换矩阵可能不会 对应于任何狄利克雷混合物。 狄利克雷混合物 用于蛋白质序列分析的一般构建 来自一组特定的蛋白质，意味着特定的 背景氨基酸组成。 该混合物应 对于蛋白质的比较来说不是最佳的 组成显着不同。 我们已经描述过 一种合理有效的调整方法 狄利克雷混合物的参数，使得它们是 与任何指定的成分一致。