Statistics of Sequence Comparison

今年完成了序列标志项目的工作， 在前几年的报告中详细介绍了这一点， 这个项目被公布了。 该项目的一个新方向于今年启动， 与研究所的Andrew Neuwald博士合作， 基因组科学与生物化学与分子学系 马里兰州医学院的生物学。 这项工作的第一个目标是开发一种改进的 大量序列的多重比对程序。 我们采用的战略有几个主要特点： (i)它采用了一种“自上而下”的调整策略， 识别所有输入序列共享的区域，然后 重新排列密切相关的子组。 这是逃跑的关键 次优陷阱，其中一组密切相关但 错配的序列会抵制改变，因为当一个序列 来自S的X被单独处理，剩余的未对准的 S的序列将X拉回未对准;（ii）它使用一个 贝叶斯统计测量比对质量，基于 最小描述长度原理与Dirichlet混合 前科 这一措施有利于更生物现实 而不是像这样， （三）使用配对评分系统;（三）它推断 有利于插入的位置特异性空位罚分 或在比对时每个序列内的缺失（indel 插入缺失在其他序列中被调用的位置。 这有利于在保守的基因组之间插入 块，可以理解为构成蛋白质 结构核心 当应用于大型数据集时，该程序 我们已经开发出运行速度更快， 平均生物学上更精确的比对 使用的程序被认为是最先进的。 介绍这项工作的论文已提交出版。 这项工作的第二个目的是扩展所描述的方法 以上是一个多对齐模型，该模型被连接到 描述表型分歧序列明显， 在统计学上与关联的对齐位置 他们的分歧。 初步研究已经开始， 这个方向。

Work was completed this year on the sequence logo project, detailed in reports of previous years, and a paper describing this project was published. A new direction for this project was launched this year in collaboration with Dr. Andrew Neuwald of the Institute for Genome Sciences and Department of Biochemistry & Molecular Biology at the University of Maryland School of Medicine. The first aim of the work was the development of an improved program for the multiple alignment of large numbers of sequence. The strategy we employed has several central features: (i) It employs a "top-down" alignment strategy that first identifies regions shared by all the input sequences, and then realigns closely related subgroups. This is key to escaping suboptimal traps, in which a set S of closely related but misaligned sequences resists change, because when a sequence X from S is dealt with individually, the remaining misaligned sequences of S pull X back into misalignment; (ii) It uses a Bayesian statistical measure of alignment quality, based on the minimum description length principle and on Dirichlet mixture priors. This measure favors more biologically realistic alignments than does, for example, the ad hoc but widely used sum-of-the-pairs scoring system; (iii) It infers position-specific gap penalties that favor insertions or deletions (indels) within each sequence at alignment positions in which indels are invoked in other sequences. This favors the placement of insertions between conserved blocks, which can be understood as making up the proteins' structural core. When applied to large datasets, the program we have developed runs significantly faster, and produces on average more biologically accurate alignments than widely used programs that have been considered the state of the art. A paper describing this work has been submitted for publication. A second aim of this work is to extend the method described above to a multiple alignment model that is articulated to describe phenotypically diverged sequences distinctly in alignment positions statistically implicated as associated with their divergence. Preliminary research has begun in this direction.