SGER: Sequence Alignment, Clustering, and Statistical Physics

[01:10903]这是SGER奖，用于支持生物信息学领域的理论研究和教育，使用的方法受统计力学的启发，不同于目前在计算生物学中使用的方法。研究的重点是序列比对和聚类。该领域的一个重要问题是如何为这些生物信息学工具获得的结果分配准确的统计显著性。PI最近开发的一种新的对齐方法和统计理论将得到加强，以创建一个更完整的统计理论，并解决统计显著性问题。这项工作的目的是开发一种更好的方法来提取适当的对准参数，其中插入，删除和替换的生物单体将被平等对待。这项工作的另一个重点是使用随机聚类模型的变体来改进聚类方法。PI的新聚类方法假设对现有数据的性质没有先验知识，并且期望对噪声数据具有更强的鲁棒性。这个SGER奖支持生物信息学领域的理论研究和研究生水平的教育。这项研究是跨学科的，目的是利用统计物理学的强大工具来解决更大的生物学问题的一个重要部分，即理解已收集的大量基因组数据的意义。PI的研究解决了能够可靠地识别不同序列之间的同源性的问题，并改进了可用于帮助识别与特定生物过程或疾病相关的基因表达模式的聚类工具。＊＊＊

0110903YuThis is a SGER award to support theoretical research and education in the area of bioinfomatics using methods inspired by statistical mechanics that differ from ones currently used in computational biology. The research centers on sequence alignment and clustering. An important problem in this area is how to assign an accurate statistical significance to a result obtained from these bioinfomatics tools. A novel alignment method and statistical theory recently developed by the PI will be enhanced to create a more complete statistical theory and to address the statistical significance problem. An aim of this work is to develop a better method to extract appropriate alignment parameters where insertions, deletions, and substitutions of biomonomers will be treated on an equal footing. Another focus of this work is to improve clustering methods using variants of the random-cluster model. The PI's new clustering method assumes no a priori knowledge of the nature of the existing data and is expected to be more robust against noisy data.%%%This SGER award supports theoretical research and graduate level education in the area of bioinformatics. The research is interdisciplinary with a view to bring the formidable tools of statistical physics to bear on an important part of the larger biological problem of understanding the meaning of the enormous amount of genome data that has been collected. The PI's research addresses the problem of being able to reliably identify homology among different sequences and to improve clustering tools that can be used to help identify gene expression patterns associated with a certain biological process or disease. ***