Precise Methods for Orthology Assessment in Large Data Sets Using Best Matches

使用最佳匹配在大数据集中进行直系同源评估的精确方法

• 批准号: 432974470
• 负责人: Professor Dr. Peter Florian Stadler
• 金额: --
• 依托单位: Lehrstuhl für Bioinformatik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/432974470?language=en
• 关键词: Precise; Methods; Orthology; Assessment; Large

Orthology detection is an important task for genome annotation, gene nomenclature, and the understanding of gene evolution. With the rapidly accelerating pace at which new genomes become available, highly efficient methods are urgently required. As demonstrated in a large body of literature, reciprocal best match methods are reasonably accurate and scale to large data sets. Nevertheless, they are far from perfect and prone to both false positive and false negative orthology calls. Drawing upon recent advances in phylogenetic combinatorics we propose here to develop practical methods to compute from reciprocal best hits (as scored by sequence (dis)similarity) the reciprocally most closely related sequences, i.e., the best matches in the proper evolutionary sense. These are directly related to orthology. The goal of the proposed work is to develop a softwarelibrary that implements this kind of data correction not only for the case of duplication-loss scenarios but also in the presence of horizontal gene transfer. To this end we will again make use of recent advances in the mathematical understanding of the orthology, reconciliation maps between trees, and event labelings. Instead of focusing on an yet another orthology assessment tool, we will focus on implementing an open source software library that is intended to make it easy for the community to include thenew algorithms into their own pipelines and tools. As a showcase application we will develop a new backend for ProteinOrtho, an orthology assessment tool maintained by the Lechner Group in Marburg.

正交学检测是基因组注释、基因命名和理解基因进化的一项重要任务。随着获得新基因组的速度迅速加快，迫切需要高效的方法。正如大量文献所证明的那样，互反最佳匹配方法具有相当高的精确度和较大的数据集规模。然而，它们还远远不是完美的，而且容易出现假阳性和假阴性的矫正学调用。利用系统发育组合学的最新进展，我们在这里建议开发实用的方法来计算相互最密切相关的序列，即在适当的进化意义上的最佳匹配。这些都与矫正学有直接关系。这项拟议工作的目标是开发一个软件库，不仅在重复丢失的情况下，而且在存在水平基因转移的情况下，实现这种数据校正。为此，我们将再次利用数学上的最新进展来理解正字法、树之间的调和映射和事件标记。与其专注于另一个矫形评估工具，我们将专注于实现一个开放源码软件库，该软件库旨在使社区能够轻松地将新算法包含到他们自己的管道和工具中。作为一个示范应用程序，我们将为ProteinOrtho开发一个新的后端，ProteinOrtho是马尔堡的Lechner Group维护的一个矫形评估工具。