Algorithmic and combinatorial problems inspired by comparative genomics.

受比较基因组学启发的算法和组合问题。

• 批准号: RGPIN-2016-04576
• 负责人: Hamel, Sylvie
• 金额: $ 1.6万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=652930
• 关键词: Algorithmic; combinatorial; problems; inspired; comparative

Abstract: Sitting at the interface of theoretical computer science and mathematics, my area of research concerns the development of algorithmic approaches and combinatorial tools for theoretical problems inspired by biological problems, coming mostly from comparative genomics. Roughly speaking, comparative genomics aims at extracting information from the comparison of genomes of different species. This information may concern genomic sequences themselves, the order of appearance of genes in different genomes, the structure of small molecules (like RNA), etc. ***The general problem of extracting meaningful information from the resulting very large mass of biological data gives rise to many important problems on combinatorial objects such as words, permutations, trees or graphs. For instance, permutations partially encode the structure of genomes. The development of efficient algorithms for the manipulation of these discrete structures is also crucial in this context. One finds that, in any one of these specific contexts, many different notions of distances have been suggested to compare the relevant objects, with the consequence that we now have to clearly characterize the ``best'' distances among these. For sure, this quality measurement of distances is closely tied to the construction of efficient algorithms fulfilling the desired goal. *** ***My research program consists in studying how one should compare and analyze such distances with respect to their principal properties, and to quantify their relevance to a given context. Concrete results of this study lead to the construction of new efficient algorithms to help solve several problems related to distances optimization. Among important uses of distances in applied contexts one looks for a way to aggregate a set of solutions of a given problem into a consensus solution which highlights common desired features, while minimizing disagreements. One original aspect of my program rests on the study of how to efficiently (algorithmically) reach these consensus solutions in various context.**

摘要：我的研究领域位于理论计算机科学和数学的交叉点，涉及受生物学问题启发的理论问题的算法方法和组合工具的开发，主要来自比较基因组学。粗略地说，比较基因组学的目的是从不同物种基因组的比较中提取信息。这些信息可能涉及基因组序列本身、不同基因组中基因出现的顺序、小分子（如 RNA）的结构等。***从产生的大量生物数据中提取有意义的信息的一般问题是引起组合对象的许多重要问题，例如单词、排列、树或图。例如，排列部分编码基因组的结构。在这种情况下，开发用于操纵这些离散结构的有效算法也至关重要。人们发现，在任何一种特定的背景下，人们都提出了许多不同的距离概念来比较相关对象，结果是我们现在必须清楚地描述这些对象之间的“最佳”距离。当然，这种距离的质量测量与实现预期目标的高效算法的构建密切相关。 *** ***我的研究项目包括研究如何比较和分析这些距离的主要属性，并量化它们与给定上下文的相关性。这项研究的具体结果导致了新的有效算法的构建，以帮助解决与距离优化相关的几个问题。在应用环境中距离的重要用途中，人们寻找一种方法将给定问题的一组解决方案聚合成一个共识解决方案，突出共同所需的特征，同时最大限度地减少分歧。我的项目的一个原创方面在于研究如何在各种背景下有效（算法地）达成这些共识解决方案。**