Efficient Algorithms for Motif Search

用于基序搜索的高效算法

• 批准号: 8324284
• 负责人: SANGUTHEVAR RAJASEKARAN
• 金额: $ 34.76万
• 依托单位: UNIVERSITY OF CONNECTICUT STORRS
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-30 至 2014-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8324284
• 关键词: Accounting; Algorithms; Amino Acid Sequence; Benchmarking; Biological; Complex; Computational Technique; Computational algorithm; DNA; DNA Sequence; Data; Data Set; Databases; Dependence; Genes; Genome; Genomics; Internet; Length; Measures; Memory; Metric; Neighborhoods; Neurons; Online Systems; Open Reading Frames; Pattern; Peptide Sequence Determination; Peptides; Plants; Process; Promoter Regions; Protein Family; Proteins; RNA Sequences; Randomized; Regulator Genes; Running; Sampling; Signal Transduction; Solutions; Sorting - Cell Movement; System; Techniques; Tertiary Protein Structure; Testing; Time; base; computing resources; design; next generation; open source; programs; supercomputer; web site

DESCRIPTION (provided by applicant): Multiple genome projects have generated large volumes of DNA, RNA and protein sequence data. While computational pattern searching techniques such as BLAST (a program for measuring sequence similarities) have enabled major discoveries such as the modularity of proteins, much more information can be gained from biological sequence data. However, due to the length and complexity of the patterns, we are limited by the computational algorithms used for motif discovery. Simple Motif Search (SMS), Planted Motif Search (PMS) and Edit-distance Motif Search (EMS) are the three principal paradigms that have been previously used for identifying short functional peptide motifs, transcriptional regulatory elements, composite regulatory patterns, DNA motifs, similarity between families of proteins, etc. Our group has been instrumental in developing algorithms for these problems. Existing pattern-search algorithms for motif search have two major shortcomings: 1) Approximate algorithms do not always identify the correct pattern, but have the advantage that they can be used to look for short and relatively large patterns in large data sets such as genomes. 2) In contrast, an exact algorithm always identifies the correct pattern, but cannot be used to identify complex data patterns in large datasets. To extract more sophisticated patterns from genomic data we need exact algorithms that can be used to analyze genomes for complex patterns with reasonable computational resources. Exact algorithms are currently limited because the run times of these algorithms are exponentially dependent on the parameters involved. For example, the currently best known algorithms for PMS and EMS (on a PC) are expected to take more than a month for patterns of length 27 and more than 5.67 years for patterns of length 31. In this project, we propose to develop the next generation SMS, PMS and EMS algorithms that identify more complex patterns in larger datasets using less computation time and memory. We also propose to develop a web based system that will incorporate PMS and EMS algorithms. Open source versions of all the algorithms and data developed will be made available to users. In addition, the web system will support online processing of queries that involve the solution of PMS and EMS.

描述(申请人提供)：多个基因组计划已经产生了大量的DNA、RNA和蛋白质序列数据。虽然像BLAST(一种测量序列相似性的程序)这样的计算模式搜索技术已经使蛋白质的模块化等重大发现成为可能，但从生物序列数据中可以获得更多的信息。然而，由于模式的长度和复杂性，我们受到用于基序发现的计算算法的限制。简单基序搜索(SMS)、种植基序搜索(PMS)和编辑距离基序搜索(EMS)是以前用于识别短功能肽基序、转录调控元件、复合调控模式、DNA基序、蛋白质家族之间的相似性等三种主要范式。我们的团队为解决这些问题开发了算法。 现有的模式搜索算法有两个主要的缺点：1)近似算法并不总是识别正确的模式，但它们的优点是可以在基因组等大型数据集中寻找较短和相对较大的模式。2)相反，精确的算法总是识别正确的模式，但不能用于识别大型数据集中的复杂数据模式。为了从基因组数据中提取更复杂的模式，我们需要能够用合理的计算资源来分析复杂模式的精确算法。精确的算法目前是有限的，因为这些算法的运行时间以指数方式依赖于所涉及的参数。例如，目前最著名的PMS和EMS算法(在PC上)预计对于长度为27的图案需要一个多月的时间，而对于长度为31的图案则需要超过5.67年的时间。在这个项目中，我们建议开发下一代SMS、PMS和EMS算法，这些算法使用更少的计算时间和内存在更大的数据集中识别更复杂的模式。我们还建议开发一个基于Web的系统，该系统将结合PMS和EMS算法。所有开发的算法和数据的开源版本将向用户提供。此外，网络系统将支持在线处理涉及PMS和EMS解决方案的查询。