ABI Innovation: New methods for multiple sequence alignment with improved accuracy and scalability

ABI Innovation：多序列比对的新方法，具有更高的准确性和可扩展性

• 批准号: 1458652
• 负责人: Tandy Warnow
• 金额: $ 86.16万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-15 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1458652&HistoricalAwards=false
• 关键词: ABI; Innovation; New; methods; multiple

Multiple sequence alignment (MSA) is one of the most basic bioinformatics steps, in which a set of molecular sequences (i.e., DNA, RNA, or amino acid sequences) are arranged inside a matrix to identify corresponding positions. MSA calculation is a fundamental first step in many biological analyses. Because of its broad applicability and importance, many MSA methods have been developed and are in wide use today. Unfortunately, many real world biological datasets have features (large size and fragmentary sequences, for example) that make accurate MSA calculation very difficult. Because poorly estimated alignments result in errors in downstream biological analyses, new MSA techniques are needed that can produce accurate alignments on difficult datasets. This project will develop MSA methods with greatly improved accuracy, and that can analyze the large and heterogeneous sequence datasets being assembled in different biology projects nationally. The project also has a substantial outreach component to women's colleges and minority serving institutions, and summer software schools to train biologists in the use of the project software.Multiple sequence alignment (MSA) and phylogeny estimation are two very basic bioinformatics problems, which sit at the intersection of machine learning, statistical estimation, and evolutionary and structural biology. MSA has particular importance in constructing evolutionary trees, understanding the function and structure of proteins, detecting interactions between proteins, and even genome assembly. Large-scale MSA and phylogeny estimation also require high performance computing and parallel algorithms, in order to provide adequate scalability. The team will develop new machine learning techniques to greatly improve MSA methods, and hence also phylogeny estimation, since it depends on accurate multiple sequence alignments. The core of this project is algorithm development, utilizing a variety of machine learning techniques (including Hidden Markov Models), statistical estimation methods (especially Bayesian MCMC and maximum likelihood), and novel algorithmic strategies, all focused on improving scalability and accuracy. More information about the project can be found at: http://tandy.cs.illinois.edu/MSAproject.html

多序列比对（MSA）是最基本的生物信息学步骤之一，其中一组分子序列（即DNA，RNA或氨基酸序列）在矩阵内布置以识别相应的位置。 MSA计算是许多生物学分析的基本第一步。由于其广泛的适用性和重要性，因此已经开发了许多MSA方法，并已广泛使用。 不幸的是，许多现实世界的生物数据集具有使MSA计算非常困难的功能（例如，大尺寸和碎片序列）。 由于估计较差的对准会导致下游生物学分析中的错误，因此需要新的MSA技术来在困难数据集上产生准确的比对。该项目将以大大提高准确性开发MSA方法，这可以分析在全国范围内不同的生物学项目中组装的大型且异质的序列数据集。该项目还针对女性大学和少数族裔服务机构以及夏季软件学校培训生物学家使用项目软件。多种序列序列对准（MSA）和系统发育估计是两个非常基本的生物信息信息问题，它们位于机器学习，统计估算，统计学估计，统计学，进化和结构生物学和结构。 MSA在构造进化树，了解蛋白质的功能和结构，检测蛋白质之间的相互作用甚至基因组组装方面特别重要。大规模的MSA和系统发育估计还需要高性能计算和平行算法，以提供足够的可伸缩性。该团队将开发新的机器学习技术以极大地改善MSA方法，因此还会进行系统发育估计，因为它取决于准确的多个序列比对。该项目的核心是算法开发，利用各种机器学习技术（包括隐藏的马尔可夫模型），统计估计方法（尤其是贝叶斯MCMC和最大似然）以及新颖的算法策略，都集中在提高可扩展性和准确性上。有关该项目的更多信息，请访问：http：//tandy.cs.illinois.edu/msaproject.html