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ITR: Computational Techniques for Applied Bioinformatics

ITR: Computational Techniques for Applied Bioinformatics
ITR:应用生物信息学计算技术
批准号:
0085910
负责人:
Tao Jiang
金额:
$48.99万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2000
资助国家:
美国
项目状态:
已结题
起止时间:
2000-09-01 至 2004-08-31

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中文摘要
翻译
该奖项为一个合作项目提供支持,该项目涉及两名计算机科学家和一名植物遗传学家,他们将为生物信息学领域的几个重要问题开发新方法、高效算法和软件工具。这项支持的工作包括研究计算范例,如四重奏方法、交互系统和近似算法,应用于基因序列的进化分析、基因复制和叶绿体基因组中的水平转移事件,叶绿体是所有植物中发现的一种含有DNA的细胞器。其他研究将通过改进和测试最近开发的序列熵估计器和基因组序列的距离度量来检查基因组的信息含量。这方面的工作将包括应用改进的方法对线粒体、病毒、叶绿体和细菌的基因组数据进行排序。其他努力将解决同时多序列比对和进化树重建的重要问题。将要开发的多序列比对方法是基于使用具有很少或没有间隙的保守区块,以及恒定条带内的多个比对。第四个领域的工作将开发有效的算法,用于计算长度高达数十亿个核苷酸的基因组序列中的短和长散布的核元素(正弦和线条)。由于必须分析大量数据,这将需要开发或适应适当的外部存储算法。随着新的分析技术产生前所未有的大量基因数据,生物、生物医学和制药研究正在经历一场重大革命。对这些信息的探索在很大程度上取决于数据分析、存储和检索的先进计算和软件技术的发展。在这种依赖下,近年来出现了一个新的跨学科研究领域--生物信息学(或计算分子生物学)。通过该奖项支持的工作预计将对该领域作出基础性和应用性的贡献。基础研究将探索和阐明解决生物信息学算法问题的新思路和新方法,应用研究将涉及植物基因组学实践中软件工具的开发和评估。虽然这些努力的目的是提高对叶绿体基因组进化的理解,但这些方法应该很容易扩展到对所有其他基因组的分析。
英文摘要
This award provides support for a collaborative project involving two computer scientists and a plant geneticist who will develop new methods, efficient algorithms, and software tools for several important problems in the field of bioinformatics. This supported work includes research into computational paradigms such as quartet methods, interactive systems, and approximation algorithms as applied to the evolutionary analysis of gene sequences, gene duplication, and horizontal transfer events in the genomes of chloroplasts, a DNA-containing organelle found in all plants. Additional studies will examine the information content of genomes by improving and testing a recently developed sequence entropy estimator and a distance metric for genomic sequences. Work in this area will include the application of the improved methods to sequence data from the genomes of mitochondria, viruses, chloroplasts and bacteria. Other efforts will address the important problem of simultaneous multiple sequence alignment and evolutionary tree reconstruction. The multiple sequence alignment approaches to be developed are based on the use of conserved blocks that have few or no gaps, and multiple alignments within a constant band. Work in a fourth area will develop efficient algorithms for computing short and long interspersed nuclear elements (SINES and LINES) in genomic sequences of lengths up to billions of nucleotides. Because of the large amounts of data that must be analyzed, this will require the development or adaptation of appropriate external memory algorithms. Biological, biomedical and pharmaceutical research is undergoing a major revolution as new analytical technologies produce unprecedented amounts of genetic data. The exploration of this information is critically dependent upon the development of advanced computational and software techniques for data analysis, storage and retrieval. From this dependency, a new interdisciplinary research field, bioinformatics (or computational molecular biology) has emerged in recent years. The work supported through this award is expected to make both fundamental and applied contributions to the field. The fundamental research will explore and explicate new ideas and methods for solving algorithmic problems in bioinformatics and the applied research will involve the development and evaluation of software tools in the practice of plant genomics. Although the efforts are aimed at improving the understanding of the evolution of chloroplast genomes, the approaches should be readily extensible to analysis of all other genomes.
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Extremal Problems on Graphs and Hypergraphs
  • 批准号:
    1855542
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $10.04万
  • 财政年份:
    2019
  • 负责人:
    Tao Jiang
  • 依托单位:
EAGER: Transcript-Based Differential Expression Analysis for Population Data Without Predefined Conditions
  • 批准号:
    1646333
  • 项目类别:
    Standard Grant
  • 资助金额:
    $20.0万
  • 财政年份:
    2016
  • 负责人:
    Tao Jiang
  • 依托单位:
Extremal problems for sparse hypergraphs and graphs
  • 批准号:
    1400249
  • 项目类别:
    Standard Grant
  • 资助金额:
    $13.32万
  • 财政年份:
    2014
  • 负责人:
    Tao Jiang
  • 依托单位:
Collaborative Research: ABI Innovation: Genome-Wide Inference of mRNA Isoforms and Abundance Estimation from Biased RNA-Seq Reads
  • 批准号:
    1262107
  • 项目类别:
    Standard Grant
  • 资助金额:
    $56.99万
  • 财政年份:
    2013
  • 负责人:
    Tao Jiang
  • 依托单位:
国内基金
海外基金
Computational Methods for Analyzing Toponome Data