Evolutionary computation and applications in bioinformatics

进化计算及其在生物信息学中的应用

• 批准号: 238298-2007
• 负责人: Wiese, Kay
• 金额: $ 1.31万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2007
• 资助国家: 加拿大
• 起止时间: 2007-01-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=365250
• 关键词: Evolutionary; computation; applications; bioinformatics

The objectives of this research program are:1) To develop improved combinatorial algorithms for problems in Bioinformatics, particularly for structure prediction of biomolecules, building on the algorithms that have been developed in my lab so far2) To extend jViz.Rna, a visualization tool for RNA secondary structureThe algorithm RnaPredict, developed in my lab and based on evolutionary computation, has been demonstrated to predict certain known RNA secondary structures with very high accuracy (up to 90%) and to have a higher prediction accuracy on certain short and medium length sequences than mfold, the current benchmark for ab initio RNA secondary structure prediction. Particularly, RnaPredict is not as prone to predicting false positives as mfold is. Rnapredict uses a number of advanced operators and representations. I plan to further evaluate, improve, and extend RnaPredict. I have developed a visualization tool for RNA secondary structure which has a number of benefits over existing tools, including: wide support of input and output formats, ability to produce high quality graphics for reproduction, interactive output for further manipulation by the end user, ability to display pseudoknots in a classical structure plot, and the ability to overlay two different structures for comparison. jViz.Rna also produces quantitative and qualitative comparisons between two structures. The quantitative comparison is currently taking into account sensitivity, specificity, and F-measure. I plan to extend jViz.RNA to incorporate a comparison of structures based on common substructures using dual graphs.

本研究计划的目标是：1）开发改进的组合算法在生物信息学的问题，特别是生物分子的结构预测，在我的实验室已经开发的算法的基础上，到目前为止2）扩展jViz.RNA，RNA二级结构的可视化工具RnaPredict算法，在我的实验室开发的和基于进化计算，已被证明预测某些已知的RNA二级结构具有非常高的准确性（高达90%），并有一个更高的预测准确率在某些短和中等长度的序列比mfold，目前的从头RNA二级结构预测的基准。特别是，RnaPredict不像mfold那样容易预测假阳性。Rnapredict使用了许多高级运算符和表示。我计划进一步评估、改进和扩展RnaPredict。我开发了一个RNA二级结构的可视化工具，它比现有的工具有很多优点，包括：广泛支持输入和输出格式，能够产生高质量的图形用于再现，交互式输出，供最终用户进一步操作，能够在经典结构图中显示伪结，以及能够覆盖两个不同的结构进行比较。RNA还产生两种结构之间的定量和定性比较。定量比较目前考虑了灵敏度、特异性和F测量。我计划扩展jViz.RNA，以使用对偶图来合并基于常见子结构的结构比较。