SGER: Context Sensitive Hidden Markov Models and Application in Computational Biology

SGER：上下文敏感的隐马尔可夫模型及其在计算生物学中的应用

• 批准号: 0636799
• 负责人: Palghat Vaidyanathan
• 金额: --
• 依托单位: California Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2008-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0636799&HistoricalAwards=false
• 关键词: SGER; Context; Sensitive; Hidden; Markov

The principles of digital signal processing have been used in genomics and proteomics by a number of researchers in the past few years. Digital .filtering and Hidden Markov Model (HMM) techniques have been applied for the identification of protein coding genes in DNA. More recently non coding genes have been emphasized by many researchers and it is now recognized that many types of non coding RNA (nc-RNA) play a major role in living organisms. Computational identification of such RNA has therefore become of great importance. These RNAs are generated from codes in the DNA, and do not code for proteins. Instead they fold into secondary structures and perform their biological function by virtue of these structures. This is what makes the computational identification of ncRNAs very challenging: it is the secondary folding structures that need to be identified rather than primary sequence structures. Such identification cannot be done with conventional HMMs because they correspond to regular grammars which are potentially incapable of identifying most secondary structures. The theory of context sensitive HMMs (cs-HMM) was recently developed towards this goal and there is strong evidence that such HMMs have great potential to identify very complicated secondary structures found in living organisms. A detailed exploration of this idea is therefore extremely timely. This is the main goal of the proposed research. For simple RNA structures such as stem-loops, tRNA cloverleaf structures, and so on, cs-HMM based algorithms have recently been developed. Algorithms that can be used for solving the alignment, scoring and training problems of csHMMs for more complex correlations will be developed in the proposed work. In order for these algorithms to be useful in biology, extensive testing on a large variety of documented sequences will also be performed. Fast algorithms for finding the optimal state sequence of an observed symbol sequence and for training pro.le-csHMMs will be developed. Recent results show that many ncRNAs play important roles in diverse gene regulatory networks. In order to build a more realistic gene regulatory network it is crucial to incorporate ncRNA genes in the network. This is another important aspect of the proposed research.The research is exploratory and unconventional, and is at the cross roads of cutting edge signal processing theory and modern bioinformatics. Its intellectual merit comes from the fact that a deep understanding of the theory of context sensitive hidden Markov models is developed and applied to a practically interesting problem in molecular biology. The impact will clearly be in theoretical signal processing as well as in biology, where non coding genes have been shown to be of great interest in medicine and gene regulation.As for broader impact, it is expected that the proposed research will lead to scholarly journal publications in international journals. Examples include the journals Bioinformatics, BMC bioinformatics, Proc. of the National Academy of Sciences, Nature biotechnology, and IEEE/ACM Transactions on Computational Biology and Bioinformatics. Many conference presentations will also emerge, and so will tutorial articles at various levels of depth and difficulties. Some of the work will be incorporated into the graduate curricula at the California Institute of Technology (Caltech).

在过去的几年中，许多研究人员已将数字信号处理的原理用于基因组学和蛋白质组学。数字。过滤和隐藏的马尔可夫模型（HMM）技术已用于鉴定DNA中蛋白质编码基因。 最近，许多研究人员强调了非编码基因，现在已经认识到，许多类型的非编码RNA（NC-RNA）在生物体中起着重要作用。 因此，这种RNA的计算识别已变得非常重要。 这些RNA是由DNA中的代码生成的，并且不为蛋白质编码。取而代之的是，它们折叠成辅助结构，并凭借这些结构来执行其生物学功能。这就是使NCRNA的计算识别非常具有挑战性的原因：需要鉴定而不是主要序列结构的次要折叠结构。这种识别不能使用常规的HMM进行，因为它们对应于常规语法，这些语法可能无法识别大多数二级结构。上下文敏感的HMM（CS-HMM）的理论最近是针对这个目标的，并且有强有力的证据表明，这种HMM具有巨大的潜力，可以识别生物体中发现的非常复杂的二级结构。因此，对这个想法的详细探索非常及时。这是拟议研究的主要目标。对于简单的RNA结构，例如干循环，tRNA cloverleaf结构等，最近已经开发了基于CS-HMM的算法。可以在拟议的工作中开发出可用于解决CSHMM的对齐，评分和训练问题的算法。为了使这些算法在生物学中有用，还将对各种各样的序列进行广泛的测试。将开发用于找到观察到的符号序列和训练pro.le-cshmms的最佳状态序列的快速算法。最近的结果表明，许多NCRNA在各种基因调节网络中起重要作用。为了构建一个更现实的基因调节网络，将NCRNA基因纳入网络至关重要。这是拟议研究的另一个重要方面。该研究是探索性和非常规的，并且位于尖端信号处理理论和现代生物信息学的跨道路上。它的智力优点源于以下事实：对上下文敏感的隐性马尔可夫模型的深刻理解是开发出来的，并应用于分子生物学中实际上有趣的问题。这种影响显然将在理论信号处理以及生物学中，在这种信号处理中，非编码基因已被证明对医学和基因调节具有极大的兴趣。除了更广泛的影响，预计拟议的研究将导致国际期刊中的学术期刊出版物。示例包括日记生物信息学，BMC生物信息学，Proc。关于计算生物学和生物信息学的国家科学院，自然生物技术学院，自然生物技术和IEEE/ACM交易。许多会议演讲也将出现，教程文章也会在各种深度和困难级别上进行。其中一些工作将纳入加利福尼亚理工学院（CALTECH）的研究生课程中。