Physically modeling cross-hybridization error in gene expression microarrays by a novel Boltzmann partition function algorithm for probe-specific position-dependent free energy

交叉杂交是基因表达测量中最重要的噪声源。 目前许多评估基因信号的方法将数据拟合到统计模型;相比之下，研究人员将开发一种基于分子动力学的算法来计算探针与特异性（即预期）靶分子以及非特异性（即非预期）靶分子之间的杂交自由能。 这种新的方法考虑到这样一个事实，即两个分子在水溶液中的杂交热力学不同于一个分子（探针）被拴在载玻片上的情况。一个新的探针特异性的位置依赖性杂交配分函数将通过动态规划算法PPH使用自由能参数从特纳，Santalucia和杉本实验室计算。配分函数说明探针和靶标之间所有可能的部分杂交以及完全杂交的（玻尔兹曼加权）总和，包括探针和靶标两者的二级结构。将PPH应用于所有探针和所有（特异性和非特异性）靶标在计算上是不可行的，因此将开发算法PPHx来计算探针和代表所有非特异性靶标的马尔可夫模型之间的杂交自由能。 从配分函数值Z立即获得包封自由能，并且可以用于从微阵列荧光强度值导出信使RNA的浓度。 高密度寡核苷酸阵列（基因表达阵列、平铺阵列、单核苷酸多态性阵列、microRNA阵列等）构成了分子生物学中发现基因及其功能的有力工具，在群体生物学、系统生物学、病理生物学和其他领域具有深远的应用。在该方法中，将荧光标记的cRNA或源自信使RNA的cDNA在玻璃载玻片上洗涤，所述玻璃载玻片上附着有数十万至数百万个短cDNA探针。荧光标记的分子和探针之间发生杂交，去除未杂交的分子，并通过光学扫描装置测量荧光强度。尽管在各种商业平台的技术改进，它仍然是不可能的推断信使RNA的浓度从微阵列荧光强度值，由于交叉杂交（也称为非特异性结合）的噪音。 一个计算机算法来计算的交叉杂交自由能将开发和实施，直接允许一个估计交叉杂交效应。 这项研究的结果将通过网络服务器和源代码的分发、期刊出版物和会议上的演讲公开。由于微阵列技术的普遍使用，这项研究将对分子生物学（遗传学，基因组学，系统生物学，种群生物学）以及疾病病理学和药物剂量和设计产生非常广泛的影响。将通过波士顿学院提供的课程和培训机会确保本科生和研究生一级的教育影响，这些课程和培训机会为女性和少数民族提供特别机会，并在每周的麻省理工学院生物信息学研讨会上提供额外的宣传。

Cross-hybridization is singly the most important source of noise in gene expression measurements. Many current approaches to assess gene signal fit data to statistical models; in contrast, the investigators will develop a thermodynamics-based algorithm to compute the hybridization free energy between probe and specific (i.e. intended) target as well as non-specific (i.e. non-intended) target molecules. This new approach takes into consideration the fact that thermodynamics of hybridization for two molecules in aequeous solution is different that than where one molecule (probe) is tethered to a glass slide. A new probe-specific position-dependent hybridization partition function will be computed by a dynamic programming algorithm PPH using free energy parameters from labs of Turner, Santalucia, and Sugimoto. The partition function accounts for (Boltzmann-weighted) sum of all possible partial as well as complete hybridizations betwenn probe and target, including secondary structure of both probe and target. Applying PPH to all probes and all (specific and non-specific) targets is not computationally feasible, so the algorithm PPHx will be developed to compute the hybridization free energy between probe and a Markov model representing all non-specific targets. Ensemble free energies are immediately obtained from partition function values Z, and can be used to derive concentrations of messenger RNA from microarray fluorescence intensity values. High-density oligonucleotide arrays (gene-expression arrays, tiling arrays, single-nucleotide polymorphism arrays, microRNA arrays, etc.) constitute a powerful tool in molecular biology for the discovery of genes and their function, with far-reaching applications in population biology, systems biology, pathobiology and other fields. In this method, fluorescently tagged cRNA or cDNA derived from messenger RNA is washed over a glass slide to which hundreds of thousands up to millions of short cDNA probes are attached. Hybridization between fluorescently tagged molecules and probes occurs, non-hybridized molecules are removed, and fluorescence intensities are measured by an optical scanning device. Despite technical improvements in various commercial platforms, it is still not possible to infer messenger RNA concentrations from microarray fluorescence intensity values, due to noise from cross-hybridization (also called non-specific binding). A computer algorithm to compute the cross-hybridization free energy will be developed and implemented, directly allowing one to estimate cross-hybridization effects. Findings from this research will be made publicly accessible through a web server and distribution of source code, by journal publications, and presentations in meetings. Due to the prevalent use of microarray technology, this research will have a very broad impact on molecular biology (genetics, genomics, systems biology, population biology) as well as to disease pathology and drug dosage and design. Educational impact at the undergraduate and graduate level will be ensured by courses and training opportunities offered at Boston College, which provide special opportunities for females and minorities, with additional outreach provided in the weekly MIT Bioinformatics Seminar.