双随机连续时间隐马尔可夫方法分析基因组拼接阵列
Doubly stochastic continuous-time hidden Markov approach for analyzing genome tiling arrays
摘要：
微阵列已经被开发出来，它可以平铺许多不同生物体的整个非重复基因组，允许在整个基因组中无偏地绘制活性转录区域或蛋白质结合位点。这些平铺阵列实验产生了大量的相关数据集，这些数据集具有许多实验伪像，这给需要创新分析方法和高效计算算法的研究人员带来了许多挑战。本文提出了一种双随机潜变量分析方法，用于基于平铺阵列数据的转录本发现和蛋白质结合区定位。该模型的独特之处在于它考虑了探针之间的实际基因组距离。此外，该模型被设计为对交叉杂交和无响应的探针具有鲁棒性，这通常会导致微阵列实验中的假阳性结果。我们将我们的模型应用于转录本发现数据集，以说明我们的方法的一致性。此外，我们将我们的方法应用到一个尖峰实验中，该实验可用作有兴趣开发和比较未来平铺阵列方法的研究人员的基准数据集。结果表明，我们的方法是非常强大的，准确的，可以用于一个单一的样品，没有控制实验，从而支付一些开销成本进行实验的平铺阵列。
英文摘要：
Microarrays have been developed that tile the entire nonrepetitive genomes of many different organisms, allowing for the unbiased mapping of active transcription regions or protein binding sites across the entire genome. These tiling array experiments produce massive correlated data sets that have many experimental artifacts, presenting many challenges to researchers that require innovative analysis methods and efficient computational algorithms. This paper presents a doubly stochastic latent variable analysis method for transcript discovery and protein binding region localization using tiling array data. This model is unique in that it considers actual genomic distance between probes. Additionally, the model is designed to be robust to cross-hybridized and nonresponsive probes, which can often lead to false-positive results in microarray experiments. We apply our model to a transcript finding data set to illustrate the consistency of our method. Additionally, we apply our method to a spike-in experiment that can be used as a benchmark data set for researchers interested in developing and comparing future tiling array methods. The results indicate that our method is very powerful, accurate and can be used on a single sample and without control experiments, thus defraying some of the overhead cost of conducting experiments on tiling arrays.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
