基于形状的ChIP-Seq峰识别
Shape-based peak identification for ChIP-Seq
摘要：
我们提出了一种新的算法，用于从ChIP-seq实验中识别结合区域。我们的方法识别统计上显着的峰值读取覆盖率的启发，在拓扑数据分析的持久性的概念，并提供了一个非参数的方法，是强大的噪声在实验中。具体来说，我们的方法减少了峰值调用问题的研究基于树的统计数据。我们证明了我们的方法在现有数据集上的准确性，并且我们表明它可以发现以前错过的区域，并且可以更清楚地区分多个结合事件。软件T-PIC（Tree shape Peak Identification for ChIP-Seq）可在http://math.berkeley.edu/~vhower/tpic.html上获得
英文摘要：
We present a new algorithm for the identification of bound regions from ChIP-seq experiments. Our method for identifying statistically significant peaks from read coverage is inspired by the notion of persistence in topological data analysis and provides a non-parametric approach that is robust to noise in experiments. Specifically, our method reduces the peak calling problem to the study of tree-based statistics derived from the data. We demonstrate the accuracy of our method on existing datasets, and we show that it can discover previously missed regions and can more clearly discriminate between multiple binding events. The software T-PIC (Tree shape Peak Identification for ChIP-Seq) is available at http://math.berkeley.edu/~vhower/tpic.html
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