PICS：ChIP-seq的概率推断
PICS: Probabilistic Inference for ChIP-seq
摘要：
ChIP-seq结合了染色质免疫沉淀和大规模并行短读测序技术，能够以比ChIP-chip更高的灵敏度、特异性和空间分辨率分析体内全基因组范围的转录因子-DNA关联。虽然ChIP-seq为研究提供了新的机会，但它也为统计分析带来了新的挑战，这些挑战来自所表征的生物系统的复杂性及其数字序列数据的可变性和偏差。我们提出了一种称为PICS（Probably Inference for ChIP-seq）的方法，用于从ChIP-seq比对读取数据中提取信息，以识别由转录因子结合的区域。PICS通过对定向读段的局部浓度进行建模来识别富集区域，并使用DNA片段长度先验信息通过贝叶斯分层t混合模型来区分紧密相邻的结合事件。它的每个事件片段长度估计也允许它从具有非典型长度的分析区域中删除。PICS使用预先计算的全基因组读段可映射性概况和截短的t分布来调整由于局部基因组重复性而缺失的读段的结合事件模型。它估计模型参数中的不确定性，这些参数可用于定义结合事件位置的置信区域和过滤估计值。最后，PICS计算相对于对照样本的每事件富集分数，并且可以使用对照样本来估计错误发现率。我们使用已发表的来自人类细胞系的GABP和FOXA 1数据集，将PICS与替代方法MACS、QuEST和CisGenome进行比较，发现PICS预测的结合位点与计算预测的结合基序更一致。
英文摘要：
ChIP-seq, which combines chromatin immunoprecipitation with massively parallel short-read sequencing, can profile in vivo genome-wide transcription factor-DNA association with higher sensitivity, specificity and spatial resolution than ChIP-chip. While it presents new opportunities for research, ChIP-seq poses new challenges for statistical analysis that derive from the complexity of the biological systems characterized and the variability and biases in its digital sequence data. We propose a method called PICS (Probabilistic Inference for ChIP-seq) for extracting information from ChIP-seq aligned-read data in order to identify regions bound by transcription factors. PICS identifies enriched regions by modeling local concentrations of directional reads, and uses DNA fragment length prior information to discriminate closely adjacent binding events via a Bayesian hierarchical t-mixture model. Its per-event fragment length estimates also allow it to remove from analysis regions that have atypical lengths. PICS uses pre-calculated, whole-genome read mappability profiles and a truncated t-distribution to adjust binding event models for reads that are missing due to local genome repetitiveness. It estimates uncertainties in model parameters that can be used to define confidence regions on binding event locations and to filter estimates. Finally, PICS calculates a per-event enrichment score relative to a control sample, and can use a control sample to estimate a false discovery rate. We compared PICS to the alternative methods MACS, QuEST, and CisGenome, using published GABP and FOXA1 data sets from human cell lines, and found that PICS' predicted binding sites were more consistent with computationally predicted binding motifs.
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