综合评估差异 ChIP-seq 工具可指导最佳算法选择。
Comprehensive assessment of differential ChIP-seq tools guides optimal algorithm selection.
机构信息
Institute for Medical Biochemistry, University of Veterinary Medicine Vienna, Vienna, Austria.
出版信息
Genome Biol. 2022 May 24;23(1):119. doi: 10.1186/s13059-022-02686-y.
BACKGROUND
The analysis of chromatin binding patterns of proteins in different biological states is a main application of chromatin immunoprecipitation followed by sequencing (ChIP-seq). A large number of algorithms and computational tools for quantitative comparison of ChIP-seq datasets exist, but their performance is strongly dependent on the parameters of the biological system under investigation. Thus, a systematic assessment of available computational tools for differential ChIP-seq analysis is required to guide the optimal selection of analysis tools based on the present biological scenario.
RESULTS
We created standardized reference datasets by in silico simulation and sub-sampling of genuine ChIP-seq data to represent different biological scenarios and binding profiles. Using these data, we evaluated the performance of 33 computational tools and approaches for differential ChIP-seq analysis. Tool performance was strongly dependent on peak size and shape as well as on the scenario of biological regulation.
CONCLUSIONS
Our analysis provides unbiased guidelines for the optimized choice of software tools in differential ChIP-seq analysis.
背景
分析不同生物状态下蛋白质的染色质结合模式是染色质免疫沉淀测序（ChIP-seq）的主要应用。目前已经有大量的算法和计算工具可用于对 ChIP-seq 数据集进行定量比较，但它们的性能很大程度上取决于所研究的生物系统的参数。因此，需要对现有的用于差异 ChIP-seq 分析的计算工具进行系统评估，以便根据当前的生物学情况指导对分析工具的最佳选择。
结果
我们通过对真实 ChIP-seq 数据进行计算机模拟和子采样创建了标准化的参考数据集，以代表不同的生物学场景和结合特征。使用这些数据，我们评估了 33 种用于差异 ChIP-seq 分析的计算工具和方法的性能。工具性能强烈依赖于峰的大小和形状，以及生物学调控的场景。
结论
我们的分析为差异 ChIP-seq 分析中软件工具的优化选择提供了无偏指南。
Zotero 插件