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比较 ChIP-exo 峰调用程序:数据质量、读取重复和结合亚型的影响。

Comparative analysis of ChIP-exo peak-callers: impact of data quality, read duplication and binding subtypes.

机构信息

Discipline of Biological Engineering, Indian Institute of Technology Gandhinagar, Palaj, Gujarat, 382355, India.

出版信息

BMC Bioinformatics. 2020 Feb 21;21(1):65. doi: 10.1186/s12859-020-3403-3.

Abstract

BACKGROUND

ChIP (Chromatin immunoprecipitation)-exo has emerged as an important and versatile improvement over conventional ChIP-seq as it reduces the level of noise, maps the transcription factor (TF) binding location in a very precise manner, upto single base-pair resolution, and enables binding mode prediction. Availability of numerous peak-callers for analyzing ChIP-exo reads has motivated the need to assess their performance and report which tool executes reasonably well for the task.

RESULTS

This study has focussed on comparing peak-callers that report direct binding events with those that report indirect binding events. The effect of strandedness of reads and duplication of data on the performance of peak-callers has been investigated. The number of peaks reported by each peak-caller is compared followed by a comparison of the annotated motifs present in the reported peaks. The significance of peaks is assessed based on the presence of a motif in top peaks. Indirect binding tools have been compared on the basis of their ability to identify annotated motifs and predict mode of protein-DNA interaction.

CONCLUSION

By studying the output of the peak-callers investigated in this study, it is concluded that the tools that use self-learning algorithms, i.e. the tools that estimate all the essential parameters from the aligned reads, perform better than the algorithms which require formation of peak-pairs. The latest tools that account for indirect binding of TFs appear to be an upgrade over the available tools, as they are able to reveal valuable information about the mode of binding in addition to direct binding. Furthermore, the quality of ChIP-exo reads have important consequences on the output of data analysis.

摘要

背景

ChIP-exo(染色质免疫沉淀-外切酶)技术作为一种重要且通用的技术,相对于传统的 ChIP-seq 技术有了很大的改进,因为它降低了噪音水平,以极高的精确度(精确到单个碱基对)绘制转录因子(TF)的结合位置,并能预测结合模式。由于有许多用于分析 ChIP-exo 读取的峰调用程序,因此需要评估它们的性能,并报告哪些工具在该任务中执行效果较好。

结果

本研究重点比较了报告直接结合事件的峰调用程序和报告间接结合事件的峰调用程序。研究了读取的链特性和数据重复对峰调用程序性能的影响。比较了每个峰调用程序报告的峰的数量,然后比较了报告的峰中存在的注释基序。根据在顶部峰中存在基序来评估峰的显著性。基于识别注释基序和预测蛋白-DNA 相互作用模式的能力来比较间接结合工具。

结论

通过研究本研究中调查的峰调用程序的输出,可以得出结论,即使用自学习算法的工具(即从对齐的读取中估计所有基本参数的工具)比需要形成峰对的算法性能更好。最新的能够解释 TF 间接结合的工具似乎比现有工具有所升级,因为它们除了直接结合之外,还能够揭示关于结合模式的有价值的信息。此外,ChIP-exo 读取的质量对数据分析的输出有重要影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3c1/7035708/a713d86c418c/12859_2020_3403_Fig1_HTML.jpg

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