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使用 SCIDDO 快速检测差异染色质域。

Fast detection of differential chromatin domains with SCIDDO.

机构信息

Institute for Medical Biometry and Bioinformatics, Heinrich Heine University, 40225 Düsseldorf, Germany.

Max Planck Institute for Informatics, Saarland Informatics Campus, 66123 Saarbrücken, Germany.

出版信息

Bioinformatics. 2021 Jun 9;37(9):1198-1205. doi: 10.1093/bioinformatics/btaa960.

Abstract

MOTIVATION

The generation of genome-wide maps of histone modifications using chromatin immunoprecipitation sequencing is a standard approach to dissect the complexity of the epigenome. Interpretation and differential analysis of histone datasets remains challenging due to regulatory meaningful co-occurrences of histone marks and their difference in genomic spread. To ease interpretation, chromatin state segmentation maps are a commonly employed abstraction combining individual histone marks. We developed the tool SCIDDO as a fast, flexible and statistically sound method for the differential analysis of chromatin state segmentation maps.

RESULTS

We demonstrate the utility of SCIDDO in a comparative analysis that identifies differential chromatin domains (DCD) in various regulatory contexts and with only moderate computational resources. We show that the identified DCDs correlate well with observed changes in gene expression and can recover a substantial number of differentially expressed genes (DEGs). We showcase SCIDDO's ability to directly interrogate chromatin dynamics, such as enhancer switches in downstream analysis, which simplifies exploring specific questions about regulatory changes in chromatin. By comparing SCIDDO to competing methods, we provide evidence that SCIDDO's performance in identifying DEGs via differential chromatin marking is more stable across a range of cell-type comparisons and parameter cut-offs.

AVAILABILITY AND IMPLEMENTATION

The SCIDDO source code is openly available under github.com/ptrebert/sciddo.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

动机

使用染色质免疫沉淀测序生成全基因组组蛋白修饰图谱是剖析表观基因组复杂性的标准方法。由于调控相关的组蛋白标记的共同出现及其在基因组上的分布差异,组蛋白数据集的解释和差异分析仍然具有挑战性。为了便于解释,染色质状态分割图谱是一种常用的抽象概念,它结合了单个组蛋白标记。我们开发了工具 SCIDDO,作为一种快速、灵活且具有统计学意义的方法,用于差异分析染色质状态分割图谱。

结果

我们在一个比较分析中展示了 SCIDDO 的实用性,该分析在各种调控环境中并仅使用适度的计算资源来识别差异染色质域(DCD)。我们表明,鉴定出的 DCD 与观察到的基因表达变化高度相关,并且可以恢复大量差异表达基因(DEG)。我们展示了 SCIDDO 直接询问染色质动态的能力,例如在下游分析中的增强子开关,这简化了探索染色质调控变化的具体问题。通过将 SCIDDO 与竞争方法进行比较,我们提供了证据表明,SCIDDO 通过差异染色质标记识别 DEG 的性能在一系列细胞类型比较和参数截止值下更加稳定。

可用性和实现

SCIDDO 的源代码在 github.com/ptrebert/sciddo 上公开可用。

补充信息

补充数据可在生物信息学在线获得。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcc0/8189691/0abe53e956ba/btaa960f1.jpg

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