Department of Biomedical and Health Sciences, University of Vermont, 106 Carrigan Drive, 302 Rowell, VT, 04505, Burlington, USA.
Department of Biochemistry, University of Vermont, Burlington, VT, USA.
BMC Genomics. 2023 Jan 25;24(1):43. doi: 10.1186/s12864-023-09109-7.
Epigenomic profiling assays such as ChIP-seq have been widely used to map the genome-wide enrichment profiles of chromatin-associated proteins and posttranslational histone modifications. Sequencing depth is a key parameter in experimental design and quality control. However, due to variable sequencing depth requirements across experimental conditions, it can be challenging to determine optimal sequencing depth, particularly for projects involving multiple targets or cell types.
We developed the peaksat R package to provide target read depth estimates for epigenomic experiments based on the analysis of peak saturation curves. We applied peaksat to establish the distinctive read depth requirements for ChIP-seq studies of histone modifications in different cell lines. Using peaksat, we were able to estimate the target read depth required per library to obtain high-quality peak calls for downstream analysis. In addition, peaksat was applied to other sequence-enrichment methods including CUT&RUN and ATAC-seq.
peaksat addresses a need for researchers to make informed decisions about whether their sequencing data has been generated to an adequate depth and subsequently sufficient meaningful peaks, and failing that, how many more reads would be required per library. peaksat is applicable to other sequence-based methods that include calling peaks in their analysis.
ChIP-seq 等表观基因组分析技术已广泛用于绘制染色质相关蛋白和翻译后组蛋白修饰的全基因组富集图谱。测序深度是实验设计和质量控制的关键参数。然而，由于不同实验条件下的测序深度要求不同，确定最佳测序深度可能具有挑战性，特别是对于涉及多个目标或细胞类型的项目。
我们开发了 peaksat R 包，该包基于峰饱和曲线分析，为表观基因组实验提供目标读取深度估计。我们应用 peaksat 来确定不同细胞系中组蛋白修饰的 ChIP-seq 研究的独特读取深度要求。使用 peaksat，我们能够估计每个文库所需的目标读取深度，以获得下游分析的高质量峰调用。此外，peaksat 还应用于其他序列富集方法，包括 CUT&RUN 和 ATAC-seq。
peaksat 满足了研究人员对其测序数据是否已达到足够深度以及随后是否有足够有意义的峰的信息决策需求，如果没有，每个文库还需要多少个读取。peaksat 适用于其他包括在其分析中调用峰的基于序列的方法。
