Department of Biomedical and Health Sciences, College of Nursing and Health Sciences, University of Vermont, Burlington, VT, USA.
Cellular Molecular Biomedical Sciences Program, University of Vermont, Burlington, VT, 05405, USA.
BMC Res Notes. 2021 Sep 20;14(1):366. doi: 10.1186/s13104-021-05781-8.
Among the different methods to profile the genome-wide patterns of transcription factor binding and histone modifications in cells and tissues, CUT&RUN has emerged as a more efficient approach that allows for a higher signal-to-noise ratio using fewer number of cells compared to ChIP-seq. The results from CUT&RUN and other related sequence enrichment assays requires comprehensive quality control (QC) and comparative analysis of data quality across replicates. While several computational tools currently exist for read mapping and analysis, a systematic reporting of data quality is lacking. Our aims were to (1) compare methods for using frozen versus fresh cells for CUT&RUN and (2) to develop an easy-to-use pipeline for assessing data quality.
We compared a workflow for CUT&RUN with fresh and frozen samples, and present an R package called ssvQC for quality control and comparison of data quality derived from CUT&RUN and other enrichment-based sequence data. Using ssvQC, we evaluate results from different CUT&RUN protocols for transcription factors and histone modifications from fresh and frozen tissue samples. Overall, this process facilitates evaluation of data quality across datasets and permits inspection of peak calling analysis, replicate analysis of different data types. The package ssvQC is readily available at https://github.com/FrietzeLabUVM/ssvQC .
在分析细胞和组织中转录因子结合和组蛋白修饰的全基因组图谱的不同方法中，CUT&RUN 方法的出现更为高效，与 ChIP-seq 相比，它使用更少的细胞数量即可获得更高的信噪比。CUT&RUN 和其他相关序列富集分析的结果需要全面的质量控制（QC）和跨重复数据质量的比较分析。虽然目前有几个用于读取映射和分析的计算工具，但缺乏对数据质量的系统报告。我们的目标是（1）比较使用新鲜和冷冻细胞进行 CUT&RUN 的方法，（2）开发一种用于评估数据质量的简单易用的管道。
我们比较了新鲜和冷冻样本的 CUT&RUN 工作流程，并提出了一个名为 ssvQC 的 R 包，用于 CUT&RUN 和其他基于富集的序列数据的质量控制和数据质量比较。使用 ssvQC，我们评估了来自新鲜和冷冻组织样本的不同 CUT&RUN 方案的转录因子和组蛋白修饰的结果。总体而言，该流程促进了数据集之间的数据质量评估，并允许检查峰调用分析、不同数据类型的重复分析。ssvQC 包可在 https://github.com/FrietzeLabUVM/ssvQC 上获得。
