ssvQC: an integrated CUT&RUN quality control workflow for histone modifications and transcription factors
| Autor: | Joseph R. Boyd, Hilde Schjerven, Seth Frietze, Princess D. Rodriguez |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Quality Control
Chromatin Immunoprecipitation Science (General) Computer science QH301-705.5 media_common.quotation_subject computer.software_genre Data type General Biochemistry Genetics and Molecular Biology Workflow Q1-390 Data visualization Quality (business) Biology (General) Data quality control media_common business.industry High-Throughput Nucleotide Sequencing General Medicine Replicate CUT&RUN Histone Code ChIP-seq Research Note Data quality Data analysis Medicine Data mining business computer Peak calling Transcription Factors |
| Zdroj: | BMC Research Notes, Vol 14, Iss 1, Pp 1-7 (2021) BMC Research Notes |
| ISSN: | 1756-0500 |
| Popis: | Objective 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. Results 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. |
| Databáze: | OpenAIRE |
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