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An integrated CUT&RUN quality control workflow for histone modifications and transcription factors

ABSTRACT: An integrated CUT&RUN quality control workflow for histone modifications and transcription factors

PROVIDER: PRJNA722185 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR14243163_1.fastq.gz	Fastqsanger.gz
	SRR14243163_2.fastq.gz	Fastqsanger.gz
	SRR14243164_1.fastq.gz	Fastqsanger.gz
	SRR14243164_2.fastq.gz	Fastqsanger.gz
	SRR14243165_1.fastq.gz	Fastqsanger.gz

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An integrated CUT&RUN quality control workflow for histone modifications and transcription factors

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ssvQC: an integrated CUT&RUN quality control workflow for histone modifications and transcription factors.

Project description:ObjectiveAmong 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.ResultsWe 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 .

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Genome-wide profiling of transcription factor-DNA binding interactions in Candida albicans: a comprehensive CUT&RUN method and data analysis workflow

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