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Best practices for eCLIP experiments and analysis

ABSTRACT: Best practices for eCLIP experiments and analysis

PROVIDER: PRJNA421341 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR6458660_1.fastq.gz	Fastqsanger.gz
	SRR6458660_2.fastq.gz	Fastqsanger.gz
	SRR6458670_1.fastq.gz	Fastqsanger.gz
	SRR6458670_2.fastq.gz	Fastqsanger.gz
	SRR6458680_1.fastq.gz	Fastqsanger.gz

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Project description:Enhanced cross-linking immunoprecipitation (eCLIP) featuring a size-matched input control has been recently applied to profile the binding sites of more than one hundred RNA binding proteins (RBPs). However computational pipelines and quality control metrics needed to process CLIP data at scale have yet to be well defined. Here, we describe our ENCODE eCLIP processing pipeline (https://github.com/YeoLab/eclip), enabling users to go from raw reads to processed peaks that are enriched above paired input, reproducible across biological replicates, and can be directly compared against the public ENCODE eCLIP resource. In particular, we discuss processing steps designed to address common artifacts, including properly quantifying unique RNA fragments bound by both unique genomic- and repetitive element-mapped reads. Using manual quality annotation of 350 ENCODE eCLIP experiments, we develop metrics for quality assessment of eCLIP experiments prior to and after sequencing, including library yield, number of unique fragments in the library, total binding relative information, and biological reproducibility. In particular, we quantify the commonly believed linkage between depth of sequencing and peak discovery, and derive methods for estimating required sequencing depth based on pre-sequencing metrics. Finally we provide recommendations for the common question of integrating RBP binding information with RNA-seq to generate splicing maps representing the positional effect of binding on alternative splicing. These pipelines and QC metrics enable large-scale processing and analysis of eCLIP data, and will help to standardize rigorous analysis of RBP binding data.

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Best practices for differential accessibility analysis in single-cell epigenomics

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Project description:R-loops represent an abundant class of large non-B DNA structures in genomes. Even though they form transiently and at modest frequencies, interfering with R-loop formation or dissolution significantly impacts genome stability. Addressing the mechanism(s) of R-loop-mediated genome destabilization requires a precise characterization of their distribution in genomes. A number of independent methods have been developed to visualize and map R-loops, but their results are at times discordant, leading to confusion. Here we review the main existing methodologies underlying R-loop mapping and assess their limitations and the robustness of existing datasets. We offer a set of best practices to improve the reproducibility of maps, hoping that such guidelines could be useful for authors and referees alike. Finally, we offer a possible resolution to the apparent contradictions in R-loop mapping outcomes between antibody-based and RNase H1-based mapping approaches.

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