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FLARE: A fast and flexible peak-calling pipeline for identifying RNA editing foci

ABSTRACT: FLARE: A fast and flexible peak-calling pipeline for identifying RNA editing foci

PROVIDER: PRJNA889435 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR21863810.fastq.gz	Fastqsanger.gz
	SRR21863811.fastq.gz	Fastqsanger.gz
	SRR21863812.fastq.gz	Fastqsanger.gz

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FLARE: A fast and flexible peak-calling pipeline for identifying RNA editing foci

Project description:Experimental methods for discovering RNA Binding Protein (RBP) binding sites on target RNAs have recently emerged which employ fusions of RBPs to RNA-editing enzymes (such asAPOBEC1 or ADAR) to “label” mRNA. However, off-target editing, genetic variants and sequencing errors can lead to false positives when using data derived from such approaches, and highlight a need for a robust, statistical approach to prioritizing confident binding sites.

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peakC: a flexible, non-parametric peak calling package for 4C and Capture-C data

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peakC: a flexible, non-parametric peak calling package for 4C and Capture-C data

Project description:High-resolution methods such as 4C and Capture-C enable the study of chromatin loops such as those formed between promoters and enhancers or CTCF/cohesin binding sites. An important aspect of 4C/CapC analyses is the identification of robust peaks in the data for the identification of chromatin loops. Here we present an R package for the analysis of 4C/CapC data. We generated 4C data for 10 viewpoints in 2 tissues in triplicate to test our methods. We developed a non-parametric peak caller based on rank-products. Sampling analysis shows that not read depth but template quality is the most important determinant of success in 4C experiments. By performing peak calling on single experiments we show that the peak calling results are similar to the replicate experiments, but that false positive rates are significantly reduced by performing replicates.

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FLARE: a fast and flexible workflow for identifying RNA editing foci.

Project description:BackgroundFusion of RNA-binding proteins (RBPs) to RNA base-editing enzymes (such as APOBEC1 or ADAR) has emerged as a powerful tool for the discovery of RBP binding sites. However, current methods that analyze sequencing data from RNA-base editing experiments are vulnerable to false positives due to off-target editing, genetic variation and sequencing errors.ResultsWe present FLagging Areas of RNA-editing Enrichment (FLARE), a Snakemake-based pipeline that builds on the outputs of the SAILOR edit site discovery tool to identify regions statistically enriched for RNA editing. FLARE can be configured to analyze any type of RNA editing, including C to U and A to I. We applied FLARE to C-to-U editing data from a RBFOX2-APOBEC1 STAMP experiment, to show that our approach attains high specificity for detecting RBFOX2 binding sites. We also applied FLARE to detect regions of exogenously introduced as well as endogenous A-to-I editing.ConclusionsFLARE is a fast and flexible workflow that identifies significantly edited regions from RNA-seq data. The FLARE codebase is available at https://github.com/YeoLab/FLARE .

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peakC: a flexible, non-parametric peak calling package for 4C and Capture-C data.

Project description:It is becoming increasingly clear that chromosome organization plays an important role in gene regulation. High-resolution methods such as 4C, Capture-C and promoter capture Hi-C (PCHiC) enable the study of chromatin loops such as those formed between promoters and enhancers or CTCF/cohesin binding sites. An important aspect of 4C/Capture-C/PCHiC analyses is the reliable identification of chromatin loops, preferably not based on visual inspection of a DNA contact profile, but on reproducible statistical analysis that robustly scores interaction peaks in the non-uniform contact background. Here, we present peakC, an R package for the analysis of 4C/Capture-C/PCHiC data. We generated 4C data for 13 viewpoints in two tissues in at least triplicate to test our methods. We developed a non-parametric peak caller based on rank-products. Sampling analysis shows that not read depth but template quality is the most important determinant of success in 4C experiments. By performing peak calling on single experiments we show that the peak calling results are similar to the replicate experiments, but that false positive rates are significantly reduced by performing replicates. Our software is user-friendly and enables robust peak calling for one-vs-all chromosome capture experiments. peakC is available at: https://github.com/deWitLab/peakC.

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Project description:Objectives: To identify gene expression changes in acne flare-up patients, thereby exploring the mechanisms of acne flare-up after treatment. Methods: 11 acne patients and 3 healthy people were divided into 4 groups (group1: 4 with flare-up, group2: 4 with improvement, group3: 3 without obvious changes, group4: healthy control). Peripheral blood of patients before and after isotretinoin or minocycline were collected. RNA-seq were used to detect the gene expression. We applied data in self-contrast and intergroup comparisons. Results: In the self-contrast of group1, 22 upregulated genes were involved in Toll-like receptor signaling pathway and inflammatory response. Comparing group1 and group3 before treatment, 1778 upregulated genes enriched in Th17 cell differentiation, while 57 downregulated genes enriched in defensive response to organism. Conclusions: The gene expression profiles of acne flare-up patients changed. Inflammatory, immune responses played a prominent role in acne flare-up process and relatively weak defensive response to microbes, comedogenesis might be risk factors.

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