Detection of statistically robust interactions from diverse RNA-DNA ligation data [RADICL-seq]
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PROVIDER: PRJNA1140596 | ENA |
REPOSITORIES: ENA
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Project description:Detection of statistically robust interactions from diverse RNA-DNA ligation data
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Project description:Chromatin-localized RNAs play diverse roles in gene regulation and nuclear architecture. Mapping genome-wide RNA-DNA interactions is possible using a variety of molecular methods, including using bridging oligonucleotides to ligate RNA and DNA in proximity. While molecular methods have progressed, a robust computational method for calling biologically meaningful RNA-DNA interactions from these data is lacking. Herein, we present RADIAnT, a reads-to-interactions pipeline for analyzing RNA-DNA ligation data. RADIAnT calls interactions against a dataset-specific, unified background which considers RNA binding site-TSS distance and genomic region bias. By scaling the background with RNA abundance, RADIAnT is sensitive enough to detect specific interactions of lowly expressed transcripts, while remaining specific enough to discount false positive interactions of highly abundant RNAs. RADIAnT outperforms previously proposed methods in the accurate recall of genome-wide Malat1-DNA interactions, and in a use-case, was utilized to identify dynamic chromatin-associated RNAs in the physiologically- and pathlologically-relevant process of endothelial-to-mesenchymal transition.
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Project description:Chromatin-localized RNAs play diverse roles in gene regulation and nuclear architecture. Mapping genome-wide RNA-DNA interactions is possible using a variety of molecular methods, including using bridging oligonucleotides to ligate RNA and DNA in proximity. While molecular methods have progressed, a robust computational method for calling biologically meaningful RNA-DNA interactions from these data is lacking. Herein, we present RADIAnT, a reads-to-interactions pipeline for analyzing RNA-DNA ligation data. RADIAnT calls interactions against a dataset-specific, unified background which considers RNA binding site-TSS distance and genomic region bias. By scaling the background with RNA abundance, RADIAnT is sensitive enough to detect specific interactions of lowly expressed transcripts, while remaining specific enough to discount false positive interactions of highly abundant RNAs. RADIAnT outperforms previously proposed methods in the accurate recall of genome-wide Malat1-DNA interactions, and in a use-case, was utilized to identify dynamic chromatin-associated RNAs in the physiologically- and pathlologically-relevant process of endothelial-to-mesenchymal transition.
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2016-05-18 | E-GEOD-81503 | biostudies-arrayexpress
Project description:Capture Hi-C (CHi-C) is a state-of-the art method for profiling chromosomal interactions involving targeted regions of interest (such as gene promoters) globally and at high resolution. Signal detection in CHi-C data involves a number of statistical challenges that are not observed when using other Hi-C-like techniques. We present a background model, and algorithms for normalisation and multiple testing that are specifically adapted to CHi-C experiments, in which many spatially dispersed regions are captured, such as in Promoter CHi-C. We implement these procedures in CHiCAGO (http://regulatorygenomicsgroup.org/chicago), an open-source package for robust interaction detection in CHi-C. We validate CHiCAGO by showing that promoter-interacting regions detected with this method are enriched for regulatory features and disease-associated SNPs.
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