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DeepC: Predicting chromatin interactions using megabase scaled deep neural networks and transfer learning (Tiled-C)

ABSTRACT: DeepC: Predicting chromatin interactions using megabase scaled deep neural networks and transfer learning (Tiled-C)

PROVIDER: PRJNA565433 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR10119342_1.fastq.gz	Fastqsanger.gz
	SRR10119342_2.fastq.gz	Fastqsanger.gz
	SRR10119343_1.fastq.gz	Fastqsanger.gz
	SRR10119343_2.fastq.gz	Fastqsanger.gz
	SRR10119344_1.fastq.gz	Fastqsanger.gz

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DeepC: Predicting chromatin interactions using megabase scaled deep neural networks and transfer learning (Tiled-C)

Project description:Understanding 3D genome structure requires high throughput, genome-wide approaches. However, assays for all vs. all chromatin interaction mapping are expensive and time consuming, which severely restricts their usage for large-scale mutagenesis screens or for mapping the impact of sequence variants. Computational models sophisticated enough to grasp the determinants of chromatin folding provide a unique window into the functional determinants of 3D genome structure as well as the effects of genome variation. A chromatin interaction predictor should work at the base pair level but also incorporate large-scale genomic context to simultaneously capture the large scale and intricate structures of chromatin architecture. Similarly, to be a flexible and generalisable approach it should also be applicable to data it has not been explicitly trained on. To develop a model with these properties, we designed a deep neuronal network (deepC) that utilizes transfer learning to accurately predict chromatin interactions from DNA sequence at megabase scale. The model generalizes well to unseen chromosomes and works across cell types, Hi-C data resolutions and a range of sequencing depths. DeepC integrates DNA sequence context on an unprecedented scale, bridging the different levels of resolution from base pairs to TADs. We demonstrate how this model allows us to investigate sequence determinants of chromatin folding at genome-wide scale and to predict the importance of regulatory elements and the impact of sequence variations.

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DeepC: Predicting chromatin interactions using megabase scaled deep neural networks and transfer learning (NG Capture-C)

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Project description:This SuperSeries is composed of the SubSeries listed below.

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Project description:Predicting the impact of noncoding genetic variation requires interpreting it in the context of three-dimensional genome architecture. We have developed deepC, a transfer-learning-based deep neural network that accurately predicts genome folding from megabase-scale DNA sequence. DeepC predicts domain boundaries at high resolution, learns the sequence determinants of genome folding and predicts the impact of both large-scale structural and single base-pair variations.

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Tiled Capture-C

Project description:Tiled Capture-C

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