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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.

2020-07-08 | GSE137436 | GEO

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

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

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DeepC: predicting 3D genome folding using megabase-scale transfer learning

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DeepC: predicting 3D genome folding using megabase-scale transfer learning

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

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DeepC: predicting 3D genome folding using megabase-scale transfer learning.

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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Project description:Investigation of whole genome-derived tiled peptide arrays to identify epitopes associated with autoantibody reactivity in NSCLC as a potential means for early detection. Arrays consisted of 2,781,902 tiled peptides representing 20,193 proteins encoded in the human genome. The detailed analysis in this study is further described in Yan et al. Whole genome-derived tiled peptide arrays detect pre-diagnostic autoantibody signatures in non-small cell lung cancer. Cancer Res. 2019 Feb 5. pii: canres.1536.2018

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E. coli MG1655 transcriptome inferred from a tiled microarray

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