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Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

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Predicting Chromatin Interactions from DNA Sequence Using DeepC.

Ron Schwessinger¹

¹MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.

Methods in Molecular Biology (Clifton, N.J.)

|February 1, 2023

Summary

This summary is machine-generated.

We present a deep learning workflow, deepC, to predict 3D genome structure and gene regulation from DNA sequence. This method accurately models chromatin interactions and the impact of genetic variants on genome architecture.

Keywords:

Chromatin interactions Deep neural networks DeepC Gene regulation Genomic variation Machine learning

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Chromatin Interaction Analysis with Paired-End Tag Sequencing ChIA-PET for Mapping Chromatin Interactions and Understanding Transcription Regulation

Chromatin Interaction Analysis with Paired-End Tag Sequencing ChIA-PET for Mapping Chromatin Interactions and Understanding Transcription Regulation

Published on: April 30, 2012

Area of Science:

Genomics
Computational Biology
Epigenetics

Background:

Understanding 3D genome structure is crucial for deciphering noncoding genetic variant roles in disease.
Current experimental methods for chromatin interaction mapping are resource-intensive, limiting large-scale genomic variation studies.
Existing computational models struggle with coarse resolution or capturing long-range DNA sequence dependencies.

Purpose of the Study:

To provide a detailed workflow for predicting 3D genome architecture using the deepC deep neural network.
To enable accurate prediction of cell type-specific chromatin interactions from DNA sequence alone.
To demonstrate the application of deepC in assessing the impact of sequence variations on genome structure.

Main Methods:

Utilizing deepC, a deep neural network with dilated convolutional layers for large sequence context interpretation at single base pair resolution.
Employing transfer learning with pre-trained convolutional weights on diverse chromatin features across cell types.
Implementing a comprehensive workflow including data pre-processing, model training, and prediction of chromatin interactions.

Main Results:

deepC accurately predicts chromatin interactions from DNA sequence at megabase scale.
Transfer learning enables cell type-specific chromatin interaction prediction without cell type-specific training data.
The workflow facilitates the analysis of sequence variations' effects on genome architecture.

Conclusions:

The deepC workflow offers a scalable and efficient computational approach to study 3D genome organization.
This method advances the understanding of noncoding variant effects on gene regulation and disease.
deepC provides a powerful tool for predicting and analyzing genome architecture and its variations.