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Chromatin

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DNAcycP: a deep learning tool for DNA cyclizability prediction.

Nucleic acids research
DNA mechanical properties play a critical role in every aspect of DNA-dependent biological processes. Recently a high throughput assay named loop-seq has been developed to quantify the intrinsic bendability of a massive number of DNA fragments simult...

Convolutional Neural Networks for Classifying Chromatin Morphology in Live-Cell Imaging.

Methods in molecular biology (Clifton, N.J.)
Chromatin is highly structured, and changes in its organization are essential in many cellular processes, including cell division. Recently, advances in machine learning have enabled researchers to automatically classify chromatin morphology in fluor...

TempoMAGE: a deep learning framework that exploits the causal dependency between time-series data to predict histone marks in open chromatin regions at time-points with missing ChIP-seq datasets.

Bioinformatics (Oxford, England)
MOTIVATION: Identifying histone tail modifications using ChIP-seq is commonly used in time-series experiments in development and disease. These assays, however, cover specific time-points leaving intermediate or early stages with missing information....

Epitome: predicting epigenetic events in novel cell types with multi-cell deep ensemble learning.

Nucleic acids research
The accumulation of large epigenomics data consortiums provides us with the opportunity to extrapolate existing knowledge to new cell types and conditions. We propose Epitome, a deep neural network that learns similarities of chromatin accessibility ...

iDHS-Deep: an integrated tool for predicting DNase I hypersensitive sites by deep neural network.

Briefings in bioinformatics
DNase I hypersensitive site (DHS) refers to the hypersensitive region of chromatin for the DNase I enzyme. It is an important part of the noncoding region and contains a variety of regulatory elements, such as promoter, enhancer, and transcription fa...

A sequence-based deep learning approach to predict CTCF-mediated chromatin loop.

Briefings in bioinformatics
Three-dimensional (3D) architecture of the chromosomes is of crucial importance for transcription regulation and DNA replication. Various high-throughput chromosome conformation capture-based methods have revealed that CTCF-mediated chromatin loops a...

Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles.

Briefings in bioinformatics
The exploration of three-dimensional chromatin interaction and organization provides insight into mechanisms underlying gene regulation, cell differentiation and disease development. Advances in chromosome conformation capture technologies, such as h...

DeepYY1: a deep learning approach to identify YY1-mediated chromatin loops.

Briefings in bioinformatics
The protein Yin Yang 1 (YY1) could form dimers that facilitate the interaction between active enhancers and promoter-proximal elements. YY1-mediated enhancer-promoter interaction is the general feature of mammalian gene control. Recently, some comput...

Deep-joint-learning analysis model of single cell transcriptome and open chromatin accessibility data.

Briefings in bioinformatics
Simultaneous profiling transcriptomic and chromatin accessibility information in the same individual cells offers an unprecedented resolution to understand cell states. However, computationally effective methods for the integration of these inherent ...