Dense neural networks for predicting chromatin conformation

Farré, Pau; Heurteau, Alexandre; Cuvier, Olivier; Emberly, Eldon

doi:10.1186/s12859-018-2286-z

Cited by 19 publications

(9 citation statements)

References 51 publications

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“…A predictor of chromatin state representative sequences. A DNN model was built with one convolution filter (forward model) to predict a 1D chromatin state sequence representation of the chromatin structure in Drosophila [ 50 ]. This model consisted of one convolution layer and a fully connected neural network and was trained to predict a \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$w \times w$\end{document} Hi-C contact matrix using a genomic sequence of length \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$3w$\end{document} and its overlapping peaks with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$M$\end{document} chromatin factors.…”

Section: Deep Learning Model Interpretation In Bioinformaticsmentioning

confidence: 99%

Interpretation of deep learning in genomics and epigenomics

Talukder

Barham

et al. 2020

Briefings in Bioinformatics

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Machine learning methods have been widely applied to big data analysis in genomics and epigenomics research. Although accuracy and efficiency are common goals in many modeling tasks, model interpretability is especially important to these studies towards understanding the underlying molecular and cellular mechanisms. Deep neural networks (DNNs) have recently gained popularity in various types of genomic and epigenomic studies due to their capabilities in utilizing large-scale high-throughput bioinformatics data and achieving high accuracy in predictions and classifications. However, DNNs are often challenged by their potential to explain the predictions due to their black-box nature. In this review, we present current development in the model interpretation of DNNs, focusing on their applications in genomics and epigenomics. We first describe state-of-the-art DNN interpretation methods in representative machine learning fields. We then summarize the DNN interpretation methods in recent studies on genomics and epigenomics, focusing on current data- and computing-intensive topics such as sequence motif identification, genetic variations, gene expression, chromatin interactions and non-coding RNAs. We also present the biological discoveries that resulted from these interpretation methods. We finally discuss the advantages and limitations of current interpretation approaches in the context of genomic and epigenomic studies. Contact:xiaoman@mail.ucf.edu, haihu@cs.ucf.edu

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Section: Deep Learning Model Interpretation In Bioinformaticsmentioning

confidence: 99%

Interpretation of deep learning in genomics and epigenomics

Talukder

Barham

et al. 2020

Briefings in Bioinformatics

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“…Neural networks of multiple layers of decision-making nodes are able to learn features in a dataset and have been used to infer gene expressions, and similar techniques have been applied to reduce noise in ChIP-seq data [77]. The ability to predict gene expressions [78], and epigenetic features such as chromatin structures [79], and enhancer sites [80] theoretically make deep learning also applicable in identifying peaks in Chem-seq data, considering the presence of high-background noise and nonuniform read distributions. Just as sequencing data can be thought of as images, they can be treated as inputs for convolutional neural networks and be processed similarly.…”

Section: Perspectivesmentioning

confidence: 99%

The Road Not Taken with Pyrrole-Imidazole Polyamides: Off-Target Effects and Genomic Binding

Lin

Nagase

2020

Biomolecules

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The high sequence specificity of minor groove-binding N-methylpyrrole-N-methylimidazole polyamides have made significant advances in cancer and disease biology, yet there have been few comprehensive reports on their off-target effects, most likely as a consequence of the lack of available tools in evaluating genomic binding, an essential aspect that has gone seriously underexplored. Compared to other N-heterocycles, the off-target effects of these polyamides and their specificity for the DNA minor groove and primary base pair recognition require the development of new analytical methods, which are missing in the field today. This review aims to highlight the current progress in deciphering the off-target effects of these N-heterocyclic molecules and suggests new ways that next-generating sequencing can be used in addressing off-target effects.

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“…The machine learning approaches used in these works include generalized linear models (Ibn-Salem & Andrade-Navarro, 2019), random forest (Bkhetan & Plewczynski, 2018;, other ensemble models (Whalen, Truty & Pollard, 2016), and neural networks: multi-layer perceptron , dense neural networks (Zeng, Wu & Jiang, 2018;Farré et al, 2018;Li, Wong & Jiang, 2019), convolutional neural networks (Schreiber et al, 2017), generative adversarial networks (Liu, Lv & Jiang, 2019), and recurrent neural networks (Cristescu et al, 2018;Singh et al, 2019;Gan, Li & Jiang, 2019).…”

Section: Introductionmentioning

confidence: 99%

A machine learning framework for the prediction of chromatin folding inDrosophilausing epigenetic features

Rozenwald

Galitsyna

Sapunov

et al. 2020

PeerJ Computer Science

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Technological advances have lead to the creation of large epigenetic datasets, including information about DNA binding proteins and DNA spatial structure. Hi-C experiments have revealed that chromosomes are subdivided into sets of self-interacting domains called Topologically Associating Domains (TADs). TADs are involved in the regulation of gene expression activity, but the mechanisms of their formation are not yet fully understood. Here, we focus on machine learning methods to characterize DNA folding patterns in Drosophila based on chromatin marks across three cell lines. We present linear regression models with four types of regularization, gradient boosting, and recurrent neural networks (RNN) as tools to study chromatin folding characteristics associated with TADs given epigenetic chromatin immunoprecipitation data. The bidirectional long short-term memory RNN architecture produced the best prediction scores and identified biologically relevant features. Distribution of protein Chriz (Chromator) and histone modification H3K4me3 were selected as the most informative features for the prediction of TADs characteristics. This approach may be adapted to any similar biological dataset of chromatin features across various cell lines and species. The code for the implemented pipeline, Hi-ChiP-ML, is publicly available: https://github.com/MichalRozenwald/Hi-ChIP-ML

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Dense neural networks for predicting chromatin conformation

Cited by 19 publications

References 51 publications

Interpretation of deep learning in genomics and epigenomics

Interpretation of deep learning in genomics and epigenomics

The Road Not Taken with Pyrrole-Imidazole Polyamides: Off-Target Effects and Genomic Binding

A machine learning framework for the prediction of chromatin folding inDrosophilausing epigenetic features

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