Chromatin accessibility prediction via a hybrid deep convolutional neural network

Qiao, Liu; Xia, Fei; Yin, Qijin; Jiang, Rui

doi:10.1093/bioinformatics/btx679

Cited by 76 publications

(46 citation statements)

References 32 publications

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“…Furthermore, the underlying sequence features of these peaks were extracted using neural networks and were shown to recapitulate known TF motifs. This confirmed that TFs play an important role in gene regulation through open, accessible chromatin [78,79]. Fine tuning of parameters is essential for all aforementioned tools [9,33], as the width of open chromatin varies Signal tracks are generated from BAM file (Raw) and bias corrected by HINT-ATAC (Bias corrected).…”

Section: Core Analysis: Peak Callingmentioning

confidence: 71%

From reads to insight: a hitchhiker’s guide to ATAC-seq data analysis

et al. 2020

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Assay of Transposase Accessible Chromatin sequencing (ATAC-seq) is widely used in studying chromatin biology, but a comprehensive review of the analysis tools has not been completed yet. Here, we discuss the major steps in ATAC-seq data analysis, including pre-analysis (quality check and alignment), core analysis (peak calling), and advanced analysis (peak differential analysis and annotation, motif enrichment, footprinting, and nucleosome position analysis). We also review the reconstruction of transcriptional regulatory networks with multiomics data and highlight the current challenges of each step. Finally, we describe the potential of single-cell ATAC-seq and highlight the necessity of developing ATAC-seq specific analysis tools to obtain biologically meaningful insights.

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Section: Core Analysis: Peak Callingmentioning

confidence: 71%

From reads to insight: a hitchhiker’s guide to ATAC-seq data analysis

et al. 2020

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“…And it is very meaningful to explain biological meaning in the process of training of CNN with visualization. Recently, many studies on biological computing prediction classifications Li et al 2017a;Liu et al 2017c;Zeng et al 2018) involving CNNs have used convolution kernels of the first layer to extract informative motifs from massive sequence data sets. These followed on from the heuristic work from Deepbind (Alipanahi et al 2015) that generated a position weight matrix (PWM) by aligning all matched sequence segments and calculating the frequency for each kernel.…”

Section: Learned and Analyzed Motifs From Cnn Kernelmentioning

confidence: 99%

Gene2vec: gene subsequence embedding for prediction of mammalian N⁶-methyladenosine sites from mRNA

Zou

Xing

Wei

et al. 2018

RNA

449

214

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N 6 -Methyladenosine (m 6 A) refers to methylation modification of the adenosine nucleotide acid at the nitrogen-6 position. Many conventional computational methods for identifying N 6 -methyladenosine sites are limited by the small amount of data available. Taking advantage of the thousands of m 6 A sites detected by high-throughput sequencing, it is now possible to discover the characteristics of m 6 A sequences using deep learning techniques. To the best of our knowledge, our work is the first attempt to use word embedding and deep neural networks for m 6 A prediction from mRNA sequences. Using four deep neural networks, we developed a model inferred from a larger sequence shifting window that can predict m 6 A accurately and robustly. Four prediction schemes were built with various RNA sequence representations and optimized convolutional neural networks. The soft voting results from the four deep networks were shown to outperform all of the stateof-the-art methods. We evaluated these predictors mentioned above on a rigorous independent test data set and proved that our proposed method outperforms the state-of-the-art predictors. The training, independent, and cross-species testing data sets are much larger than in previous studies, which could help to avoid the problem of overfitting. Furthermore, an online prediction web server implementing the four proposed predictors has been built and is available at http://server. malab.cn/Gene2vec/.

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“…In the recent years, CNNs have been successfully used in a wide spectrum of fields such as computer vision (Razavian, et al, 2014) and natural language processing (Vinyals, et al, 2015). In bioinformatics, CNNs have been used to predict regulatory elements , chromatin accessibility (Liu, et al, 2018) and epigenetic states of a DNA fragment Zhou and Troyanskaya, 2015) , as well as explain functional implications of genetic variants (Zhou and Troyanskaya, 2015).…”

Section: Introductionmentioning

confidence: 99%

Integrating distal and proximal information to predict gene expression via a densely connected convolutional neural network

Zeng

Wang

Jiang

2018

Preprint

Self Cite

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Motivation: Interactions among such cis-regulatory elements as enhancers and promoters are main driving forces shaping context-specific chromatin structure and gene expression. Although there have been computational methods for predicting gene expression from genomic and epigenomic information, most of them overlook long-range enhancer-promoter interactions, due to the difficulty in precisely linking regulatory enhancers to target genes. Recently, a novel high-throughput experimental approach named HiChIP has been developed and generating comprehensive data on high-resolution interactions between promoters and distal enhancers. On the other hand, plenty of studies have suggested that deep learning achieves state-of-the-art performance in epigenomic signal prediction, and thus promoting the understanding of regulatory elements. In consideration of these two factors, we integrate proximal promoter sequences and HiChIP distal enhancer-promoter interactions to accurately model gene expression. Results: We propose DeepExpression, a densely connected convolutional neural network to predict gene expression using both promoter sequences and enhancer-promoter interactions. We demonstrate that our model consistently outperforms baseline methods not only in the classification of binary gene expression status but also in the regression of continuous gene expression levels, in both cross-validation experiments and cross-cell lines predictions. We show that sequential promoter information is more informative than experimental enhancer information while enhancer-promoter interactions are most beneficial from those within ±100 kbp around the TSS of a gene. We finally visualize motifs in both promoter and enhancer regions and show the match of identified sequence signatures and known motifs. We expect to see a wide spectrum of applications using HiChIP data in deciphering the mechanism of gene regulation.

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Chromatin accessibility prediction via a hybrid deep convolutional neural network

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 76 publications

References 32 publications

From reads to insight: a hitchhiker’s guide to ATAC-seq data analysis

From reads to insight: a hitchhiker’s guide to ATAC-seq data analysis

Gene2vec: gene subsequence embedding for prediction of mammalian N⁶-methyladenosine sites from mRNA

Integrating distal and proximal information to predict gene expression via a densely connected convolutional neural network

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Chromatin accessibility prediction via a hybrid deep convolutional neural network

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 76 publications

References 32 publications

From reads to insight: a hitchhiker’s guide to ATAC-seq data analysis

From reads to insight: a hitchhiker’s guide to ATAC-seq data analysis

Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA

Integrating distal and proximal information to predict gene expression via a densely connected convolutional neural network

Contact Info

Product

Resources

About

Gene2vec: gene subsequence embedding for prediction of mammalian N⁶-methyladenosine sites from mRNA