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Denoising genome-wide histone ChIP-seq with convolutional neural networks
[article]
2016
bioRxiv
pre-print
Preserved Fulltext
Results: We introduce a convolutional denoising algorithm, Coda, that uses convolutional neural networks to learn a mapping from suboptimal to high-quality histone ChIP-seq data. ...
Motivation: Chromatin immunoprecipitation sequencing (ChIP-seq) experiments are commonly used to obtain genome-wide profiles of histone modifications associated with different types of functional genomic ...
Acknowledgements We thank Jin-Wook Lee for his assistance with the AQUAS pipeline and Kyle Loh, Irene Kaplow, and Nasa Sinnott-Armstrong for their helpful feedback and suggestions. ...
doi:10.1101/052118
fatcat:rfhhlvtwozfzxkywsbho5oakgm
Denoising genome-wide histone ChIP-seq with convolutional neural networks
2017
Bioinformatics
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Results: We introduce a convolutional denoising algorithm, Coda, that uses convolutional neural networks to learn a mapping from suboptimal to high-quality histone ChIP-seq data. ...
Motivation: Chromatin immune-precipitation sequencing (ChIP-seq) experiments are commonly used to obtain genome-wide profiles of histone modifications associated with different types of functional genomic ...
Acknowledgements We thank Jin-Wook Lee for his assistance with the AQUAS pipeline and Kyle Loh, Irene Kaplow and Nasa Sinnott-Armstrong for their helpful feedback and suggestions. ...
doi:10.1093/bioinformatics/btx243
pmid:28881977
pmcid:PMC5870713
fatcat:bqthdodvz5b3pixyjqsa7ipis4
AtacWorks: A deep convolutional neural network toolkit for epigenomics
[article]
2019
bioRxiv
pre-print
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AtacWorks uses a deep neural network to learn a mapping between noisy ATAC-seq data and corresponding higher-coverage or higher-quality data. ...
We introduce AtacWorks (https://github.com/clara-genomics/AtacWorks), a method to denoise and identify accessible chromatin from low-coverage or low-quality ATAC-seq data. ...
We thank Ronald Lebofsky and Giulia Schiroli for assistance in generating dscATAC-seq data. We thank Yan Hu for critical reading of the manuscript. ...
doi:10.1101/829481
fatcat:2adiemixg5afnkskgd44tsxz3e
CNN-Peaks: ChIP-Seq peak detection pipeline using convolutional neural networks that imitate human visual inspection
2020
Scientific Reports
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ChIP-seq is one of the core experimental resources available to understand genome-wide epigenetic interactions and identify the functional elements associated with diseases. ...
Recently developed convolutional neural networks (CNN), which are capable of achieving human-like classification accuracy, can be applied to this challenging problem. ...
To resolve this issue, we designed a new approach for calling the peaks in ChIP-seq data based on a convolutional neural network architecture that mimics human visual inspection. ...
doi:10.1038/s41598-020-64655-4
pmid:32404971
fatcat:o53fuem5rnaepcnmgvtdrqi6ku
Computational biology: deep learning
2017
Emerging Topics in Life Sciences
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This exciting class of methods, based on artificial neural networks, quickly became popular due to its competitive performance in prediction problems. ...
In pioneering early work, applying simple network architectures to abundant data already provided gains over traditional counterparts in functional genomics, image analysis, and medical diagnostics. ...
genome-wide
histone ChIP-seq with
convolutional neural
networks [15]
CNN
ChIP-seq
ChIP-seq
Denoise ChiP-seq
data
DNA binding
DeepEnhancer DeepEnhancer: predicting
enhancers by
convolutional ...
doi:10.1042/etls20160025
pmid:33525807
pmcid:PMC7289034
fatcat:qnw2yndsp5aqlnxxshtaipzctu
Deep learning-based enhancement of epigenomics data with AtacWorks
2021
Nature Communications
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AbstractATAC-seq is a widely-applied assay used to measure genome-wide chromatin accessibility; however, its ability to detect active regulatory regions can depend on the depth of sequencing coverage and ...
Here we introduce AtacWorks, a deep learning toolkit to denoise sequencing coverage and identify regulatory peaks at base-pair resolution from low cell count, low-coverage, or low-quality ATAC-seq data ...
We thank Ronald Lebofsky and Giulia Schiroli for assistance in generating dscATAC-seq data. We thank Yan Hu for critical reading of the paper. ...
doi:10.1038/s41467-021-21765-5
pmid:33686069
fatcat:6vmjxm7ikrcsbftyxz73z2uzfa
Deep Learning for Genomics: A Concise Overview
[article]
2018
arXiv
pre-print
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In this paper, we briefly discuss the strengths of different deep learning models from a genomic perspective so as to fit each particular task with a proper deep architecture, and remark on practical considerations ...
In parallel with the urgent demand for robust algorithms, deep learning has succeeded in a variety of fields such as vision, speech, and text processing. ...
A collaboratively written review paper on deep learning, genomics, and precision medicine, now available at https://greenelab.github.io/deep-review/ ...
arXiv:1802.00810v2
fatcat:u6s7pz2p6jdxzodz5k34it2hiu
Deep Learning Methodologies for Genomic Data Prediction: Review
2021
Journal of Artificial Intelligence for Medical Sciences
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With the introduction of high-throughput sequencing techniques, researchers now can analyze and produce a large amount of genomic datasets and this has aided the classification of genomic studies as a ...
We outline the limitations of deep learning methodologies when dealing with genomic data and we conclude that advancement in deep learning methodologies will help rejuvenate genomic research and build ...
There are many data types that are readily available, such as genomic assays (RNA-seq expression), transcription factor (TF) binding chip-seq data and chromatin accessibility assays (DNase-seq, MNase-seq ...
doi:10.2991/jaims.d.210512.001
fatcat:cyadysvs6bbo7bz6gpcbmjvbuy
Revealing Hi-C subcompartments by imputing high-resolution inter-chromosomal chromatin interactions
[article]
2018
bioRxiv
pre-print
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Recent high-resolution genome-wide mapping of chromatin interactions using Hi-C has revealed that chromosomes in the human genome are spatially segregated into distinct subcompartments. ...
Sniper revealed that chromosomal regions with conserved and more dynamic subcompartment annotations across cell types have different patterns of functional genomic features. ...
We then compared the prediction from SNIPER with histone mark ChIP-seq and DNA replication timing data in GM12878 (Fig. 2D ) obtained from ENCODE (Consortium, 2012) . ...
doi:10.1101/505503
fatcat:zahkmmctr5e3nltiaz6cafwchm
Methods for ChIP-seq analysis: A practical workflow and advanced applications
2020
Methods
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Genome-wide analysis of histone modifications, such as enhancer analysis and genome-wide chromatin state annotation, enables systematic analysis of how the epigenomic landscape contributes to cell identity ...
Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is a central method in epigenomic research. ...
For example, Coda [85] encodes a generative noise process and recovers 305 signals in ChIP-seq data using convolutional neural networks. ...
doi:10.1016/j.ymeth.2020.03.005
pmid:32240773
fatcat:f5tok4lqbjgj5nn3sg5pddfix4
Deep learning applications in single-cell omics data analysis
[article]
2021
bioRxiv
pre-print
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We examine DL applications in a variety of single-cell omics (genomics, transcriptomics, proteomics, metabolomics and multi-omics integration) and address whether DL techniques will prove to be advantageous ...
Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is a widely used technique for mapping TFs, histone changes, and other protein-DNA interactions for genome-wide mapping (Furey 2012 ). ...
Convolutional Neural Network (CNNs) CNNs (LeCun and Bengio 1995) are specialized types of networks that use convolution (the mathematical operation) instead of tensor multiplication (which is done in FFNNs ...
doi:10.1101/2021.11.26.470166
fatcat:3bmpecoza5dedbmwm62jwhfm4e
Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities
2019
Information Fusion
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Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2018 pre-print arXiv:1807.00123v1 fatcat:zrpz2xp7ojgyjdq62xm23zvlmqOther Versions
These dimensions include a myriad of properties describing genome, epigenome, transcriptome, microbiome, phenotype, and lifestyle. ...
It uses a deep convolutional neural network on the sequence to obtain probability predictions of DNase-seq signal. ...
DeepSEA [133] trains a deep convolutional neural network on genomic sequence to predict epigenomic state. It can predict both transcription factor binding and histone modification status. ...
doi:10.1016/j.inffus.2018.09.012
pmid:30467459
pmcid:PMC6242341
fatcat:mjhnzxxv4fbrlgufb7vkg3pz5u
Cross-species regulatory sequence activity prediction
[article]
2019
bioRxiv
pre-print
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Here, we develop a strategy to train deep convolutional neural networks simultaneously on multiple genomes and apply it to learn sequence predictors for large compendia of human and mouse data. ...
We further demonstrate a novel and powerful transfer learning approach to use mouse regulatory models to analyze human genetic variants associated with molecular phenotypes and disease. ...
We can compute this score for every dataset using two forward passes of the convolutional neural network. ...
doi:10.1101/660563
fatcat:vbdyabx6vndnzd4i25hqbc7aku
AIKYATAN: mapping distal regulatory elements using convolutional learning on GPU
2019
BMC Bioinformatics
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Plus, convolutional neural networks (CNN) provide the best-in-class accuracy, superior to the vanilla variant. ...
Specifically, we develop a binary classifier that classifies genome sequences as distal regulatory regions or not, given their histone modifications' combinatorial signatures. ...
The ChIP-seq reads of these histone modifications give us their enhancement level. ...
doi:10.1186/s12859-019-3049-1
pmid:31590652
pmcid:PMC6781298
fatcat:ymqqul65xjg4lc446yyfwvs3fi
Integrated Analysis of Whole Genome and Epigenome Data Using Machine Learning Technology: Toward the Establishment of Precision Oncology
2021
Frontiers in Oncology
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With the completion of the International Human Genome Project, we have entered what is known as the post-genome era, and efforts to apply genomic information to medicine have become more active. ...
In particular, with the announcement of the Precision Medicine Initiative by U.S. ...
neural network. ...
doi:10.3389/fonc.2021.666937
pmid:34055633
pmcid:PMC8149908
fatcat:qxjeqxbxpvbcblv3ysrt4lp5pm
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