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Denoising genome-wide histone ChIP-seq with convolutional neural networks [article]

Pang Wei Koh, Emma Pierson, Anshul Kundaje
2016 bioRxiv   pre-print
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

Pang Wei Koh, Emma Pierson, Anshul Kundaje
2017 Bioinformatics  
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]

Avantika Lal, Zachary D Chiang, Nikolai Yakovenko, Fabiana M Duarte, Johnny Israeli, Jason D Buenrostro
2019 bioRxiv   pre-print
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

Dongpin Oh, J. Seth Strattan, Junho K. Hur, José Bento, Alexander Eckehart Urban, Giltae Song, J. Michael Cherry
2020 Scientific Reports  
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

William Jones, Kaur Alasoo, Dmytro Fishman, Leopold Parts
2017 Emerging Topics in Life Sciences  
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

Avantika Lal, Zachary D. Chiang, Nikolai Yakovenko, Fabiana M. Duarte, Johnny Israeli, Jason D. Buenrostro
2021 Nature Communications  
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]

Tianwei Yue, Haohan Wang
2018 arXiv   pre-print
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

Yusuf Aleshinloye Abass, Steve A. Adeshina
2021 Journal of Artificial Intelligence for Medical Sciences  
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]

Kyle Xiong, Jian Ma
2018 bioRxiv   pre-print
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

Ryuichiro Nakato, Toyonori Sakata
2020 Methods  
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]

Nafiseh Erfanian, A. Ali Heydari, Pablo Ianez, Afshin Derakhshani, Mohammad Ghasemigol, Mohsen Farahpour, Saeed Nasseri, Hossein Safarpour, Amirhossein Sahebkar
2021 bioRxiv   pre-print
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

Marinka Zitnik, Francis Nguyen, Bo Wang, Jure Leskovec, Anna Goldenberg, Michael M. Hoffman
2019 Information Fusion  
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]

David R Kelley
2019 bioRxiv   pre-print
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

Chih-Hao Fang, Nawanol Theera-Ampornpunt, Michael A. Roth, Ananth Grama, Somali Chaterji
2019 BMC Bioinformatics  
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

Ken Asada, Syuzo Kaneko, Ken Takasawa, Hidenori Machino, Satoshi Takahashi, Norio Shinkai, Ryo Shimoyama, Masaaki Komatsu, Ryuji Hamamoto
2021 Frontiers in Oncology  
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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