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Computational Epigenomics and Epitranscriptomics

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Cover of 'Computational Epigenomics and Epitranscriptomics'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 DNA Methylation Data Analysis Using Msuite.
  3. Altmetric Badge
    Chapter 2 Interactive DNA Methylation Array Analysis with ShinyÉPICo
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    Chapter 3 Predicting Chromatin Interactions from DNA Sequence Using DeepC
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    Chapter 4 Integrating Single-Cell Methylome and Transcriptome Data with MAPLE
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    Chapter 5 Quantitative Comparison of Multiple Chromatin Immunoprecipitation-Sequencing (ChIP-seq) Experiments with spikChIP
  7. Altmetric Badge
    Chapter 6 A Guide to MethylationToActivity: A Deep Learning Framework That Reveals Promoter Activity Landscapes from DNA Methylomes in Individual Tumors.
  8. Altmetric Badge
    Chapter 7 DNA Modification Patterns Filtering and Analysis Using DNAModAnnot.
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    Chapter 8 Methylome Imputation by Methylation Patterns
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    Chapter 9 Sequoia: A Framework for Visual Analysis of RNA Modifications from Direct RNA Sequencing Data.
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    Chapter 10 Predicting Pseudouridine Sites with Porpoise.
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    Chapter 11 Pseudouridine Identification and Functional Annotation with PIANO.
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    Chapter 12 Analyzing mRNA Epigenetic Sequencing Data with TRESS.
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    Chapter 13 Nanopore Direct RNA Sequencing Data Processing and Analysis Using MasterOfPores.
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    Chapter 14 Data Analysis Pipeline for Detection and Quantification of Pseudouridine (ψ) in RNA by HydraPsiSeq.
  16. Altmetric Badge
    Chapter 15 Analysis of RNA Sequences and Modifications Using NASE
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    Chapter 16 Mapping of RNA Modifications by Direct Nanopore Sequencing and JACUSA2.
Attention for Chapter 6: A Guide to MethylationToActivity: A Deep Learning Framework That Reveals Promoter Activity Landscapes from DNA Methylomes in Individual Tumors.
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Chapter title
A Guide to MethylationToActivity: A Deep Learning Framework That Reveals Promoter Activity Landscapes from DNA Methylomes in Individual Tumors.
Chapter number 6
Book title
Computational Epigenomics and Epitranscriptomics
Published in
Methods in molecular biology, January 2023
DOI 10.1007/978-1-0716-2962-8_6
Pubmed ID
Book ISBNs
978-1-07-162961-1, 978-1-07-162962-8
Authors

Dieseldorff Jones, Karissa, Putnam, Daniel, Williams, Justin, Chen, Xiang

Abstract

Genome-wide DNA methylomes have contributed greatly to tumor detection and subclassification. However, interpreting the biological impact of the DNA methylome at the individual gene level remains a challenge. MethylationToActivity (M2A) is a pipeline that uses convolutional neural networks to infer H3K4me3 and H3K27ac enrichment from DNA methylomes and thus infer promoter activity. It was shown to be highly accurate and robust in revealing promoter activity landscapes in various pediatric and adult cancers. The following will present a user-friendly guide through the model pipeline.

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 2 15%
Researcher 2 15%
Student > Ph. D. Student 1 8%
Other 1 8%
Student > Doctoral Student 1 8%
Other 1 8%
Unknown 5 38%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 23%
Agricultural and Biological Sciences 2 15%
Chemistry 1 8%
Engineering 1 8%
Unknown 6 46%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 February 2023.
All research outputs
#21,023,132
of 23,660,057 outputs
Outputs from Methods in molecular biology
#10,164
of 13,343 outputs
Outputs of similar age
#350,948
of 442,198 outputs
Outputs of similar age from Methods in molecular biology
#400
of 518 outputs
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