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Computational Epigenomics and Epitranscriptomics

Overview of attention for book
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.
  9. Altmetric Badge
    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 9: Sequoia: A Framework for Visual Analysis of RNA Modifications from Direct RNA Sequencing Data.
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  • High Attention Score compared to outputs of the same age and source (92nd percentile)

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Chapter title
Sequoia: A Framework for Visual Analysis of RNA Modifications from Direct RNA Sequencing Data.
Chapter number 9
Book title
Computational Epigenomics and Epitranscriptomics
Published in
Methods in molecular biology, January 2023
DOI 10.1007/978-1-0716-2962-8_9
Pubmed ID
Book ISBNs
978-1-07-162961-1, 978-1-07-162962-8

Koonchanok, Ratanond, Daulatabad, Swapna Vidhur, Reda, Khairi, Janga, Sarath Chandra


Oxford Nanopore-based long-read direct RNA sequencing protocols are being increasingly used to study the dynamics of RNA metabolic processes due to improvements in read lengths, increased throughput, decreasing cost, ease of library preparation, and convenience. Long-read sequencing enables single-molecule-based detection of posttranscriptional changes, promising novel insights into the functional roles of RNA. However, fulfilling this potential will necessitate the development of new tools for analyzing and exploring this type of data. Although there are tools that allow users to analyze signal information, such as comparing raw signal traces to a nucleotide sequence, they don't facilitate studying each individual signal instance in each read or perform analysis of signal clusters based on signal similarity. Therefore, we present Sequoia, a visual analytics application that allows users to interactively analyze signals originating from nanopore sequencers and can readily be extended to both RNA and DNA sequencing datasets. Sequoia combines a Python-based backend with a multi-view graphical interface that allows users to ingest raw nanopore sequencing data in Fast5 format, cluster sequences based on electric-current similarities, and drill-down onto signals to find attributes of interest. In this tutorial, we illustrate each individual step involved in running Sequoia and in the process dissect input data characteristics. We show how to generate Nanopore sequencing-based visualizations by leveraging dimensionality reduction and parameter tuning to separate modified RNA sequences from their unmodified counterparts. Sequoia's interactive features enhance nanopore-based computational methodologies. Sequoia enables users to construct rationales and hypotheses and develop insights about the dynamic nature of RNA from the visual analysis. Sequoia is available at https://github.com/dnonatar/Sequoia .

Twitter Demographics

The data shown below were collected from the profiles of 11 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 1 100%
Readers by discipline Count As %
Computer Science 1 100%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 06 February 2023.
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Outputs from Methods in molecular biology
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Altmetric has tracked 23,577,761 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 13,423 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 84% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 438,347 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 510 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.