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RNA Methylation

Overview of attention for book
Cover of 'RNA Methylation'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 LC-MS Analysis of Methylated RNA
  3. Altmetric Badge
    Chapter 2 Comparative Analysis of Ribonucleic Acid Digests (CARD) by Mass Spectrometry
  4. Altmetric Badge
    Chapter 3 Liquid Chromatography-Mass Spectrometry for Analysis of RNA Adenosine Methylation
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    Chapter 4 Genome-Wide Location Analyses of N6-Methyladenosine Modifications (m6A-Seq)
  6. Altmetric Badge
    Chapter 5 Mapping m6A at Individual-Nucleotide Resolution Using Crosslinking and Immunoprecipitation (miCLIP)
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    Chapter 6 Detection and Quantification of N 6-Methyladenosine in Messenger RNA by TLC
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    Chapter 7 Illustrating the Epitranscriptome at Nucleotide Resolution Using Methylation-iCLIP (miCLIP)
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    Chapter 8 Detection of 5-Methylcytosine in Specific Poly(A) RNAs by Bisulfite Sequencing
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    Chapter 9 Transcriptome-Wide Detection of 5-Methylcytosine by Bisulfite Sequencing
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    Chapter 10 Analysis of High-Throughput RNA Bisulfite Sequencing Data
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    Chapter 11 Statistical Methods for Transcriptome-Wide Analysis of RNA Methylation by Bisulfite Sequencing
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    Chapter 12 High-Throughput Mapping of 2′-O-Me Residues in RNA Using Next-Generation Sequencing (Illumina RiboMethSeq Protocol)
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    Chapter 13 RiboMeth-seq: Profiling of 2′-O-Me in RNA
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    Chapter 14 In Silico Identification of RNA Modifications from High-Throughput Sequencing Data Using HAMR
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    Chapter 15 High-Throughput Small RNA Sequencing Enhanced by AlkB-Facilitated RNA de-Methylation (ARM-Seq)
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    Chapter 16 Transcriptome-Wide Mapping of N 1-Methyladenosine Methylome
  18. Altmetric Badge
    Chapter 17 In Vitro Assays for RNA Methyltransferase Activity
  19. Altmetric Badge
    Chapter 18 Crosslinking Methods to Identify RNA Methyltransferase Targets In Vivo
  20. Altmetric Badge
    Chapter 19 Methylated mRNA Nucleotides as Regulators for Ribosomal Translation
  21. Altmetric Badge
    Chapter 20 Automated Chemical Solid-Phase Synthesis and Deprotection of 5-Hydroxymethylcytosine-Containing RNA
Attention for Chapter 14: In Silico Identification of RNA Modifications from High-Throughput Sequencing Data Using HAMR
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Chapter title
In Silico Identification of RNA Modifications from High-Throughput Sequencing Data Using HAMR
Chapter number 14
Book title
RNA Methylation
Published in
Methods in molecular biology, March 2017
DOI 10.1007/978-1-4939-6807-7_14
Pubmed ID
Book ISBNs
978-1-4939-6805-3, 978-1-4939-6807-7
Authors

Kuksa, Pavel P., Leung, Yuk Yee, Vandivier, Lee E., Anderson, Zachary, Gregory, Brian D., Wang, Li-San, Pavel P. Kuksa, Yuk Yee Leung, Lee E. Vandivier, Zachary Anderson, Brian D. Gregory, Li-San Wang

Editors

Alexandra Lusser

Abstract

RNA molecules are often altered post-transcriptionally by the covalent modification of their nucleotides. These modifications are known to modulate the structure, function, and activity of RNAs. When reverse transcribed into cDNA during RNA sequencing library preparation, atypical (modified) ribonucleotides that affect Watson-Crick base pairing will interfere with reverse transcriptase (RT), resulting in cDNA products with mis-incorporated bases or prematurely terminated RNA products. These interactions with RT can therefore be inferred from mismatch patterns in the sequencing reads, and are distinguishable from simple base-calling errors, single-nucleotide polymorphisms (SNPs), or RNA editing sites. Here, we describe a computational protocol for the in silico identification of modified ribonucleotides from RT-based RNA-seq read-out using the High-throughput Analysis of Modified Ribonucleotides (HAMR) software. HAMR can identify these modifications transcriptome-wide with single nucleotide resolution, and also differentiate between different types of modifications to predict modification identity. Researchers can use HAMR to identify and characterize RNA modifications using RNA-seq data from a variety of common RT-based sequencing protocols such as Poly(A), total RNA-seq, and small RNA-seq.

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X Demographics

The data shown below were collected from the profiles of 3 X users 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 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 45%
Student > Ph. D. Student 3 14%
Student > Master 3 14%
Student > Bachelor 1 5%
Professor > Associate Professor 1 5%
Other 0 0%
Unknown 4 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 32%
Agricultural and Biological Sciences 6 27%
Computer Science 1 5%
Medicine and Dentistry 1 5%
Neuroscience 1 5%
Other 1 5%
Unknown 5 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 08 April 2017.
All research outputs
#14,056,410
of 22,962,258 outputs
Outputs from Methods in molecular biology
#3,955
of 13,136 outputs
Outputs of similar age
#167,268
of 308,511 outputs
Outputs of similar age from Methods in molecular biology
#64
of 303 outputs
Altmetric has tracked 22,962,258 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,136 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 68% 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 308,511 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 303 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.