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Transcriptome Data Analysis

Overview of attention for book
Cover of 'Transcriptome Data Analysis'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Comparison of Gene Expression Profiles in Nonmodel Eukaryotic Organisms with RNA-Seq
  3. Altmetric Badge
    Chapter 2 Microarray Data Analysis for Transcriptome Profiling
  4. Altmetric Badge
    Chapter 3 Pathway and Network Analysis of Differentially Expressed Genes in Transcriptomes
  5. Altmetric Badge
    Chapter 4 QuickRNASeq: Guide for Pipeline Implementation and for Interactive Results Visualization
  6. Altmetric Badge
    Chapter 5 Tracking Alternatively Spliced Isoforms from Long Reads by SpliceHunter
  7. Altmetric Badge
    Chapter 6 RNA-Seq-Based Transcript Structure Analysis with TrBorderExt
  8. Altmetric Badge
    Chapter 7 Analysis of RNA Editing Sites from RNA-Seq Data Using GIREMI
  9. Altmetric Badge
    Chapter 8 Bioinformatic Analysis of MicroRNA Sequencing Data
  10. Altmetric Badge
    Chapter 9 Microarray-Based MicroRNA Expression Data Analysis with Bioconductor
  11. Altmetric Badge
    Chapter 10 Identification and Expression Analysis of Long Intergenic Noncoding RNAs
  12. Altmetric Badge
    Chapter 11 Analysis of RNA-Seq Data Using TEtranscripts
  13. Altmetric Badge
    Chapter 12 Computational Analysis of RNA–Protein Interactions via Deep Sequencing
  14. Altmetric Badge
    Chapter 13 Predicting Gene Expression Noise from Gene Expression Variations
  15. Altmetric Badge
    Chapter 14 A Protocol for Epigenetic Imprinting Analysis with RNA-Seq Data
  16. Altmetric Badge
    Chapter 15 Single-Cell Transcriptome Analysis Using SINCERA Pipeline
  17. Altmetric Badge
    Chapter 16 Mathematical Modeling and Deconvolution of Molecular Heterogeneity Identifies Novel Subpopulations in Complex Tissues
Attention for Chapter 11: Analysis of RNA-Seq Data Using TEtranscripts
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Chapter title
Analysis of RNA-Seq Data Using TEtranscripts
Chapter number 11
Book title
Transcriptome Data Analysis
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7710-9_11
Pubmed ID
Book ISBNs
978-1-4939-7709-3, 978-1-4939-7710-9
Authors

Ying Jin, Molly Hammell

Abstract

Transposable elements (TE) are mobile genetic elements that can readily change their genomic position. When not properly silenced, TEs can contribute a substantial portion to the cell's transcriptome, but are typically ignored in most RNA-seq data analyses. One reason for leaving TE-derived reads out of RNA-seq analyses is the complexities involved in properly aligning short sequencing reads to these highly repetitive regions. Here we describe a method for including TE-derived reads in RNA-seq differential expression analysis using an open source software package called TEtranscripts. TEtranscripts is designed to assign both uniquely and ambiguously mapped reads to all possible gene and TE-derived transcripts in order to statistically infer the correct gene/TE abundances. Here, we provide a detailed tutorial of TEtranscripts using a published qPCR validated dataset.Barbara McClintock laid the foundation for TE research with her discoveries in maize of mobile genetic elements capable of inserting into novel locations in the genome, altering the expression of nearby genes [1]. Since then, our appreciation of the contribution of repetitive TE-derived sequences to eukaryotic genomes has vastly increased. With the publication of the first human genome draft by the Human Genome Project, it was determined that nearly half of the human genome is derived from TE sequences [2, 3], with varying levels of repetitive DNA present in most plant and animal species. More recent studies looking at distantly related TE-like sequences have estimated that up to two thirds of the human genome might be repeat-derived [4], with the vast majority of these sequences attributed to retrotransposons that require transcription as part of the mobilization process, as discussed below.

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Mendeley readers

The data shown below were compiled from readership statistics for 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 73 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 16%
Researcher 11 15%
Other 6 8%
Student > Bachelor 5 7%
Professor 5 7%
Other 9 12%
Unknown 25 34%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 20 27%
Agricultural and Biological Sciences 18 25%
Neuroscience 3 4%
Immunology and Microbiology 1 1%
Computer Science 1 1%
Other 2 3%
Unknown 28 38%
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 07 March 2018.
All research outputs
#15,494,712
of 23,026,672 outputs
Outputs from Methods in molecular biology
#5,390
of 13,170 outputs
Outputs of similar age
#269,811
of 442,363 outputs
Outputs of similar age from Methods in molecular biology
#596
of 1,499 outputs
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