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Transposons and Retrotransposons

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Cover of 'Transposons and Retrotransposons'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Study of Transposable Elements and Their Genomic Impact
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    Chapter 2 Bacterial Group II Introns: Identification and Mobility Assay.
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    Chapter 3 In Silico Methods to Identify Exapted Transposable Element Families. - PubMed - NCBI
  5. Altmetric Badge
    Chapter 4 Retrotransposon Capture Sequencing (RC-Seq): A Targeted, High-Throughput Approach to Resolve Somatic L1 Retrotransposition in Humans
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    Chapter 5 Long Interspersed Element Sequencing (L1-Seq): A Method to Identify Somatic LINE-1 Insertions in the Human Genome
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    Chapter 6 Combining Amplification Typing of L1 Active Subfamilies (ATLAS) with High-Throughput Sequencing
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    Chapter 7 RNA-Seq Analysis to Measure the Expression of SINE Retroelements
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    Chapter 8 Qualitative and Quantitative Assays of Transposition and Homologous Recombination of the Retrotransposon Tf1 in Schizosaccharomyces pombe
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    Chapter 9 LINE Retrotransposition Assays in Saccharomyces cerevisiae
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    Chapter 10 LINE-1 Cultured Cell Retrotransposition Assay.
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    Chapter 11 L1 Retrotransposition in Neural Progenitor Cells. - PubMed - NCBI
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    Chapter 12 Characterization of Engineered L1 Retrotransposition Events: The Recovery Method
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    Chapter 13 SINE Retrotransposition: Evaluation of Alu Activity and Recovery of De Novo Inserts.
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    Chapter 14 The Engineered SVA Trans-mobilization Assay.
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    Chapter 15 Detection of LINE-1 RNAs by Northern Blot. - PubMed - NCBI
  17. Altmetric Badge
    Chapter 16 Monitoring Long Interspersed Nuclear Element 1 Expression During Mouse Embryonic Stem Cell Differentiation
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    Chapter 17 Immunodetection of Human LINE-1 Expression in Cultured Cells and Human Tissues.
  19. Altmetric Badge
    Chapter 18 Cellular Localization of Engineered Human LINE-1 RNA and Proteins.
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    Chapter 19 Purification of L1-Ribonucleoprotein Particles (L1-RNPs) from Cultured Human Cells
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    Chapter 20 Characterization of L1-Ribonucleoprotein Particles.
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    Chapter 21 LEAP: L1 Element Amplification Protocol
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    Chapter 22 Biochemical Approaches to Study LINE-1 Reverse Transcriptase Activity In Vitro.
  24. Altmetric Badge
    Chapter 23 Methylated DNA Immunoprecipitation Analysis of Mammalian Endogenous Retroviruses
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    Chapter 24 Profiling DNA Methylation and Hydroxymethylation at Retrotransposable Elements
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    Chapter 25 A Large-Scale Functional Screen to Identify Epigenetic Repressors of Retrotransposon Expression
  27. Altmetric Badge
    Chapter 26 Reprogramming of Human Fibroblasts to Induced Pluripotent Stem Cells with Sleeping Beauty Transposon-Based Stable Gene Delivery
Attention for Chapter 7: RNA-Seq Analysis to Measure the Expression of SINE Retroelements
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  • In the top 25% of all research outputs scored by Altmetric
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  • High Attention Score compared to outputs of the same age and source (89th percentile)

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Chapter title
RNA-Seq Analysis to Measure the Expression of SINE Retroelements
Chapter number 7
Book title
Transposons and Retrotransposons
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3372-3_7
Pubmed ID
Book ISBNs
978-1-4939-3370-9, 978-1-4939-3372-3
Authors

Román, Ángel Carlos, Morales-Hernández, Antonio, Fernández-Salguero, Pedro M, Ángel Carlos Román Ph.D., Antonio Morales-Hernández, Pedro M. Fernández-Salguero, Ángel Carlos Román

Editors

Jose L. Garcia-Pérez

Abstract

The intrinsic features of retroelements, like their repetitive nature and disseminated presence in their host genomes, demand the use of advanced methodologies for their bioinformatic and functional study. The short length of SINE (short interspersed elements) retrotransposons makes such analyses even more complex. Next-generation sequencing (NGS) technologies are currently one of the most widely used tools to characterize the whole repertoire of gene expression in a specific tissue. In this chapter, we will review the molecular and computational methods needed to perform NGS analyses on SINE elements. We will also describe new methods of potential interest for researchers studying repetitive elements. We intend to outline the general ideas behind the computational analyses of NGS data obtained from SINE elements, and to stimulate other scientists to expand our current knowledge on SINE biology using RNA-seq and other NGS tools.

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X Demographics

The data shown below were collected from the profiles of 2 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 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 25%
Student > Ph. D. Student 4 20%
Student > Master 3 15%
Other 2 10%
Professor > Associate Professor 2 10%
Other 3 15%
Unknown 1 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 30%
Biochemistry, Genetics and Molecular Biology 5 25%
Medicine and Dentistry 3 15%
Computer Science 2 10%
Economics, Econometrics and Finance 1 5%
Other 1 5%
Unknown 2 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 25 February 2016.
All research outputs
#3,927,321
of 22,849,304 outputs
Outputs from Methods in molecular biology
#1,005
of 13,127 outputs
Outputs of similar age
#67,523
of 393,600 outputs
Outputs of similar age from Methods in molecular biology
#154
of 1,470 outputs
Altmetric has tracked 22,849,304 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 13,127 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done particularly well, scoring higher than 92% 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 393,600 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 1,470 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.