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Pseudogenes

Overview of attention for book
Cover of 'Pseudogenes'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Pseudogene Redux with New Biological Significance
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    Chapter 2 Contribution of Pseudogenes to Sequence Diversity
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    Chapter 3 Computational Methods for Pseudogene Annotation Based on Sequence Homology
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    Chapter 4 Computational Methods of Identification of Pseudogenes Based on Functionality: Entropy and GC Content.
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    Chapter 5 Methods of Identification of Pseudogenes Based on Functionality: Hybridization of 18S rRNA and mRNA During Translation
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    Chapter 6 Whole-Genome Identification of Neutrally Evolving Pseudogenes Using the Evolutionary Measure dN/dS
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    Chapter 7 Methods to study the occurrence and the evolution of pseudogenes through a phylogenetic approach.
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    Chapter 8 Methods for Detecting Transcribed Pseudogenes: PCR on Regions of High Sequence Similarity Followed by Cloning and Sequencing
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    Chapter 9 RNA Amplification for Pseudogene Detection Using RNA-Seq.
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    Chapter 10 GENCODE Pseudogenes
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    Chapter 11 Methods to Detect Transcribed Pseudogenes: RNA-Seq Discovery Allows Learning Through Features
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    Chapter 12 Proteomics techniques for the detection of translated pseudogenes.
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    Chapter 13 Pseudogenes as Competitive Endogenous RNAs: Target Prediction and Validation.
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    Chapter 14 Pseudogenes: A Novel Source of trans-Acting Antisense RNAs
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    Chapter 15 Pseudogene-Derived Endogenous siRNAs and Their Function
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    Chapter 16 Methods to Study Translated Pseudogenes: In Vitro Translation, Fusion with a Tag/Reporter Gene, and Complementation Assay
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    Chapter 17 Targeted and robust amplification of mitochondrial DNA in the presence of nuclear-encoded mitochondrial pseudogenes using φ29 DNA polymerases.
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    Chapter 18 Pseudogenes
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    Chapter 19 Mutational Analysis of CYP21A2 Gene and CYP21A1P Pseudogene: Long-range PCR on Genomic DNA.
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    Chapter 20 PMS2 Gene Mutational Analysis: Direct cDNA Sequencing to Circumvent Pseudogene Interference
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    Chapter 21 Dealing with Pseudogenes in Molecular Diagnostics in the Next-Generation Sequencing Era
Attention for Chapter 10: GENCODE Pseudogenes
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Chapter title
GENCODE Pseudogenes
Chapter number 10
Book title
Pseudogenes
Published in
Methods in molecular biology, April 2014
DOI 10.1007/978-1-4939-0835-6_10
Pubmed ID
Book ISBNs
978-1-4939-0834-9, 978-1-4939-0835-6
Authors

Adam Frankish, Jennifer Harrow, Frankish A, Harrow J, Adam Frankish Ph.D.

Editors

Laura Poliseno

Abstract

Historically pseudogenes were believed to represent nonfunctional genomic fossils; however, there is emerging evidence that many of them could be biologically active. This possibility has ignited interest in pseudogene loci and made the need for their high-quality annotation more pressing as an accurate knowledge of all pseudogenes in the human reference genome sequence facilitates confident functional analysis. GENCODE have undertaken the first genome-wide pseudogene assignment for protein-coding genes combining both large-scale manual annotation and computational pseudogene prediction pipelines. Multiple computational predictions provide an unbiased set of hints for manual annotators to investigate, both during first-pass annotation and as part of QC to identify any potential missing pseudogene loci. Where a pseudogene is identified, the extent of its homology to the parent locus is fully investigated by a manual annotator; a pseudogene model is built and assigned to one of eight pseudogene biotypes depending on the mechanism of creation and on the presence of locus-specific transcriptional or proteomic data. The high-quality, information-rich set of pseudogenes created has been integrated with ENCODE functional genomics data, specifically expression level, transcription factor and RNA polymerase II binding, and chromatin marks. In this way we have been able to identify some pseudogenes that possess conventional characteristics of functionality as well as others with interesting patterns of partial activity, which might suggest that putatively inactive loci could be gaining a novel function, for example as long noncoding RNAs. The activity data associated with every pseudogene is stored in the psiDR resource.

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X Demographics

The data shown below were collected from the profiles of 5 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 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 30%
Other 4 17%
Student > Ph. D. Student 2 9%
Student > Postgraduate 2 9%
Professor > Associate Professor 2 9%
Other 3 13%
Unknown 3 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 39%
Medicine and Dentistry 4 17%
Agricultural and Biological Sciences 3 13%
Computer Science 3 13%
Neuroscience 1 4%
Other 0 0%
Unknown 3 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 23 June 2014.
All research outputs
#7,133,687
of 22,756,196 outputs
Outputs from Methods in molecular biology
#2,163
of 13,089 outputs
Outputs of similar age
#69,990
of 227,086 outputs
Outputs of similar age from Methods in molecular biology
#20
of 145 outputs
Altmetric has tracked 22,756,196 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 13,089 research outputs from this source. They receive a mean Attention Score of 3.3. This one has done well, scoring higher than 83% 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 227,086 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 68% of its contemporaries.
We're also able to compare this research output to 145 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.