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Post-Transcriptional Gene Regulation

Overview of attention for book
Cover of 'Post-Transcriptional Gene Regulation'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction to Bioinformatics Resources for Post-transcriptional Regulation of Gene Expression.
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    Chapter 2 Post-Transcriptional Gene Regulation
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    Chapter 3 Transcriptional Regulation with CRISPR/Cas9 Effectors in Mammalian Cells.
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    Chapter 4 Studying the Translatome with Polysome Profiling.
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    Chapter 5 Exploring Ribosome Positioning on Translating Transcripts with Ribosome Profiling.
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    Chapter 6 Post-Transcriptional Gene Regulation
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    Chapter 7 Use of the pBUTR Reporter System for Scalable Analysis of 3' UTR-Mediated Gene Regulation.
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    Chapter 8 Comprehensive Identification of RNA-Binding Proteins by RNA Interactome Capture.
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    Chapter 9 Identifying RBP Targets with RIP-seq.
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    Chapter 10 PAR-CLIP: A Method for Transcriptome-Wide Identification of RNA Binding Protein Interaction Sites.
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    Chapter 11 Profiling the Binding Sites of RNA-Binding Proteins with Nucleotide Resolution Using iCLIP.
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    Chapter 12 A Pipeline for PAR-CLIP Data Analysis.
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    Chapter 13 Capture and Identification of miRNA Targets by Biotin Pulldown and RNA-seq.
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    Chapter 14 Post-Transcriptional Gene Regulation
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    Chapter 15 Genome-Wide Analysis of A-to-I RNA Editing.
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    Chapter 16 Nucleotide-Level Profiling of m5C RNA Methylation
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    Chapter 17 Probing N (6)-methyladenosine (m(6)A) RNA Modification in Total RNA with SCARLET.
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    Chapter 18 Genome-Wide Identification of Alternative Polyadenylation Events Using 3'T-Fill.
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    Chapter 19 Genome-Wide Profiling of Alternative Translation Initiation Sites.
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    Chapter 20 Post-Transcriptional Gene Regulation
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    Chapter 21 Visualizing mRNA Dynamics in Live Neurons and Brain Tissues.
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    Chapter 22 Single-Molecule Live-Cell Visualization of Pre-mRNA Splicing.
Attention for Chapter 6: Post-Transcriptional Gene Regulation
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Chapter title
Post-Transcriptional Gene Regulation
Chapter number 6
Book title
Post-Transcriptional Gene Regulation
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3067-8_6
Pubmed ID
Book ISBNs
978-1-4939-3066-1, 978-1-4939-3067-8
Authors

Martinez-Nunez, Rocio T, Sanford, Jeremy R, Rocio T. Martinez-Nunez, Jeremy R. Sanford

Editors

Erik Dassi

Abstract

Gene expression profiling is widely used as a measure of the protein output of cells. However, it is becoming more evident that there are multiple layers of post-transcriptional gene regulation that greatly impact protein output (Battle et al., Science 347:664-667, 2014; Khan et al., Science 342:1100-1104, 2013; Vogel et al., Mol Syst Biol 6:400, 2010). Alternative splicing (AS) impacts the expression of protein coding genes in several ways. Firstly, AS increases exponentially the coding-capacity of genes generating multiple transcripts from the same genomic sequence. Secondly, alternatively spliced mRNAs are subjected differentially to RNA-degradation via pathways such as nonsense mediated decay (AS-NMD) or microRNAs (Shyu et al., EMBO J 27:471-481, 2008). And thirdly, cytoplasmic export from the nucleus and translation are regulated in an isoform-specific manner, adding an extra layer of regulation that impacts the protein output of the cell (Martin and Ephrussi, Cell 136:719-730, 2009; Sterne-Weiler et al., Genome Res 23:1615-1623, 2013). These data highlight the need of a method that allows analyzing both the nuclear events (AS) and the cytoplasmic fate (polyribosome-binding) of individual mRNA isoforms.In order to determine how alternative splicing determines the polyribosome association of mRNA isoforms we developed Frac-seq. Frac-seq combines subcellular fractionation and high throughput RNA sequencing (RNA-seq). Frac-seq gives a window onto the translational fate of specific alternatively spliced isoforms on a genome-wide scale. There is evidence of preferential translation of specific mRNA isoforms (Coldwell and Morley, Mol Cell Biol 26:8448-8460, 2006; Sanford et al., Genes Dev 18:755-768; Zhong et al., Mol Cell 35:1-10, 2009; Michlewski et al., Mol Cell 30:179-189, 2008); the advantage of Frac-seq is that it allows analyzing the binding of alternatively spliced isoforms to polyribosomes and comparing their relative abundance to the cytosolic fraction. Polyribosomes are resolved by sucrose gradient centrifugation of cytoplasmic extracts, subsequent reading and extraction. The total mRNA fraction is taken prior ultracentrifugation as a measure of all mRNAs present in the sample. Both populations of RNAs are then isolated using phenol-chloroform precipitation; polyadenylated RNAs are selected and converted into libraries and sequenced. Bioinformatics analysis is then performed to measure alternatively spliced isoforms; several tools can be used such as MISO, RSEM, or Cufflinks (Katz et al., Nat Methods 7:1009-1015, 2010; Li and Dewey, BMC Bioinformatics 12:323, 2011; Trapnell et al., Nat Protoc 7:562-578, 2012). Comparison of total mRNAs and polyribosome-bound mRNAs can be used as a measure of the polyribosome association of specific isoforms based on the presence/absence of specific alternative splicing events in each fraction. Frac-seq shows that not all isoforms from a gene are equally loaded into polyribosomes, that mRNA preferential loading does not always correlate to its expression in the cytoplasm and that the presence of specific events such as microRNA binding sites or Premature Termination Codons determine the loading of specific isoforms into polyribosomes.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 1 4%
Unknown 23 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 25%
Other 3 13%
Student > Master 3 13%
Lecturer 2 8%
Researcher 2 8%
Other 3 13%
Unknown 5 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 38%
Agricultural and Biological Sciences 6 25%
Medicine and Dentistry 3 13%
Computer Science 1 4%
Neuroscience 1 4%
Other 0 0%
Unknown 4 17%
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 09 July 2016.
All research outputs
#14,856,861
of 22,880,230 outputs
Outputs from Methods in molecular biology
#4,701
of 13,132 outputs
Outputs of similar age
#219,020
of 393,712 outputs
Outputs of similar age from Methods in molecular biology
#469
of 1,471 outputs
Altmetric has tracked 22,880,230 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,132 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 59% 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 393,712 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,471 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.