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X-Chromosome Inactivation

Overview of attention for book
Cover of 'X-Chromosome Inactivation'

Table of Contents

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    Book Overview
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    Chapter 1 Screening for Factors Involved in X Chromosome Inactivation Using Haploid ESCs
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    Chapter 2 Unbiased Genetic Screen to Identify Factors Involved in X-Chromosome Inactivation Using a Pooled Bar-Coded shRNA Library
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    Chapter 3 ChIRP-MS: RNA-Directed Proteomic Discovery
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    Chapter 4 Manipulation of Xist Imprinting in Mouse Preimplantation Embryos
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    Chapter 5 Somatic Cell Nuclear Transfer in Mice: Basic Protocol and Its Modification for Correcting X Chromosome Inactivation Status
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    Chapter 6 Live Imaging of Xist RNA
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    Chapter 7 Live Imaging of X-Chromosome Inactivation and Reactivation Kinetics
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    Chapter 8 Visualizing the Dynamics of Inactive X Chromosomes in Living Cells Using Antibody-Based Fluorescent Probes
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    Chapter 9 Visualization of Hidden Epitopes at the Inactive X Chromosome
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    Chapter 10 Chromosome Spread Analyses of Meiotic Sex Chromosome Inactivation
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    Chapter 11 Simultaneous RNA–DNA FISH in Mouse Preimplantation Embryos
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    Chapter 12 Combined Immunofluorescence, RNA FISH, and DNA FISH in Preimplantation Mouse Embryos
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    Chapter 13 RNA-FISH and Immunofluorescence of Mouse Preimplantation and Postimplantation Embryos
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    Chapter 14 Experimental Analysis of Imprinted Mouse X-Chromosome Inactivation
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    Chapter 15 X-Chromosome Inactivation and Escape from X Inactivation in Mouse
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    Chapter 16 Practical Analysis of Hi-C Data: Generating A/B Compartment Profiles
Attention for Chapter 16: Practical Analysis of Hi-C Data: Generating A/B Compartment Profiles
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  • High Attention Score compared to outputs of the same age and source (88th percentile)

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Chapter title
Practical Analysis of Hi-C Data: Generating A/B Compartment Profiles
Chapter number 16
Book title
X-Chromosome Inactivation
Published in
Methods in molecular biology, September 2018
DOI 10.1007/978-1-4939-8766-5_16
Pubmed ID
Book ISBNs
978-1-4939-8765-8, 978-1-4939-8766-5
Authors

Hisashi Miura, Rawin Poonperm, Saori Takahashi, Ichiro Hiratani, Miura, Hisashi, Poonperm, Rawin, Takahashi, Saori, Hiratani, Ichiro

Abstract

Recent advances in next-generation sequencing (NGS) and chromosome conformation capture (3C) analysis have led to the development of Hi-C, a genome-wide version of the 3C method. Hi-C has identified new levels of chromosome organization such as A/B compartments, topologically associating domains (TADs) as well as large megadomains on the inactive X chromosome, while allowing the identification of chromatin loops at the genome scale. Despite its powerfulness, Hi-C data analysis is much more involved compared to conventional NGS applications such as RNA-seq or ChIP-seq and requires many more steps. This presents a significant hurdle for those who wish to implement Hi-C technology into their laboratory. On the other hand, genomics data repository sites sometimes contain processed Hi-C data sets, allowing researchers to perform further analysis without the need for high-spec workstations and servers. In this chapter, we provide a detailed description on how to calculate A/B compartment profiles from processed Hi-C data on the autosomes and the active/inactive X chromosomes.

X Demographics

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 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 54 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 26%
Researcher 8 15%
Student > Master 7 13%
Student > Doctoral Student 5 9%
Student > Bachelor 3 6%
Other 5 9%
Unknown 12 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 29 54%
Agricultural and Biological Sciences 7 13%
Medicine and Dentistry 3 6%
Sports and Recreations 1 2%
Neuroscience 1 2%
Other 1 2%
Unknown 12 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 20 January 2019.
All research outputs
#7,370,120
of 25,692,343 outputs
Outputs from Methods in molecular biology
#2,240
of 14,321 outputs
Outputs of similar age
#120,710
of 349,462 outputs
Outputs of similar age from Methods in molecular biology
#30
of 253 outputs
Altmetric has tracked 25,692,343 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 14,321 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done well, scoring higher than 84% 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 349,462 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 65% of its contemporaries.
We're also able to compare this research output to 253 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.