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Transcription Factor Regulatory Networks

Overview of attention for book
Cover of 'Transcription Factor Regulatory Networks'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Detecting protein-protein interactions/complex components using mass spectrometry coupled techniques.
  3. Altmetric Badge
    Chapter 2 Analysis of Transcription Factor Networks Using IVV Method
  4. Altmetric Badge
    Chapter 3 Next-generation sequencing coupled with a cell-free display technology for reliable interactome of translational factors.
  5. Altmetric Badge
    Chapter 4 Chromatin immunoprecipitation protocol for Mammalian cells.
  6. Altmetric Badge
    Chapter 5 Detecting Protein-DNA Interactions Using a Modified Yeast One-Hybrid System.
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    Chapter 6 RNA Sequencing: From Sample Preparation to Analysis
  8. Altmetric Badge
    Chapter 7 Detecting Expressed Genes Using CAGE.
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    Chapter 8 A tutorial to identify nonlinear associations in gene expression time series data.
  10. Altmetric Badge
    Chapter 9 Inference of TFRNs (2)
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    Chapter 10 Identification of the minimal connected network of transcription factors by transcriptomic and genomic data integration.
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    Chapter 11 Modeling and Simulation Using CellDesigner
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    Chapter 12 Human Genome Network Platform: A Resource for TFRN Analysis.
  14. Altmetric Badge
    Chapter 13 Identification of Transcription Factors Activated in Thymic Epithelial Cells During Embryonic Thymus Development
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    Chapter 14 Analysis of NFATc1-Centered Transcription Factor Regulatory Networks in Osteoclast Formation
  16. Altmetric Badge
    Chapter 15 Transcriptional Regulation in Adipogenesis Through PPARγ-Dependent and -Independent Mechanisms by Prostaglandins.
  17. Altmetric Badge
    Chapter 16 Analysis of TFRNs Associated with Steroid Hormone-Related Cancers.
  18. Altmetric Badge
    Chapter 17 Repositioning Monocyte TFRN into Fibroblasts
Attention for Chapter 1: Detecting protein-protein interactions/complex components using mass spectrometry coupled techniques.
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Chapter title
Detecting protein-protein interactions/complex components using mass spectrometry coupled techniques.
Chapter number 1
Book title
Transcription Factor Regulatory Networks
Published in
Methods in molecular biology, January 2014
DOI 10.1007/978-1-4939-0805-9_1
Pubmed ID
24927830
Book ISBNs
978-1-4939-0804-2, 978-1-4939-0805-9
Authors

Zhibin Ning, Brett Hawley, Cheng-Kang Chiang, Deeptee Seebun, Daniel Figeys, Ning Z, Hawley B, Chiang CK, Seebun D, Figeys D, Ning, Zhibin, Hawley, Brett, Chiang, Cheng-Kang, Seebun, Deeptee, Figeys, Daniel

Abstract

Proteins play important roles in biochemical processes. Most biological functions are realized through protein-protein interactions (PPI). Co-immunoprecipitation is the most straightforward method to detect PPI. With the development of modern mass spectrometry (MS), throughput, sensitivity, and confidence for the detection of PPI can be readily achieved by scaling up traditional antibody-based strategies. Herein, we describe a typical workflow for general PPI detection using mass spectrometry coupled techniques, covering from Co-immunoprecipitation (Co-IP), to gel display, in-gel digestion, liquid chromatography mass spectrometry (LC-MS) analysis, as well as result interpretation and statistic filtering. This protocol provides an overview of the technique as well as practical tips.

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

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 23%
Professor 3 14%
Researcher 3 14%
Student > Ph. D. Student 2 9%
Professor > Associate Professor 2 9%
Other 3 14%
Unknown 4 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 32%
Medicine and Dentistry 3 14%
Agricultural and Biological Sciences 2 9%
Immunology and Microbiology 1 5%
Computer Science 1 5%
Other 2 9%
Unknown 6 27%
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 27 January 2015.
All research outputs
#17,722,431
of 22,757,541 outputs
Outputs from Methods in molecular biology
#7,186
of 13,089 outputs
Outputs of similar age
#220,815
of 305,255 outputs
Outputs of similar age from Methods in molecular biology
#262
of 597 outputs
Altmetric has tracked 22,757,541 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,089 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 39th percentile – i.e., 39% of its peers scored the same or lower than it.
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 305,255 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 597 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 50% of its contemporaries.

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