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Statistical Genomics

Overview of attention for book
Cover of 'Statistical Genomics'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Overview of Sequence Data Formats
  3. Altmetric Badge
    Chapter 2 Integrative Exploratory Analysis of Two or More Genomic Datasets
  4. Altmetric Badge
    Chapter 3 Study Design for Sequencing Studies
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    Chapter 4 Genomic Annotation Resources in R/Bioconductor
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    Chapter 5 The Gene Expression Omnibus Database
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    Chapter 6 A Practical Guide to The Cancer Genome Atlas (TCGA)
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    Chapter 7 Working with Oligonucleotide Arrays
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    Chapter 8 Meta-Analysis in Gene Expression Studies
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    Chapter 9 Practical Analysis of Genome Contact Interaction Experiments
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    Chapter 10 Quantitative Comparison of Large-Scale DNA Enrichment Sequencing Data
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    Chapter 11 Variant Calling From Next Generation Sequence Data
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    Chapter 12 Genome-Scale Analysis of Cell-Specific Regulatory Codes Using Nuclear Enzymes
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    Chapter 13 NGS-QC Generator: A Quality Control System for ChIP-Seq and Related Deep Sequencing-Generated Datasets
  15. Altmetric Badge
    Chapter 14 Operating on Genomic Ranges Using BEDOPS
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    Chapter 15 GMAP and GSNAP for Genomic Sequence Alignment: Enhancements to Speed, Accuracy, and Functionality
  17. Altmetric Badge
    Chapter 16 Visualizing Genomic Data Using Gviz and Bioconductor
  18. Altmetric Badge
    Chapter 17 Introducing Machine Learning Concepts with WEKA
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    Chapter 18 Experimental Design and Power Calculation for RNA-seq Experiments
  20. Altmetric Badge
    Chapter 19 It’s DE-licious: A Recipe for Differential Expression Analyses of RNA-seq Experiments Using Quasi-Likelihood Methods in edgeR
Attention for Chapter 12: Genome-Scale Analysis of Cell-Specific Regulatory Codes Using Nuclear Enzymes
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Chapter title
Genome-Scale Analysis of Cell-Specific Regulatory Codes Using Nuclear Enzymes
Chapter number 12
Book title
Statistical Genomics
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3578-9_12
Pubmed ID
Book ISBNs
978-1-4939-3576-5, 978-1-4939-3578-9
Authors

Songjoon Baek, Myong-Hee Sung, Baek, Songjoon, Sung, Myong-Hee

Editors

Ewy Mathé, Sean Davis

Abstract

High-throughput sequencing technologies have made it possible for biologists to generate genome-wide profiles of chromatin features at the nucleotide resolution. Enzymes such as nucleases or transposes have been instrumental as a chromatin-probing agent due to their ability to target accessible chromatin for cleavage or insertion. On the scale of a few hundred base pairs, preferential action of the nuclear enzymes on accessible chromatin allows mapping of cell state-specific accessibility in vivo. Such accessible regions contain functionally important regulatory sites, including promoters and enhancers, which undergo active remodeling for cells adapting in a dynamic environment. DNase-seq and the more recent ATAC-seq are two assays that are gaining popularity. Deep sequencing of DNA libraries from these assays, termed genomic footprinting, has been proposed to enable the comprehensive construction of protein occupancy profiles over the genome at the nucleotide level. Recent studies have discovered limitations of genomic footprinting which reduce the scope of detectable proteins. In addition, the identification of putative factors that bind to the observed footprints remains challenging. Despite these caveats, the methodology still presents significant advantages over alternative techniques such as ChIP-seq or FAIRE-seq. Here we describe computational approaches and tools for analysis of chromatin accessibility and genomic footprinting. Proper experimental design and assay-specific data analysis ensure the detection sensitivity and maximize retrievable information. The enzyme-based chromatin profiling approaches represent a powerful and evolving methodology which facilitates our understanding of how the genome is regulated.

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The data shown below were collected from the profiles of 4 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 7 35%
Student > Ph. D. Student 5 25%
Student > Bachelor 3 15%
Student > Master 2 10%
Student > Doctoral Student 1 5%
Other 1 5%
Unknown 1 5%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 45%
Agricultural and Biological Sciences 5 25%
Neuroscience 2 10%
Pharmacology, Toxicology and Pharmaceutical Science 1 5%
Medicine and Dentistry 1 5%
Other 1 5%
Unknown 1 5%
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 24 March 2016.
All research outputs
#14,255,539
of 22,858,915 outputs
Outputs from Methods in molecular biology
#4,194
of 13,128 outputs
Outputs of similar age
#206,052
of 393,637 outputs
Outputs of similar age from Methods in molecular biology
#417
of 1,470 outputs
Altmetric has tracked 22,858,915 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,128 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 64% 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 393,637 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
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 gotten more attention than average, scoring higher than 68% of its contemporaries.