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Statistical Genomics

Overview of attention for book
Cover of 'Statistical Genomics'

Table of Contents

  1. Altmetric Badge
    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
  7. Altmetric Badge
    Chapter 6 A Practical Guide to The Cancer Genome Atlas (TCGA)
  8. Altmetric Badge
    Chapter 7 Working with Oligonucleotide Arrays
  9. Altmetric Badge
    Chapter 8 Meta-Analysis in Gene Expression Studies
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    Chapter 9 Practical Analysis of Genome Contact Interaction Experiments
  11. Altmetric Badge
    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
  16. Altmetric Badge
    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
  19. Altmetric Badge
    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 10: Quantitative Comparison of Large-Scale DNA Enrichment Sequencing Data
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Chapter title
Quantitative Comparison of Large-Scale DNA Enrichment Sequencing Data
Chapter number 10
Book title
Statistical Genomics
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3578-9_10
Pubmed ID
27008016
Book ISBNs
978-1-4939-3576-5, 978-1-4939-3578-9
Authors

Matthias Lienhard, Lukas Chavez, Lienhard, Matthias, Chavez, Lukas

Editors

Ewy Mathé, Sean Davis

Abstract

DNA enrichment followed by sequencing (DNA-IP seq) is a versatile tool in molecular biology with a wide variety of applications. Computational analysis of differential DNA enrichment between conditions is important for identifying epigenetic alterations in disease compared to healthy controls and for revealing dynamic epigenetic modifications throughout normal and distorted cell differentiation and development. We present a protocol for genome-wide comparative analysis of DNA-IP sequencing data to identify statistically significant differential sequencing coverage between two conditions by considering variation across replicates. The protocol provides a detailed description for the comparative analysis of DNA-IP sequencing data including basic data processing, quality controls, and identification of differential enrichment using the Bioconductor package "MEDIPS".

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

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 33%
Professor 1 17%
Student > Bachelor 1 17%
Student > Master 1 17%
Researcher 1 17%
Other 0 0%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 33%
Medicine and Dentistry 2 33%
Neuroscience 1 17%
Agricultural and Biological Sciences 1 17%
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 24 March 2016.
All research outputs
#18,449,393
of 22,858,915 outputs
Outputs from Methods in molecular biology
#7,923
of 13,128 outputs
Outputs of similar age
#284,485
of 393,637 outputs
Outputs of similar age from Methods in molecular biology
#846
of 1,470 outputs
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So far Altmetric has tracked 13,128 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 24th percentile – i.e., 24% of its peers scored the same or lower than it.
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