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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
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    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
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    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
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    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 11: Variant Calling From Next Generation Sequence Data
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Chapter title
Variant Calling From Next Generation Sequence Data
Chapter number 11
Book title
Statistical Genomics
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3578-9_11
Pubmed ID
27008017
Book ISBNs
978-1-4939-3576-5, 978-1-4939-3578-9
Authors

Nancy F. Hansen, Hansen, Nancy F, Hansen, Nancy F.

Editors

Ewy Mathé, Sean Davis

Abstract

The use of next generation nucleotide sequencing to discover and genotype small sequence variants has led to numerous insights into the molecular causes of various diseases. This chapter describes the use of freely available software to align next generation sequencing reads to a reference and then to use the resulting alignments to call, annotate, view, and filter small sequence variants. The suggested variant calling workflow includes read alignment with novoalign, the removal of polymerase chain reaction duplicate sequences with samtools or bamUtils, and the detection of variants with Freebayes or bam2mpg software. ANNOVAR is then used to annotate the predicted variants using gene models, population frequencies, and predicted mutation severity, producing variant files which can be viewed and filtered with the variant display tool VarSifter.

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X Demographics

The data shown below were collected from the profile of 1 X user 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 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Australia 1 5%
Unknown 20 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 29%
Student > Ph. D. Student 6 29%
Lecturer > Senior Lecturer 1 5%
Student > Bachelor 1 5%
Professor 1 5%
Other 2 10%
Unknown 4 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 38%
Biochemistry, Genetics and Molecular Biology 4 19%
Neuroscience 2 10%
Computer Science 1 5%
Medicine and Dentistry 1 5%
Other 1 5%
Unknown 4 19%
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 25 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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