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Systems Genetics

Overview of attention for book
Cover of 'Systems Genetics'

Table of Contents

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    Book Overview
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    Chapter 1 Resources for Systems Genetics
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    Chapter 2 Heterogeneous Stock Populations for Analysis of Complex Traits
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    Chapter 3 Mouse Genome Informatics (MGI): Resources for Mining Mouse Genetic, Genomic, and Biological Data in Support of Primary and Translational Research
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    Chapter 4 GeneNetwork: A Toolbox for Systems Genetics
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    Chapter 5 Complex Trait Analyses of the Collaborative Cross: Tools and Databases
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    Chapter 6 Integrative Functional Genomics for Systems Genetics in GeneWeaver.org
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    Chapter 7 A Suite of Tools for Biologists That Improve Accessibility and Visualization of Large Systems Genetics Datasets: Applications to the Hybrid Mouse Diversity Panel
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    Chapter 8 Expression QTLs Mapping and Analysis: A Bayesian Perspective
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    Chapter 9 Epigenetics and Control of RNAs
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    Chapter 10 Integrating Multidimensional Data Sources to Identify Genes Regulating Complex Phenotypes
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    Chapter 11 RNA-Seq in the Collaborative Cross
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    Chapter 12 QTL Mapping and Identification of Candidate Genes in DO Mice: A Use Case Model Derived from a Benzene Toxicity Experiment
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    Chapter 13 Visualization of Results from Systems Genetics Studies in Chromosomal Context
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    Chapter 14 Using Baseline Transcriptional Connectomes in Rat to Identify Genetic Pathways Associated with Predisposition to Complex Traits
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    Chapter 15 Precise Network Modeling of Systems Genetics Data Using the Bayesian Network Webserver
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    Chapter 16 Systems Genetics as a Tool to Identify Master Genetic Regulators in Complex Disease
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    Chapter 17 Genomic Control of Retinal Cell Number: Challenges, Protocol, and Results
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    Chapter 18 Systems Genetics Analysis to Identify the Genetic Modulation of a Glaucoma-Associated Gene
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    Chapter 19 Genetic Dissection of Variation in Hippocampal Intra- and Infrapyramidal Mossy Fibers in the Mouse
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    Chapter 20 Complex Genetics of Cardiovascular Traits in Mice: F2-Mapping of QTLs and Their Underlying Genes
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    Chapter 21 Systems Genetics of Liver Fibrosis
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    Chapter 22 Systems Genetics Analysis of Iron and Its Regulation in Brain and Periphery
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    Chapter 23 Systems Genetics of Obesity
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    Chapter 24 Social Interactions and Indirect Genetic Effects on Complex Juvenile and Adult Traits
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    Chapter 25 Complex Genetics of Behavior: BXDs in the Automated Home-Cage
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    Chapter 26 Integrative Analysis of Genetic, Genomic, and Phenotypic Data for Ethanol Behaviors: A Network-Based Pipeline for Identifying Mechanisms and Potential Drug Targets
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    Chapter 27 Dissection of Host Susceptibility to Bacterial Infections and Its Toxins
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    Chapter 28 The Collaborative Cross Resource for Systems Genetics Research of Infectious Diseases
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    Chapter 29 Using Systems Genetics to Understanding the Etiology of Complex Disease
Attention for Chapter 11: RNA-Seq in the Collaborative Cross
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Chapter title
RNA-Seq in the Collaborative Cross
Chapter number 11
Book title
Systems Genetics
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-6427-7_11
Pubmed ID
Book ISBNs
978-1-4939-6425-3, 978-1-4939-6427-7

Richard Green, Courtney Wilkins, Martin T. Ferris, Michael Gale Jr., Green, Richard, Wilkins, Courtney, Ferris, Martin T, Gale, Michael, Michael Gale


Klaus Schughart, Robert W. Williams


The Collaborative Cross (CC) is a large panel of inbred mouse strains currently being developed for multiple areas of research. Scientists are taking integrated omics-style approaches to collecting data in order to obtain a deeper understanding of the biological mechanisms underlying a number of diverse disease phenotypes. As the cost of the next generation sequencing (NGS) decreases, RNA-sequencing (RNA-seq) has become the favored approach to transcriptomic analyses versus microarrays due to increases in sensitivity and resolution. This is particularly the case with newly defined genomes, where experimental annotation has not caught up to the new microarray platforms. Traditional RNA-seq approaches are not ideal when working with results from collaborative cross studies, as the genomes across individual strains differ considerably. In this chapter we will provide an overview of how to effectively perform RNA-seq analysis from data obtained from the CC mice.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 100%
Professor 1 100%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 1 100%
Immunology and Microbiology 1 100%

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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 06 January 2017.
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