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Stem Cell Transcriptional Networks

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Cover of 'Stem Cell Transcriptional Networks'

Table of Contents

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    Book Overview
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    Chapter 1 Efficient library preparation for next-generation sequencing analysis of genome-wide epigenetic and transcriptional landscapes in embryonic stem cells.
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    Chapter 2 Analysis of next-generation sequencing data using galaxy.
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    Chapter 3 edgeR for Differential RNA-seq and ChIP-seq Analysis: An Application to Stem Cell Biology.
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    Chapter 4 Use Model-Based Analysis of ChIP-Seq (MACS) to Analyze Short Reads Generated by Sequencing Protein-DNA Interactions in Embryonic Stem Cells.
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    Chapter 5 Spatial Clustering for Identification of ChIP-Enriched Regions (SICER) to Map Regions of Histone Methylation Patterns in Embryonic Stem Cells
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    Chapter 6 Identifying Stem Cell Gene Expression Patterns and Phenotypic Networks with AutoSOME
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    Chapter 7 Visualization and Clustering of High-Dimensional Transcriptome Data Using GATE
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    Chapter 8 Interpreting and Visualizing ChIP-seq Data with the seqMINER Software.
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    Chapter 9 A Description of the Molecular Signatures Database (MSigDB) Web Site
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    Chapter 10 Use of Genome-Wide RNAi Screens to Identify Regulators of Embryonic Stem Cell Pluripotency and Self-Renewal
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    Chapter 11 Correlating Histone Modification Patterns with Gene Expression Data During Hematopoiesis
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    Chapter 12 In Vitro Maturation and In Vitro Fertilization of Mouse Oocytes and Preimplantation Embryo Culture
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    Chapter 13 Derivation and manipulation of trophoblast stem cells from mouse blastocysts.
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    Chapter 14 Conversion of epiblast stem cells to embryonic stem cells using growth factors and small molecule inhibitors.
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    Chapter 15 Generation of Induced Pluripotent Stem Cells Using Chemical Inhibition and Three Transcription Factors
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    Chapter 16 Transdifferentiation of Mouse Fibroblasts and Hepatocytes to Functional Neurons
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    Chapter 17 Direct Lineage Conversion of Pancreatic Exocrine to Endocrine Beta Cells In Vivo with Defined Factors
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    Chapter 18 Direct Reprogramming of Cardiac Fibroblasts to Cardiomyocytes Using MicroRNAs.
  20. Altmetric Badge
    Chapter 19 Reprogramming Somatic Cells into Pluripotent Stem Cells Using miRNAs.
Attention for Chapter 1: Efficient library preparation for next-generation sequencing analysis of genome-wide epigenetic and transcriptional landscapes in embryonic stem cells.
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Chapter title
Efficient library preparation for next-generation sequencing analysis of genome-wide epigenetic and transcriptional landscapes in embryonic stem cells.
Chapter number 1
Book title
Stem Cell Transcriptional Networks
Published in
Methods in molecular biology, January 2014
DOI 10.1007/978-1-4939-0512-6_1
Pubmed ID
Book ISBNs
978-1-4939-0511-9, 978-1-4939-0512-6
Authors

Benjamin L Kidder, Keji Zhao, Benjamin L. Kidder, Kidder, Benjamin L., Zhao, Keji

Abstract

Gene expression in embryonic stem (ES) cells is regulated in part by a network of transcription factors, epigenetic regulators, and histone modifications that influence the underlying chromatin in a way that is conducive or repressive for transcription. Advances in next-generation sequencing technology have allowed for the genome-wide analysis of chromatin constituents and protein-DNA interactions at high resolution in ES cells and other stem cells. While many studies have surveyed genome-wide profiles of a few factors and expression changes at a fixed time point in undifferentiated ES cells, few have utilized an integrative approach to simultaneously survey protein-DNA interactions, histone modifications, and expression programs during ES cell self-renewal and differentiation. To identify transcriptional networks that regulate pluripotency and differentiation, it is important to generate high-quality genome-wide maps of transcription factors, chromatin factors, and histone modifications and to survey global gene expression profiles. Here, to interrogate genome-wide profiles of chromatin features and to survey global gene expression programs in ES cells, we describe protocols for efficient library construction for next-generation sequencing of ChIP-Seq and RNA-Seq samples.

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

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Professor 2 25%
Lecturer 1 13%
Student > Ph. D. Student 1 13%
Student > Master 1 13%
Researcher 1 13%
Other 0 0%
Unknown 2 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 25%
Agricultural and Biological Sciences 2 25%
Computer Science 1 13%
Immunology and Microbiology 1 13%
Unknown 2 25%
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 20 April 2014.
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#20,228,822
of 22,754,104 outputs
Outputs from Methods in molecular biology
#9,863
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Outputs of similar age
#264,768
of 305,238 outputs
Outputs of similar age from Methods in molecular biology
#405
of 597 outputs
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