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Deep Sequencing Data Analysis

Overview of attention for book
Cover of 'Deep Sequencing Data Analysis'

Table of Contents

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    Book Overview
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    Chapter 1 An Introduction to High-Throughput Sequencing Experiments: Design and Bioinformatics Analysis
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    Chapter 2 Compressing Resequencing Data with GReEn
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    Chapter 3 On the Accuracy of Short Read Mapping
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    Chapter 4 Statistical Modeling of Coverage in High-Throughput Data
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    Chapter 5 Assembly Algorithms for Deep Sequencing Data: Basics and Pitfalls
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    Chapter 6 Short Read Mapping for Exome Sequencing
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    Chapter 7 Profiling Short Tandem Repeats from Short Reads
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    Chapter 8 Exome sequencing analysis: a guide to disease variant detection.
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    Chapter 9 Identifying RNA Editing Sites in miRNAs by Deep Sequencing
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    Chapter 10 Identifying Differential Alternative Splicing Events from RNA Sequencing Data Using RNASeq-MATS.
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    Chapter 11 Optimizing Detection of Transcription Factor-Binding Sites in ChIP-seq Experiments
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    Chapter 12 Statistical Analysis of ChIP-seq Data with MOSAiCS
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    Chapter 13 Detection of Reverse Transcriptase Termination Sites Using cDNA Ligation and Massive Parallel Sequencing
Attention for Chapter 11: Optimizing Detection of Transcription Factor-Binding Sites in ChIP-seq Experiments
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Citations

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Chapter title
Optimizing Detection of Transcription Factor-Binding Sites in ChIP-seq Experiments
Chapter number 11
Book title
Deep Sequencing Data Analysis
Published in
Methods in molecular biology, January 2013
DOI 10.1007/978-1-62703-514-9_11
Pubmed ID
23872976
Book ISBNs
978-1-62703-513-2, 978-1-62703-514-9
Authors

Aleksi Kallio, Laura L. Elo, Kallio, Aleksi, Elo, Laura L.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Russia 1 6%
Unknown 16 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 24%
Other 3 18%
Student > Ph. D. Student 3 18%
Professor 2 12%
Student > Doctoral Student 1 6%
Other 2 12%
Unknown 2 12%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 35%
Agricultural and Biological Sciences 5 29%
Computer Science 3 18%
Immunology and Microbiology 1 6%
Unknown 2 12%

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