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Algorithms for Next-Generation Sequencing Data

Overview of attention for book
Cover of 'Algorithms for Next-Generation Sequencing Data'

Table of Contents

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    Book Overview
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    Chapter 1 Algorithms for Indexing Highly Similar DNA Sequences
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    Chapter 2 Full-Text Indexes for High-Throughput Sequencing
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    Chapter 3 Searching and Indexing Circular Patterns
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    Chapter 4 De Novo NGS Data Compression
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    Chapter 5 Cloud Storage-Management Techniques for NGS Data
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    Chapter 6 Probabilistic Models for Error Correction of Nonuniform Sequencing Data
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    Chapter 7 DNA-Seq Error Correction Based on Substring Indices
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    Chapter 8 Error Correction in Methylation Profiling From NGS Bisulfite Protocols
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    Chapter 9 Comparative Assessment of Alignment Algorithms for NGS Data: Features, Considerations, Implementations, and Future
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    Chapter 10 CUSHAW Suite: Parallel and Efficient Algorithms for NGS Read Alignment
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    Chapter 11 String-Matching and Alignment Algorithms for Finding Motifs in NGS Data
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    Chapter 12 The Contig Assembly Problem and Its Algorithmic Solutions
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    Chapter 13 An Efficient Approach to Merging Paired-End Reads and Incorporation of Uncertainties
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    Chapter 14 Assembly-Free Techniques for NGS Data
Attention for Chapter 7: DNA-Seq Error Correction Based on Substring Indices
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Citations

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Chapter title
DNA-Seq Error Correction Based on Substring Indices
Chapter number 7
Book title
Algorithms for Next-Generation Sequencing Data
Published by
Springer, Cham, January 2017
DOI 10.1007/978-3-319-59826-0_7
Book ISBNs
978-3-31-959824-6, 978-3-31-959826-0
Authors

David Weese, Marcel H. Schulz, Hugues Richard

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 1 33%
Researcher 1 33%
Unknown 1 33%
Readers by discipline Count As %
Computer Science 2 67%
Unknown 1 33%

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