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Deep Sequencing Data Analysis

Overview of attention for book
Attention for Chapter 8: Exome sequencing analysis: a guide to disease variant detection.
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Chapter title
Exome sequencing analysis: a guide to disease variant detection.
Chapter number 8
Book title
Deep Sequencing Data Analysis
Published in
Methods in molecular biology, July 2013
DOI 10.1007/978-1-62703-514-9_8
Pubmed ID
Book ISBNs
978-1-62703-513-2, 978-1-62703-514-9
Authors

Isakov O, Perrone M, Shomron N, Ofer Isakov, Marie Perrone, Noam Shomron, Isakov, Ofer, Perrone, Marie, Shomron, Noam

Abstract

Whole exome sequencing presents a powerful tool to study rare genetic disorders. The most challenging part of using exome sequencing for the purpose of disease-causing variant detection is analyzing, interpreting, and filtering the large number of detected variants. In this chapter we provide a comprehensive description of the various steps required for such an analysis. We address strategies in selecting samples to sequence, and technical considerations involved in exome sequencing. We then discuss how to identify variants, and methods for first annotating detected variants using characteristics such as allele frequency, location in the genome, and predicted severity, and then classifying and prioritizing the detected variants based on those annotations. Finally, we review possible gene annotations that may help to establish a relationship between genes carrying high-priority variants and the phenotype in question, in order to identify the most likely causative mutations.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 1 2%
Germany 1 2%
Unknown 42 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 18%
Student > Master 7 16%
Student > Bachelor 7 16%
Student > Postgraduate 3 7%
Student > Doctoral Student 2 5%
Other 9 20%
Unknown 8 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 14 32%
Agricultural and Biological Sciences 9 20%
Medicine and Dentistry 5 11%
Chemistry 2 5%
Linguistics 1 2%
Other 5 11%
Unknown 8 18%