Chapter title |
Reproducible, Scalable Fusion Gene Detection from RNA-Seq.
|
---|---|
Chapter number | 13 |
Book title |
Cancer Gene Profiling
|
Published in |
Methods in molecular biology, January 2016
|
DOI | 10.1007/978-1-4939-3204-7_13 |
Pubmed ID | |
Book ISBNs |
978-1-4939-3203-0, 978-1-4939-3204-7
|
Authors |
Vladan Arsenijevic, Brandi N. Davis-Dusenbery |
Editors |
Robert Grützmann, Christian Pilarsky |
Abstract |
Chromosomal rearrangements resulting in the creation of novel gene products, termed fusion genes, have been identified as driving events in the development of multiple types of cancer. As these gene products typically do not exist in normal cells, they represent valuable prognostic and therapeutic targets. Advances in next-generation sequencing and computational approaches have greatly improved our ability to detect and identify fusion genes. Nevertheless, these approaches require significant computational resources. Here we describe an approach which leverages cloud computing technologies to perform fusion gene detection from RNA sequencing data at any scale. We additionally highlight methods to enhance reproducibility of bioinformatics analyses which may be applied to any next-generation sequencing experiment. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 6 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 2 | 33% |
Researcher | 2 | 33% |
Student > Master | 1 | 17% |
Unknown | 1 | 17% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 2 | 33% |
Biochemistry, Genetics and Molecular Biology | 1 | 17% |
Computer Science | 1 | 17% |
Medicine and Dentistry | 1 | 17% |
Unknown | 1 | 17% |