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.
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Arsenijevic, V., Davis-Dusenbery, B.N. (2016). Reproducible, Scalable Fusion Gene Detection from RNA-Seq. In: Grützmann, R., Pilarsky, C. (eds) Cancer Gene Profiling. Methods in Molecular Biology, vol 1381. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3204-7_13
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DOI: https://doi.org/10.1007/978-1-4939-3204-7_13
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-3203-0
Online ISBN: 978-1-4939-3204-7
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