Abstract
Fusion transcripts that are frequent in cancer can be exploited to understand the mechanisms of malignancy and can serve as diagnostic or prognostic markers. Several algorithms have been developed to predict fusion transcripts from DNA or RNA data. The majority of these algorithms align sequencing reads to the reference transcriptome for predicting fusions; however, this results in several undetected fusions due to the highly perturbed nature of cancer genomes. Here, we describe a novel method that uses a k-mer based algorithm to predict fusion transcripts accurately using the unaligned reads from the regular RNA-seq data analysis pipelines.
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Acknowledgments
The authors are grateful to Sanjit Pandey for providing systems administrative support to Linux and Windows servers and to the Bioinformatics and Systems Biology core at the University of Nebraska Medical Center (UNMC). The authors also acknowledge the Holland Computing Center of the University of Nebraska for computational resources. This work was supported by the development funds to CG from UNMC and partial support from National Institutes of Health grants [P20GM103427, 1P30GM110768, P30CA036727, and 2P01AG029531].
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Vellichirammal, N.N., Albahrani, A., Li, Y., Guda, C. (2020). Identification of Fusion Transcripts from Unaligned RNA-Seq Reads Using ChimeRScope. In: Li, H., Elfman, J. (eds) Chimeric RNA. Methods in Molecular Biology, vol 2079. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9904-0_2
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DOI: https://doi.org/10.1007/978-1-4939-9904-0_2
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