Abstract
Chromosomal rearrangements which shape the genomes of cancer cells often result in fusion genes. Several recent studies have proposed using oligo microarrays targeting fusion junctions to detect fusion genes present in a sample. These approaches design a microarray targeted to discover known fusion genes by using a probe for each possible fusion junction. The hybridization of a sample to one of these probes suggests the presence of a fusion gene. Application of this approach is impractical to detect de-novo gene fusions due to the tremendous number of possible fusion junctions. In this paper we develop a novel approach related to string barcoding which reduces the number of probes necessary for de-novo gene fusion detection by a factor of 3000. The key idea behind our approach is that we utilize probes which match multiple fusion genes where each fusion gene is represented by a unique combination of probes.
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References
Borneman, J., Chrobak, M., Della Vedova, G., Figueroa, A., Jiang, T.: Probe selection algorithms with applications in the analysis of microbial communities. Bioinformatics 17(suppl. 1), S39–S48 (2001)
UCSC Genome Browser, http://genome.ucsc.edu/
Chen, W., et al.: Mapping translocation breakpoints by next-generation sequencing. Genome Res. 18(7), 1143–1149 (2008)
Chun, S.-M.M., et al.: Identification of leukemia-specific fusion gene transcripts with a novel oligonucleotide array. Mol. Diagn. Ther. 11(1), 21–28 (2007)
Dasgupta, B., Konwar, K.M., Mandoiu, I.I., Shvartsman, A.A.: Highly scalable algorithms for robust string barcoding. Int. J. Bioinform. Res. Appl. 1(2), 145–161 (2005)
Lancia, G., Rizzi, R.: The approximability of the string barcoding problem. Algorithms Mol. Biol. 1, 12 (2006)
Lu, Q., et al.: A sensitive array-based assay for identifying multiple tmprss2:erg fusion gene variants. Nucleic Acids Res. 36(20), e130 (2008)
Maher, C.A., et al.: Transcriptome sequencing to detect gene fusions in cancer. Nature 458(7234), 97–101 (2009)
Mitelman, F., Johansson, B., Mertens, F.: The impact of translocations and gene fusions on cancer causation. Nat. Rev. Cancer 7(4), 233–245 (2007)
Nasedkina, T.V., et al.: Identification of chromosomal translocations in leukemias by hybridization with oligonucleotide microarrays. Haematologica 87(4), 363–372 (2002)
Nasedkina, T.V., et al.: Clinical screening of gene rearrangements in childhood leukemia by using a multiplex polymerase chain reaction-microarray approach. Clin. Cancer Res. 9(15), 5620–5629 (2003)
Rash, S., Gusfield, D.: String barcoding: uncovering optimal virus signatures. In: RECOMB 2002: Proceedings of the sixth annual international conference on Computational biology, pp. 254–261. ACM, New York (2002)
Reis-Filho, J.S.: Next-generation sequencing. Breast Cancer Res 11(suppl. 3), S12 (2009)
Shi, R.Z., Morrissey, J.M., Rowley, J.D.: Screening and quantification of multiple chromosome translocations in human leukemia. Clin. Chem. 49(7), 1066–1073 (2003)
Skotheim, R.I., et al.: A universal assay for detection of oncogenic fusion transcripts by oligo microarray analysis. Mol. Cancer 8, 5 (2009)
Teixeira, M.R.: Recurrent fusion oncogenes in carcinomas. Crit. Rev. Oncog. 12(3-4), 257–271 (2006)
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He, D., Eskin, E. (2010). Effective Algorithms for Fusion Gene Detection. In: Moulton, V., Singh, M. (eds) Algorithms in Bioinformatics. WABI 2010. Lecture Notes in Computer Science(), vol 6293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15294-8_26
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DOI: https://doi.org/10.1007/978-3-642-15294-8_26
Publisher Name: Springer, Berlin, Heidelberg
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