Gene fusions created by somatic genomic rearrangements are known to play an important role in the onset and development of some cancers, such as lymphomas and sarcomas. RNA-Seq (whole transcriptome shotgun sequencing) is proving to be a useful tool for the discovery of novel gene fusions in cancer transcriptomes. However, algorithmic methods for the discovery of gene fusions using RNA-Seq data remain underdeveloped. We have developed deFuse, a novel computational method for fusion discovery in tumor RNA-Seq data. Unlike existing methods that use only unique best-hit alignments and consider only fusion boundaries at the ends of known exons, deFuse considers all alignments and all possible locations for fusion boundaries. As a result, deFuse is able to identify fusion sequences with demonstrably better sensitivity than previous approaches. To increase the specificity of our approach, we curated a list of 60 true positive and 61 true negative fusion sequences (as confirmed by RT-PCR), and have trained an adaboost classifier on 11 novel features of the sequence data. The resulting classifier has an estimated value of 0.91 for the area under the ROC curve. We have used deFuse to discover gene fusions in 40 ovarian tumor samples, one ovarian cancer cell line, and three sarcoma samples. We report herein the first gene fusions discovered in ovarian cancer. We conclude that gene fusions are not infrequent events in ovarian cancer and that these events have the potential to substantially alter the expression patterns of the genes involved; gene fusions should therefore be considered in efforts to comprehensively characterize the mutational profiles of ovarian cancer transcriptomes.
Pubmed ID: 21625565 RIS Download
Mesh terms: Algorithms | Base Sequence | Carcinoma | Cell Line, Tumor | Female | Gene Expression Regulation, Neoplastic | Humans | Male | Melanoma | Molecular Sequence Data | Mutation | Oncogene Fusion | Ovarian Neoplasms | Prostatic Neoplasms | Sarcoma | Sequence Analysis, RNA | Skin Neoplasms
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Repository of raw sequencing data from the next generation of sequencing platforms including including Roche 454 GS System, Illumina Genome Analyzer, Applied Biosystems SOLiD System, Helicos Heliscope, Complete Genomics, and Pacific Biosciences SMRT. In addition to raw sequence data, SRA now stores alignment information in the form of read placements on a reference sequence. Data submissions are welcome. SRA is NIH''''s primary archive of high-throughput sequencing data and is part of the international partnership of archives (INSDC) at the NCBI, the European Bioinformatics Institute and the DNA Database of Japan. Data submitted to any of the three organizations are shared among them. NCBI announced that due to budget constraints, it would be discontinuing SRA but NIH has since committed interim funding for SRA in its current form until October 1, 2011. In addition, NCBI has been working with staff from other NIH Institutes and NIH grantees to develop an approach to continue archiving a widely used subset of next generation sequencing data after October 1, 2011.
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Web tool for an organized view of the transcriptome. Collection of the computationally identified transcripts from the same locus. Information on protein similarities, gene expression, cDNA clones, and genomic location. System for automatically partitioning GenBank sequences into a non redundant set of gene oriented clusters.
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