Sequencing methods and datasets to improve functional interpretation of sleeping beauty mutagenesis screens |
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Authors: | Jesse D Riordan Luke J Drury Ryan P Smith Benjamin T Brett Laura M Rogers Todd E Scheetz Adam J Dupuy |
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Institution: | .Department of Anatomy and Cell Biology, University of Iowa, Iowa City, IA 52242 USA ;.Center for Bioinformatics and Computational Biology, University of Iowa, Iowa City, IA 52242 USA ;.Department of Ophthalmology and Visual Sciences; Roy J. & Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA 52242 USA |
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Abstract: | BackgroundAnimal models of cancer are useful to generate complementary datasets for comparison to human tumor data. Insertional mutagenesis screens, such as those utilizing the Sleeping Beauty (SB) transposon system, provide a model that recapitulates the spontaneous development and progression of human disease. This approach has been widely used to model a variety of cancers in mice. Comprehensive mutation profiles are generated for individual tumors through amplification of transposon insertion sites followed by high-throughput sequencing. Subsequent statistical analyses identify common insertion sites (CISs), which are predicted to be functionally involved in tumorigenesis. Current methods utilized for SB insertion site analysis have some significant limitations. For one, they do not account for transposon footprints – a class of mutation generated following transposon remobilization. Existing methods also discard quantitative sequence data due to uncertainty regarding the extent to which it accurately reflects mutation abundance within a heterogeneous tumor. Additionally, computational analyses generally assume that all potential insertion sites have an equal probability of being detected under non-selective conditions, an assumption without sufficient relevant data. The goal of our study was to address these potential confounding factors in order to enhance functional interpretation of insertion site data from tumors.ResultsWe describe here a novel method to detect footprints generated by transposon remobilization, which revealed minimal evidence of positive selection in tumors. We also present extensive characterization data demonstrating an ability to reproducibly assign semi-quantitative information to individual insertion sites within a tumor sample. Finally, we identify apparent biases for detection of inserted transposons in several genomic regions that may lead to the identification of false positive CISs.ConclusionThe information we provide can be used to refine analyses of data from insertional mutagenesis screens, improving functional interpretation of results and facilitating the identification of genes important in cancer development and progression.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-1150) contains supplementary material, which is available to authorized users. |
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Keywords: | Sleeping Beauty Mutagenesis Mouse models of cancer Tumor clonality Transposon remobilization High-throughput sequencing |
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