Sequencing methods and datasets to improve functional interpretation of sleeping beauty mutagenesis screens

BMC Genomics

Adam J Dupuy³  Todd E Scheetz¹  Laura M Rogers³  Benjamin T Brett²  Ryan P Smith³  Luke J Drury³  Jesse D Riordan³ 

[1] Department of Ophthalmology and Visual Sciences; Roy J. & Lucille A. Carver College of Medicine, University of Iowa, Iowa City IA 52242, USA;Center for Bioinformatics and Computational Biology, University of Iowa, Iowa City IA 52242, USA;Department of Anatomy and Cell Biology, University of Iowa, Iowa City IA 52242, USA

关键词: High-throughput sequencing;    Transposon remobilization;    Tumor clonality;    Mouse models of cancer;    Mutagenesis;    Sleeping Beauty;   

Others  :  1122633
DOI  :  10.1186/1471-2164-15-1150

 received in 2014-08-14, accepted in 2014-12-16,  发布年份 2014

【 摘 要 】

Background

Animal 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.

Results

We 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.

Conclusion

The 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.

【 授权许可】

   
2014 Riordan et al.; licensee BioMed Central.

【 图 表 】

Figure 1.

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Figure 3.

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