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Transmicron: Accurate prediction of insertion probabilities improves detection of cancer driver genes from transposon mutagenesis screens

ABSTRACT: Transposon screens are powerful in vivo assays used to identify loci driving carcinogenesis. These loci are identified as Common Insertion Sites (CIS), i.e. regions with more transposon insertions than expected by chance. However, the identification of CIS is strongly affected by biases in the insertion behaviour of transposon systems. Here, we introduce Transmicron, a novel method that differs from previous methods by i) modelling neutral insertion rates based on chromatin accessibility, transcriptional activity, and sequence context, and ii) estimating oncogenic selection for each genomic region using Poisson regression to model insertion counts while controlling for neutral insertion rates. To assess the benefits of our approach, we generated a dataset applying two different transposon systems under comparable conditions. Benchmarking for enrichment of known cancer genes showed improved performance of Transmicron against state-of-the-art methods. Modelling neutral insertion rates allowed for better control of false positives and stronger agreement of the results between transposon systems. Moreover, using Poisson regression to consider intra-sample and inter-sample information proved beneficial in small and moderately-sized datasets. Transmicron is open-source and freely available. Overall, this study contributes to the understanding of transposon biology and introduces a novel approach to use this knowledge for discovering cancer driver genes.

ORGANISM(S): Mus musculus

PROVIDER: GSE214379 | GEO | 2022/10/01

REPOSITORIES: GEO

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Project description:The transposon site hybridization (TraSH) technique (Sassetti, CM et al. 2001. PNAS 98:12712-7) was utilized to identify genes important for the survival of Y. pestis within murine macrophages. A transposon library was created with ~31,500 Y. pestis KIM6+ insertion mutants. A portion of the Y. pestis transposon insertion mutant library was used to infect BMMs and the surviving bacteria (output pool) were recovered. TraSH was used to compare the output pool to a portion of the library that was not subjected to selection (input pool) in order to identify Y. pestis genes important for survival in macrophages. Each end of the transposon used for mutagenesis contains an outward-reading T7 RNA polymerase promoter. RNAs transcribed from the T7 promoters are complementary to the chromosomal DNA flanking each transposon in the library, so the RNAs can be used as “targets” to identify the approximate position of each transposon insertion in the mutant pool. Differentially labeled targets generated from the output and input pools are competitively hybridized to the 70-mer oligonucleotide microarrays obtained from Pathogen Functional Genomics Resource Center/J. Craig Venter Institute. Genes important for survival of Y. pestis in macrophages are identified by determining the ratio of the signal intensities for the output and input targets hybridizing to a given probe. A transposon library was created with ~31,500 Y. pestis KIM6+ insertion mutants. A portion of the Y. pestis transposon insertion mutant library was used to infect BMMs and the surviving bacteria (output pool) were recovered. TraSH was used to compare the output pool to a portion of the library that was not subjected to selection (input pool). Each end of the transposon used for mutagenesis contains an outward-reading T7 RNA polymerase promoter. RNAs transcribed from the T7 promoters are complementary to the chromosomal DNA flanking each transposon in the library, so the RNAs was used as “targets” to identify the approximate position of each transposon insertion in the mutant pool. Differentially labeled targets generated from the output and input pools are competitively hybridized to the 70-mer oligonucleotide microarrays obtained from Pathogen Functional Genomics Resource Center/J. Craig Venter Institute. Genes important for survival of Y. pestis in macrophages are identified by determining the ratio of the signal intensities for the output and input targets hybridizing to a given probe.

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Transmicron: Accurate prediction of insertion probabilities improves detection of cancer driver genes from transposon mutagenesis screens

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