Applications of DNA integrating elements: Facing the bias bully

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

Transposon screens are powerful in vivo assays used to identify loci driving carcinogenesis. These loci are identified as Common Insertion Sites (CISs), i.e. regions with more transposon insertions than expected by chance. However, the identification of CISs is 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.

Integration site selection by retroviruses and transposable elements in eukaryotes

Transposable elements and retroviruses are found in most genomes, can be pathogenic and are widely used as gene-delivery and functional genomics tools. Exploring whether these genetic elements target specific genomic sites for integration and how this preference is achieved is crucial to our understanding of genome evolution, somatic genome plasticity in cancer and ageing, host-parasite interactions and genome engineering applications. High-throughput profiling of integration sites by next-generation sequencing, combined with large-scale genomic data mining and cellular or biochemical approaches, has revealed that the insertions are usually non-random. The DNA sequence, chromatin and nuclear context, and cellular proteins cooperate in guiding integration in eukaryotic genomes, leading to a remarkable diversity of insertion site distribution and evolutionary strategies.

TRIP through the chromatin: a high throughput exploration of enhancer regulatory landscapes

Enhancers are regulatory elements that promote gene expression in a spatio-temporal way and are involved in a wide range of developmental and disease processes. Both the identification and subsequent functional dissection of enhancers are key steps in understanding these processes. Several high-throughput approaches were recently developed for these purposes; however, in almost all cases enhancers are being tested outside their native chromatin context. Until recently, the analysis of enhancer activities at their native genomic locations was low throughput, laborious and time-consuming. Here, we discuss the potential of a powerful approach, TRIP, to study the functioning of enhancers in their native chromatin environments by introducing sensor constructs directly in the genome. TRIP allows for simultaneously analyzing the quantitative readout of numerous sensor constructs integrated at random locations in the genome. The high-throughput and flexible nature of TRIP opens up potential to study different aspects of enhancer biology at an unprecedented level.