Dataset Information

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 (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.

SUBMITTER: Bredthauer C

PROVIDER: S-EPMC9976929 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Bredthauer Carl C Fischer Anja A Ahari Ata Jadid AJ Cao Xueqi X Weber Julia J Rad Lena L Rad Roland R Wachutka Leonhard L Gagneur Julien J

Nucleic acids research 20230201 4

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, transcriptio ...[more]

PMID: 36617985

Similar Datasets

Project description:Retroviral insertional mutagenesis screens, which identify genes involved in tumor development in mice, have yielded a substantial number of retroviral integration sites, and this number is expected to grow substantially due to the introduction of high-throughput screening techniques. The data of various retroviral insertional mutagenesis screens are compiled in the publicly available Retroviral Tagged Cancer Gene Database (RTCGD). Integrally analyzing these screens for the presence of common insertion sites (CISs, i.e., regions in the genome that have been hit by viral insertions in multiple independent tumors significantly more than expected by chance) requires an approach that corrects for the increased probability of finding false CISs as the amount of available data increases. Moreover, significance estimates of CISs should be established taking into account both the noise, arising from the random nature of the insertion process, as well as the bias, stemming from preferential insertion sites present in the genome and the data retrieval methodology. We introduce a framework, the kernel convolution (KC) framework, to find CISs in a noisy and biased environment using a predefined significance level while controlling the family-wise error (FWE) (the probability of detecting false CISs). Where previous methods use one, two, or three predetermined fixed scales, our method is capable of operating at any biologically relevant scale. This creates the possibility to analyze the CISs in a scale space by varying the width of the CISs, providing new insights in the behavior of CISs across multiple scales. Our method also features the possibility of including models for background bias. Using simulated data, we evaluate the KC framework using three kernel functions, the Gaussian, triangular, and rectangular kernel function. We applied the Gaussian KC to the data from the combined set of screens in the RTCGD and found that 53% of the CISs do not reach the significance threshold in this combined setting. Still, with the FWE under control, application of our method resulted in the discovery of eight novel CISs, which each have a probability less than 5% of being false detections.

Project description:McrA is one of three functions that restrict modified foreign DNA in Escherichia coli K-12, affecting both methylated and hydroxymethylated substrates. We present here the first systematic analysis of the functional organization of McrA by using the GPS-LS insertion scanning system. We collected in-frame insertions of five amino acids at 46 independent locations and C-terminal truncations at 20 independent locations in the McrA protein. Each mutant was assayed for in vivo restriction of both methylated and hydroxymethylated bacteriophage (M.HpaII-modified lambda and T4gt, respectively) and for induction of the E. coli SOS response in the presence of M.HpaII methylation, indicative of DNA damage. Our findings suggest the presence of an N-terminal DNA-binding domain and a C-terminal catalytic nuclease domain connected by a linker region largely tolerant of amino acid insertions. DNA damage inflicted by a functional C-terminal domain is required for restriction of phage T4gt. Disruption of the N-terminal domain abolishes restriction of both substrates. Surprisingly, truncation mutations that spare the N-terminal domain do not mediate DNA damage, as measured by SOS induction, but nevertheless partially restrict M.HpaII-modified lambda in vivo. We suggest a common explanation for this "restriction without damage" and a similar observation seen in vivo with McrB, a component of another of the modified-DNA restriction functions. Briefly, we propose that unproductive site-specific binding of the protein to a vulnerable position in the lambda genome disrupts the phage development program at an early stage. We also identified a single mutant, carrying an insertion in the N-terminal domain, which could fully restrict lambda but did not restrict T4gt at all. This mutant may have a selective impairment in substrate recognition, distinguishing methylated from hydroxymethylated substrates. The study shows that the technically easy insertion scanning method can provide a rich source of functional information when coupled with effective phenotype tests.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets