Browse
Submit Data
Databases
API
Help

Dataset Information

20 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Generating realistic null hypothesis of cancer mutational landscapes using SigProfilerSimulator

ABSTRACT: Background Performing a statistical test requires a null hypothesis. In cancer genomics, a key challenge is the fast generation of accurate somatic mutational landscapes that can be used as a realistic null hypothesis for making biological discoveries. Results Here we present SigProfilerSimulator, a powerful tool that is capable of simulating the mutational landscapes of thousands of cancer genomes at different resolutions within seconds. Applying SigProfilerSimulator to 2144 whole-genome sequenced cancers reveals: (i) that most doublet base substitutions are not due to two adjacent single base substitutions but likely occur as single genomic events; (ii) that an extended sequencing context of ±?2 bp is required to more completely capture the patterns of substitution mutational signatures in human cancer; (iii) information on false-positive discovery rate of commonly used bioinformatics tools for detecting driver genes. Conclusions SigProfilerSimulator’s breadth of features allows one to construct a tailored null hypothesis and use it for evaluating the accuracy of other bioinformatics tools or for downstream statistical analysis for biological discoveries. SigProfilerSimulator is freely available at https://github.com/AlexandrovLab/SigProfilerSimulator with an extensive documentation at https://osf.io/usxjz/wiki/home/.

SUBMITTER: Bergstrom E

PROVIDER: S-EPMC7539472 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Beyond the Hypercube: Evolutionary Accessibility of Fitness Landscapes with Realistic Mutational Networks.

Project description:Evolutionary pathways describe trajectories of biological evolution in the space of different variants of organisms (genotypes). The probability of existence and the number of evolutionary pathways that lead from a given genotype to a better-adapted genotype are important measures of accessibility of local fitness optima and the reproducibility of evolution. Both quantities have been studied in simple mathematical models where genotypes are represented as binary sequences of two types of basic units, and the network of permitted mutations between the genotypes is a hypercube graph. However, it is unclear how these results translate to the biologically relevant case in which genotypes are represented by sequences of more than two units, for example four nucleotides (DNA) or 20 amino acids (proteins), and the mutational graph is not the hypercube. Here we investigate accessibility of the best-adapted genotype in the general case of K > 2 units. Using computer generated and experimental fitness landscapes we show that accessibility of the global fitness maximum increases with K and can be much higher than for binary sequences. The increase in accessibility comes from the increase in the number of indirect trajectories exploited by evolution for higher K. As one of the consequences, the fraction of genotypes that are accessible increases by three orders of magnitude when the number of units K increases from 2 to 16 for landscapes of size N ? 106 genotypes. This suggests that evolution can follow many different trajectories on such landscapes and the reconstruction of evolutionary pathways from experimental data might be an extremely difficult task.

| S-EPMC5147777 | biostudies-literature

RenderGAN: Generating Realistic Labeled Data.

Project description:Deep Convolutional Neuronal Networks (DCNNs) are showing remarkable performance on many computer vision tasks. Due to their large parameter space, they require many labeled samples when trained in a supervised setting. The costs of annotating data manually can render the use of DCNNs infeasible. We present a novel framework called RenderGAN that can generate large amounts of realistic, labeled images by combining a 3D model and the Generative Adversarial Network framework. In our approach, image augmentations (e.g., lighting, background, and detail) are learned from unlabeled data such that the generated images are strikingly realistic while preserving the labels known from the 3D model. We apply the RenderGAN framework to generate images of barcode-like markers that are attached to honeybees. Training a DCNN on data generated by the RenderGAN yields considerably better performance than training it on various baselines.

| S-EPMC7805882 | biostudies-literature

Generating realistic scaled complex networks.

Project description:Research on generative models plays a central role in the emerging field of network science, studying how statistical patterns found in real networks could be generated by formal rules. Output from these generative models is then the basis for designing and evaluating computational methods on networks including verification and simulation studies. During the last two decades, a variety of models has been proposed with an ultimate goal of achieving comprehensive realism for the generated networks. In this study, we (a) introduce a new generator, termed ReCoN; (b) explore how ReCoN and some existing models can be fitted to an original network to produce a structurally similar replica, (c) use ReCoN to produce networks much larger than the original exemplar, and finally (d) discuss open problems and promising research directions. In a comparative experimental study, we find that ReCoN is often superior to many other state-of-the-art network generation methods. We argue that ReCoN is a scalable and effective tool for modeling a given network while preserving important properties at both micro- and macroscopic scales, and for scaling the exemplar data by orders of magnitude in size.

| S-EPMC6225971 | biostudies-other

Nuclear topology modulates the mutational landscapes of cancer genomes.

Project description:Nuclear organization of genomic DNA affects processes of DNA damage and repair, yet its effects on mutational landscapes in cancer genomes remain unclear. Here we analyzed genome-wide somatic mutations from 366 samples of six cancer types. We found that lamina-associated regions, which are typically localized at the nuclear periphery, displayed higher somatic mutation frequencies than did the interlamina regions at the nuclear core. This effect was observed even after adjustment for features such as GC percentage, chromatin, and replication timing. Furthermore, mutational signatures differed between the nuclear core and periphery, thus indicating differences in the patterns of DNA-damage or DNA-repair processes. For instance, smoking and UV-related signatures, as well as substitutions at certain motifs, were more enriched in the nuclear periphery. Thus, the nuclear architecture may influence mutational landscapes in cancer genomes beyond the previously described effects of chromatin structure and replication timing.

| S-EPMC5744871 | biostudies-literature

Evolution of new regulatory functions on biophysically realistic fitness landscapes.

Project description:Gene expression is controlled by networks of regulatory proteins that interact specifically with external signals and DNA regulatory sequences. These interactions force the network components to co-evolve so as to continually maintain function. Yet, existing models of evolution mostly focus on isolated genetic elements. In contrast, we study the essential process by which regulatory networks grow: the duplication and subsequent specialization of network components. We synthesize a biophysical model of molecular interactions with the evolutionary framework to find the conditions and pathways by which new regulatory functions emerge. We show that specialization of new network components is usually slow, but can be drastically accelerated in the presence of regulatory crosstalk and mutations that promote promiscuous interactions between network components.Gene networks evolve by transcription factor (TF) duplication and divergence of their binding site specificities, but little is known about the global constraints at play. Here, the authors study the coevolution of TFs and binding sites using a biophysical-evolutionary approach, and show that the emerging complex fitness landscapes strongly influence regulatory evolution with a role for crosstalk.

| S-EPMC5548793 | biostudies-literature

Modelling mutational landscapes of human cancers in vitro.

Project description:Experimental models that recapitulate mutational landscapes of human cancers are needed to decipher the rapidly expanding data on human somatic mutations. We demonstrate that mutation patterns in immortalised cell lines derived from primary murine embryonic fibroblasts (MEFs) exposed in vitro to carcinogens recapitulate key features of mutational signatures observed in human cancers. In experiments with several cancer-causing agents we obtained high genome-wide concordance between human tumour mutation data and in vitro data with respect to predominant substitution types, strand bias and sequence context. Moreover, we found signature mutations in well-studied human cancer driver genes. To explore endogenous mutagenesis, we used MEFs ectopically expressing activation-induced cytidine deaminase (AID) and observed an excess of AID signature mutations in immortalised cell lines compared to their non-transgenic counterparts. MEF immortalisation is thus a simple and powerful strategy for modelling cancer mutation landscapes that facilitates the interpretation of human tumour genome-wide sequencing data.

| S-EPMC5259794 | biostudies-other

Recombination and mutational robustness in neutral fitness landscapes.

Project description:Mutational robustness quantifies the effect of random mutations on fitness. When mutational robustness is high, most mutations do not change fitness or have only a minor effect on it. From the point of view of fitness landscapes, robust genotypes form neutral networks of almost equal fitness. Using deterministic population models it has been shown that selection favors genotypes inside such networks, which results in increased mutational robustness. Here we demonstrate that this effect is massively enhanced by recombination. Our results are based on a detailed analysis of mesa-shaped fitness landscapes, where we derive precise expressions for the dependence of the robustness on the landscape parameters for recombining and non-recombining populations. In addition, we carry out numerical simulations on different types of random holey landscapes as well as on an empirical fitness landscape. We show that the mutational robustness of a genotype generally correlates with its recombination weight, a new measure that quantifies the likelihood for the genotype to arise from recombination. We argue that the favorable effect of recombination on mutational robustness is a highly universal feature that may have played an important role in the emergence and maintenance of mechanisms of genetic exchange.

| S-EPMC6711544 | biostudies-other

Complex correlations: replication timing and mutational landscapes during cancer and genome evolution.

Project description:A recent flurry of reports correlates replication timing (RT) with mutation rates during both evolution and cancer. Specifically, point mutations and copy number losses correlate with late replication, while copy number gains and other rearrangements correlate with early replication. In some cases, plausible mechanisms have been proposed. Point mutation rates may reflect temporal variation in repair mechanisms. Transcription-induced double-strand breaks are expected to occur in transcriptionally active early replicating chromatin. Fusion partners are generally in close proximity, and chromatin in close proximity replicates at similar times. However, temporal enrichment of copy number gains and losses remains an enigma. Moreover, many conclusions are compromised by a lack of matched RT and sequence datasets, the filtering out of developmental variation in RT, and the use of somatic cell lines to make inferences about germline evolution.

| S-EPMC4140690 | biostudies-literature

Biochemical and functional characterization of glycosylation-associated mutational landscapes in colon cancer.

Project description:The molecular basis of aberrant protein glycosylation, a pathological alteration widespread in colorectal cancers (CRC), and the mechanisms by which it contributes to tumor progression remain largely unknown. We performed targeted re-sequencing of 430 glycosylation-associated genes in a series of patient-derived CRC cell lines (N?=?31) and matched primary tumor tissues, identifying 12 new significantly mutated glycosylation-associated genes in colon cancer. In particular, we observed an enrichment of mutations in genes (B3GNT2, B4GALT2, ST6GALNAC2) involved in the biosynthesis of N- and Cores 1-3 O-linked glycans in the colon, accounting for ~16% of the CRCs tested. Analysis of independent large-scale tumor tissue datasets confirmed recurrent mutations within these genes in colon and other gastrointestinal cancers. Systematic biochemical and phenotypic characterization of the candidate wild-type and mutant glycosyltransferases demonstrated these mutations as either markedly altering protein localization, post-translational modification, encoded enzymatic activities and/or the migratory potential of colon carcinoma cells. These findings suggest that functionally deleterious mutations in glycosyltransferase genes in part underlie aberrant glycosylation, and contribute to the pathogenesis of molecular subsets of colon and other gastrointestinal malignancies.

| S-EPMC4804330 | biostudies-literature

An omnibus test for the global null hypothesis.

Project description:Global hypothesis tests are a useful tool in the context of clinical trials, genetic studies, or meta-analyses, when researchers are not interested in testing individual hypotheses, but in testing whether none of the hypotheses is false. There are several possibilities how to test the global null hypothesis when the individual null hypotheses are independent. If it is assumed that many of the individual null hypotheses are false, combination tests have been recommended to maximize power. If, however, it is assumed that only one or a few null hypotheses are false, global tests based on individual test statistics are more powerful (e.g. Bonferroni or Simes test). However, usually there is no a priori knowledge on the number of false individual null hypotheses. We therefore propose an omnibus test based on cumulative sums of the transformed p-values. We show that this test yields an impressive overall performance. The proposed method is implemented in an R-package called omnibus.

| S-EPMC6676337 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data