Dataset Information

SV-Bay: structural variant detection in cancer genomes using a Bayesian approach with correction for GC-content and read mappability.

ABSTRACT:

Motivation

Whole genome sequencing of paired-end reads can be applied to characterize the landscape of large somatic rearrangements of cancer genomes. Several methods for detecting structural variants with whole genome sequencing data have been developed. So far, none of these methods has combined information about abnormally mapped read pairs connecting rearranged regions and associated global copy number changes automatically inferred from the same sequencing data file. Our aim was to create a computational method that could use both types of information, i.e. normal and abnormal reads, and demonstrate that by doing so we can highly improve both sensitivity and specificity rates of structural variant prediction.

Results

We developed a computational method, SV-Bay, to detect structural variants from whole genome sequencing mate-pair or paired-end data using a probabilistic Bayesian approach. This approach takes into account depth of coverage by normal reads and abnormalities in read pair mappings. To estimate the model likelihood, SV-Bay considers GC-content and read mappability of the genome, thus making important corrections to the expected read count. For the detection of somatic variants, SV-Bay makes use of a matched normal sample when it is available. We validated SV-Bay on simulated datasets and an experimental mate-pair dataset for the CLB-GA neuroblastoma cell line. The comparison of SV-Bay with several other methods for structural variant detection demonstrated that SV-Bay has better prediction accuracy both in terms of sensitivity and false-positive detection rate.

Availability and implementation

https://github.com/InstitutCurie/SV-Bay

Contact

valentina.boeva@inserm.fr

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Iakovishina D

PROVIDER: S-EPMC4896370 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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Publications

SV-Bay: structural variant detection in cancer genomes using a Bayesian approach with correction for GC-content and read mappability.

Iakovishina Daria D Janoueix-Lerosey Isabelle I Barillot Emmanuel E Regnier Mireille M Boeva Valentina V

Bioinformatics (Oxford, England) 20160106 7

<h4>Motivation</h4>Whole genome sequencing of paired-end reads can be applied to characterize the landscape of large somatic rearrangements of cancer genomes. Several methods for detecting structural variants with whole genome sequencing data have been developed. So far, none of these methods has combined information about abnormally mapped read pairs connecting rearranged regions and associated global copy number changes automatically inferred from the same sequencing data file. Our aim was to ...[more]

PMID: 26740523

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Project description:BackgroundAs a key parameter of genome sequence variation, the GC content of bacterial genomes has been investigated for over half a century, and many hypotheses have been put forward to explain this GC content variation and its relationship to other fundamental processes. Previously, we classified eubacteria into dnaE-based groups (the dimeric combination of DNA polymerase III alpha subunits), according to a hypothesis where GC content variation is essentially governed by genome replication and DNA repair mechanisms. Further investigation led to the discovery that two major mutator genes, polC and dnaE2, may be responsible for genomic GC content variation. Consequently, an in-depth analysis was conducted to evaluate various potential intrinsic and extrinsic factors in association with GC content variation among eubacterial genomes.ResultsMutator genes, especially those with dominant effects on the mutation spectra, are biased towards either GC or AT richness, and they alter genomic GC content in the two opposite directions. Increased bacterial genome size (or gene number) appears to rely on increased genomic GC content; however, it is unclear whether the changes are directly related to certain environmental pressures. Certain environmental and bacteriological features are related to GC content variation, but their trends are more obvious when analyzed under the dnaE-based grouping scheme. Most terrestrial, plant-associated, and nitrogen-fixing bacteria are members of the dnaE1|dnaE2 group, whereas most pathogenic or symbiotic bacteria in insects, and those dwelling in aquatic environments, are largely members of the dnaE1|polV group.ConclusionOur studies provide several lines of evidence indicating that DNA polymerase III ? subunit and its isoforms participating in either replication (such as polC) or SOS mutagenesis/translesion synthesis (such as dnaE2), play dominant roles in determining GC variability. Other environmental or bacteriological factors, such as genome size, temperature, oxygen requirement, and habitat, either play subsidiary roles or rely indirectly on different mutator genes to fine-tune the GC content. These results provide a comprehensive insight into mechanisms of GC content variation and the robustness of eubacterial genomes in adapting their ever-changing environments over billions of years.

Project description:The characterization of functional elements in genomes relies on the identification of the footprints of natural selection. In this quest, taking into account neutral evolutionary processes such as mutation and genetic drift is crucial because these forces can generate patterns that may obscure or mimic signatures of selection. In mammals, and probably in many eukaryotes, another such confounding factor called GC-Biased Gene Conversion (gBGC) has been documented. This mechanism generates patterns identical to what is expected under selection for higher GC-content, specifically in highly recombining genomic regions. Recent results have suggested that a mysterious selective force favouring higher GC-content exists in Bacteria but the possibility that it could be gBGC has been excluded. Here, we show that gBGC is probably at work in most if not all bacterial species. First we find a consistent positive relationship between the GC-content of a gene and evidence of intra-genic recombination throughout a broad spectrum of bacterial clades. Second, we show that the evolutionary force responsible for this pattern is acting independently from selection on codon usage, and could potentially interfere with selection in favor of optimal AU-ending codons. A comparison with data from human populations shows that the intensity of gBGC in Bacteria is comparable to what has been reported in mammals. We propose that gBGC is not restricted to sexual Eukaryotes but also widespread among Bacteria and could therefore be an ancestral feature of cellular organisms. We argue that if gBGC occurs in bacteria, it can account for previously unexplained observations, such as the apparent non-equilibrium of base substitution patterns and the heterogeneity of gene composition within bacterial genomes. Because gBGC produces patterns similar to positive selection, it is essential to take this process into account when studying the evolutionary forces at work in bacterial genomes.

Project description:BackgroundThe purpose of the present study was to examine the GC content of substituted bases (sbGC) in the core genomes of 35 bacterial species. Each species, or core genome, constituted genomes from at least 10 strains. We also wanted to explore whether sbGC for each strain was associated with the corresponding species' core genome GC content (cgGC). We present a simple mathematical model that estimates sbGC from cgGC. The model assumes only that the estimated sbGC is a function of cgGC proportional to fixed AT→GC (α) and GC → AT (β) mutation rates. Non-linear regression was used to estimate parameters α and β from the empirical data described above.ResultsWe found that sbGC for each strain showed a non-linear association with the corresponding cgGC with a bias towards higher GC content for most core genomes (66.3% of the strains), assuming as a null-hypothesis that sbGC should be approximately equal to cgGC. The most GC rich core genomes (i.e. approximately %GC > 60), on the other hand, exhibited slightly less GC-biased sbGC than expected. The best fitted regression model indicates that GC → AT mutation rates β = (1.91 ± 0.13) p < 0.001 are approximately (1.91/0.79) = 2.42 times as high, on average, as AT→GC α = (- 0.79 ± 0.25) p < 0.001 mutation rates. Whether the observed sbGC GC-bias for all but the most GC-rich prokaryotic species is due to selection, compensating for the GC → AT mutation bias, and/or selective neutral processes is currently debated. Residual standard error was found to be σ = 0.076 indicating estimated errors of sbGC to be approximately within ±15.2% GC (95% confidence interval) for the strains of all species in the study.ConclusionNot only did our mathematical model give reasonable estimates of sbGC it also provides further support to previous observations that mutation rates in prokaryotes exhibit a universal GC → AT bias that appears to be remarkably consistent between taxa.

Dataset Information

SV-Bay: structural variant detection in cancer genomes using a Bayesian approach with correction for GC-content and read mappability.

Motivation

Results

Availability and implementation

Contact

Supplementary information

Publications

SV-Bay: structural variant detection in cancer genomes using a Bayesian approach with correction for GC-content and read mappability.

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