Characterization of a strain-specific CD-1 reference genome reveals potential inter- and intra-strain functional variability

BACKGROUND

CD-1 is an outbred mouse stock that is frequently used in toxicology, pharmacology, and fundamental biomedical research. Although inbred strains are typically better suited for such studies due to minimal genetic variability, outbred stocks confer practical advantages over inbred strains, such as improved breeding performance and low cost. Knowledge of the full genetic variability of CD-1 would make it more useful in toxicology, pharmacology, and fundamental biomedical research.

RESULTS

We performed deep genomic DNA sequencing of CD-1 mice and used the data to identify genome-wide SNPs, indels, and germline transposable elements relative to the mm10 reference genome. We used multiple genome-wide sequencing data types and previously published CD-1 SNPs to validate our called variants. We used the called variants to construct a strain-specific CD-1 reference genome, which we show can improve mappability and reduce experimental biases from genome-wide sequencing data derived from CD-1 mice. Based on previously published ChIP-seq and ATAC-seq data, we find evidence that genetic variation between CD-1 mice can lead to alterations in transcription factor binding. We also identified a number of variants in the coding region of genes which could have effects on translation of genes.

CONCLUSIONS

We have identified millions of previously unidentified CD-1 variants with the potential to confound studies involving CD-1. We used the identified variants to construct a CD-1-specific reference genome, which can improve accuracy and reduce bias when aligning genomics data derived from CD-1 mice.

A comprehensive analysis of copy number variations in diverse apple populations

BACKGROUND

As an important source of genetic variation, copy number variation (CNV) can alter the dosage of DNA segments, which in turn may affect gene expression level and phenotype. However, our knowledge of CNV in apple is still limited. Here, we obtained high-confidence CNVs and investigated their functional impact based on genome resequencing data of two apple populations, cultivars and wild relatives.

RESULTS

In this study, we identified 914,610 CNVs comprising 14,839 CNV regions (CNVRs) from 346 apple accessions, including 289 cultivars and 57 wild relatives. CNVRs summed to 71.19 Mb, accounting for 10.03% of the apple genome. Under the low linkage disequilibrium (LD) with nearby SNPs, they could also accurately reflect the population structure of apple independent of SNPs. Furthermore, A total of 3,621 genes were covered by CNVRs and functionally involved in biological processes such as defense response, reproduction and metabolic processes. In addition, the population differentiation index ([Formula: see text]) analysis between cultivars and wild relatives revealed 127 CN-differentiated genes, which may contribute to trait differences in these two populations.

CONCLUSIONS

This study was based on identification of CNVs from 346 diverse apple accessions, which to our knowledge was the largest dataset for CNV analysis in apple. Our work presented the first comprehensive CNV map and provided valuable resources for understanding genomic variations in apple.

Recommendations for the Use of in Silico Approaches for Next-Generation Sequencing Bioinformatic Pipeline Validation: A Joint Report of the Association for Molecular Pathology, Association for Pathology Informatics, and College of American Pathologists

In silico approaches for next-generation sequencing (NGS) data modeling have utility in the clinical laboratory as a tool for clinical assay validation. In silico NGS data can take a variety of forms, including pure simulated data or manipulated data files in which variants are inserted into existing data files. In silico data enable simulation of a range of variants that may be difficult to obtain from a single physical sample. Such data allow laboratories to more accurately test the performance of clinical bioinformatics pipelines without sequencing additional cases. For example, clinical laboratories may use in silico data to simulate low variant allele fraction variants to test the analytical sensitivity of variant calling software or simulate a range of insertion/deletion sizes to determine the performance of insertion/deletion calling software. In this article, the Working Group reviews the different types of in silico data with their strengths and limitations, methods to generate in silico data, and how data can be used in the clinical molecular diagnostic laboratory. Survey data indicate how in silico NGS data are currently being used. Finally, potential applications for which in silico data may become useful in the future are presented.

PerSVade: personalized structural variant detection in any species of interest

Structural variants (SVs) underlie genomic variation but are often overlooked due to difficult detection from short reads. Most algorithms have been tested on humans, and it remains unclear how applicable they are in other organisms. To solve this, we develop perSVade (personalized structural variation detection), a sample-tailored pipeline that provides optimally called SVs and their inferred accuracy, as well as small and copy number variants. PerSVade increases SV calling accuracy on a benchmark of six eukaryotes. We find no universal set of optimal parameters, underscoring the need for sample-specific parameter optimization. PerSVade will facilitate SV detection and study across diverse organisms.

kngMap: Sensitive and Fast Mapping Algorithm for Noisy Long Reads Based on the K-Mer Neighborhood Graph

With the rapid development of single molecular sequencing (SMS) technologies such as PacBio single-molecule real-time and Oxford Nanopore sequencing, the output read length is continuously increasing, which has dramatical potentials on cutting-edge genomic applications. Mapping these reads to a reference genome is often the most fundamental and computing-intensive step for downstream analysis. However, these long reads contain higher sequencing errors and could more frequently span the breakpoints of structural variants (SVs) than those of shorter reads, leading to many unaligned reads or reads that are partially aligned for most state-of-the-art mappers. As a result, these methods usually focus on producing local mapping results for the query read rather than obtaining the whole end-to-end alignment. We introduce kngMap, a novel k-mer neighborhood graph-based mapper that is specifically designed to align long noisy SMS reads to a reference sequence. By benchmarking exhaustive experiments on both simulated and real-life SMS datasets to assess the performance of kngMap with ten other popular SMS mapping tools (e.g., BLASR, BWA-MEM, and minimap2), we demonstrated that kngMap has higher sensitivity that can align more reads and bases to the reference genome; meanwhile, kngMap can produce consecutive alignments for the whole read and span different categories of SVs in the reads. kngMap is implemented in C++ and supports multi-threading; the source code of kngMap can be downloaded for free at: https://github.com/zhang134/kngMap for academic usage.

Detecting structural variations with precise breakpoints using low-depth WGS data from a single oxford nanopore MinION flowcell

Structural variation (SV) is a major cause of genetic disorders. In this paper, we show that low-depth (specifically, 4×) whole-genome sequencing using a single Oxford Nanopore MinION flow cell suffices to support sensitive detection of SV, particularly pathogenic SV for supporting clinical diagnosis. When using 4× ONT WGS data, existing SV calling software often fails to detect pathogenic SV, especially in the form of long deletion, terminal deletion, duplication, and unbalanced translocation. Our new SV calling software SENSV can achieve high sensitivity for all types of SV and a breakpoint precision typically ± 100 bp; both features are important for clinical concerns. The improvement achieved by SENSV stems from several new algorithms. We evaluated SENSV and other software using both real and simulated data. The former was based on 24 patient samples, each diagnosed with a genetic disorder. SENSV found the pathogenic SV in 22 out of 24 cases (all heterozygous, size from hundreds of kbp to a few Mbp), reporting breakpoints within 100 bp of the true answers. On the other hand, no existing software can detect the pathogenic SV in more than 10 out of 24 cases, even when the breakpoint requirement is relaxed to ± 2000 bp.

A benchmark of structural variation detection by long reads through a realistic simulated model

Accurate simulations of structural variation distributions and sequencing data are crucial for the development and benchmarking of new tools. We develop Sim-it, a straightforward tool for the simulation of both structural variation and long-read data. These simulations from Sim-it reveal the strengths and weaknesses for current available structural variation callers and long-read sequencing platforms. With these findings, we develop a new method (combiSV) that can combine the results from structural variation callers into a superior call set with increased recall and precision, which is also observed for the latest structural variation benchmark set developed by the GIAB Consortium.

SimFFPE and FilterFFPE: improving structural variant calling in FFPE samples

BACKGROUND

Artifact chimeric reads are enriched in next-generation sequencing data generated from formalin-fixed paraffin-embedded (FFPE) samples. Previous work indicated that these reads are characterized by erroneous split-read support that is interpreted as evidence of structural variants. Thus, a large number of false-positive structural variants are detected. To our knowledge, no tool is currently available to specifically call or filter structural variants in FFPE samples. To overcome this gap, we developed 2 R packages: SimFFPE and FilterFFPE.

RESULTS

SimFFPE is a read simulator, specifically designed for next-generation sequencing data from FFPE samples. A mixture of characteristic artifact chimeric reads, as well as normal reads, is generated. FilterFFPE is a filtration algorithm, removing artifact chimeric reads from sequencing data while keeping real chimeric reads. To evaluate the performance of FilterFFPE, we performed structural variant calling with 3 common tools (Delly, Lumpy, and Manta) with and without prior filtration with FilterFFPE. After applying FilterFFPE, the mean positive predictive value improved from 0.27 to 0.48 in simulated samples and from 0.11 to 0.27 in real samples, while sensitivity remained basically unchanged or even slightly increased.

CONCLUSIONS

FilterFFPE improves the performance of SV calling in FFPE samples. It was validated by analysis of simulated and real data.

Linearization of genome sequence graphs revisited

The need to include the genetic variation within a population into a reference genome led to the concept of a genome sequence graph. Nodes of such a graph are labeled with DNA sequences occurring in represented genomes. Due to double-stranded nature of DNA, each node may be oriented in one of two possible ways, resulting in marking one end of the labeling sequence as in-side and the other as out-side. Edges join pairs of sides and reflect adjacency between node sequences in genomes constituting the graph. Linearization of a sequence graph aims at orienting and ordering graph nodes in a way that makes it more efficient for visualization and further analysis, e.g. access and traversal. We propose a new linearization algorithm, called ALIBI – Algorithm for Linearization by Incremental graph BuIlding. The evaluation shows that ALIBI is computationally very efficient and generates high-quality results.

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We propose ALIBI – a new algorithm for linearization of genome sequence graphs

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ALIBI yields less feedback arcs and reversing joins than competing methods

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ALIBI shows high efficiency and scales well to large graphs

An integrated approach for copy number variation discovery in parent-offspring trios

Whole-genome sequencing (WGS) of parent-offspring trios has become widely used to identify causal copy number variations (CNVs) in rare and complex diseases. Existing CNV detection approaches usually do not make effective use of Mendelian inheritance in parent-offspring trios and yield low accuracy. In this study, we propose a novel integrated approach, TrioCNV2, for jointly detecting CNVs from WGS data of the parent-offspring trio. TrioCNV2 first makes use of the read depth and discordant read pairs to infer approximate locations of CNVs and then employs the split read and local de novo assembly approaches to refine the breakpoints. We use the real WGS data of two parent-offspring trios to demonstrate TrioCNV2's performance and compare it with other CNV detection approaches. The software TrioCNV2 is implemented using a combination of Java and R and is freely available from the website at https://github.com/yongzhuang/TrioCNV2.

SVLR: Genome Structural Variant Detection Using Long-Read Sequencing Data

Genome structural variants (SVs) have great impacts on human phenotype and diversity, and have been linked to numerous diseases. Long-read sequencing technologies arise to make it possible to find SVs of as long as 10,000 nucleotides. Thus, long read-based SV detection has been drawing attention of many recent research projects, and many tools have been developed for long reads to detect SVs recently. In this article, we present a new method, called SVLR, to detect SVs based on long-read sequencing data. Comparing with existing methods, SVLR can detect three new kinds of SVs: block replacements, block interchanges, and translocations. Although these new SVs are structurally more complicated, SVLR achieves accuracies that are comparable with those of the classic SVs. Moreover, for the classic SVs that can be detected by state-of-the-art methods (e.g., SVIM and Sniffles), our experiments demonstrate recall improvements of up to 38% without harming the precisions (i.e., >78%). We also point out three directions to further improve SV detection in the future. Source codes: https://github.com/GWYSDU/SVLR.

VISOR: a versatile haplotype-aware structural variant simulator for short- and long-read sequencing

SUMMARY

VISOR is a tool for haplotype-specific simulations of simple and complex structural variants (SVs). The method is applicable to haploid, diploid or higher ploidy simulations for bulk or single-cell sequencing data. SVs are implanted into FASTA haplotypes at single-basepair resolution, optionally with nearby single-nucleotide variants. Short or long reads are drawn at random from these haplotypes using standard error profiles. Double- or single-stranded data can be simulated and VISOR supports the generation of haplotype-tagged BAM files. The tool further includes methods to interactively visualize simulated variants in single-stranded data. The versatility of VISOR is unmet by comparable tools and it lays the foundation to simulate haplotype-resolved cancer heterogeneity data in bulk or at single-cell resolution.

AVAILABILITY AND IMPLEMENTATION

VISOR is implemented in python 3.6, open-source and freely available at https://github.com/davidebolo1993/VISOR. Documentation is available at https://davidebolo1993.github.io/visordoc/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

PASA: Identifying More Credible Structural Variants of Hedou12

Although plenty of structural variant detecting approaches for human genomes can be looked up in the literatures, little has been acknowledged on the effectiveness of those structural variant softwares for plant genomes. Moreover, it has been demonstrated frequent occurrences for those structural variant detecting softwares to find too many false structural variants. In this paper, we devote to detect deletions, insertions, and inversions, in total of three kinds of structural variants occurring in Hedou12 genome in contrast to Williams82 genome. To find more potential structural variants, we try to develop new principles to detect discordant and split read map sets supporting structural variants. Aiming to enhance the precision of structural variant detections, we propose two new sequencing characteristic based probability models, which use the sequencing parameters of Hedou12 genome as well as the parameters for Hedou12 paired-end reads to be aligned onto Williams82, to evaluate the probability for a potential structural variant to occur in. To remove the false members from those potential structural variants, we propose a set cover problem model to describe formally on which potential structural variants it should accept to achieve as high as possible a probability summation. This will achieve a solution with more credible structural variants, which can be verified by comparing with DELLY version 0.5.8 and LUMPY version 0.2.2.3. Our algorithm has been verified to be able to find deletions, insertions, and inversions in Hedou12 in contrast to Williams82 DELLY as well as LUMPY fails to find.

ScanITD: Detecting internal tandem duplication with robust variant allele frequency estimation

BACKGROUND

Internal tandem duplications (ITDs) are tandem duplications within coding exons and are important prognostic markers and drug targets for acute myeloid leukemia (AML). Next-generation sequencing has enabled the discovery of ITD at single-nucleotide resolution. ITD allele frequency is used in the risk stratification of patients with AML; higher ITD allele frequency is associated with poorer clinical outcomes. However, the ITD allele frequency data are often unavailable to treating physicians and the detection of ITDs with accurate variant allele frequency (VAF) estimation remains challenging for short-read sequencing.

RESULTS

Here we present the ScanITD approach, which performs a stepwise seed-and-realignment procedure for ITD detection with accurate VAF prediction. The evaluations on simulated and real data demonstrate that ScanITD outperforms 3 state-of-the-art ITD detectors, especially for VAF estimation. Importantly, ScanITD yields better accuracy than general-purpose structural variation callers for predicting ITD size range duplications.

CONCLUSIONS

ScanITD enables the accurate identification of ITDs with robust VAF estimation. ScanITD is written in Python and is open-source software that is freely accessible at https://github.com/ylab-hi/ScanITD.

SVIM: structural variant identification using mapped long reads

Motivation

Structural variants are defined as genomic variants larger than 50 bp. They have been shown to affect more bases in any given genome than single-nucleotide polymorphisms or small insertions and deletions. Additionally, they have great impact on human phenotype and diversity and have been linked to numerous diseases. Due to their size and association with repeats, they are difficult to detect by shotgun sequencing, especially when based on short reads. Long read, single-molecule sequencing technologies like those offered by Pacific Biosciences or Oxford Nanopore Technologies produce reads with a length of several thousand base pairs. Despite the higher error rate and sequencing cost, long-read sequencing offers many advantages for the detection of structural variants. Yet, available software tools still do not fully exploit the possibilities.

Results

We present SVIM, a tool for the sensitive detection and precise characterization of structural variants from long-read data. SVIM consists of three components for the collection, clustering and combination of structural variant signatures from read alignments. It discriminates five different variant classes including similar types, such as tandem and interspersed duplications and novel element insertions. SVIM is unique in its capability of extracting both the genomic origin and destination of duplications. It compares favorably with existing tools in evaluations on simulated data and real datasets from Pacific Biosciences and Nanopore sequencing machines.

Availability and implementation

The source code and executables of SVIM are available on Github: github.com/eldariont/svim. SVIM has been implemented in Python 3 and published on bioconda and the Python Package Index.

Supplementary information

Supplementary data are available at Bioinformatics online.

SVSR: A Program to Simulate Structural Variations and Generate Sequencing Reads for Multiple Platforms

Structural variation accounts for a major fraction of mutations in the human genome and confers susceptibility to complex diseases. Next generation sequencing along with the rapid development of computational methods provides a cost-effective procedure to detect such variations. Simulation of structural variations and sequencing reads with real characteristics is essential for benchmarking the computational methods. Here, we develop a new program, SVSR, to simulate five types of structural variations (indels, tandem duplication, CNVs, inversions, and translocations) and SNPs for the human genome and to generate sequencing reads with features from popular platforms (Illumina, SOLiD, 454, and Ion Torrent). We adopt a selection model trained from real data to predict copy number states, starting from the first site of a particular genome to the end. Furthermore, we utilize references of microbial genomes to produce insertion fragments and design probabilistic models to imitate inversions and translocations. Moreover, we create platform-specific errors and base quality profiles to generate normal, tumor, or normal-tumor mixture reads. Experimental results show that SVSR could capture more features that are realistic and generate datasets with satisfactory quality scores. SVSR is able to evaluate the performance of structural variation detection methods and guide the development of new computational methods.

MVSC: A Multi-variation Simulator of Cancer Genome

BACKGROUND

Many forms of variations exist in the genome, which are the main causes of individual phenotypic differences. The detection of variants, especially those located in the tumor genome, still faces many challenges due to the complexity of the genome structure. Thus, the performance assessment of variation detection tools using next-generation sequencing platforms is urgently needed.

METHOD

We have created a software package called the Multi-Variation Simulator of Cancer genomes (MVSC) to simulate common genomic variants, including single nucleotide polymorphisms, small insertion and deletion polymorphisms, and structural variations (SVs), which are analogous to human somatically acquired variations. Three sets of variations embedded in genomic sequences in different periods were dynamically and sequentially simulated one by one.

RESULTS

In cancer genome simulation, complex SVs are important because this type of variation is characteristic of the tumor genome structure. Overlapping variations of different sizes can also coexist in the same genome regions, adding to the complexity of cancer genome architecture. Our results show that MVSC can efficiently simulate a variety of genomic variants that cannot be simulated by existing software packages.

CONCLUSION

The MVSC-simulated variants can be used to assess the performance of existing tools designed to detect SVs in next-generation sequencing data, and we also find that MVSC is memory and time-efficient compared with similar software packages.

SVXplorer: Three-tier approach to identification of structural variants via sequential recombination of discordant cluster signatures

The identification of structural variants using short-read data remains challenging. Most approaches that use discordant paired-end sequences ignore non-trivial signatures presented by variants containing 3 breakpoints, such as those generated by various copy-paste and cut-paste mechanisms. This can result in lower precision and sensitivity in the identification of the more common structural variants such as deletions and duplications. We present SVXplorer, which uses a graph-based clustering approach streamlined by the integration of non-trivial signatures from discordant paired-end alignments, split-reads and read depth information to improve upon existing methods. We show that SVXplorer is more sensitive and precise compared to several existing approaches on multiple real and simulated datasets. SVXplorer is available for download at https://github.com/kunalkathuria/SVXplorer.

NanoVar: accurate characterization of patients' genomic structural variants using low-depth nanopore sequencing

The recent advent of third-generation sequencing technologies brings promise for better characterization of genomic structural variants by virtue of having longer reads. However, long-read applications are still constrained by their high sequencing error rates and low sequencing throughput. Here, we present NanoVar, an optimized structural variant caller utilizing low-depth (8X) whole-genome sequencing data generated by Oxford Nanopore Technologies. NanoVar exhibits higher structural variant calling accuracy when benchmarked against current tools using low-depth simulated datasets. In patient samples, we successfully validate structural variants characterized by NanoVar and uncover normal alternative sequences or alleles which are present in healthy individuals.

SECNVs: A Simulator of Copy Number Variants and Whole-Exome Sequences From Reference Genomes

Copy number variants are duplications and deletions of the genome that play an important role in phenotypic changes and human disease. Many software applications have been developed to detect copy number variants using either whole-genome sequencing or whole-exome sequencing data. However, there is poor agreement in the results from these applications. Simulated datasets containing copy number variants allow comprehensive comparisons of the operating characteristics of existing and novel copy number variant detection methods. Several software applications have been developed to simulate copy number variants and other structural variants in whole-genome sequencing data. However, none of the applications reliably simulate copy number variants in whole-exome sequencing data. We have developed and tested Simulator of Exome Copy Number Variants (SECNVs), a fast, robust and customizable software application for simulating copy number variants and whole-exome sequences from a reference genome. SECNVs is easy to install, implements a wide range of commands to customize simulations, can output multiple samples at once, and incorporates a pipeline to output rearranged genomes, short reads and BAM files in a single command. Variants generated by SECNVs are detected with high sensitivity and precision by tools commonly used to detect copy number variants. SECNVs is publicly available at https://github.com/YJulyXing/SECNVs.

PostSV: A Post-Processing Approach for Filtering Structural Variations

Genomic structural variations are significant causes of genome diversity and complex diseases. With advances in sequencing technologies, many algorithms have been designed to identify structural differences using next-generation sequencing (NGS) data. Due to repetitions in the human genome and the short reads produced by NGS, the discovery of structural variants (SVs) by state-of-the-art SV callers is not always accurate. To improve performance, multiple SV callers are often used to detect variants. However, most SV callers suffer from high false-positive rates, which diminishes the overall performance, especially in low-coverage genomes. In this article, we propose a post-processing classification–based algorithm that can be used to filter structural variation predictions produced by SV callers. Novel features are defined from putative SV predictions using reads at the local regions around the breakpoints. Several classifiers are employed to classify the candidate predictions and remove false positives. We test our classifier models on simulated and real genomes and show that the proposed approach improves the performance of state-of-the-art algorithms.

SyRI: finding genomic rearrangements and local sequence differences from whole-genome assemblies

Genomic differences range from single nucleotide differences to complex structural variations. Current methods typically annotate sequence differences ranging from SNPs to large indels accurately but do not unravel the full complexity of structural rearrangements, including inversions, translocations, and duplications, where highly similar sequence changes in location, orientation, or copy number. Here, we present SyRI, a pairwise whole-genome comparison tool for chromosome-level assemblies. SyRI starts by finding rearranged regions and then searches for differences in the sequences, which are distinguished for residing in syntenic or rearranged regions. This distinction is important as rearranged regions are inherited differently compared to syntenic regions.

Evaluating nanopore sequencing data processing pipelines for structural variation identification

BACKGROUND

Structural variations (SVs) account for about 1% of the differences among human genomes and play a significant role in phenotypic variation and disease susceptibility. The emerging nanopore sequencing technology can generate long sequence reads and can potentially provide accurate SV identification. However, the tools for aligning long-read data and detecting SVs have not been thoroughly evaluated.

RESULTS

Using four nanopore datasets, including both empirical and simulated reads, we evaluate four alignment tools and three SV detection tools. We also evaluate the impact of sequencing depth on SV detection. Finally, we develop a machine learning approach to integrate call sets from multiple pipelines. Overall SV callers' performance varies depending on the SV types. For an initial data assessment, we recommend using aligner minimap2 in combination with SV caller Sniffles because of their speed and relatively balanced performance. For detailed analysis, we recommend incorporating information from multiple call sets to improve the SV call performance.

CONCLUSIONS

We present a workflow for evaluating aligners and SV callers for nanopore sequencing data and approaches for integrating multiple call sets. Our results indicate that additional optimizations are needed to improve SV detection accuracy and sensitivity, and an integrated call set can provide enhanced performance. The nanopore technology is improving, and the sequencing community is likely to grow accordingly. In turn, better benchmark call sets will be available to more accurately assess the performance of available tools and facilitate further tool development.

SVEngine: an efficient and versatile simulator of genome structural variations with features of cancer clonal evolution

Background

Simulating genome sequence data with variant features facilitates the development and benchmarking of structural variant analysis programs. However, there are only a few data simulators that provide structural variants in silico and even fewer that provide variants with different allelic fraction and haplotypes.

Findings

We developed SVEngine, an open-source tool to address this need. SVEngine simulates next-generation sequencing data with embedded structural variations. As input, SVEngine takes template haploid sequences (FASTA) and an external variant file, a variant distribution file, and/or a clonal phylogeny tree file (NEWICK) as input. Subsequently, it simulates and outputs sequence contigs (FASTAs), sequence reads (FASTQs), and/or post-alignment files (BAMs). All of the files contain the desired variants, along with BED files containing the ground truth. SVEngine's flexible design process enables one to specify size, position, and allelic fraction for deletions, insertions, duplications, inversions, and translocations. Finally, SVEngine simulates sequence data that replicate the characteristics of a sequencing library with mixed sizes of DNA insert molecules. To improve the compute speed, SVEngine is highly parallelized to reduce the simulation time.

Conclusions

We demonstrated the versatile features of SVEngine and its improved runtime comparisons with other available simulators. SVEngine's features include the simulation of locus-specific variant frequency designed to mimic the phylogeny of cancer clonal evolution. We validated SVEngine's accuracy by simulating genome-wide structural variants of NA12878 and a heterogeneous cancer genome. Our evaluation included checking various sequencing mapping features such as coverage change, read clipping, insert size shift, and neighboring hanging read pairs for representative variant types. Structural variant callers Lumpy and Manta and tumor heterogeneity estimator THetA2 were able to perform realistically on the simulated data. SVEngine is implemented as a standard Python package and is freely available for academic use .

SQUID: transcriptomic structural variation detection from RNA-seq

Transcripts are frequently modified by structural variations, which lead to fused transcripts of either multiple genes, known as a fusion gene, or a gene and a previously non-transcribed sequence. Detecting these modifications, called transcriptomic structural variations (TSVs), especially in cancer tumor sequencing, is an important and challenging computational problem. We introduce SQUID, a novel algorithm to predict both fusion-gene and non-fusion-gene TSVs accurately from RNA-seq alignments. SQUID unifies both concordant and discordant read alignments into one model and doubles the precision on simulation data compared to other approaches. Using SQUID, we identify novel non-fusion-gene TSVs on TCGA samples.

Natural Variation in the Distribution and Abundance of Transposable Elements Across the Caenorhabditis elegans Species

Transposons are mobile DNA elements that generate both adaptive and deleterious phenotypic variation thereby driving genome evolution. For these reasons, genomes have mechanisms to regulate transposable element (TE) activity. Approximately 12–16% of the Caenorhabditis elegans genome is composed of TEs, of which the majority are likely inactive. However, most studies of TE activity have been conducted in the laboratory strain N2, which limits our knowledge of the effects of these mobile elements across natural populations. We analyzed the distribution and abundance of TEs in 208 wild C. elegans strains to better understand how transposons contribute to variation in natural populations. We identified 3,397 TEs as compared with the reference strain, of which 2,771 are novel insertions and 241 are TEs that have been excised in at least one wild strain. Likely because of their hypothesized deleterious effects, we find that TEs are found at low allele frequencies throughout the population, and we predict functional effects of TE insertions. The abundances of TEs reflect their activities, and these data allowed us to perform both genome-wide association mappings and rare variant correlations to reveal several candidate genes that impact TE regulation, including small regulatory piwi-interacting RNAs and chromatin factors. Because TE variation in natural populations could underlie phenotypic variation for organismal and behavioral traits, the transposons that we identified and their regulatory mechanisms can be used in future studies to explore the genomics of complex traits and evolutionary changes.

A comparative evaluation of genome assembly reconciliation tools

Background

The majority of eukaryotic genomes are unfinished due to the algorithmic challenges of assembling them. A variety of assembly and scaffolding tools are available, but it is not always obvious which tool or parameters to use for a specific genome size and complexity. It is, therefore, common practice to produce multiple assemblies using different assemblers and parameters, then select the best one for public release. A more compelling approach would allow one to merge multiple assemblies with the intent of producing a higher quality consensus assembly, which is the objective of assembly reconciliation.

Results

Several assembly reconciliation tools have been proposed in the literature, but their strengths and weaknesses have never been compared on a common dataset. We fill this need with this work, in which we report on an extensive comparative evaluation of several tools. Specifically, we evaluate contiguity, correctness, coverage, and the duplication ratio of the merged assembly compared to the individual assemblies provided as input.

Conclusions

None of the tools we tested consistently improved the quality of the input GAGE and synthetic assemblies. Our experiments show an increase in contiguity in the consensus assembly when the original assemblies already have high quality. In terms of correctness, the quality of the results depends on the specific tool, as well as on the quality and the ranking of the input assemblies. In general, the number of misassemblies ranges from being comparable to the best of the input assembly to being comparable to the worst of the input assembly.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-017-1213-3) contains supplementary material, which is available to authorized users.

Pysim-sv: a package for simulating structural variation data with GC-biases

Background

Structural variations (SVs) are wide-spread in human genomes and may have important implications in disease-related and evolutionary studies. High-throughput sequencing (HTS) has become a major platform for SV detection and simulation serves as a powerful and cost-effective approach for benchmarking SV detection algorithms. Accurate performance assessment by simulation requires the simulator capable of generating simulation data with all important features of real data, such GC biases in HTS data and various complexities in tumor data. However, no available package has systematically addressed all issues in data simulation for SV benchmarking.

Results

Pysim-sv is a package for simulating HTS data to evaluate performance of SV detection algorithms. Pysim-sv can introduce a wide spectrum of germline and somatic genomic variations. The package contains functionalities to simulate tumor data with aneuploidy and heterogeneous subclones, which is very useful in assessing algorithm performance in tumor studies. Furthermore, Pysim-sv can introduce GC-bias, the most important and prevalent bias in HTS data, in the simulated HTS data.

Conclusions

Pysim-sv provides an unbiased toolkit for evaluating HTS-based SV detection algorithms.

Detection and validation of structural variations in bovine whole-genome sequence data

BACKGROUND

Several examples of structural variation (SV) affecting phenotypic traits have been reported in cattle. Currently the identification of SV from whole-genome sequence data (WGS) suffers from a high false positive rate. Our aim was to construct a high quality set of SV calls in cattle using WGS data. First, we tested two SV detection programs, Breakdancer and Pindel, and the overlap of these methods, on simulated sequence data to determine their precision and sensitivity. We then identified population SV from WGS of 252 Holstein and 64 Jersey bulls based on the overlapping calls from the two programs. In addition, we validated an overlapped SV set in 28 twice-sequenced Holstein individuals, and in another two validated sets (one for each breed) that were transmitted from sire to son. We also tested whether highly conserved gene sets across eukaryotes and recently expanded gene families in bovine were depleted and enriched, respectively, for SV.

RESULTS

In empirical WGS data, 17,518 SV covering 27.36 Mb were found in the Holstein population and 4285 SV covering 8.74 Mb in the Jersey population, of which 4.62 Mb of SV overlapped between Holsteins and Jerseys. A total of 11,534 candidate SV covering 5.64 Mb were validated in the 28 twice-sequenced individuals, while 3.49 and 0.67 Mb of SV were validated from Holstein and Jersey sire-son transmission, respectively. Only eight of 237 core eukaryotic genes had at least a 50-bp overlap with an SV from our validated sets, suggesting that conserved genes are depleted for SV (p < 0.05). In addition, we observed that recently expanded gene families were significantly more associated with SV than other genes. Long interspersed nuclear elements-1 were enriched for deletions when compared to the rest of the genome (p = 0.0035).

CONCLUSIONS

We reported SV from 252 Holstein and 64 Jersey individuals. A considerable proportion of Jersey population SV (53.5%) were also found in Holstein. In contrast, about 76.90% sire-son transmission validated SV were present in Jerseys and Holsteins. The enrichment of SV in expanding gene families suggests that SV can be a source of genetic variation for evolution.

Population scale mapping of transposable element diversity reveals links to gene regulation and epigenomic variation

Variation in the presence or absence of transposable elements (TEs) is a major source of genetic variation between individuals. Here, we identified 23,095 TE presence/absence variants between 216 Arabidopsis accessions. Most TE variants were rare, and we find these rare variants associated with local extremes of gene expression and DNA methylation levels within the population. Of the common alleles identified, two thirds were not in linkage disequilibrium with nearby SNPs, implicating these variants as a source of novel genetic diversity. Many common TE variants were associated with significantly altered expression of nearby genes, and a major fraction of inter-accession DNA methylation differences were associated with nearby TE insertions. Overall, this demonstrates that TE variants are a rich source of genetic diversity that likely plays an important role in facilitating epigenomic and transcriptional differences between individuals, and indicates a strong genetic basis for epigenetic variation.

DOI:http://dx.doi.org/10.7554/eLife.20777.001

SRinversion: a tool for detecting short inversions by splitting and re-aligning poorly mapped and unmapped sequencing reads

MOTIVATION

Rapid development in sequencing technologies has dramatically improved our ability to detect genetic variants in human genome. However, current methods have variable sensitivities in detecting different types of genetic variants. One type of such genetic variants that is especially hard to detect is inversions. Analysis of public databases showed that few short inversions have been reported so far. Unlike reads that contain small insertions or deletions, which will be considered through gap alignment, reads carrying short inversions often have poor mapping quality or are unmapped, thus are often not further considered. As a result, the majority of short inversions might have been overlooked and require special algorithms for their detection.

RESULTS

Here, we introduce SRinversion, a framework to analyze poorly mapped or unmapped reads by splitting and re-aligning them for the purpose of inversion detection. SRinversion is very sensitive to small inversions and can detect those less than 10 bp in size. We applied SRinversion to both simulated data and high-coverage sequencing data from the 1000 Genomes Project and compared the results with those from Pindel, BreakDancer, DELLY, Gustaf and MID. A better performance of SRinversion was achieved for both datasets for the detection of small inversions.

AVAILABILITY AND IMPLEMENTATION

SRinversion is implemented in Perl and is publicly available at http://paed.hku.hk/genome/software/SRinversion/index.html CONTACT: yangwl@hku.hkSupplementary information: Supplementary data are available at Bioinformatics online.

PSSV: a novel pattern-based probabilistic approach for somatic structural variation identification

MOTIVATION

Whole genome DNA-sequencing (WGS) of paired tumor and normal samples has enabled the identification of somatic DNA changes in an unprecedented detail. Large-scale identification of somatic structural variations (SVs) for a specific cancer type will deepen our understanding of driver mechanisms in cancer progression. However, the limited number of WGS samples, insufficient read coverage, and the impurity of tumor samples that contain normal and neoplastic cells, limit reliable and accurate detection of somatic SVs.

RESULTS

We present a novel pattern-based probabilistic approach, PSSV, to identify somatic structural variations from WGS data. PSSV features a mixture model with hidden states representing different mutation patterns; PSSV can thus differentiate heterozygous and homozygous SVs in each sample, enabling the identification of those somatic SVs with heterozygous mutations in normal samples and homozygous mutations in tumor samples. Simulation studies demonstrate that PSSV outperforms existing tools. PSSV has been successfully applied to breast cancer data to identify somatic SVs of key factors associated with breast cancer development.

AVAILABILITY AND IMPLEMENTATION

An R package of PSSV is available at http://www.cbil.ece.vt.edu/software.htm CONTACT: xuan@vt.eduSupplementary information: Supplementary data are available at Bioinformatics online.

IntSIM: An Integrated Simulator of Next-Generation Sequencing Data

OBJECTIVE

Next-generation sequencing data has been widely used for DNA variant discovery and tumor study through computational tools. Effective simulation of such data with many realistic features is very necessary for testing existing tools and guiding the development of new tools.

METHODS

We present an integrated simulation system, IntSIM, to simulate common DNA variants and to generate sequencing reads for mixture genomes. IntSIM has three novel features in comparison with other simulation programs: 1) it is able to simulate both germline and somatic variants in the same sequence, 2) it deals with tumor purity so as to generate reads corresponding to heterogeneous genomes and also produce tumor-normal matched samples, and 3) it simulates correlations among SNPs, among CNVs/CNAs based on HMM models trained from real sequencing genomes, and can simulates broad and focal CNV/CNA events.

RESULTS

The simulation data of IntSIM can reflect characteristics observed from real data and are consistent with input parameters. The IntSIM software package is freely available at http://intsim.sourceforge.net/.

CONCLUSION

Based on a great number of experiments, IntSIM performs better than other program for some scenarios, such as simulation of heterozygous SNPs, CNVs/CNAs, and can achieve some functions that other programs cannot achieve.

SIGNIFICANCE

Simulation with IntSIM can be expected to evaluate performance of methods in detecting various types of variants, analyzing tumor samples, and especially providing a realistic assessment of effect of tumor purity on identification of somatic mutations.

Altools: a user friendly NGS data analyser

BACKGROUND

Genotyping by re-sequencing has become a standard approach to estimate single nucleotide polymorphism (SNP) diversity, haplotype structure and the biodiversity and has been defined as an efficient approach to address geographical population genomics of several model species. To access core SNPs and insertion/deletion polymorphisms (indels), and to infer the phyletic patterns of speciation, most such approaches map short reads to the reference genome. Variant calling is important to establish patterns of genome-wide association studies (GWAS) for quantitative trait loci (QTLs), and to determine the population and haplotype structure based on SNPs, thus allowing content-dependent trait and evolutionary analysis. Several tools have been developed to investigate such polymorphisms as well as more complex genomic rearrangements such as copy number variations, presence/absence variations and large deletions. The programs available for this purpose have different strengths (e.g. accuracy, sensitivity and specificity) and weaknesses (e.g. low computation speed, complex installation procedure and absence of a user-friendly interface). Here we introduce Altools, a software package that is easy to install and use, which allows the precise detection of polymorphisms and structural variations.

RESULTS

Altools uses the BWA/SAMtools/VarScan pipeline to call SNPs and indels, and the dnaCopy algorithm to achieve genome segmentation according to local coverage differences in order to identify copy number variations. It also uses insert size information from the alignment of paired-end reads and detects potential large deletions. A double mapping approach (BWA/BLASTn) identifies precise breakpoints while ensuring rapid elaboration. Finally, Altools implements several processes that yield deeper insight into the genes affected by the detected polymorphisms. Altools was used to analyse both simulated and real next-generation sequencing (NGS) data and performed satisfactorily in terms of positive predictive values, sensitivity, the identification of large deletion breakpoints and copy number detection.

CONCLUSIONS

Altools is fast, reliable and easy to use for the mining of NGS data. The software package also attempts to link identified polymorphisms and structural variants to their biological functions thus providing more valuable information than similar tools.

Structural variation detection using next-generation sequencing data: A comparative technical review

Structural variations (SVs) are mutations in the genome of size at least fifty nucleotides. They contribute to the phenotypic differences among healthy individuals, cause severe diseases and even cancers by breaking or linking genes. Thus, it is crucial to systematically profile SVs in the genome. In the past decade, many next-generation sequencing (NGS)-based SV detection methods have been proposed due to the significant cost reduction of NGS experiments and their ability to unbiasedly detect SVs to the base-pair resolution. These SV detection methods vary in both sensitivity and specificity, since they use different SV-property-dependent and library-property-dependent features. As a result, predictions from different SV callers are often inconsistent. Besides, the noises in the data (both platform-specific sequencing error and artificial chimeric reads) impede the specificity of SV detection. Poorly characterized regions in the human genome (e.g., repeat regions) greatly impact the reads mapping and in turn affect the SV calling accuracy. Calling of complex SVs requires specialized SV callers. Apart from accuracy, processing speed of SV caller is another factor deciding its usability. Knowing the pros and cons of different SV calling techniques and the objectives of the biological study are essential for biologists and bioinformaticians to make informed decisions. This paper describes different components in the SV calling pipeline and reviews the techniques used by existing SV callers. Through simulation study, we also demonstrate that library properties, especially insert size, greatly impact the sensitivity of different SV callers. We hope the community can benefit from this work both in designing new SV calling methods and in selecting the appropriate SV caller for specific biological studies.

Joint detection of copy number variations in parent-offspring trios

MOTIVATION

Whole genome sequencing (WGS) of parent-offspring trios is a powerful approach for identifying disease-associated genes via detecting copy number variations (CNVs). Existing approaches, which detect CNVs for each individual in a trio independently, usually yield low-detection accuracy. Joint modeling approaches leveraging Mendelian transmission within the parent-offspring trio can be an efficient strategy to improve CNV detection accuracy.

RESULTS

In this study, we developed TrioCNV, a novel approach for jointly detecting CNVs in parent-offspring trios from WGS data. Using negative binomial regression, we modeled the read depth signal while considering both GC content bias and mappability bias. Moreover, we incorporated the family relationship and used a hidden Markov model to jointly infer CNVs for three samples of a parent-offspring trio. Through application to both simulated data and a trio from 1000 Genomes Project, we showed that TrioCNV achieved superior performance than existing approaches.

AVAILABILITY AND IMPLEMENTATION

The software TrioCNV implemented using a combination of Java and R is freely available from the website at https://github.com/yongzhuang/TrioCNV CONTACT: ydwang@hit.edu.cn

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Konnector v2.0: pseudo-long reads from paired-end sequencing data

Background

Reading the nucleotides from two ends of a DNA fragment is called paired-end tag (PET) sequencing. When the fragment length is longer than the combined read length, there remains a gap of unsequenced nucleotides between read pairs. If the target in such experiments is sequenced at a level to provide redundant coverage, it may be possible to bridge these gaps using bioinformatics methods. Konnector is a local de novo assembly tool that addresses this problem. Here we report on version 2.0 of our tool.

Results

Konnector uses a probabilistic and memory-efficient data structure called Bloom filter to represent a k-mer spectrum - all possible sequences of length k in an input file, such as the collection of reads in a PET sequencing experiment. It performs look-ups to this data structure to construct an implicit de Bruijn graph, which describes (k-1) base pair overlaps between adjacent k-mers. It traverses this graph to bridge the gap between a given pair of flanking sequences.

Conclusions

Here we report the performance of Konnector v2.0 on simulated and experimental datasets, and compare it against other tools with similar functionality. We note that, representing k-mers with 1.5 bytes of memory on average, Konnector can scale to very large genomes. With our parallel implementation, it can also process over a billion bases on commodity hardware.

Local sequence assembly reveals a high-resolution profile of somatic structural variations in 97 cancer genomes

Genomic structural variations (SVs) are pervasive in many types of cancers. Characterizing their underlying mechanisms and potential molecular consequences is crucial for understanding the basic biology of tumorigenesis. Here, we engineered a local assembly-based algorithm (laSV) that detects SVs with high accuracy from paired-end high-throughput genomic sequencing data and pinpoints their breakpoints at single base-pair resolution. By applying laSV to 97 tumor-normal paired genomic sequencing datasets across six cancer types produced by The Cancer Genome Atlas Research Network, we discovered that non-allelic homologous recombination is the primary mechanism for generating somatic SVs in acute myeloid leukemia. This finding contrasts with results for the other five types of solid tumors, in which non-homologous end joining and microhomology end joining are the predominant mechanisms. We also found that the genes recursively mutated by single nucleotide alterations differed from the genes recursively mutated by SVs, suggesting that these two types of genetic alterations play different roles during cancer progression. We further characterized how the gene structures of the oncogene JAK1 and the tumor suppressors KDM6A and RB1 are affected by somatic SVs and discussed the potential functional implications of intergenic SVs.

A Sparse Model Based Detection of Copy Number Variations From Exome Sequencing Data

GOAL

Whole-exome sequencing provides a more cost-effective way than whole-genome sequencing for detecting genetic variants, such as copy number variations (CNVs). Although a number of approaches have been proposed to detect CNVs from whole-genome sequencing, a direct adoption of these approaches to whole-exome sequencing will often fail because exons are separately located along a genome. Therefore, an appropriate method is needed to target the specific features of exome sequencing data.

METHODS

In this paper, a novel sparse model based method is proposed to discover CNVs from multiple exome sequencing data. First, exome sequencing data are represented with a penalized matrix approximation, and technical variability and random sequencing errors are assumed to follow a generalized Gaussian distribution. Second, an iteratively reweighted least squares algorithm is used to estimate the solution.

RESULTS

The method is tested and validated on both synthetic and real data, and compared with other approaches including CoNIFER, XHMM, and cn.MOPS. The test demonstrates that the proposed method outperform other approaches.

CONCLUSION

The proposed sparse model can detect CNVs from exome sequencing data with high power and precision. Significance: Sparse model can target the specific features of exome sequencing data. The software codes are freely available at http://www.tulane.edu/ wyp/software/Exon_CNV.m.

BatAlign: an incremental method for accurate alignment of sequencing reads

Structural variations (SVs) play a crucial role in genetic diversity. However, the alignments of reads near/across SVs are made inaccurate by the presence of polymorphisms. BatAlign is an algorithm that integrated two strategies called 'Reverse-Alignment' and 'Deep-Scan' to improve the accuracy of read-alignment. In our experiments, BatAlign was able to obtain the highest F-measures in read-alignments on mismatch-aberrant, indel-aberrant, concordantly/discordantly paired and SV-spanning data sets. On real data, the alignments of BatAlign were able to recover 4.3% more PCR-validated SVs with 73.3% less callings. These suggest BatAlign to be effective in detecting SVs and other polymorphic-variants accurately using high-throughput data. BatAlign is publicly available at https://goo.gl/a6phxB.

BreakSeek: a breakpoint-based algorithm for full spectral range INDEL detection

Although recent developed algorithms have integrated multiple signals to improve sensitivity for insertion and deletion (INDEL) detection, they are far from being perfect and still have great limitations in detecting a full size range of INDELs. Here we present BreakSeek, a novel breakpoint-based algorithm, which can unbiasedly and efficiently detect both homozygous and heterozygous INDELs, ranging from several base pairs to over thousands of base pairs, with accurate breakpoint and heterozygosity rate estimations. Comprehensive evaluations on both simulated and real datasets revealed that BreakSeek outperformed other existing methods on both sensitivity and specificity in detecting both small and large INDELs, and uncovered a significant amount of novel INDELs that were missed before. In addition, by incorporating sophisticated statistic models, we for the first time investigated and demonstrated the importance of handling false and conflicting signals for multi-signal integrated methods.

Robust and exact structural variation detection with paired-end and soft-clipped alignments: SoftSV compared with eight algorithms

Structural variation (SV) plays an important role in genetic diversity among the population in general and specifically in diseases such as cancer. Modern next-generation sequencing (NGS) technologies provide paired-end sequencing data at high depth with increasing read lengths. This development enabled the analysis of split-reads to detect SV breakpoints with single-nucleotide resolution. But ambiguous mappings and breakpoint sequences with further co-occurring mutations hamper split-read alignments against a reference sequence. The trade-off between high sensitivity and low false-positive rate is problematic and often requires a lot of fine-tuning of the analysis method based on knowledge about its algorithm and the characteristics of the data set. We present SoftSV, a method for exact breakpoint detection for small and large deletions, inversions, tandem duplications and inter-chromosomal translocations, which relies solely on the mutual alignment of soft-clipped reads within the neighborhood of discordantly mapped paired-end reads. Unlike other SV detection algorithms, our approach does not require thresholds regarding sequencing coverage or mapping quality. We evaluate SoftSV together with eight approaches (Breakdancer, Clever, CREST, Delly, GASVPro, Pindel, Socrates and SoftSearch) on simulated and real data sets. Our results show that sensitive and reliable SV detection is subject to many different factors like read length, sequence coverage and SV type. While most programs have their individual drawbacks, our greedy approach turns out to be the most robust and sensitive on many experimental setups. Sensitivities above 85% and positive predictive values between 80 and 100% could be achieved consistently for all SV types on simulated data sets starting at relatively short 75 bp reads and low 10-15× sequence coverage.

GROM-RD: resolving genomic biases to improve read depth detection of copy number variants

Amplifications or deletions of genome segments, known as copy number variants (CNVs), have been associated with many diseases. Read depth analysis of next-generation sequencing (NGS) is an essential method of detecting CNVs. However, genome read coverage is frequently distorted by various biases of NGS platforms, which reduce predictive capabilities of existing approaches. Additionally, the use of read depth tools has been somewhat hindered by imprecise breakpoint identification. We developed GROM-RD, an algorithm that analyzes multiple biases in read coverage to detect CNVs in NGS data. We found non-uniform variance across distinct GC regions after using existing GC bias correction methods and developed a novel approach to normalize such variance. Although complex and repetitive genome segments complicate CNV detection, GROM-RD adjusts for repeat bias and uses a two-pipeline masking approach to detect CNVs in complex and repetitive segments while improving sensitivity in less complicated regions. To overcome a typical weakness of RD methods, GROM-RD employs a CNV search using size-varying overlapping windows to improve breakpoint resolution. We compared our method to two widely used programs based on read depth methods, CNVnator and RDXplorer, and observed improved CNV detection and breakpoint accuracy for GROM-RD. GROM-RD is available at http://grigoriev.rutgers.edu/software/.

SCNVSim: somatic copy number variation and structure variation simulator

Background

Somatically acquired structure variations (SVs) and copy number variations (CNVs) can induce genetic changes that are directly related to tumor genesis. Somatic SV/CNV detection using next-generation sequencing (NGS) data still faces major challenges introduced by tumor sample characteristics, such as ploidy, heterogeneity, and purity. A simulated cancer genome with known SVs and CNVs can serve as a benchmark for evaluating the performance of existing somatic SV/CNV detection tools and developing new methods.

Results

SCNVSim is a tool for simulating somatic CNVs and structure variations SVs. Other than multiple types of SV and CNV events, the tool is capable of simulating important features related to tumor samples including aneuploidy, heterogeneity and purity.

Conclusions

SCNVSim generates the genomes of a cancer cell population with detailed information of copy number status, loss of heterozygosity (LOH), and event break points, which is essential for developing and evaluating somatic CNV and SV detection methods in cancer genomics studies.

BSSV: Bayesian based somatic structural variation identification with whole genome DNA-seq data

High coverage whole genome DNA-sequencing enables identification of somatic structural variation (SSV) more evident in paired tumor and normal samples. Recent studies show that simultaneous analysis of paired samples provides a better resolution of SSV detection than subtracting shared SVs. However, available tools can neither identify all types of SSVs nor provide any rank information regarding their somatic features. In this paper, we have developed a Bayesian framework, by integrating read alignment information from both tumor and normal samples, called BSSV, to calculate the significance of each SSV. Tested by simulated data, the precision of BSSV is comparable to that of available tools and the false negative rate is significantly lowered. We have also applied this approach to The Cancer Genome Atlas breast cancer data for SSV detection. Many known breast cancer specific mutated genes like RAD51, BRIP1, ER, PGR and PTPRD have been successfully identified.