Sensitive and accurate detection of copy number variants using read depth of coverage

Phylogenetic quantification of intra-tumour heterogeneity

Intra-tumour genetic heterogeneity is the result of ongoing evolutionary change within each cancer. The expansion of genetically distinct sub-clonal populations may explain the emergence of drug resistance, and if so, would have prognostic and predictive utility. However, methods for objectively quantifying tumour heterogeneity have been missing and are particularly difficult to establish in cancers where predominant copy number variation prevents accurate phylogenetic reconstruction owing to horizontal dependencies caused by long and cascading genomic rearrangements. To address these challenges, we present MEDICC, a method for phylogenetic reconstruction and heterogeneity quantification based on a Minimum Event Distance for Intra-tumour Copy-number Comparisons. Using a transducer-based pairwise comparison function, we determine optimal phasing of major and minor alleles, as well as evolutionary distances between samples, and are able to reconstruct ancestral genomes. Rigorous simulations and an extensive clinical study show the power of our method, which outperforms state-of-the-art competitors in reconstruction accuracy, and additionally allows unbiased numerical quantification of tumour heterogeneity. Accurate quantification and evolutionary inference are essential to understand the functional consequences of tumour heterogeneity. The MEDICC algorithms are independent of the experimental techniques used and are applicable to both next-generation sequencing and array CGH data.

Copy number variation analysis in the context of electronic medical records and large-scale genomics consortium efforts

The goal of this paper is to review recent research on copy number variations (CNVs) and their association with complex and rare diseases. In the latter part of this paper, we focus on how large biorepositories such as the electronic medical record and genomics (eMERGE) consortium may be best leveraged to systematically mine for potentially pathogenic CNVs, and we end with a discussion of how such variants might be reported back for inclusion in electronic medical records as part of medical history.

Segmentor3IsBack: an R package for the fast and exact segmentation of Seq-data

Background

Change point problems arise in many genomic analyses such as the detection of copy number variations or the detection of transcribed regions. The expanding Next Generation Sequencing technologies now allow to locate change points at the nucleotide resolution.

Results

Because of its complexity which is almost linear in the sequence length when the maximal number of segments is constant, and as its performance had been acknowledged for microarrays, we propose to use the Pruned Dynamic Programming algorithm for Seq-experiment outputs. This requires the adaptation of the algorithm to the negative binomial distribution with which we model the data. We show that if the dispersion in the signal is known, the PDP algorithm can be used, and we provide an estimator for this dispersion. We describe a compression framework which reduces the time complexity without modifying the accuracy of the segmentation. We propose to estimate the number of segments via a penalized likelihood criterion. We illustrate the performance of the proposed methodology on RNA-Seq data.

Conclusions

We illustrate the results of our approach on a real dataset and show its good performance. Our algorithm is available as an R package on the CRAN repository.

DB2: a probabilistic approach for accurate detection of tandem duplication breakpoints using paired-end reads

Background

With the advent of paired-end high throughput sequencing, it is now possible to identify various types of structural variation on a genome-wide scale. Although many methods have been proposed for structural variation detection, most do not provide precise boundaries for identified variants. In this paper, we propose a new method, Distribution Based detection of Duplication Boundaries (DB²), for accurate detection of tandem duplication breakpoints, an important class of structural variation, with high precision and recall.

Results

Our computational experiments on simulated data show that DB² outperforms state-of-the-art methods in terms of finding breakpoints of tandem duplications, with a higher positive predictive value (precision) in calling the duplications’ presence. In particular, DB²’s prediction of tandem duplications is correct 99% of the time even for very noisy data, while narrowing down the space of possible breakpoints within a margin of 15 to 20 bps on the average. Most of the existing methods provide boundaries in ranges that extend to hundreds of bases with lower precision values. Our method is also highly robust to varying properties of the sequencing library and to the sizes of the tandem duplications, as shown by its stable precision, recall and mean boundary mismatch performance. We demonstrate our method’s efficacy using both simulated paired-end reads, and those generated from a melanoma sample and two ovarian cancer samples. Newly discovered tandem duplications are validated using PCR and Sanger sequencing.

Conclusions

Our method, DB², uses discordantly aligned reads, taking into account the distribution of fragment length to predict tandem duplications along with their breakpoints on a donor genome. The proposed method fine tunes the breakpoint calls by applying a novel probabilistic framework that incorporates the empirical fragment length distribution to score each feasible breakpoint. DB² is implemented in Java programming language and is freely available at http://mendel.gene.cwru.edu/laframboiselab/software.php.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-175) contains supplementary material, which is available to authorized users.

Estimating optimal window size for analysis of low-coverage next-generation sequence data

MOTIVATION

Current high-throughput sequencing has greatly transformed genome sequence analysis. In the context of very low-coverage sequencing (<0.1×), performing 'binning' or 'windowing' on mapped short sequences ('reads') is critical to extract genomic information of interest for further evaluation, such as copy-number alteration analysis. If the window size is too small, many windows will exhibit zero counts and almost no pattern can be observed. In contrast, if the window size is too wide, the patterns or genomic features will be 'smoothed out'. Our objective is to identify an optimal window size in between the two extremes.

RESULTS

We assume the reads density to be a step function. Given this model, we propose a data-based estimation of optimal window size based on Akaike's information criterion (AIC) and cross-validation (CV) log-likelihood. By plotting the AIC and CV log-likelihood curve as a function of window size, we are able to estimate the optimal window size that minimizes AIC or maximizes CV log-likelihood. The proposed methods are of general purpose and we illustrate their application using low-coverage next-generation sequence datasets from real tumour samples and simulated datasets.

AVAILABILITY AND IMPLEMENTATION

An R package to estimate optimal window size is available at http://www1.maths.leeds.ac.uk/∼arief/R/win/.

seqCNA: an R package for DNA copy number analysis in cancer using high-throughput sequencing

Background

Deviations in the amount of genomic content that arise during tumorigenesis, called copy number alterations, are structural rearrangements that can critically affect gene expression patterns. Additionally, copy number alteration profiles allow insight into cancer discrimination, progression and complexity. On data obtained from high-throughput sequencing, improving quality through GC bias correction and keeping false positives to a minimum help build reliable copy number alteration profiles.

Results

We introduce seqCNA, a parallelized R package for an integral copy number analysis of high-throughput sequencing cancer data. The package includes novel methodology on (i) filtering, reducing false positives, and (ii) GC content correction, improving copy number profile quality, especially under great read coverage and high correlation between GC content and copy number. Adequate analysis steps are automatically chosen based on availability of paired-end mapping, matched normal samples and genome annotation.

Conclusions

seqCNA, available through Bioconductor, provides accurate copy number predictions in tumoural data, thanks to the extensive filtering and better GC bias correction, while providing an integrated and parallelized workflow.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-178) contains supplementary material, which is available to authorized users.

Identification of pathogen genomic variants through an integrated pipeline

BACKGROUND

Whole-genome sequencing represents a powerful experimental tool for pathogen research. We present methods for the analysis of small eukaryotic genomes, including a streamlined system (called Platypus) for finding single nucleotide and copy number variants as well as recombination events.

RESULTS

We have validated our pipeline using four sets of Plasmodium falciparum drug resistant data containing 26 clones from 3D7 and Dd2 background strains, identifying an average of 11 single nucleotide variants per clone. We also identify 8 copy number variants with contributions to resistance, and report for the first time that all analyzed amplification events are in tandem.

CONCLUSIONS

The Platypus pipeline provides malaria researchers with a powerful tool to analyze short read sequencing data. It provides an accurate way to detect SNVs using known software packages, and a novel methodology for detection of CNVs, though it does not currently support detection of small indels. We have validated that the pipeline detects known SNVs in a variety of samples while filtering out spurious data. We bundle the methods into a freely available package.

Common copy number variation detection from multiple sequenced samples

Common copy number variations (CNVs) are small regions of genomic variations at the same loci across multiple samples, which can be detected with high resolution from next-generation sequencing (NGS) technique. Multiple sequencing data samples are often available from genomic studies; examples include sequences from multiple platforms and sequences from multiple individuals. By integrating complementary information from multiple data samples, detection power can be potentially improved. However, most of current CNV detection methods often process an individual sequence sample, or two samples in an abnormal versus matched normal study; researches on detecting common CNVs across multiple samples have been very limited but are much needed. In this paper, we propose a novel method to detect common CNVs from multiple sequencing samples by exploiting the concurrency of genomic variations in read depth signals derived from multiple NGS data. We use a penalized sparse regression model to fit multiple read depth profiles, based on which common CNV identification is formulated as a change-point detection problem. Finally, we validate the proposed method on both simulation and real data, showing that it can give both higher detection power and better break point estimation over several published CNV detection methods.

Effector specialization in a lineage of the Irish potato famine pathogen

Accelerated gene evolution is a hallmark of pathogen adaptation following a host jump. Here, we describe the biochemical basis of adaptation and specialization of a plant pathogen effector after its colonization of a new host. Orthologous protease inhibitor effectors from the Irish potato famine pathogen, Phytophthora infestans, and its sister species, Phytophthora mirabilis, which is responsible for infection of Mirabilis jalapa, are adapted to protease targets unique to their respective host plants. Amino acid polymorphisms in both the inhibitors and their target proteases underpin this biochemical specialization. Our results link effector specialization to diversification and speciation of this plant pathogen.

Copy-number variation in the pathogenesis of autism spectrum disorder

Autism spectrum disorder is a neurodevelopmental disorder present in 1% of the population, characterized by impairments in reciprocal social interaction, communication deficits and restricted patterns of behavior. Approximately 10% of the autism spectrum disorder population is thought to have large chromosomal rearrangements. Copy-number variations (CNV) alter the genome structure either by duplication or deletion of a chromosomal region. The association between CNV and autism susceptibility has become more apparent through the use of methods based on comparative genomic hybridization in screening CNV. The nature of the high CNV rate in the human genome is partly explained by non-allelic homologous recombination between flanking repeated sequences derived from multiple copies of transposons or mobile genetic elements. There are hotspots for CNV in the human genome, such as 16p11.2 and 22q11.2. Genes involved in CNV are supposed to have copy-number dose-dependent effects on the behavior of affected individuals. Animal models give insight into the possible interactions between core genetic loci and additional factors contributing to the phenotypes of each individual. If affected genes code for cellular signaling molecules, reducing the dosage in the intracellular signaling pathway may result in the malfunction of the nervous system. The genetic background of autism spectrum disorder is highly heterogenic and most common or rare CNV do not lead to autism spectrum disorders in the majority of cases, but may occasionally increase the risk of developing an autism spectrum disorder.

TIGRA: a targeted iterative graph routing assembler for breakpoint assembly

Recent progress in next-generation sequencing has greatly facilitated our study of genomic structural variation. Unlike single nucleotide variants and small indels, many structural variants have not been completely characterized at nucleotide resolution. Deriving the complete sequences underlying such breakpoints is crucial for not only accurate discovery, but also for the functional characterization of altered alleles. However, our current ability to determine such breakpoint sequences is limited because of challenges in aligning and assembling short reads. To address this issue, we developed a targeted iterative graph routing assembler, TIGRA, which implements a set of novel data analysis routines to achieve effective breakpoint assembly from next-generation sequencing data. In our assessment using data from the 1000 Genomes Project, TIGRA was able to accurately assemble the majority of deletion and mobile element insertion breakpoints, with a substantively better success rate and accuracy than other algorithms. TIGRA has been applied in the 1000 Genomes Project and other projects and is freely available for academic use.

Parametric modeling of whole-genome sequencing data for CNV identification

Copy number variants (CNVs) constitute an important class of genetic variants in human genome and are shown to be associated with complex diseases. Whole-genome sequencing provides an unbiased way of identifying all the CNVs that an individual carries. In this paper, we consider parametric modeling of the read depth (RD) data from whole-genome sequencing with the aim of identifying the CNVs, including both Poisson and negative-binomial modeling of such count data. We propose a unified approach of using a mean-matching variance stabilizing transformation to turn the relatively complicated problem of sparse segment identification for count data into a sparse segment identification problem for a sequence of Gaussian data. We apply the optimal sparse segment identification procedure to the transformed data in order to identify the CNV segments. This provides a computationally efficient approach for RD-based CNV identification. Simulation results show that this approach often results in a small number of false identifications of the CNVs and has similar or better performances in identifying the true CNVs when compared with other RD-based approaches. We demonstrate the methods using the trio data from the 1000 Genomes Project.

PSCC: sensitive and reliable population-scale copy number variation detection method based on low coverage sequencing

Background

Copy number variations (CNVs) represent an important type of genetic variation that deeply impact phenotypic polymorphisms and human diseases. The advent of high-throughput sequencing technologies provides an opportunity to revolutionize the discovery of CNVs and to explore their relationship with diseases. However, most of the existing methods depend on sequencing depth and show instability with low sequence coverage. In this study, using low coverage whole-genome sequencing (LCS) we have developed an effective population-scale CNV calling (PSCC) method.

Methodology/Principal Findings

In our novel method, two-step correction was used to remove biases caused by local GC content and complex genomic characteristics. We chose a binary segmentation method to locate CNV segments and designed combined statistics tests to ensure the stable performance of the false positive control. The simulation data showed that our PSCC method could achieve 99.7%/100% and 98.6%/100% sensitivity and specificity for over 300 kb CNV calling in the condition of LCS (∼2×) and ultra LCS (∼0.2×), respectively. Finally, we applied this novel method to analyze 34 clinical samples with an average of 2× LCS. In the final results, all the 31 pathogenic CNVs identified by aCGH were successfully detected. In addition, the performance comparison revealed that our method had significant advantages over existing methods using ultra LCS.

Conclusions/Significance

Our study showed that PSCC can sensitively and reliably detect CNVs using low coverage or even ultra-low coverage data through population-scale sequencing.

Copy number polymorphism in plant genomes

Copy number variants (CNVs) are genomic rearrangements resulting from gains or losses of DNA segments. Typically, the term refers to rearrangements of sequences larger than 1 kb. This type of polymorphism has recently been shown to be a key contributor to intra-species genetic variation, along with single-nucleotide polymorphisms and short insertion-deletion polymorphisms. Over the last decade, a growing number of studies have highlighted the importance of copy number variation (CNV) as a factor affecting human phenotype and individual CNVs have been linked to risks for severe diseases. In plants, the exploration of the extent and role of CNV is still just beginning. Initial genomic analyses indicate that CNVs are prevalent in plants and have greatly affected plant genome evolution. Many CNV events have been observed in outcrossing and autogamous species. CNVs are usually found on all chromosomes, with CNV hotspots interspersed with regions of very low genetic variation. Although CNV is mainly associated with intergenic regions, many CNVs encompass protein-coding genes. The collected data suggest that CNV mainly affects the members of large families of functionally redundant genes. Thus, the effects of individual CNV events on phenotype are usually modest. Nevertheless, there are many cases in which CNVs for specific genes have been linked to important traits such as flowering time, plant height and resistance to biotic and abiotic stress. Recent reports suggest that CNVs may form rapidly in response to stress.

Inferring short tandem repeat variation from paired-end short reads

The advances of high-throughput sequencing offer an unprecedented opportunity to study genetic variation. This is challenged by the difficulty of resolving variant calls in repetitive DNA regions. We present a Bayesian method to estimate repeat-length variation from paired-end sequence read data. The method makes variant calls based on deviations in sequence fragment sizes, allowing the analysis of repeats at lengths of relevance to a range of phenotypes. We demonstrate the method’s ability to detect and quantify changes in repeat lengths from short read genomic sequence data across genotypes. We use the method to estimate repeat variation among 12 strains of Arabidopsis thaliana and demonstrate experimentally that our method compares favourably against existing methods. Using this method, we have identified all repeats across the genome, which are likely to be polymorphic. In addition, our predicted polymorphic repeats also included the only known repeat expansion in A. thaliana, suggesting an ability to discover potential unstable repeats.

Detection of structural DNA variation from next generation sequencing data: a review of informatic approaches

Next generation sequencing (NGS), or massively paralleled sequencing, refers to a collective group of methods in which numerous sequencing reactions take place simultaneously, resulting in enormous amounts of sequencing data for a small fraction of the cost of Sanger sequencing. Typically short (50-250 bp), NGS reads are first mapped to a reference genome, and then variants are called from the mapped data. While most NGS applications focus on the detection of single nucleotide variants (SNVs) or small insertions/deletions (indels), structural variation, including translocations, larger indels, and copy number variation (CNV), can be identified from the same data. Structural variation detection can be performed from whole genome NGS data or "targeted" data including exomes or gene panels. However, while targeted sequencing greatly increases sequencing coverage or depth of particular genes, it may introduce biases in the data that require specialized informatic analyses. In the past several years, there have been considerable advances in methods used to detect structural variation, and a full range of variants from SNVs to balanced translocations to CNV can now be detected with reasonable sensitivity from either whole genome or targeted NGS data. Such methods are being rapidly applied to clinical testing where they can supplement or in some cases replace conventional fluorescence in situ hybridization or array-based testing. Here we review some of the informatics approaches used to detect structural variation from NGS data.

SomatiCA: identifying, characterizing and quantifying somatic copy number aberrations from cancer genome sequencing data

Whole genome sequencing of matched tumor-normal sample pairs is becoming routine in cancer research. However, analysis of somatic copy-number changes from sequencing data is still challenging because of insufficient sequencing coverage, unknown tumor sample purity and subclonal heterogeneity. Here we describe a computational framework, named SomatiCA, which explicitly accounts for tumor purity and subclonality in the analysis of somatic copy-number profiles. Taking read depths (RD) and lesser allele frequencies (LAF) as input, SomatiCA will output 1) admixture rate for each tumor sample, 2) somatic allelic copy-number for each genomic segment, 3) fraction of tumor cells with subclonal change in each somatic copy number aberration (SCNA), and 4) a list of substantial genomic aberration events including gain, loss and LOH. SomatiCA is available as a Bioconductor R package at http://www.bioconductor.org/packages/2.13/bioc/html/SomatiCA.html.

Assembly errors cause false tandem duplicate regions in the chicken (Gallus gallus) genome sequence

The complexity of eukaryote genomes makes assembly errors inevitable in the process of constructing reference genomes. Next-generation sequencing (NGS) could provide an efficient way to validate previously assembled genomes. Here, we exploited NGS data to interrogate the chicken reference genome and identified 35 pairs of nearly identical regions with >99.5 % sequence similarity and a median size of 109 kb. Several lines of evidence, including read depth, the composition of junction sequences, and sequence similarity, suggest that these regions present genome assembly errors and should be excluded from forthcoming genomic studies.

WaveCNV: allele-specific copy number alterations in primary tumors and xenograft models from next-generation sequencing

Motivation: Copy number variations (CNVs) are a major source of genomic variability and are especially significant in cancer. Until recently microarray technologies have been used to characterize CNVs in genomes. However, advances in next-generation sequencing technology offer significant opportunities to deduce copy number directly from genome sequencing data. Unfortunately cancer genomes differ from normal genomes in several aspects that make them far less amenable to copy number detection. For example, cancer genomes are often aneuploid and an admixture of diploid/non-tumor cell fractions. Also patient-derived xenograft models can be laden with mouse contamination that strongly affects accurate assignment of copy number. Hence, there is a need to develop analytical tools that can take into account cancer-specific parameters for detecting CNVs directly from genome sequencing data.

Results: We have developed WaveCNV, a software package to identify copy number alterations by detecting breakpoints of CNVs using translation-invariant discrete wavelet transforms and assign digitized copy numbers to each event using next-generation sequencing data. We also assign alleles specifying the chromosomal ratio following duplication/loss. We verified copy number calls using both microarray (correlation coefficient 0.97) and quantitative polymerase chain reaction (correlation coefficient 0.94) and found them to be highly concordant. We demonstrate its utility in pancreatic primary and xenograft sequencing data.

Availability and implementation: Source code and executables are available at https://github.com/WaveCNV. The segmentation algorithm is implemented in MATLAB, and copy number assignment is implemented Perl.

Contact:lakshmi.muthuswamy@gmail.com

Supplementary information:Supplementary data are available at Bioinformatics online.

Detectable clonal mosaicism in the human genome

Human genetic mosaicism is the presence of two or more cellular populations with distinct genotypes in an individual who developed from a single fertilized ovum. While initially observed across a spectrum of rare genetic disorders, detailed assessment of data from genome-wide association studies now reveal that detectable clonal mosaicism involving large structural alterations (>2 Mb) can also be seen in populations of apparently healthy individuals. The first generation of descriptive studies has generated new interest in understanding the molecular basis of the affected genomic regions, percent of the cellular subpopulation involved, and developmental timing of the underlying mutational event, which could reveal new insights into the initiation, clonal expansion, and phenotypic manifestations of mosaic events. Early evidence indicates detectable clonal mosaicism increases in frequency with age and could preferentially occur in males. The observed pattern of recurrent events affecting specific chromosomal regions indicates some regions are more susceptible to these events, which could reflect inter-individual differences in genomic stability. Moreover, it is also plausible that the presence of large structural events could be associated with cancer risk. The characterization of detectable genetic mosaicism reveals that there could be important dynamic changes in the human genome associated with the aging process, which could be associated with risk for common disorders, such as cancer, cardiovascular disease, diabetes, and neurological disorders.

Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives

Copy number variation (CNV) is a prevalent form of critical genetic variation that leads to an abnormal number of copies of large genomic regions in a cell. Microarray-based comparative genome hybridization (arrayCGH) or genotyping arrays have been standard technologies to detect large regions subject to copy number changes in genomes until most recently high-resolution sequence data can be analyzed by next-generation sequencing (NGS). During the last several years, NGS-based analysis has been widely applied to identify CNVs in both healthy and diseased individuals. Correspondingly, the strong demand for NGS-based CNV analyses has fuelled development of numerous computational methods and tools for CNV detection. In this article, we review the recent advances in computational methods pertaining to CNV detection using whole genome and whole exome sequencing data. Additionally, we discuss their strengths and weaknesses and suggest directions for future development.

MATCHCLIP: locate precise breakpoints for copy number variation using CIGAR string by matching soft clipped reads

Copy number variations (CNVs) are associated with many complex diseases. Next generation sequencing data enable one to identify precise CNV breakpoints to better under the underlying molecular mechanisms and to design more efficient assays. Using the CIGAR strings of the reads, we develop a method that can identify the exact CNV breakpoints, and in cases when the breakpoints are in a repeated region, the method reports a range where the breakpoints can slide. Our method identifies the breakpoints of a CNV using both the positions and CIGAR strings of the reads that cover breakpoints of a CNV. A read with a long soft clipped part (denoted as S in CIGAR) at its 3′(right) end can be used to identify the 5′(left)-side of the breakpoints, and a read with a long S part at the 5′ end can be used to identify the breakpoint at the 3′-side. To ensure both types of reads cover the same CNV, we require the overlapped common string to include both of the soft clipped parts. When a CNV starts and ends in the same repeated regions, its breakpoints are not unique, in which case our method reports the left most positions for the breakpoints and a range within which the breakpoints can be incremented without changing the variant sequence. We have implemented the methods in a C++ package intended for the current Illumina Miseq and Hiseq platforms for both whole genome and exon-sequencing. Our simulation studies have shown that our method compares favorably with other similar methods in terms of true discovery rate, false positive rate and breakpoint accuracy. Our results from a real application have shown that the detected CNVs are consistent with zygosity and read depth information. The software package is available at http://statgene.med.upenn.edu/softprog.html.

Chromosomal structural variations during progression of a prostate epithelial cell line to a malignant metastatic state inactivate the NF2, NIPSNAP1, UGT2B17, and LPIN2 genes

Prostate cancer is the second highest cause of male cancer deaths in the United States. A significant number of tumors advance to a highly invasive and metastatic stage, which is typically resistant to traditional cancer therapeutics. In order to identify chromosomal structural variants that may contribute to prostate cancer progression we sequenced the genomes of a HPV-18 immortalized nonmalignant human prostate epithelial cell line, RWPE1, and compared it to its malignant, metastatic derivative, WPE1-NB26. There were a total of 34 large (> 1 Mbp) and 38 small copy number variants (<100 kbp) in WPE1-NB26 that were not present in the precursor cell line. We also identified and validated 46 structural variants present in the two cell lines, of which 23 were unique to WPE1-NB26. Structural variants unique to the malignant cell line inactivated: (1) the neurofibromin2 (NF2) gene, a known tumor suppressor; (2) its neighboring gene NIPSNAP1, another putative tumor suppressor that inhibits TRPV6, an anti-apoptotic oncogene implicated in prostate cancer progression; (3) UGT2B17, a gene that inactivates dihydrotestosterone, a known activator of prostate cancer progression; and (4) LPIN2, a phosphatidic acid phosphatase and a co-factor of PGC1a that is important for lipid metabolism and for suppressing autoinflammation. Our results illustrate the value of comparing the genomes of defined related pairs of cell lines to discover chromosomal structural variants that may contribute to cancer progression.

Challenges for implementing next-generation sequencing-based genome diagnostics: it's also the people, not just the machines

The scope of next-generation DNA sequencing (NGS) is transitioning from research to diagnostics (and beyond), but the conditions for routine clinical application have not been clearly defined. Technological limitations for sequencing a patient's DNA fast and affordably are rapidly disappearing. At the same time, more and more is known about the role of DNA variation in disease susceptibility, disease development and response to treatment. Consequently, more and more pediatricians, cardiologists and other medical specialists would like to apply NGS-based diagnostics. The standard, comprehensive and easy-to-handle genetic test these specialists are looking for, however, is not yet available. Molecular diagnostic laboratories have started to implement NGS into their routine workflows, but are also becoming increasingly aware that the context in which they operate is changing. It becomes apparent that the major challenges are not in the technology, but rather in anticipating the changing scope and scale. Developing the infrastructure to sustainably perform NGS-based diagnostics in a changing technological, clinical and societal context is therefore more relevant than defining minimal performance criteria or standard analysis pipelines. Implementing NGS-based diagnostics comes with novel applications, emerging service models and reconfiguration of professional roles, and should thus be considered in the context of future healthcare. Here, we present the key elements for transition of NGS from research to diagnostics.

The Growing Importance of CNVs: New Insights for Detection and Clinical Interpretation

Differences between genomes can be due to single nucleotide variants, translocations, inversions, and copy number variants (CNVs, gain or loss of DNA). The latter can range from sub-microscopic events to complete chromosomal aneuploidies. Small CNVs are often benign but those larger than 500 kb are strongly associated with morbid consequences such as developmental disorders and cancer. Detecting CNVs within and between populations is essential to better understand the plasticity of our genome and to elucidate its possible contribution to disease. Hence there is a need for better-tailored and more robust tools for the detection and genome-wide analyses of CNVs. While a link between a given CNV and a disease may have often been established, the relative CNV contribution to disease progression and impact on drug response is not necessarily understood. In this review we discuss the progress, challenges, and limitations that occur at different stages of CNV analysis from the detection (using DNA microarrays and next-generation sequencing) and identification of recurrent CNVs to the association with phenotypes. We emphasize the importance of germline CNVs and propose strategies to aid clinicians to better interpret structural variations and assess their clinical implications.

PeSV-Fisher: identification of somatic and non-somatic structural variants using next generation sequencing data

Next-generation sequencing technologies expedited research to develop efficient computational tools for the identification of structural variants (SVs) and their use to study human diseases. As deeper data is obtained, the existence of higher complexity SVs in some genomes becomes more evident, but the detection and definition of most of these complex rearrangements is still in its infancy. The full characterization of SVs is a key aspect for discovering their biological implications. Here we present a pipeline (PeSV-Fisher) for the detection of deletions, gains, intra- and inter-chromosomal translocations, and inversions, at very reasonable computational costs. We further provide comprehensive information on co-localization of SVs in the genome, a crucial aspect for studying their biological consequences. The algorithm uses a combination of methods based on paired-reads and read-depth strategies. PeSV-Fisher has been designed with the aim to facilitate identification of somatic variation, and, as such, it is capable of analysing two or more samples simultaneously, producing a list of non-shared variants between samples. We tested PeSV-Fisher on available sequencing data, and compared its behaviour to that of frequently deployed tools (BreakDancer and VariationHunter). We have also tested this algorithm on our own sequencing data, obtained from a tumour and a normal blood sample of a patient with chronic lymphocytic leukaemia, on which we have also validated the results by targeted re-sequencing of different kinds of predictions. This allowed us to determine confidence parameters that influence the reliability of breakpoint predictions.

Availability

PeSV-Fisher is available at http://gd.crg.eu/tools.

Whole-genome sequences of DA and F344 rats with different susceptibilities to arthritis, autoimmunity, inflammation and cancer

DA (D-blood group of Palm and Agouti, also known as Dark Agouti) and F344 (Fischer) are two inbred rat strains with differences in several phenotypes, including susceptibility to autoimmune disease models and inflammatory responses. While these strains have been extensively studied, little information is available about the DA and F344 genomes, as only the Brown Norway (BN) and spontaneously hypertensive rat strains have been sequenced to date. Here we report the sequencing of the DA and F344 genomes using next-generation Illumina paired-end read technology and the first de novo assembly of a rat genome. DA and F344 were sequenced with an average depth of 32-fold, covered 98.9% of the BN reference genome, and included 97.97% of known rat ESTs. New sequences could be assigned to 59 million positions with previously unknown data in the BN reference genome. Differences between DA, F344, and BN included 19 million positions in novel scaffolds, 4.09 million single nucleotide polymorphisms (SNPs) (including 1.37 million new SNPs), 458,224 short insertions and deletions, and 58,174 structural variants. Genetic differences between DA, F344, and BN, including high-impact SNPs and short insertions and deletions affecting >2500 genes, are likely to account for most of the phenotypic variation between these strains. The new DA and F344 genome sequencing data should facilitate gene discovery efforts in rat models of human disease.

Reprever: resolving low-copy duplicated sequences using template driven assembly

Genomic sequence duplication is an important mechanism for genome evolution, often resulting in large sequence variations with implications for disease progression. Although paired-end sequencing technologies are commonly used for structural variation discovery, the discovery of novel duplicated sequences remains an unmet challenge. We analyze duplicons starting from identified high-copy number variants. Given paired-end mapped reads, and a candidate high-copy region, our tool, Reprever, identifies (a) the insertion breakpoints where the extra duplicons inserted into the donor genome and (b) the actual sequence of the duplicon. Reprever resolves ambiguous mapping signatures from existing homologs, repetitive elements and sequencing errors to identify breakpoint. At each breakpoint, Reprever reconstructs the inserted sequence using profile hidden Markov model (PHMM)-based guided assembly. In a test on 1000 artificial genomes with simulated duplication, Reprever could identify novel duplicates up to 97% of genomes within 3 bp positional and 1% sequence errors. Validation on 680 fosmid sequences identified and reconstructed eight duplicated sequences with high accuracy. We applied Reprever to reanalyzing a re-sequenced data set from the African individual NA18507 to identify >800 novel duplicates, including insertions in genes and insertions with additional variation. polymerase chain reaction followed by capillary sequencing validated both the insertion locations of the strongest predictions and their predicted sequence.

CNV-TV: a robust method to discover copy number variation from short sequencing reads

Background

Copy number variation (CNV) is an important structural variation (SV) in human genome. Various studies have shown that CNVs are associated with complex diseases. Traditional CNV detection methods such as fluorescence in situ hybridization (FISH) and array comparative genomic hybridization (aCGH) suffer from low resolution. The next generation sequencing (NGS) technique promises a higher resolution detection of CNVs and several methods were recently proposed for realizing such a promise. However, the performances of these methods are not robust under some conditions, e.g., some of them may fail to detect CNVs of short sizes. There has been a strong demand for reliable detection of CNVs from high resolution NGS data.

Results

A novel and robust method to detect CNV from short sequencing reads is proposed in this study. The detection of CNV is modeled as a change-point detection from the read depth (RD) signal derived from the NGS, which is fitted with a total variation (TV) penalized least squares model. The performance (e.g., sensitivity and specificity) of the proposed approach are evaluated by comparison with several recently published methods on both simulated and real data from the 1000 Genomes Project.

Conclusion

The experimental results showed that both the true positive rate and false positive rate of the proposed detection method do not change significantly for CNVs with different copy numbers and lengthes, when compared with several existing methods. Therefore, our proposed approach results in a more reliable detection of CNVs than the existing methods.

CNVrd, a read-depth algorithm for assigning copy-number at the FCGR locus: population-specific tagging of copy number variation at FCGR3B

The extent of contribution from common gene copy number (CN) variants in human disease is currently unresolved. Part of the reason for this is the technical difficulty in directly measuring CN variation (CNV) using molecular methods, and the lack of single nucleotide polymorphisms (SNPs) that can tag complex CNV that has arisen multiple times on different SNP haplotypes. One CNV locus implicated in human disease is FCGR. Here we aimed to use next-generation sequencing (NGS) data from the 1000 Genomes Project to assign CN at FCGR3A and FCGR3B and to comprehensively assess the ability of SNPs to tag specific CN variants. A read-depth algorithm was developed (CNVrd) and validated on a subset of HapMap samples using CN assignments that had previously been determined using molecular and microarray methods. At 7 out of 9 other complex loci there was >90% concordance with microarray data. However, given that some prior knowledge of CN is required, the generalizability of CNVrd is limited and should be applied to other complex CNV loci with caution. Subsequently, CN was assigned et FCGR3B using CNVrd in a total of 952 samples from the 1000 Genomes Project, using three classes and SNPs that correlated with duplication were identified. The best tag SNP was observed in the Mexican-American sample set for duplication at FCGR3B. This SNP (rs117435514, r² = 0.79) also tagged similar duplication in Chinese and Japanese (r² = 0.35–0.60), but not in Caucasian or African. No tag SNP for duplication at FCGR3A or deletion at FCGR3B was identified in any population. We conclude that it is possible to tag CNV at the FCGR locus, but CN and SNPs have to be characterized and correlated on a population-specific basis.

Increased CNV-region deletions in mild cognitive impairment (MCI) and Alzheimer's disease (AD) subjects in the ADNI sample

We investigated the genome-wide distribution of CNVs in the Alzheimer's disease (AD) Neuroimaging Initiative (ADNI) sample (146 with AD, 313 with Mild Cognitive Impairment (MCI), and 181 controls). Comparison of single CNVs between cases (MCI and AD) and controls shows overrepresentation of large heterozygous deletions in cases (p-value<0.0001). The analysis of CNV-Regions identifies 44 copy number variable loci of heterozygous deletions, with more CNV-Regions among affected than controls (p=0.005). Seven of the 44 CNV-Regions are nominally significant for association with cognitive impairment. We validated and confirmed our main findings with genome re-sequencing of selected patients and controls. The functional pathway analysis of the genes putatively affected by deletions of CNV-Regions reveals enrichment of genes implicated in axonal guidance, cell-cell adhesion, neuronal morphogenesis and differentiation. Our findings support the role of CNVs in AD, and suggest an association between large deletions and the development of cognitive impairment.

Comparative studies of copy number variation detection methods for next-generation sequencing technologies

Copy number variation (CNV) has played an important role in studies of susceptibility or resistance to complex diseases. Traditional methods such as fluorescence in situ hybridization (FISH) and array comparative genomic hybridization (aCGH) suffer from low resolution of genomic regions. Following the emergence of next generation sequencing (NGS) technologies, CNV detection methods based on the short read data have recently been developed. However, due to the relatively young age of the procedures, their performance is not fully understood. To help investigators choose suitable methods to detect CNVs, comparative studies are needed. We compared six publicly available CNV detection methods: CNV-seq, FREEC, readDepth, CNVnator, SegSeq and event-wise testing (EWT). They are evaluated both on simulated and real data with different experiment settings. The receiver operating characteristic (ROC) curve is employed to demonstrate the detection performance in terms of sensitivity and specificity, box plot is employed to compare their performances in terms of breakpoint and copy number estimation, Venn diagram is employed to show the consistency among these methods, and F-score is employed to show the overlapping quality of detected CNVs. The computational demands are also studied. The results of our work provide a comprehensive evaluation on the performances of the selected CNV detection methods, which will help biological investigators choose the best possible method.

A survey of copy-number variation detection tools based on high-throughput sequencing data

Copy-number variation (CNV) is a major class of genomic variation with potentially important functional consequences in both normal and diseased populations. Remarkable advances in development of next-generation sequencing (NGS) platforms provide an unprecedented opportunity for accurate, high-resolution characterization of CNVs. In this unit, we give an overview of available computational tools for detection of CNVs and discuss comparative advantages and disadvantages of different approaches.

The mutation rate of mycobacterial repetitive unit loci in strains of M. tuberculosis from cynomolgus macaque infection

Background

Mycobacterial interspersed repetitive units (MIRUs) are minisatellites within the Mycobacterium tuberculosis (Mtb) genome. Copy number variation (CNV) in MIRU loci is used for epidemiological typing, making the rate of variation important for tracking the transmission of Mtb strains. In this study, we developed and assessed a whole-genome sequencing (WGS) approach to detect MIRU CNV in Mtb. We applied this methodology to a panel of Mtb strains isolated from the macaque model of tuberculosis (TB), the animal model that best mimics human disease. From these data, we have estimated the rate of MIRU variation in the host environment, providing a benchmark rate for future epidemiologic work.

Results

We assessed variation at the 24 MIRU loci used for typing in a set of Mtb strains isolated from infected cynomolgus macaques. We previously performed WGS of these strains and here have applied both read depth (RD) and paired-end mapping (PEM) metrics to identify putative copy number variants. To assess the relative power of these approaches, all MIRU loci were resequenced using Sanger sequencing. We detected two insertion/deletion events both of which could be identified as candidates by PEM criteria. With these data, we estimate a MIRU mutation rate of 2.70 × 10^-03 (95% CI: 3.30 × 10^-04- 9.80 × 10^-03) per locus, per year.

Conclusion

Our results represent the first experimental estimate of the MIRU mutation rate in Mtb. This rate is comparable to the highest previous estimates gathered from epidemiologic data and meta-analyses. Our findings allow for a more rigorous interpretation of data gathered from MIRU typing.

A Poisson hierarchical modelling approach to detecting copy number variation in sequence coverage data

Background

The advent of next generation sequencing technology has accelerated efforts to map and catalogue copy number variation (CNV) in genomes of important micro-organisms for public health. A typical analysis of the sequence data involves mapping reads onto a reference genome, calculating the respective coverage, and detecting regions with too-low or too-high coverage (deletions and amplifications, respectively). Current CNV detection methods rely on statistical assumptions (e.g., a Poisson model) that may not hold in general, or require fine-tuning the underlying algorithms to detect known hits. We propose a new CNV detection methodology based on two Poisson hierarchical models, the Poisson-Gamma and Poisson-Lognormal, with the advantage of being sufficiently flexible to describe different data patterns, whilst robust against deviations from the often assumed Poisson model.

Results

Using sequence coverage data of 7 Plasmodium falciparum malaria genomes (3D7 reference strain, HB3, DD2, 7G8, GB4, OX005, and OX006), we showed that empirical coverage distributions are intrinsically asymmetric and overdispersed in relation to the Poisson model. We also demonstrated a low baseline false positive rate for the proposed methodology using 3D7 resequencing data and simulation. When applied to the non-reference isolate data, our approach detected known CNV hits, including an amplification of the PfMDR1 locus in DD2 and a large deletion in the CLAG3.2 gene in GB4, and putative novel CNV regions. When compared to the recently available FREEC and cn.MOPS approaches, our findings were more concordant with putative hits from the highest quality array data for the 7G8 and GB4 isolates.

Conclusions

In summary, the proposed methodology brings an increase in flexibility, robustness, accuracy and statistical rigour to CNV detection using sequence coverage data.

Quantifying copy number variations using a hidden Markov model with inhomogeneous emission distributions

Copy number variations (CNVs) are a significant source of genetic variation and have been found frequently associated with diseases such as cancers and autism. High-throughput sequencing data are increasingly being used to detect and quantify CNVs; however, the distributional properties of the data are not fully understood. A hidden Markov model (HMM) is proposed using inhomogeneous emission distributions based on negative binomial regression to account for the sequencing biases. The model is tested on the whole genome sequencing data and simulated data sets. An algorithm for CNV detection is implemented in the R package CNVfinder. The model based on negative binomial regression is shown to provide a good fit to the data and provides competitive performance compared with methods based on normalization of read counts.

CNVeM: copy number variation detection using uncertainty of read mapping

Copy number variations (CNVs) are widely known to be an important mediator for diseases and traits. The development of high-throughput sequencing (HTS) technologies has provided great opportunities to identify CNV regions in mammalian genomes. In a typical experiment, millions of short reads obtained from a genome of interest are mapped to a reference genome. The mapping information can be used to identify CNV regions. One important challenge in analyzing the mapping information is the large fraction of reads that can be mapped to multiple positions. Most existing methods either only consider reads that can be uniquely mapped to the reference genome or randomly place a read to one of its mapping positions. Therefore, these methods have low power to detect CNVs located within repeated sequences. In this study, we propose a probabilistic model, CNVeM, that utilizes the inherent uncertainty of read mapping. We use maximum likelihood to estimate locations and copy numbers of copied regions and implement an expectation-maximization (EM) algorithm. One important contribution of our model is that we can distinguish between regions in the reference genome that differ from each other by as little as 0.1%. As our model aims to predict the copy number of each nucleotide, we can predict the CNV boundaries with high resolution. We apply our method to simulated datasets and achieve higher accuracy compared to CNVnator. Moreover, we apply our method to real data from which we detected known CNVs. To our knowledge, this is the first attempt to predict CNVs at nucleotide resolution and to utilize uncertainty of read mapping.

A computational method for detecting copy number variations using scale-space filtering

Background

As next-generation sequencing technology made rapid and cost-effective sequencing available, the importance of computational approaches in finding and analyzing copy number variations (CNVs) has been amplified. Furthermore, most genome projects need to accurately analyze sequences with fairly low-coverage read data. It is urgently needed to develop a method to detect the exact types and locations of CNVs from low coverage read data.

Results

Here, we propose a new CNV detection method, CNV_SS, which uses scale-space filtering. The scale-space filtering is evaluated by applying to the read coverage data the Gaussian convolution for various scales according to a given scaling parameter. Next, by differentiating twice and finding zero-crossing points, inflection points of scale-space filtered read coverage data are calculated per scale. Then, the types and the exact locations of CNVs are obtained by analyzing the finger print map, the contours of zero-crossing points for various scales.

Conclusions

The performance of CNV_SS showed that FNR and FPR stay in the range of 1.27% to 2.43% and 1.14% to 2.44%, respectively, even at a relatively low coverage (0.5x ≤C ≤2x). CNV_SS gave also much more effective results than the conventional methods in the evaluation of FNR, at 3.82% at least and 76.97% at most even when the coverage level of read data is low. CNV_SS source code is freely available from http://dblab.hallym.ac.kr/CNV SS/.

Detection of common copy number variation with application to population clustering from next generation sequencing data

Copy number variation (CNV) is a structural variation in human genome that has been associated with many complex diseases. In this paper we present a method to detect common copy number variation from next generation sequencing data. First, copy number variations are detected from each individual sample, which is formulated as a total variation penalized least square problem. Second, the common copy number discovery from multiple samples is obtained using source separation techniques such as the non-negative matrix factorization (NMF). Finally, the method is applied to population clustering. The results on real data analysis show that two family trio with different ancestries can be clustered into two ethnic groups based on their common CNVs, demonstrating the potential of the proposed method for application to population genetics.

CoNVEX: copy number variation estimation in exome sequencing data using HMM

Background

One of the main types of genetic variations in cancer is Copy Number Variations (CNV). Whole exome sequenicng (WES) is a popular alternative to whole genome sequencing (WGS) to study disease specific genomic variations. However, finding CNV in Cancer samples using WES data has not been fully explored.

Results

We present a new method, called CoNVEX, to estimate copy number variation in whole exome sequencing data. It uses ratio of tumour and matched normal average read depths at each exonic region, to predict the copy gain or loss. The useful signal produced by WES data will be hindered by the intrinsic noise present in the data itself. This limits its capacity to be used as a highly reliable CNV detection source. Here, we propose a method that consists of discrete wavelet transform (DWT) to reduce noise. The identification of copy number gains/losses of each targeted region is performed by a Hidden Markov Model (HMM).

Conclusion

HMM is frequently used to identify CNV in data produced by various technologies including Array Comparative Genomic Hybridization (aCGH) and WGS. Here, we propose an HMM to detect CNV in cancer exome data. We used modified data from 1000 Genomes project to evaluate the performance of the proposed method. Using these data we have shown that CoNVEX outperforms the existing methods significantly in terms of precision. Overall, CoNVEX achieved a sensitivity of more than 92% and a precision of more than 50%.

A survey of tools for variant analysis of next-generation genome sequencing data

Recent advances in genome sequencing technologies provide unprecedented opportunities to characterize individual genomic landscapes and identify mutations relevant for diagnosis and therapy. Specifically, whole-exome sequencing using next-generation sequencing (NGS) technologies is gaining popularity in the human genetics community due to the moderate costs, manageable data amounts and straightforward interpretation of analysis results. While whole-exome and, in the near future, whole-genome sequencing are becoming commodities, data analysis still poses significant challenges and led to the development of a plethora of tools supporting specific parts of the analysis workflow or providing a complete solution. Here, we surveyed 205 tools for whole-genome/whole-exome sequencing data analysis supporting five distinct analytical steps: quality assessment, alignment, variant identification, variant annotation and visualization. We report an overview of the functionality, features and specific requirements of the individual tools. We then selected 32 programs for variant identification, variant annotation and visualization, which were subjected to hands-on evaluation using four data sets: one set of exome data from two patients with a rare disease for testing identification of germline mutations, two cancer data sets for testing variant callers for somatic mutations, copy number variations and structural variations, and one semi-synthetic data set for testing identification of copy number variations. Our comprehensive survey and evaluation of NGS tools provides a valuable guideline for human geneticists working on Mendelian disorders, complex diseases and cancers.

Improving detection of copy-number variation by simultaneous bias correction and read-depth segmentation

Structural variation is an important class of genetic variation in mammals. High-throughput sequencing (HTS) technologies promise to revolutionize copy-number variation (CNV) detection but present substantial analytic challenges. Converging evidence suggests that multiple types of CNV-informative data (e.g. read-depth, read-pair, split-read) need be considered, and that sophisticated methods are needed for more accurate CNV detection. We observed that various sources of experimental biases in HTS confound read-depth estimation, and note that bias correction has not been adequately addressed by existing methods. We present a novel read-depth-based method, GENSENG, which uses a hidden Markov model and negative binomial regression framework to identify regions of discrete copy-number changes while simultaneously accounting for the effects of multiple confounders. Based on extensive calibration using multiple HTS data sets, we conclude that our method outperforms existing read-depth-based CNV detection algorithms. The concept of simultaneous bias correction and CNV detection can serve as a basis for combining read-depth with other types of information such as read-pair or split-read in a single analysis. A user-friendly and computationally efficient implementation of our method is freely available.

Chapter 6: Structural variation and medical genomics

Differences between individual human genomes, or between human and cancer genomes, range in scale from single nucleotide variants (SNVs) through intermediate and large-scale duplications, deletions, and rearrangements of genomic segments. The latter class, called structural variants (SVs), have received considerable attention in the past several years as they are a previously under appreciated source of variation in human genomes. Much of this recent attention is the result of the availability of higher-resolution technologies for measuring these variants, including both microarray-based techniques, and more recently, high-throughput DNA sequencing. We describe the genomic technologies and computational techniques currently used to measure SVs, focusing on applications in human and cancer genomics.

cnvHiTSeq: integrative models for high-resolution copy number variation detection and genotyping using population sequencing data

Recent advances in sequencing technologies provide the means for identifying copy number variation (CNV) at an unprecedented resolution. A single next-generation sequencing experiment offers several features that can be used to detect CNV, yet current methods do not incorporate all available signatures into a unified model. cnvHiTSeq is an integrative probabilistic method for CNV discovery and genotyping that jointly analyzes multiple features at the population level. By combining evidence from complementary sources, cnvHiTSeq achieves high genotyping accuracy and a substantial improvement in CNV detection sensitivity over existing methods, while maintaining a low false discovery rate. cnvHiTSeq is available at http://sourceforge.net/projects/cnvhitseq.

Whole-genome reconstruction and mutational signatures in gastric cancer

Background

Gastric cancer is the second highest cause of global cancer mortality. To explore the complete repertoire of somatic alterations in gastric cancer, we combined massively parallel short read and DNA paired-end tag sequencing to present the first whole-genome analysis of two gastric adenocarcinomas, one with chromosomal instability and the other with microsatellite instability.

Results

Integrative analysis and de novo assemblies revealed the architecture of a wild-type KRAS amplification, a common driver event in gastric cancer. We discovered three distinct mutational signatures in gastric cancer - against a genome-wide backdrop of oxidative and microsatellite instability-related mutational signatures, we identified the first exome-specific mutational signature. Further characterization of the impact of these signatures by combining sequencing data from 40 complete gastric cancer exomes and targeted screening of an additional 94 independent gastric tumors uncovered ACVR2A, RPL22 and LMAN1 as recurrently mutated genes in microsatellite instability-positive gastric cancer and PAPPA as a recurrently mutated gene in TP53 wild-type gastric cancer.

Conclusions

These results highlight how whole-genome cancer sequencing can uncover information relevant to tissue-specific carcinogenesis that would otherwise be missed from exome-sequencing data.