CANOES: detecting rare copy number variants from whole exome sequencing data

We present CANOES, an algorithm for the detection of rare copy number variants from exome sequencing data. CANOES models read counts using a negative binomial distribution and estimates variance of the read counts using a regression-based approach based on selected reference samples in a given dataset. We test CANOES on a family-based exome sequencing dataset, and show that its sensitivity and specificity is comparable to that of XHMM. Moreover, the method is complementary to Gaussian approximation-based methods (e.g. XHMM or CoNIFER). When CANOES is used in combination with these methods, it will be possible to produce high accuracy calls, as demonstrated by a much reduced and more realistic de novo rate in results from trio data.     

Identification of copy number variants from exome sequence data

Background

With advances in next generation sequencing technologies and genomic capture techniques, exome sequencing has become a cost-effective approach for mutation detection in genetic diseases. However, computational prediction of copy number variants (CNVs) from exome sequence data is a challenging task. Whilst numerous programs are available, they have different sensitivities, and have low sensitivity to detect smaller CNVs (1–4 exons). Additionally, exonic CNV discovery using standard aCGH has limitations due to the low probe density over exonic regions. The goal of our study was to develop a protocol to detect exonic CNVs (including shorter CNVs that cover 1–4 exons), combining computational prediction algorithms and a high-resolution custom CGH array.

Results

We used six published CNV prediction programs (ExomeCNV, CONTRA, ExomeCopy, ExomeDepth, CoNIFER, XHMM) and an in-house modification to ExomeCopy and ExomeDepth (ExCopyDepth) for computational CNV prediction on 30 exomes from the 1000 genomes project and 9 exomes from primary immunodeficiency patients. CNV predictions were tested using a custom CGH array designed to capture all exons (exaCGH). After this validation, we next evaluated the computational prediction of shorter CNVs. ExomeCopy and the in-house modified algorithm, ExCopyDepth, showed the highest capability in detecting shorter CNVs. Finally, the performance of each computational program was assessed by calculating the sensitivity and false positive rate.

Conclusions

In this paper, we assessed the ability of 6 computational programs to predict CNVs, focussing on short (1–4 exon) CNVs. We also tested these predictions using a custom array targeting exons. Based on these results, we propose a protocol to identify and confirm shorter exonic CNVs combining computational prediction algorithms and custom aCGH experiments.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-661) contains supplementary material, which is available to authorized users.     

Inferring copy number and genotype in tumour exome data

Background

Using whole exome sequencing to predict aberrations in tumours is a cost effective alternative to whole genome sequencing, however is predominantly used for variant detection and infrequently utilised for detection of somatic copy number variation.

Results

We propose a new method to infer copy number and genotypes using whole exome data from paired tumour/normal samples. Our algorithm uses two Hidden Markov Models to predict copy number and genotypes and computationally resolves polyploidy/aneuploidy, normal cell contamination and signal baseline shift. Our method makes explicit detection on chromosome arm level events, which are commonly found in tumour samples. The methods are combined into a package named ADTEx (Aberration Detection in Tumour Exome). We applied our algorithm to a cohort of 17 in-house generated and 18 TCGA paired ovarian cancer/normal exomes and evaluated the performance by comparing against the copy number variations and genotypes predicted using Affymetrix SNP 6.0 data of the same samples. Further, we carried out a comparison study to show that ADTEx outperformed its competitors in terms of precision and F-measure.

Conclusions

Our proposed method, ADTEx, uses both depth of coverage ratios and B allele frequencies calculated from whole exome sequencing data, to predict copy number variations along with their genotypes. ADTEx is implemented as a user friendly software package using Python and R statistical language. Source code and sample data are freely available under GNU license (GPLv3) at http://adtex.sourceforge.net/.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-732) contains supplementary material, which is available to authorized users.     

EXCAVATOR: detecting copy number variants from whole-exome sequencing data

We developed a novel software tool, EXCAVATOR, for the detection of copy number variants (CNVs) from whole-exome sequencing data. EXCAVATOR combines a three-step normalization procedure with a novel heterogeneous hidden Markov model algorithm and a calling method that classifies genomic regions into five copy number states. We validate EXCAVATOR on three datasets and compare the results with three other methods. These analyses show that EXCAVATOR outperforms the other methods and is therefore a valuable tool for the investigation of CNVs in largescale projects, as well as in clinical research and diagnostics. EXCAVATOR is freely available at http://sourceforge.net/projects/excavatortool/.     

Dissecting the genotype in syndromic intellectual disability using whole exome sequencing in addition to genome-wide copy number analysis

When a known microimbalance affecting multiple genes is detected in a patient with syndromic intellectual disability, it is usually presumed causative for all observed features. Whole exome sequencing (WES) allows questioning this assumption. In this study of three families with children affected by unexplained syndromic intellectual disability, genome-wide copy number and subsequent analyses revealed a de novo maternal 1.1 Mb microdeletion in the 14q32 imprinted region causing a paternal UPD(14)-like phenotype, and two inherited 22q11.21 microduplications of 2.5 or 2.8 Mb. In patient 1 carrying the 14q32 microdeletion, tall stature and renal malformation were unexplained by paternal UPD(14), and there was no altered DLK1 expression or unexpected methylation status. By WES and filtering with a mining tool, a novel FBN1 missense variant was found in patient 1 and his mother, who both showed clinical features of Marfan syndrome by thorough anthropometric assessment, and a novel EYA1 missense variant as a probable cause of the renal malformation in the patient. In patient 2 with the 22q11.21 microduplication syndrome, skin hypo- and hyperpigmentation and two malignancies were only partially explained. By WES, compound heterozygous BLM stop founder mutations were detected causing Bloom syndrome. In male patient 3 carrying a 22q11.21 microduplication inherited from his unaffected father, WES identified a novel missense variant in the OPHN1 X-linked intellectual disability gene inherited from the unaffected mother as a possible additional cause for developmental delay. Thus, WES seems warranted in patients carrying microdeletions or microduplications, who have unexplained clinical features or microimbalances inherited from an unaffected parent.     

Rare germline copy number variants in colorectal cancer predisposition characterized by exome sequencing analysis

正Colorectal cancer(CRC)is one of the most common neoplasms and an important cause of mortality worldwide(http://globocan.iarc.fr/).Approximately 35%of the variation in CRC susceptibility is likely due to heritable factors(Lichtenstein et al.,2000).Genetic variations in the human genome include single nucleotide variants(SNVs),short insertions and deletions,and larger structural vari-     

Genome-wide studies of copy number variation and exome sequencing identify rare variants in BAG3 as a cause of dilated cardiomyopathy

Dilated cardiomyopathy commonly causes heart failure and is the most frequent precipitating cause of heart transplantation. Familial dilated cardiomyopathy has been shown to be caused by rare variant mutations in more than 30 genes but only ~35% of its genetic cause has been identified, principally by using linkage-based or candidate gene discovery approaches. In a multigenerational family with autosomal dominant transmission, we employed whole-exome sequencing in a proband and three of his affected family members, and genome-wide copy number variation in the proband and his affected father and unaffected mother. Exome sequencing identified 428 single point variants resulting in missense, nonsense, or splice site changes. Genome-wide copy number analysis identified 51 insertion deletions and 440 copy number variants > 1 kb. Of these, a 8733 bp deletion, encompassing exon 4 of the heat shock protein cochaperone BCL2-associated athanogene 3 (BAG3), was found in seven affected family members and was absent in 355 controls. To establish the relevance of variants in this protein class in genetic DCM, we sequenced the coding exons in BAG3 in 311 other unrelated DCM probands and identified one frameshift, two nonsense, and four missense rare variants absent in 355 control DNAs, four of which were familial and segregated with disease. Knockdown of bag3 in a zebrafish model recapitulated DCM and heart failure. We conclude that new comprehensive genomic approaches have identified rare variants in BAG3 as causative of DCM.     

Detecting common copy number variants in high-throughput sequencing data by using JointSLM algorithm

The discovery of genomic structural variants (SVs), such as copy number variants (CNVs), is essential to understand genetic variation of human populations and complex diseases. Over recent years, the advent of new high-throughput sequencing (HTS) platforms has opened many opportunities for SVs discovery, and a very promising approach consists in measuring the depth of coverage (DOC) of reads aligned to the human reference genome. At present, few computational methods have been developed for the analysis of DOC data and all of these methods allow to analyse only one sample at time. For these reasons, we developed a novel algorithm (JointSLM) that allows to detect common CNVs among individuals by analysing DOC data from multiple samples simultaneously. We test JointSLM performance on synthetic and real data and we show its unprecedented resolution that enables the detection of recurrent CNV regions as small as 500 bp in size. When we apply JointSLM to analyse chromosome one of eight genomes with different ancestry, we identify 3000 regions with recurrent CNVs of different frequency and size: hierarchical clustering on these regions segregates the eight individuals in two groups that reflect their ancestry, demonstrating the potential utility of JointSLM for population genetics studies.     

Exploration of the gene fusion landscape of glioblastoma using transcriptome sequencing and copy number data

Background

RNA-seq has spurred important gene fusion discoveries in a number of different cancers, including lung, prostate, breast, brain, thyroid and bladder carcinomas. Gene fusion discovery can potentially lead to the development of novel treatments that target the underlying genetic abnormalities.

Results

In this study, we provide comprehensive view of gene fusion landscape in 185 glioblastoma multiforme patients from two independent cohorts. Fusions occur in approximately 30-50% of GBM patient samples. In the Ivy Center cohort of 24 patients, 33% of samples harbored fusions that were validated by qPCR and Sanger sequencing. We were able to identify high-confidence gene fusions from RNA-seq data in 53% of the samples in a TCGA cohort of 161 patients. We identified 13 cases (8%) with fusions retaining a tyrosine kinase domain in the TCGA cohort and one case in the Ivy Center cohort. Ours is the first study to describe recurrent fusions involving non-coding genes. Genomic locations 7p11 and 12q14-15 harbor majority of the fusions. Fusions on 7p11 are formed in focally amplified EGFR locus whereas 12q14-15 fusions are formed by complex genomic rearrangements. All the fusions detected in this study can be further visualized and analyzed using our website: http://ivygap.swedish.org/fusions.

Conclusions

Our study highlights the prevalence of gene fusions as one of the major genomic abnormalities in GBM. The majority of the fusions are private fusions, and a minority of these recur with low frequency. A small subset of patients with fusions of receptor tyrosine kinases can benefit from existing FDA approved drugs and drugs available in various clinical trials. Due to the low frequency and rarity of clinically relevant fusions, RNA-seq of GBM patient samples will be a vital tool for the identification of patient-specific fusions that can drive personalized therapy.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-14-818) contains supplementary material, which is available to authorized users.     

Accurate, high-throughput typing of copy number variation using paralogue ratios from dispersed repeats

Recent work has demonstrated an unexpected prevalence of copy number variation in the human genome, and has highlighted the part this variation may play in predisposition to common phenotypes. Some important genes vary in number over a high range (e.g. DEFB4, which commonly varies between two and seven copies), and have posed formidable technical challenges for accurate copy number typing, so that there are no simple, cheap, high-throughput approaches suitable for large-scale screening. We have developed a simple comparative PCR method based on dispersed repeat sequences, using a single pair of precisely designed primers to amplify products simultaneously from both test and reference loci, which are subsequently distinguished and quantified via internal sequence differences. We have validated the method for the measurement of copy number at DEFB4 by comparison of results from >800 DNA samples with copy number measurements by MAPH/REDVR, MLPA and array-CGH. The new Paralogue Ratio Test (PRT) method can require as little as 10 ng genomic DNA, appears to be comparable in accuracy to the other methods, and for the first time provides a rapid, simple and inexpensive method for copy number analysis, suitable for application to typing thousands of samples in large case-control association studies.     

High resolution copy number inference in cancer using short-molecule nanopore sequencing

Genome copy number is an important source of genetic variation in health and disease. In cancer, Copy Number Alterations (CNAs) can be inferred from short-read sequencing data, enabling genomics-based precision oncology. Emerging Nanopore sequencing technologies offer the potential for broader clinical utility, for example in smaller hospitals, due to lower instrument cost, higher portability, and ease of use. Nonetheless, Nanopore sequencing devices are limited in the number of retrievable sequencing reads/molecules compared to short-read sequencing platforms, limiting CNA inference accuracy. To address this limitation, we targeted the sequencing of short-length DNA molecules loaded at optimized concentration in an effort to increase sequence read/molecule yield from a single nanopore run. We show that short-molecule nanopore sequencing reproducibly returns high read counts and allows high quality CNA inference. We demonstrate the clinical relevance of this approach by accurately inferring CNAs in acute myeloid leukemia samples. The data shows that, compared to traditional approaches such as chromosome analysis/cytogenetics, short molecule nanopore sequencing returns more sensitive, accurate copy number information in a cost effective and expeditious manner, including for multiplex samples. Our results provide a framework for short-molecule nanopore sequencing with applications in research and medicine, which includes but is not limited to, CNAs.     

rSW-seq: Algorithm for detection of copy number alterations in deep sequencing data

Background  

Recent advances in sequencing technologies have enabled generation of large-scale genome sequencing data. These data can be used to characterize a variety of genomic features, including the DNA copy number profile of a cancer genome. A robust and reliable method for screening chromosomal alterations would allow a detailed characterization of the cancer genome with unprecedented accuracy.     

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     

Integrated study of copy number states and genotype calls using high-density SNP arrays

We propose a statistical framework, named genoCN, to simultaneously dissect copy number states and genotypes using high-density SNP (single nucleotide polymorphism) arrays. There are at least two types of genomic DNA copy number differences: copy number variations (CNVs) and copy number aberrations (CNAs). While CNVs are naturally occurring and inheritable, CNAs are acquired somatic alterations most often observed in tumor tissues only. CNVs tend to be short and more sparsely located in the genome compared with CNAs. GenoCN consists of two components, genoCNV and genoCNA, designed for CNV and CNA studies, respectively. In contrast to most existing methods, genoCN is more flexible in that the model parameters are estimated from the data instead of being decided a priori. GenoCNA also incorporates two important strategies for CNA studies. First, the effects of tissue contamination are explicitly modeled. Second, if SNP arrays are performed for both tumor and normal tissues of one individual, the genotype calls from normal tissue are used to study CNAs in tumor tissue. We evaluated genoCN by applications to 162 HapMap individuals and a brain tumor (glioblastoma) dataset and showed that our method can successfully identify both types of copy number differences and produce high-quality genotype calls.     

An optimization framework for unsupervised identification of rare copy number variation from SNP array data

Copy number variants (CNVs) have roles in human disease, and DNA microarrays are important tools for identifying them. In this paper, we frame CNV identification as an objective function optimization problem. We apply our method to data from hundreds of samples, and demonstrate its ability to detect CNVs at a high level of sensitivity without sacrificing specificity. Its performance compares favorably with currently available methods and it reveals previously unreported gains and losses.     

An optimization framework for unsupervised identification of rare copy number variation from SNP array data

Background

Specific chromatin characteristics, especially the modification status of the core histone proteins, are associated with active and inactive genes. There is growing evidence that genes that respond to environmental or developmental signals may possess distinct chromatin marks. Using a T cell model and both genome-wide and gene-focused approaches, we examined the chromatin characteristics of genes that respond to T cell activation.

Results

To facilitate comparison of genes with similar basal expression levels, we used expression-profiling data to bin genes according to their basal expression levels. We found that inducible genes in the lower basal expression bins, especially rapidly induced primary response genes, were more likely than their non-responsive counterparts to display the histone modifications of active genes, have RNA polymerase II (Pol II) at their promoters and show evidence of ongoing basal elongation. There was little or no evidence for the presence of active chromatin marks in the absence of promoter Pol II on these inducible genes. In addition, we identified a subgroup of genes with active promoter chromatin marks and promoter Pol II but no evidence of elongation. Following T cell activation, we find little evidence for a major shift in the active chromatin signature around inducible gene promoters but many genes recruit more Pol II and show increased evidence of elongation.

Conclusions

These results suggest that the majority of inducible genes are primed for activation by having an active chromatin signature and promoter Pol II with or without ongoing elongation.