Optimized filtering reduces the error rate in detecting genomic variants by short-read sequencing

Distinguishing single-nucleotide variants (SNVs) from errors in whole-genome sequences remains challenging. Here we describe a set of filters, together with a freely accessible software tool, that selectively reduce error rates and thereby facilitate variant detection in data from two short-read sequencing technologies, Complete Genomics and Illumina. By sequencing the nearly identical genomes from monozygotic twins and considering shared SNVs as 'true variants' and discordant SNVs as 'errors', we optimized thresholds for 12 individual filters and assessed which of the 1,048 filter combinations were effective in terms of sensitivity and specificity. Cumulative application of all effective filters reduced the error rate by 290-fold, facilitating the identification of genetic differences between monozygotic twins. We also applied an adapted, less stringent set of filters to reliably identify somatic mutations in a highly rearranged tumor and to identify variants in the NA19240 HapMap genome relative to a reference set of SNVs.     

Reference-unbiased copy number variant analysis using CGH microarrays

Comparative genomic hybridization (CGH) microarrays have been used to determine copy number variations (CNVs) and their effects on complex diseases. Detection of absolute CNVs independent of genomic variants of an arbitrary reference sample has been a critical issue in CGH array experiments. Whole genome analysis using massively parallel sequencing with multiple ultra-high resolution CGH arrays provides an opportunity to catalog highly accurate genomic variants of the reference DNA (NA10851). Using information on variants, we developed a new method, the CGH array reference-free algorithm (CARA), which can determine reference-unbiased absolute CNVs from any CGH array platform. The algorithm enables the removal and rescue of false positive and false negative CNVs, respectively, which appear due to the effects of genomic variants of the reference sample in raw CGH array experiments. We found that the CARA remarkably enhanced the accuracy of CGH array in determining absolute CNVs. Our method thus provides a new approach to interpret CGH array data for personalized medicine.     

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.     

PattRec: An easy-to-use CNV detection tool optimized for targeted NGS assays with diagnostic purposes

Genetic laboratories use custom-commercial targeted next-generation sequencing (tg-NGS) assays to identify disease-causing variants. Although the high coverage achieved with these tests allows for the detection of copy number variants (CNVs), which account for an important proportion of the genetic burden in human diseases, an easy-to-use tool for automatic CNV detection is still lacking. This article presents a new CNV detection tool optimized for tg-NGS data: PattRec. PattRec was evaluated using a wide range of data, and its performance compared with those of other CNV detection tools. The software includes features for selecting optimal controls, discarding polymorphic CNVs prior to analysis, and filtering out deletions based on SNV zygosity, and automatically creates an in-house CNV database. There is no need for high level bioinformatic expertise and users can choose color-coded xlsx output that helps to prioritize potentially pathogenic CNVs. PattRec is presented as a Java based GUI, freely available online: https://github.com/irotero/PattRec.     

PureCN: copy number calling and SNV classification using targeted short read sequencing

Background

Matched sequencing of both tumor and normal tissue is routinely used to classify variants of uncertain significance (VUS) into somatic vs. germline. However, assays used in molecular diagnostics focus on known somatic alterations in cancer genes and often only sequence tumors. Therefore, an algorithm that reliably classifies variants would be helpful for retrospective exploratory analyses. Contamination of tumor samples with normal cells results in differences in expected allelic fractions of germline and somatic variants, which can be exploited to accurately infer genotypes after adjusting for local copy number. However, existing algorithms for determining tumor purity, ploidy and copy number are not designed for unmatched short read sequencing data.

Results

We describe a methodology and corresponding open source software for estimating tumor purity, copy number, loss of heterozygosity (LOH), and contamination, and for classification of single nucleotide variants (SNVs) by somatic status and clonality. This R package, PureCN, is optimized for targeted short read sequencing data, integrates well with standard somatic variant detection pipelines, and has support for matched and unmatched tumor samples. Accuracy is demonstrated on simulated data and on real whole exome sequencing data.

Conclusions

Our algorithm provides accurate estimates of tumor purity and ploidy, even if matched normal samples are not available. This in turn allows accurate classification of SNVs. The software is provided as open source (Artistic License 2.0) R/Bioconductor package PureCN (http://bioconductor.org/packages/PureCN/).

     

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.     

全基因组测序及其在遗传性疾病研究及诊断中的应用

最近,随着测序成本的不断降低,数据分析策略的不断提升,全基因组测序（whole-genome sequencing,WGS）已经在癌症、孟德尔遗传病、复杂疾病的致病基因检测中得到了一定运用,并逐步走向了临床诊断。全基因组测序不但可以检测编码区和非编码区的点突变（SNVs）和插入缺失（InDels）,还可以在全基因组范围内检测拷贝数变异（copy number variation,CNV）以及结构变异（structure variation,SV）。本文详细地介绍了全基因组测序的标准生物信息分析流程与方法,及其在疾病研究、临床诊断中的应用,并对全基因组测序在医学遗传学中的应用与研究进展,以及数据分析方面面临的挑战进行了概述。     

cnvCapSeq: detecting copy number variation in long-range targeted resequencing data

Targeted resequencing technologies have allowed for efficient and cost-effective detection of genomic variants in specific regions of interest. Although capture sequencing has been primarily used for investigating single nucleotide variants and indels, it has the potential to elucidate a broader spectrum of genetic variation, including copy number variants (CNVs). Various methods exist for detecting CNV in whole-genome and exome sequencing datasets. However, no algorithms have been specifically designed for contiguous target sequencing, despite its increasing importance in clinical and research applications. We have developed cnvCapSeq, a novel method for accurate and sensitive CNV discovery and genotyping in long-range targeted resequencing. cnvCapSeq was benchmarked using a simulated contiguous capture sequencing dataset comprising 21 genomic loci of various lengths. cnvCapSeq was shown to outperform the best existing exome CNV method by a wide margin both in terms of sensitivity (92.0 versus 48.3%) and specificity (99.8 versus 70.5%). We also applied cnvCapSeq to a real capture sequencing cohort comprising a contiguous 358 kb region that contains the Complement Factor H gene cluster. In this dataset, cnvCapSeq identified 41 samples with CNV, including two with duplications, with a genotyping accuracy of 99%, as ascertained by quantitative real-time PCR.     

MAC: identifying and correcting annotation for multi-nucleotide variations

Background

Next-Generation Sequencing (NGS) technologies have rapidly advanced our understanding of human variation in cancer. To accurately translate the raw sequencing data into practical knowledge, annotation tools, algorithms and pipelines must be developed that keep pace with the rapidly evolving technology. Currently, a challenge exists in accurately annotating multi-nucleotide variants (MNVs). These tandem substitutions, when affecting multiple nucleotides within a single protein codon of a gene, result in a translated amino acid involving all nucleotides in that codon. Most existing variant callers report a MNV as individual single-nucleotide variants (SNVs), often resulting in multiple triplet codon sequences and incorrect amino acid predictions. To correct potentially misannotated MNVs among reported SNVs, a primary challenge resides in haplotype phasing which is to determine whether the neighboring SNVs are co-located on the same chromosome.

Results

Here we describe MAC (Multi-Nucleotide Variant Annotation Corrector), an integrative pipeline developed to correct potentially mis-annotated MNVs. MAC was designed as an application that only requires a SNV file and the matching BAM file as data inputs. Using an example data set containing 3024 SNVs and the corresponding whole-genome sequencing BAM files, we show that MAC identified eight potentially mis-annotated SNVs, and accurately updated the amino acid predictions for seven of the variant calls.

Conclusions

MAC can identify and correct amino acid predictions that result from MNVs affecting multiple nucleotides within a single protein codon, which cannot be handled by most existing SNV-based variant pipelines. The MAC software is freely available and represents a useful tool for the accurate translation of genomic sequence to protein function.     

Enhanced CHO Clone Screening: Application of Targeted Locus Amplification and Next‐Generation Sequencing Technologies for Cell Line Development

Early analytical clone screening is important during Chinese hamster ovary (CHO) cell line development of biotherapeutic proteins to select a clonally derived cell line with most favorable stability and product quality. Sensitive sequence confirmation methods using mass spectrometry have limitations in throughput and turnaround time. Next‐generation sequencing (NGS) technologies emerged as alternatives for CHO clone analytics. We report an efficient NGS workflow applying the targeted locus amplification (TLA) strategy for genomic screening of antibody expressing CHO clones. In contrast to previously reported RNA sequencing approaches, TLA allows for targeted sequencing of genomic integrated transgenic DNA without prior locus information, robust detection of single‐nucleotide variants (SNVs) and transgenic rearrangements. During clone selection, TLA/NGS revealed CHO clones with high‐level SNVs within the antibody gene and we report in another case the utility of TLA/NGS to identify rearrangements at transgenic DNA level. We also determined detection limits for SNVs calling and the potential to identify clone contaminations by TLA/NGS. TLA/NGS also allows to identify genetically identical clones. In summary, we demonstrate that TLA/NGS is a robust screening method useful for routine clone analytics during cell line development with the potential to process up to 24 CHO clones in less than 7 workdays.     

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/.     

Allele-specific copy-number discovery from whole-genome and whole-exome sequencing

Copy-number variants (CNVs) are a major form of genetic variation and a risk factor for various human diseases, so it is crucial to accurately detect and characterize them. It is conceivable that allele-specific reads from high-throughput sequencing data could be leveraged to both enhance CNV detection and produce allele-specific copy number (ASCN) calls. Although statistical methods have been developed to detect CNVs using whole-genome sequence (WGS) and/or whole-exome sequence (WES) data, information from allele-specific read counts has not yet been adequately exploited. In this paper, we develop an integrated method, called AS-GENSENG, which incorporates allele-specific read counts in CNV detection and estimates ASCN using either WGS or WES data. To evaluate the performance of AS-GENSENG, we conducted extensive simulations, generated empirical data using existing WGS and WES data sets and validated predicted CNVs using an independent methodology. We conclude that AS-GENSENG not only predicts accurate ASCN calls but also improves the accuracy of total copy number calls, owing to its unique ability to exploit information from both total and allele-specific read counts while accounting for various experimental biases in sequence data. Our novel, user-friendly and computationally efficient method and a complete analytic protocol is freely available at https://sourceforge.net/projects/asgenseng/.     

Mutation Discovery in Regions of Segmental Cancer Genome Amplifications with CoNAn-SNV: A Mixture Model for Next Generation Sequencing of Tumors

Next generation sequencing has now enabled a cost-effective enumeration of the full mutational complement of a tumor genome-in particular single nucleotide variants (SNVs). Most current computational and statistical models for analyzing next generation sequencing data, however, do not account for cancer-specific biological properties, including somatic segmental copy number alterations (CNAs)-which require special treatment of the data. Here we present CoNAn-SNV (Copy Number Annotated SNV): a novel algorithm for the inference of single nucleotide variants (SNVs) that overlap copy number alterations. The method is based on modelling the notion that genomic regions of segmental duplication and amplification induce an extended genotype space where a subset of genotypes will exhibit heavily skewed allelic distributions in SNVs (and therefore render them undetectable by methods that assume diploidy). We introduce the concept of modelling allelic counts from sequencing data using a panel of Binomial mixture models where the number of mixtures for a given locus in the genome is informed by a discrete copy number state given as input. We applied CoNAn-SNV to a previously published whole genome shotgun data set obtained from a lobular breast cancer and show that it is able to discover 21 experimentally revalidated somatic non-synonymous mutations in a lobular breast cancer genome that were not detected using copy number insensitive SNV detection algorithms. Importantly, ROC analysis shows that the increased sensitivity of CoNAn-SNV does not result in disproportionate loss of specificity. This was also supported by analysis of a recently published lymphoma genome with a relatively quiescent karyotype, where CoNAn-SNV showed similar results to other callers except in regions of copy number gain where increased sensitivity was conferred. Our results indicate that in genomically unstable tumors, copy number annotation for SNV detection will be critical to fully characterize the mutational landscape of cancer genomes.     

A comprehensive framework for detecting copy number variants from single nucleotide polymorphism data: ‘rCNV’, a versatile r package for paralogue and CNV detection

Recent studies have highlighted the significant role of copy number variants (CNVs) in phenotypic diversity, environmental adaptation and species divergence across eukaryotes. The presence of CNVs also has the potential to introduce genotyping biases, which can pose challenges to accurate population and quantitative genetic analyses. However, detecting CNVs in genomes, particularly in non-model organisms, presents a formidable challenge. To address this issue, we have developed a statistical framework and an accompanying r software package that leverage allelic-read depth from single nucleotide polymorphism (SNP) data for accurate CNV detection. Our framework capitalises on two key principles. First, it exploits the distribution of allelic-read depth ratios in heterozygotes for individual SNPs by comparing it against an expected distribution based on binomial sampling. Second, it identifies SNPs exhibiting an apparent excess of heterozygotes under Hardy–Weinberg equilibrium. By employing multiple statistical tests, our method not only enhances sensitivity to sampling effects but also effectively addresses reference biases, resulting in optimised SNP classification. Our framework is compatible with various NGS technologies (e.g. RADseq, Exome-capture). This versatility enables CNV calling from genomes of diverse complexities. To streamline the analysis process, we have implemented our framework in the user-friendly r package ‘rCNV’, which automates the entire workflow seamlessly. We trained our models using simulated data and validated their performance on four datasets derived from different sequencing technologies, including RADseq (Chinook salmon—Oncorhynchus tshawytscha), Rapture (American lobster—Homarus americanus), Exome-capture (Norway spruce—Picea abies) and WGS (Malaria mosquito—Anopheles gambiae).     

Efficient and cost effective population resequencing by pooling and in-solution hybridization

High-throughput sequencing of targeted genomic loci in large populations is an effective approach for evaluating the contribution of rare variants to disease risk. We evaluated the feasibility of using in-solution hybridization-based target capture on pooled DNA samples to enable cost-efficient population sequencing studies. For this, we performed pooled sequencing of 100 HapMap samples across ～ 600 kb of DNA sequence using the Illumina GAIIx. Using our accurate variant calling method for pooled sequence data, we were able to not only identify single nucleotide variants with a low false discovery rate (<1%) but also accurately detect short insertion/deletion variants. In addition, with sufficient coverage per individual in each pool (30-fold) we detected 97.2% of the total variants and 93.6% of variants below 5% in frequency. Finally, allele frequencies for single nucleotide variants (SNVs) estimated from the pooled data and the HapMap genotype data were tightly correlated (correlation coefficient > = 0.995).     

Robustness of Massively Parallel Sequencing Platforms

The improvements in high throughput sequencing technologies (HTS) made clinical sequencing projects such as ClinSeq and Genomics England feasible. Although there are significant improvements in accuracy and reproducibility of HTS based analyses, the usability of these types of data for diagnostic and prognostic applications necessitates a near perfect data generation. To assess the usability of a widely used HTS platform for accurate and reproducible clinical applications in terms of robustness, we generated whole genome shotgun (WGS) sequence data from the genomes of two human individuals in two different genome sequencing centers. After analyzing the data to characterize SNPs and indels using the same tools (BWA, SAMtools, and GATK), we observed significant number of discrepancies in the call sets. As expected, the most of the disagreements between the call sets were found within genomic regions containing common repeats and segmental duplications, albeit only a small fraction of the discordant variants were within the exons and other functionally relevant regions such as promoters. We conclude that although HTS platforms are sufficiently powerful for providing data for first-pass clinical tests, the variant predictions still need to be confirmed using orthogonal methods before using in clinical applications.     

Genome-wide mapping of copy number variation in humans: comparative analysis of high resolution array platforms

Accurate and efficient genome-wide detection of copy number variants (CNVs) is essential for understanding human genomic variation, genome-wide CNV association type studies, cytogenetics research and diagnostics, and independent validation of CNVs identified from sequencing based technologies. Numerous, array-based platforms for CNV detection exist utilizing array Comparative Genome Hybridization (aCGH), Single Nucleotide Polymorphism (SNP) genotyping or both. We have quantitatively assessed the abilities of twelve leading genome-wide CNV detection platforms to accurately detect Gold Standard sets of CNVs in the genome of HapMap CEU sample NA12878, and found significant differences in performance. The technologies analyzed were the NimbleGen 4.2 M, 2.1 M and 3×720 K Whole Genome and CNV focused arrays, the Agilent 1×1 M CGH and High Resolution and 2×400 K CNV and SNP+CGH arrays, the Illumina Human Omni1Quad array and the Affymetrix SNP 6.0 array. The Gold Standards used were a 1000 Genomes Project sequencing-based set of 3997 validated CNVs and an ultra high-resolution aCGH-based set of 756 validated CNVs. We found that sensitivity, total number, size range and breakpoint resolution of CNV calls were highest for CNV focused arrays. Our results are important for cost effective CNV detection and validation for both basic and clinical applications.     

Clinical analysis of germline copy number variation in DMD using a non-conjugate hierarchical Bayesian model

Background

Detection of copy number variants (CNVs) is an important aspect of clinical testing for several disorders, including Duchenne muscular dystrophy, and is often performed using multiplex ligation-dependent probe amplification (MLPA). However, since many genetic carrier screens depend instead on next-generation sequencing (NGS) for wider discovery of small variants, they often do not include CNV analysis. Moreover, most computational techniques developed to detect CNVs from exome sequencing data are not suitable for carrier screening, as they require matched normals, very large cohorts, or extensive gene panels.

Methods

We present a computational software package, geneCNV (http://github.com/vkozareva/geneCNV), which can identify exon-level CNVs using exome sequencing data from only a few genes. The tool relies on a hierarchical parametric model trained on a small cohort of reference samples.

Results

Using geneCNV, we accurately inferred heterozygous CNVs in the DMD gene across a cohort of 15 test subjects. These results were validated against MLPA, the current standard for clinical CNV analysis in DMD. We also benchmarked the tool’s performance against other computational techniques and found comparable or improved CNV detection in DMD using data from panels ranging from 4,000 genes to as few as 8 genes.

Conclusions

geneCNV allows for the creation of cost-effective screening panels by allowing NGS sequencing approaches to generate results equivalent to bespoke genotyping assays like MLPA. By using a parametric model to detect CNVs, it also fulfills regulatory requirements to define a reference range for a genetic test. It is freely available and can be incorporated into any Illumina sequencing pipeline to create clinical assays for detection of exon duplications and deletions.