Structural Variation Discovery Research Articles

Structural variants in the Chinese population and their impact on phenotypes, diseases and population adaptation

A complete characterization of genetic variation is a fundamental goal of human genome research. Long-read sequencing has improved the sensitivity of structural variant discovery. Here, we conduct the long-read sequencing-based structural variant analysis for 405 unrelated Chinese individuals, with 68 phenotypic and clinical measurements. We discover a landscape of 132,312 nonredundant structural variants, of which 45.2% are novel. The identified structural variants are of high-quality, with an estimated false discovery rate of 3.2%. The concatenated length of all the structural variants is approximately 13.2% of the human reference genome. We annotate 1,929 loss-of-function structural variants affecting the coding sequence of 1,681 genes. We discover rare deletions in HBA1/HBA2/HBB associated with anemia. Furthermore, we identify structural variants related to immunity which differentiate the northern and southern Chinese populations. Our study describes the landscape of structural variants in the Chinese population and their contribution to phenotypes and disease.

Multiplatform discovery and regulatory function analysis of structural variations in non-small cell lung carcinoma.

Non-small cell lung carcinoma (NSCLC), the most common form of lung cancer, is the leading cause of cancer-related death worldwide. We perform whole-genome sequencing (WGS) on samples from 43 primary patients with NSCLC and matched normal samples and analyze their matched open chromatin data and transcriptome data. Our results indicate that next-generation sequencing (NGS) and the Bionano Genomics (BNG) platform should be viewed as complementary technologies in terms of structural variations detection. By creating a framework integrating these two platforms, we detect high-technical-confidence somatic structural variations (SVs) in NSCLC cases, which could aid in the efficient investigation of new candidate oncogenes, such as TRIO and SESTD1. Our findings highlight the impact of somatic SVs on NSCLC oncogenesis and lay a foundation for exploring associations among somatic SVs, gene expression, and regulatory networks in patients with NSCLC.

Analysis of Hi-C Data for Discovery of Structural Variations in Cancer.

Structural variations (SVs) are large genomic rearrangements that can be challenging to identify with current short read sequencing technology due to various confounding factors such as existence of genomic repeats and complex SV structures. Hi-C breakfinder is the first computational tool that utilizes the technology of high-throughput chromatin conformation capture assay (Hi-C) to systematically identify SVs, without being interfered by regular confounding factors. SVs change the spatial distance of genomic regions and cause discontinuous signals in Hi-C, which are difficult to analyze by routine informatics practice. Here we provide step-by-step guidance for how to identify SVs using Hi-C data and how to reconstruct Hi-C maps in the presence of SVs.

ASHLEYS: automated quality control for single-cell Strand-seq data.

SummarySingle-cell DNA template strand sequencing (Strand-seq) enables chromosome length haplotype phasing, construction of phased assemblies, mapping sister-chromatid exchange events and structural variant discovery. The initial quality control of potentially thousands of single-cell libraries is still done manually by domain experts. ASHLEYS automates this tedious task, delivers near-expert performance and labels even large datasets in seconds.Availability and implementationgithub.com/friendsofstrandseq/ashleys-qc, MIT license.Supplementary information Supplementary data are available at Bioinformatics online.

Expectations and blind spots for structural variation detection from long-read assemblies and short-read genome sequencing technologies

Virtually all genome sequencing efforts in national biobanks, complex and Mendelian disease programs, and medical genetic initiatives are reliant upon short-read whole-genome sequencing (srWGS), which presents challenges for the detection of structural variants (SVs) relative to emerging long-read WGS (lrWGS) technologies. Given this ubiquity of srWGS in large-scale genomics initiatives, we sought to establish expectations for routine SV detection from this data type by comparison with lrWGS assembly, as well as to quantify the genomic properties and added value of SVs uniquely accessible to each technology. Analyses from the Human Genome Structural Variation Consortium (HGSVC) of three families captured ~11,000 SVs per genome from srWGS and ~25,000 SVs per genome from lrWGS assembly. Detection power and precision for SV discovery varied dramatically by genomic context and variant class: 9.7% of the current GRCh38 reference is defined by segmental duplication (SD) and simple repeat (SR), yet 91.4% of deletions that were specifically discovered by lrWGS localized to these regions. Across the remaining 90.3% of reference sequence, we observed extremely high (93.8%) concordance between technologies for deletions in these datasets. In contrast, lrWGS was superior for detection of insertions across all genomic contexts. Given that non-SD/SR sequences encompass 95.9% of currently annotated disease-associated exons, improved sensitivity from lrWGS to discover novel pathogenic deletions in these currently interpretable genomic regions is likely to be incremental. However, these analyses highlight the considerable added value of assembly-based lrWGS to create new catalogs of insertions and transposable elements, as well as disease-associated repeat expansions in genomic sequences that were previously recalcitrant to routine assessment.

ClipSV: improving structural variation detection by read extension, spliced alignment and tree-based decision rules.

Structural variation (SV), which consists of genomic variation from 50 to millions of base pairs, confers considerable impacts on human diseases, complex traits and evolution. Accurately detecting SV is a fundamental step to characterize the features of individual genomes. Currently, several methods have been proposed to detect SVs using the next-generation sequencing (NGS) platform. However, due to the short length of sequencing reads and the complexity of SV content, the SV-detecting tools are still limited by low sensitivity, especially for insertion detection. In this study, we developed a novel tool, ClipSV, to improve SV discovery. ClipSV utilizes a read extension and spliced alignment approach to overcoming the limitation of read length. By reconstructing long sequences from SV-associated short reads, ClipSV discovers deletions and short insertions from the long sequence alignments. To comprehensively characterize insertions, ClipSV implements tree-based decision rules that can efficiently utilize SV-containing reads. Based on the evaluations of both simulated and real sequencing data, ClipSV exhibited an overall better performance compared to currently popular tools, especially for insertion detection. As NGS platform represents the mainstream sequencing capacity for routine genomic applications, we anticipate ClipSV will serve as an important tool for SV characterization in future genomic studies.

Impact of lossy compression of nanopore raw signal data on basecalling and consensus accuracy.

Nanopore sequencing provides a real-time and portable solution to genomic sequencing, enabling better assembly, structural variant discovery and modified base detection than second generation technologies. The sequencing process generates a huge amount of data in the form of raw signal contained in fast5 files, which must be compressed to enable efficient storage and transfer. Since the raw data is inherently noisy, lossy compression has potential to significantly reduce space requirements without adversely impacting performance of downstream applications. We explore the use of lossy compression for nanopore raw data using two state-of-the-art lossy time-series compressors, and evaluate the tradeoff between compressed size and basecalling/consensus accuracy. We test several basecallers and consensus tools on a variety of datasets at varying depths of coverage, and conclude that lossy compression can provide 35-50% further reduction in compressed size of raw data over the state-of-the-art lossless compressor with negligible impact on basecalling accuracy (≲0.2% reduction) and consensus accuracy (≲0.002% reduction). In addition, we evaluate the impact of lossy compression on methylation calling accuracy and observe that this impact is minimal for similar reductions in compressed size, although further evaluation with improved benchmark datasets is required for reaching a definite conclusion. The results suggest the possibility of using lossy compression, potentially on the nanopore sequencing device itself, to achieve significant reductions in storage and transmission costs while preserving the accuracy of downstream applications. The code is available at https://github.com/shubhamchandak94/lossy_compression_evaluation. Supplementary data are available at Bioinformatics online.

Chromosome-scale, haplotype-resolved assembly of human genomes

Haplotype-resolved or phased genome assembly provides a complete picture of genomes and their complex genetic variations. However, current algorithms for phased assembly either do not generate chromosome-scale phasing or require pedigree information, which limits their application. We present a method named diploid assembly (DipAsm) that uses long, accurate reads and long-range conformation data for single individuals to generate a chromosome-scale phased assembly within 1 day. Applied to four public human genomes, PGP1, HG002, NA12878 and HG00733, DipAsm produced haplotype-resolved assemblies with minimum contig length needed to cover 50% of the known genome (NG50) up to 25 Mb and phased ~99.5% of heterozygous sites at 98–99% accuracy, outperforming other approaches in terms of both contiguity and phasing completeness. We demonstrate the importance of chromosome-scale phased assemblies for the discovery of structural variants (SVs), including thousands of new transposon insertions, and of highly polymorphic and medically important regions such as the human leukocyte antigen (HLA) and killer cell immunoglobulin-like receptor (KIR) regions. DipAsm will facilitate high-quality precision medicine and studies of individual haplotype variation and population diversity.

The structural variation landscape in 492 Atlantic salmon genomes

Structural variants (SVs) are a major source of genetic and phenotypic variation, but remain challenging to accurately type and are hence poorly characterized in most species. We present an approach for reliable SV discovery in non-model species using whole genome sequencing and report 15,483 high-confidence SVs in 492 Atlantic salmon (Salmo salar L.) sampled from a broad phylogeographic distribution. These SVs recover population genetic structure with high resolution, include an active DNA transposon, widely affect functional features, and overlap more duplicated genes retained from an ancestral salmonid autotetraploidization event than expected. Changes in SV allele frequency between wild and farmed fish indicate polygenic selection on behavioural traits during domestication, targeting brain-expressed synaptic networks linked to neurological disorders in humans. This study offers novel insights into the role of SVs in genome evolution and the genetic architecture of domestication traits, along with resources supporting reliable SV discovery in non-model species.

A Practical Guide for Structural Variation Detection in the Human Genome.

Profiling genetic variants-including single nucleotide variants, small insertions and deletions, copy number variations, and structural variations (SVs)-from both healthy individuals and individuals with disease is a key component of genetic and biomedical research. SVs are large-scale changes in the genome and involve breakage and rejoining of DNA fragments. They may affect thousands to millions of nucleotides and can lead to loss, gain, and reshuffling of genes and regulatory elements. SVs are known to impact gene expression and potentially result in altered phenotypes and diseases. Therefore, identifying SVs from the human genomes is particularly important. In this review, I describe advantages and disadvantages of the available high-throughput assays for the discovery of SVs, which are the most challenging genetic alterations to detect. A practical guide is offered to suggest the most suitable strategies for discovering different types of SVs including common germline, rare, somatic, and complex variants. I also discuss factors to be considered, such as cost and performance, for different strategies when designing experiments. Last, I present several approaches to identify potential SV artifacts caused by samples, experimental procedures, and computational analysis. © 2020 Wiley Periodicals LLC.

Long-read-based human genomic structural variation detection with cuteSV

Long-read sequencing is promising for the comprehensive discovery of structural variations (SVs). However, it is still non-trivial to achieve high yields and performance simultaneously due to the complex SV signatures implied by noisy long reads. We propose cuteSV, a sensitive, fast, and scalable long-read-based SV detection approach. cuteSV uses tailored methods to collect the signatures of various types of SVs and employs a clustering-and-refinement method to implement sensitive SV detection. Benchmarks on simulated and real long-read sequencing datasets demonstrate that cuteSV has higher yields and scaling performance than state-of-the-art tools. cuteSV is available at https://github.com/tjiangHIT/cuteSV.

Discovery and population genomics of structural variation in a songbird genus

Structural variation (SV) constitutes an important type of genetic mutations providing the raw material for evolution. Here, we uncover the genome-wide spectrum of intra- and interspecific SV segregating in natural populations of seven songbird species in the genus Corvus. Combining short-read (N = 127) and long-read re-sequencing (N = 31), as well as optical mapping (N = 16), we apply both assembly- and read mapping approaches to detect SV and characterize a total of 220,452 insertions, deletions and inversions. We exploit sampling across wide phylogenetic timescales to validate SV genotypes and assess the contribution of SV to evolutionary processes in an avian model of incipient speciation. We reveal an evolutionary young (~530,000 years) cis-acting 2.25-kb LTR retrotransposon insertion reducing expression of the NDP gene with consequences for premating isolation. Our results attest to the wealth and evolutionary significance of SV segregating in natural populations and highlight the need for reliable SV genotyping.

SVJedi: genotyping structural variations with long reads.

Studies on structural variants (SVs) are expanding rapidly. As a result, and thanks to third generation sequencing technologies, the number of discovered SVs is increasing, especially in the human genome. At the same time, for several applications such as clinical diagnoses, it is important to genotype newly sequenced individuals on well-defined and characterized SVs. Whereas several SV genotypers have been developed for short read data, there is a lack of such dedicated tool to assess whether known SVs are present or not in a new long read sequenced sample, such as the one produced by Pacific Biosciences or Oxford Nanopore Technologies. We present a novel method to genotype known SVs from long read sequencing data. The method is based on the generation of a set of representative allele sequences that represent the two alleles of each structural variant. Long reads are aligned to these allele sequences. Alignments are then analyzed and filtered out to keep only informative ones, to quantify and estimate the presence of each SV allele and the allele frequencies. We provide an implementation of the method, SVJedi, to genotype SVs with long reads. The tool has been applied to both simulated and real human datasets and achieves high genotyping accuracy. We show that SVJedi obtains better performances than other existing long read genotyping tools and we also demonstrate that SV genotyping is considerably improved with SVJedi compared to other approaches, namely SV discovery and short read SV genotyping approaches. https://github.com/llecompte/SVJedi.git. Supplementary data are available at Bioinformatics online.

Current Affairs of Microbial Genome-Wide Association Studies: Approaches, Bottlenecks and Analytical Pitfalls.

Microbial genome-wide association studies (mGWAS) are a new and exciting research field that is adapting human GWAS methods to understand how variations in microbial genomes affect host or pathogen phenotypes, such as drug resistance, virulence, host specificity and prognosis. Several computational tools and methods have been developed or adapted from human GWAS to facilitate the discovery of novel mutations and structural variations that are associated with the phenotypes of interest. However, no comprehensive, end-to-end, user-friendly tool is currently available. The development of a broadly applicable pipeline presents a real opportunity among computational biologists. Here, (i) we review the prominent and promising tools, (ii) discuss analytical pitfalls and bottlenecks in mGWAS, (iii) provide insights into the selection of appropriate tools, (iv) highlight the gaps that still need to be filled and how users and developers can work together to overcome these bottlenecks. Use of mGWAS research can inform drug repositioning decisions as well as accelerate the discovery and development of more effective vaccines and antimicrobials for pressing infectious diseases of global health significance, such as HIV, TB, influenza, and malaria.

Critical length in long-read resequencing.

Long-read sequencing has substantial advantages for structural variant discovery and phasing of variants compared to short-read technologies, but the required and optimal read length has not been assessed. In this work, we used long reads simulated from human genomes and evaluated structural variant discovery and variant phasing using current best practice bioinformatics methods. We determined that optimal discovery of structural variants from human genomes can be obtained with reads of minimally 20 kb. Haplotyping variants across genes only reaches its optimum from reads of 100 kb. These findings are important for the design of future long-read sequencing projects.

PostSV: A Post-Processing Approach for Filtering Structural Variations.

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

The minimum weight clique partition problem and its application to structural variant calling

The calling of genomic structural variants (SV) in high-throughput sequencing data necessitates prior discovery of abnormally aligned discordant read pair clusters that indicate candidate SVs. Some methods for SV discovery collect these candidate variants by heuristically searching for maximal cliques in an undirected graph, with nodes representing discordant read pairs and edges between vertices indicating that the read pairs overlap. This approach works well for identifying clusters that overlap with noisy mapping artefacts, but could miss distinct variant clusters that are created due to complex structural variants or overlapping breakpoints of distinct SVs. In this paper, we consider the minimum weight clique partition problem and its application to the problem of discordant read pair clustering. Our results demonstrate that methods which approximate or heuristically solve this problem can enhance the predictive abilities of structural variant calling algorithms.

The minimum weight clique partition problem and its application to structural variant calling

The calling of genomic structural variants (SV) in high-throughput sequencing data necessitates prior discovery of abnormally aligned discordant read pair clusters that indicate candidate SVs. Some methods for SV discovery collect these candidate variants by heuristically searching for maximal cliques in an undirected graph, with nodes representing discordant read pairs and edges between vertices indicating that the read pairs overlap. This approach works well for identifying clusters that overlap with noisy mapping artefacts, but could miss distinct variant clusters that are created due to complex structural variants or overlapping breakpoints of distinct SVs. In this paper, we consider the minimum weight clique partition problem and its application to the problem of discordant read pair clustering. Our results demonstrate that methods which approximate or heuristically solve this problem can enhance the predictive abilities of structural variant calling algorithms.

Promising prospects of nanopore sequencing for algal hologenomics and structural variation discovery

BackgroundThe MinION Access Program (MAP, 2014–2016) allowed selected users to test the prospects of long nanopore reads for diverse organisms and applications through the rapid development of improving chemistries. In 2014, faced with a fragmented Illumina assembly for the chloroplast genome of the green algal holobiont Caulerpa ashmeadii, we applied to the MAP to test the prospects of nanopore reads to investigate such intricacies, as well as further explore the hologenome of this species with native and hybrid approaches.ResultsThe chloroplast genome could only be resolved as a circular molecule in nanopore assemblies, which also revealed structural variants (i.e. chloroplast polymorphism or heteroplasmy). Signal and Illumina polishing of nanopore-assembled organelle genomes (chloroplast and mitochondrion) reflected the importance of coverage on final quality and current limitations. In hybrid assembly, our modest nanopore data sets showed encouraging results to improve assembly length, contiguity, repeat content, and binning of the larger nuclear and bacterial genomes. Profiling of the holobiont with nanopore or Illumina data unveiled a dominant Rhodospirillaceae (Alphaproteobacteria) species among six putative endosymbionts. While very fragmented, the cumulative hybrid assembly length of C. ashmeadii’s nuclear genome reached 24.4 Mbp, including 2.1 Mbp in repeat, ranging closely with GenomeScope’s estimate (> 26.3 Mbp, including 4.8 Mbp in repeat).ConclusionOur findings relying on a very modest number of nanopore R9 reads as compared to current output with newer chemistries demonstrate the promising prospects of the technology for the assembly and profiling of an algal hologenome and resolution of structural variation. The discovery of polymorphic ‘chlorotypes’ in C. ashmeadii, most likely mediated by homing endonucleases and/or retrohoming by reverse transcriptases, represents the first report of chloroplast heteroplasmy in the siphonous green algae. Improving contiguity of C. ashmeadii’s nuclear and bacterial genomes will require deeper nanopore sequencing to greatly increase the coverage of these larger genomic compartments.

Abstract 1658: Performance evaluation of assembly based structural variation discovery in paired tumor normal samples

Abstract Structural variations (SVs) are well known to contribute to genetic diversity of human populations, affect biological functions, and cause various human disorders. However, accurately identifying SVs with correct sizes and locations in the human genome, particularly in cancer samples, remains a challenge due to the complexities of the human genome, limitations of sequencing technologies, and drawbacks of analysis methods. Recent advancement of next-generation sequencing technologies has significantly reduced the sequencing cost, while substantially increased the lengths of the sequencing reads. Therefore, using de novo assembly based approaches for discovering a full spectrum of SVs in human genome becomes tempting. While various assembly methods have been developed and proposed for general use by the community, the relative efficiency and predictive accuracy of SVs calling based on these assembly methods have not been fully evaluated. In this study, we applied several popular de novo assembly tools to the sequencing read data that were generated using multiple sequencing technologies with technical replicates for a paired tumor-normal samples. Assemblies and SVs callsets were generated, and repeatability in the SVs of the technical replicates and reproducibility across sequencing sites were evaluated. Results showed that there were substantial variabilities between SVs callsets based on various assemblies within sequencing platform and cross sequencing sites. We also observed considerable variabilities between assembly based SVs callsets and alignment based SVs callsets. The sources of these variabilities will be further discussed. These results allow better understanding of the impacts of de novo assembly methods on SVs calling, thus providing a better insight to precision medicine. Citation Format: Chunlin Xiao. Performance evaluation of assembly based structural variation discovery in paired tumor normal samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1658.