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1. NPSV: A simulation-driven approach to genotyping structural variants in whole-genome sequencing data

   https://doi.org/10.1093/gigascience/giab046  

   Linderman, Michael D; Paudyal, Crystal; Shakeel, Musab; Kelley, William; Bashir, Ali; Gelb, Bruce D (July 2021, GigaScience)

   Abstract Background Structural variants (SVs) play a causal role in numerous diseases but are difficult to detect and accurately genotype (determine zygosity) in whole-genome next-generation sequencing data. SV genotypers that assume that the aligned sequencing data uniformly reflect the underlying SV or use existing SV call sets as training data can only partially account for variant and sample-specific biases. Results We introduce NPSV, a machine learning–based approach for genotyping previously discovered SVs that uses next-generation sequencing simulation to model the combined effects of the genomic region, sequencer, and alignment pipeline on the observed SV evidence. We evaluate NPSV alongside existing SV genotypers on multiple benchmark call sets. We show that NPSV consistently achieves or exceeds state-of-the-art genotyping accuracy across SV call sets, samples, and variant types. NPSV can specifically identify putative de novo SVs in a trio context and is robust to offset SV breakpoints. Conclusions Growing SV databases and the increasing availability of SV calls from long-read sequencing make stand-alone genotyping of previously identified SVs an increasingly important component of genome analyses. By treating potential biases as a “black box” that can be simulated, NPSV provides a framework for accurately genotyping a broad range of SVs in both targeted and genome-scale applications. 

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2. Structural Variants Contribute to Phenotypic Variation in Maize

   https://doi.org/10.1111/mec.17662  

   Catlin, Nathan S; Agha, Husain I; Platts, Adrian E; Munasinghe, Manisha; Hirsch, Candice N; Josephs, Emily B (December 2025, Molecular Ecology)

   ABSTRACT Comprehensively identifying the loci shaping trait variation has been challenging, in part because standard approaches often miss many types of genetic variants. Structural variants (SVs), especially transposable elements (TEs), are likely to affect phenotypic variation but we lack methods that can detect polymorphic SVs and TEs using short‐read sequencing data. Here, we used a whole genome alignment between two maize genotypes to identify polymorphic SVs and then genotyped a large maize diversity panel for these variants using short‐read sequencing data. After characterising SV variation in the panel, we identified SV polymorphisms that are associated with life history traits and genotype‐by‐environment (GxE) interactions. While most of the SVs associated with traits contained TEs, only two of the SVs had boundaries that clearly matched TE breakpoints indicative of a TE insertion, while the other polymorphisms were likely caused by deletions. One of the SVs that appeared to be caused by a TE insertion had the most associations with gene expression compared to other trait‐associated SVs. All of the SVs associated with traits were in linkage disequilibrium with nearby single nucleotide polymorphisms (SNPs), suggesting that the approach used here did not identify unique associations that would have been missed in a SNP association study. Overall, we have (1) created a technique to genotype SV polymorphisms across a large diversity panel using support from genomic short‐read sequencing alignments and (2) connected this presence/absence SV variation to diverse traits and GxE interactions. 

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3. SVDSS: structural variation discovery in hard-to-call genomic regions using sample-specific strings from accurate long reads

   https://doi.org/10.1038/s41592-022-01674-1  

   Denti, Luca; Khorsand, Parsoa; Bonizzoni, Paola; Hormozdiari*, Fereydoun*; Chikhi, Rayan* (January 2023, Nature Methods)

   Structural variants (SVs) account for a large amount of sequence variability across genomes and play an important role in human genomics and precision medicine. Despite intense efforts over the years, the discovery of SVs in individuals remains challenging due to the diploid and highly repetitive structure of the human genome, and by the presence of SVs that vastly exceed sequencing read lengths. However, the recent introduction of low-error long-read sequencing technologies such as PacBio HiFi may finally enable these barriers to be overcome. Here we present SV discovery with sample-specific strings (SVDSS)—a method for discovery of SVs from long-read sequencing technologies (for example, PacBio HiFi) that combines and effectively leverages mapping-free, mapping-based and assembly-based methodologies for overall superior SV discovery performance. Our experiments on several human samples show that SVDSS outperforms state-of-the-art mapping-based methods for discovery of insertion and deletion SVs in PacBio HiFi reads and achieves notable improvements in calling SVs in repetitive regions of the genome. 

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4. HQAlign: aligning nanopore reads for SV detection using current-level modeling

   https://doi.org/10.1093/bioinformatics/btad580  

   Joshi, Dhaivat; Diggavi, Suhas; Chaisson, Mark J; Kannan, Sreeram (October 2023, Bioinformatics)

   Alkan, Can (Ed.)

   Abstract MotivationDetection of structural variants (SVs) from the alignment of sample DNA reads to the reference genome is an important problem in understanding human diseases. Long reads that can span repeat regions, along with an accurate alignment of these long reads play an important role in identifying novel SVs. Long-read sequencers, such as nanopore sequencing, can address this problem by providing very long reads but with high error rates, making accurate alignment challenging. Many errors induced by nanopore sequencing have a bias because of the physics of the sequencing process and proper utilization of these error characteristics can play an important role in designing a robust aligner for SV detection problems. In this article, we design and evaluate HQAlign, an aligner for SV detection using nanopore sequenced reads. The key ideas of HQAlign include (i) using base-called nanopore reads along with the nanopore physics to improve alignments for SVs, (ii) incorporating SV-specific changes to the alignment pipeline, and (iii) adapting these into existing state-of-the-art long-read aligner pipeline, minimap2 (v2.24), for efficient alignments. ResultsWe show that HQAlign captures about 4%–6% complementary SVs across different datasets, which are missed by minimap2 alignments while having a standalone performance at par with minimap2 for real nanopore reads data. For the common SV calls between HQAlign and minimap2, HQAlign improves the start and the end breakpoint accuracy by about 10%–50% for SVs across different datasets. Moreover, HQAlign improves the alignment rate to 89.35% from minimap2 85.64% for nanopore reads alignment to recent telomere-to-telomere CHM13 assembly, and it improves to 86.65% from 83.48% for nanopore reads alignment to GRCh37 human genome. Availability and implementationhttps://github.com/joshidhaivat/HQAlign.git. 

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5. High-coverage nanopore sequencing of samples from the 1000 Genomes Project to build a comprehensive catalog of human genetic variation

   https://doi.org/10.1101/gr.279273.124  

   Gustafson, Jonas A; Gibson, Sophia B; Damaraju, Nikhita; Zalusky, Miranda PG; Hoekzema, Kendra; Twesigomwe, David; Yang, Lei; Snead, Anthony A; Richmond, Phillip A; De_Coster, Wouter; et al (November 2024, Genome Research)

   Fewer than half of individuals with a suspected Mendelian or monogenic condition receive a precise molecular diagnosis after comprehensive clinical genetic testing. Improvements in data quality and costs have heightened interest in using long-read sequencing (LRS) to streamline clinical genomic testing, but the absence of control data sets for variant filtering and prioritization has made tertiary analysis of LRS data challenging. To address this, the 1000 Genomes Project (1KGP) Oxford Nanopore Technologies Sequencing Consortium aims to generate LRS data from at least 800 of the 1KGP samples. Our goal is to use LRS to identify a broader spectrum of variation so we may improve our understanding of normal patterns of human variation. Here, we present data from analysis of the first 100 samples, representing all 5 superpopulations and 19 subpopulations. These samples, sequenced to an average depth of coverage of 37× and sequence read N50 of 54 kbp, have high concordance with previous studies for identifying single nucleotide and indel variants outside of homopolymer regions. Using multiple structural variant (SV) callers, we identify an average of 24,543 high-confidence SVs per genome, including shared and private SVs likely to disrupt gene function as well as pathogenic expansions within disease-associated repeats that were not detected using short reads. Evaluation of methylation signatures revealed expected patterns at known imprinted loci, samples with skewed X-inactivation patterns, and novel differentially methylated regions. All raw sequencing data, processed data, and summary statistics are publicly available, providing a valuable resource for the clinical genetics community to discover pathogenic SVs. 

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