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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. 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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3. 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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4. 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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5. Genetic Variant Detection Over Generations: Sparsity-Constrained Optimization Using Block-Coordinate Descent

   https://doi.org/10.1109/MeMeA57477.2023.10171853  

   Aburidi, Mohammed; Banuelos, Mario; Sindi, Suzanne; Marcia, Roummel (June 2023, 2023 IEEE Conference on Medical Measurements and Applications (MeMeA))

   Structural variants (SVs) are rearrangements of regions in an individual’s genome signal. SVs are an important source of genetic diversity and disease in humans and other mammalian species. The SV detection process is susceptible to sequencing and mapping errors, especially when the average number of reads supporting each variant is low (i.e. low-coverage settings), which leads to high false-positive rates. Besides their rarity in the human genome, they are shared between related individuals. Thus, it’s advantageous to devise algorithms that focus on close relatives. In this paper, we develop a constrained-optimization method to detect germline SVs in genetic signals by considering multiple related people. First, we exploit familial relationships by considering a biologically realistic scenario of three generations of related individuals (a grandparent, a parent, and a child). Second, we pose the problem as a constrained optimization problem regularized by a sparsity-promoting penalty. Our framework demonstrates improvements in predicting SVs in related individuals and uncovering true SVs from false positives on both simulated and real genetic signals from the 1000 Genomes Project with low coverage. Further, our block-coordinate descent approach produces results with equal accuracy to the 3D projections of the solution, demonstrating feasibility for more complex and higher-dimensional pedigrees. 

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