Tools and Techniques for Structural Variant Analysis

The human genome contains a high degree of variation, encompassing both small-scale changes, such as single nucleotide variants, and much larger alterations known as structural variants (SVs). As a major source of genetic diversity, SVs typically span 50 base pairs or more and include insertions, deletions, duplications, inversions, translocations, and complex rearrangements¹. While not all SVs contribute to disease, many play an important role in both rare and common disorders. Understanding and identifying these variations is crucial for studying their role in human health.

Challenges
Over the past several years, SV detection has drastically improved with the advent of new technologies, but it continues to face persistent obstacles. “Detecting SVs presents significant challenges due to their inherent complexity and variability,” stated Mark Kiel, M.D., Ph.D., Co-Founder and Chief Scientific Officer of Genomenon. “Accurately identifying these variants requires tools capable of handling this broad spectrum of size and complexity, yet most testing methodologies are optimized for detecting smaller variants.”

Another major challenge is identifying SVs in scientific literature. With no universal standard for structural variant nomenclature, detection is difficult. “Variants may be described using ISCN (International System for Human Cytogenomic Nomenclature), HGVS (Human Genomic Variation Society), or natural language, and these naming conventions can evolve over time,” explained Kiel. “This lack of consistency makes comprehensive and accurate identification of SVs in literature a moving target.”

Technologies for SV Detection
In the past, scientists identified SVs using karyotyping, array-comparative genomic hybridization (array-CGH), representational oligonucleotide microarray analysis (ROMA), and other techniques that have been largely replaced with newer approaches^1,2. The latest generation of tools has enhanced SV detection by resolving many of the earlier challenges and delivering consistent and accurate results.

Long-Read Sequencing
Today, sequencing-based methods are the go-to standard for finding structural variants. The most common sequencing approaches for detecting structural variants have traditionally involved short-read mapping³. As Kiel noted earlier, these methodologies are typically designed for detecting shorter variants, and their limited length makes it difficult to identify large or complex genomic rearrangements. This has led to the growing adoption of long-read sequencing platforms, such as those from PacBio and Oxford Nanopore Technologies^4,5.

“Long-read sequencing technologies are becoming pivotal in identifying complex SVs, particularly in regions of the genome previously inaccessible to short-read methods,” stated Kiel. “These advancements allow for accurate characterization of breakpoints, repetitive sequences, and large insertions or deletions that are critical in diagnosing genetic disorders.”

Combining long-read and short-read technologies can also be an effective strategy for SV detection that leverages the strengths of both methods³. Long reads facilitate improved mapping and the detection of large or complex structural variants, while short reads offer higher breakpoint resolution and greater precision. In addition, the enhanced performance and affordability of long-read technologies have made them indispensable for detecting structural variants in both large-scale and clinical research settings.

Artificial Intelligence Tools
AI and computational advancements have also enhanced SV detection by addressing challenges in identifying and interpreting SVs within scientific literature. “Our AI-powered tools and Mastermind software address these issues by recognizing the diverse ways SVs are named,” explained Kiel. These tools accurately interpret multiple nomenclature systems and identify relevant publications, providing scientists with reliable access to critical information⁶. “This capability empowers scientists to overcome the challenges associated with SV complexity and inconsistent naming conventions, streamlining variant detection and enhancing research outcomes,” shared Kiel.

The foundation for these tools was laid during Genomenon’s early development, when the software engineering team created a technology called genomic language processing (GLP). Similar to natural language processing but focused on genetic nomenclature, GLP enables more precise detection of genetic variants. Building on this, the team has recently implemented cutting-edge AI techniques to improve detection sensitivity and reduce false positives.

Kiel noted that these advances strengthen their ability to extract and interpret structural variant metadata from the literature, leading to a deeper understanding of their clinical and research significance. The expanded metadata now includes ISCN and HGVS representations, zygosity, mosaicism, diseases, risk estimates, somatic characteristics, and variant details. “These improvements provide researchers and clinicians with deeper, more actionable insights, further solidifying our tools as indispensable resources in genomics,” Kiel stated.

Emerging Tools for SV Analysis
Among the technologies enhancing structural variant analysis is Bionano’s optical genome mapping (OGM), which runs on the Stratys and Saphyr systems. OGM detects structural variants by labeling ultra-high molecular weight DNA with fluorescent markers, linearizing it in nanochannel arrays, and analyzing label patterns. It offers high-resolution, genome-wide SV detection and can be integrated with sequencing and microarrays for comprehensive genomic analysis.

An alternative emerging approach is Nabsys’s electronic genome mapping (EGM), which uses the OhmX Platform to detect structural variants by measuring voltage changes as DNA passes through a nanodetector. Then tags attached to DNA create resistance changes, which enable high-resolution SV detection. EGM eliminates optical limitations, provides high-resolution mapping, and offers scalability for large-scale genomic analysis.

Recent Advances
Along with these developments, new trends and innovations are helping to shape the future of SV detection. “The next wave of advancements in structural variant detection will focus on enhancing the precision and clinical utility of SV analysis in diagnostics,” stated Kiel. This includes the increased application of long-read technologies for identifying complex SVs.

An additional area of progress, as Kiel described, is combining genomic, transcriptomic, and epigenomic data to enhance the clinical interpretation of SVs through multi-omics integration. For instance, integrating structural variant detection with RNA sequencing reveals downstream gene expression changes, strengthening the connection between SVs and disease phenotypes. “Additionally, graph-based reference genomes are gaining traction, offering a more accurate representation of genetic diversity and improving the detection of clinically relevant variants across diverse populations,” noted Kiel.

He stated that greater consistency in nomenclature across publications and clinical diagnoses will enhance the identification of similar variants and improve clinical genetics' ability to classify structural variants accurately. Furthermore, continued improvements in sequencing technology will also boost structural variant detection.

Real-World Impact
Structural variant detection also plays a vital role in clinical genomics, with real-world applications demonstrating how key technologies can directly impact patient outcomes. One notable success story comes from Rady Children’s Institute for Genomic Medicine, where clinicians used Genomenon's Mastermind software to help diagnose a child’s immune deficiency. “The information also allowed the medical team caring for the child to pursue a targeted therapy that drastically improved the patient’s condition,” emphasized Kiel. While other interpretation tools overlooked this study, Mastermind successfully identified it, demonstrating the precision and reach of Genomenon’s AI-powered approach.

Long-read sequencing has also proven effective in real-world structural variant detection across a variety of clinical scenarios. It has resolved balanced translocations in rare Mendelian disorders⁷, streamlined diagnosis of imprinting syndromes by assessing SVs and methylation⁸, and identified pathogenic SVs in critically ill children with rapid turnaround⁹. It has also uncovered repeat expansions in hereditary ataxia, expanding both diagnostic yield and phenotypic understanding¹⁰.

Additionally, OGM has shown strong clinical utility. In a study of 104 neural tube defect cases, OGM identified diagnostic or candidate structural variants in 30% of them¹¹. These included SVs that affect key developmental genes and revealed new insights into neural tube defect pathogenesis, as well as the value of OGM in detecting variants often missed by traditional genomic technologies.

Filling in the Gaps
Progress in the field has been accelerated by technological breakthroughs that have resolved many of the initial challenges. However, the path to clinical utility for SV analysis still requires bridging important gaps. A key challenge is the lack of standardized methods for validating and benchmarking SV detection tools, which leads to inconsistencies across sequencing platforms and analysis pipelines. As mentioned earlier, the lack of a universal nomenclature for SVs also remains a major gap that must be addressed to support accurate interpretation in both clinical and research contexts. Another major hurdle is interpretation. “While many SVs are identified, their clinical significance often remains unclear, especially for novel or rare variants,” Kiel stated. He emphasized the need for robust, clinically validated databases linking SVs to phenotypes and disease outcomes to improve diagnostic reliability.

Finally, Kiel called for better integration of SV analysis into clinical workflows, stressing that tools must translate raw genomic data into clear, actionable reports for healthcare providers. This is especially important for diagnosing and managing genetic conditions such as rare inherited disorders, cancers, and developmental syndromes. “Addressing these gaps—including standardizing nomenclature—will be instrumental in advancing the role of structural variant detection in clinical diagnostics, paving the way for more precise, personalized, and impactful patient care,” Kiel concluded.


References

1. Escaramís G, Docampo E, Rabionet R. A decade of structural variants: description, history and methods to detect structural variation. Brief Funct Genomics. 2015;14(5):305-314. doi:10.1093/bfgp/elv014
2. Ho SS, Urban AE, Mills RE. Structural variation in the sequencing era. Nat Rev Genet. 2020;21(3):171-189. doi:10.1038/s41576-019-0180-9
3. Mahmoud M, Gobet N, Cruz-Dávalos DI, Mounier N, Dessimoz C, Sedlazeck FJ. Structural variant calling: the long and the short of it. Genome Biol. 2019;20(1):246. Published 2019 Nov 20. doi:10.1186/s13059-019-1828-7
4. Sedlazeck FJ, Rescheneder P, Smolka M, et al. Accurate detection of complex structural variations using single-molecule sequencing. Nat Methods. 2018;15(6):461-468. doi:10.1038/s41592-018-0001-7
5. Logsdon GA, Vollger MR, Eichler EE. Long-read human genome sequencing and its applications. Nat Rev Genet. 2020;21(10):597-614. doi:10.1038/s41576-020-0236-x
6. Chunn LM, Nefcy DC, Scouten RW, et al. Mastermind: A Comprehensive Genomic Association Search Engine for Empirical Evidence Curation and Genetic Variant Interpretation. Front Genet. 2020;11:577152. Published 2020 Nov 13. doi:10.3389/fgene.2020.577152
7. Vollger MR, Korlach J, Eldred KC, et al. Synchronized long-read genome, methylome, epigenome, and transcriptome for resolving a Mendelian condition. Preprint. bioRxiv. 2023;2023.09.26.559521. Published 2023 Sep 27. doi:10.1101/2023.09.26.559521
8. Paschal CR, Zalusky MPG, Beck AE, et al. Concordance of Whole-Genome Long-Read Sequencing with Standard Clinical Testing for Prader-Willi and Angelman Syndromes. J Mol Diagn. 2025;27(3):166-176. doi:10.1016/j.jmoldx.2024.12.003
9. Kamolvisit W, Cheawsamoot C, Chetruengchai W, et al. Singleton rapid long-read genome sequencing as first tier genetic test for critically Ill children with suspected genetic diseases. Eur J Hum Genet. Published online February 27, 2025. doi:10.1038/s41431-025-01818-9
10. Saffie-Awad P, Moller A, Daida K, et al. Identification of GGC Repeat Expansions in ZFHX3 Among Chilean Movement Disorder Patients. Preprint. medRxiv. 2025;2025.03.17.25323863. Published 2025 Mar 19. doi:10.1101/2025.03.17.25323863
11. Sahajpal NS, Dean J, Hilton B, et al. Optical genome mapping identifies rare structural variants in neural tube defects. Genome Res. Published online March 19, 2025. doi:10.1101/gr.279318.124