SV Analysis

Overview

SV (Structural Variant) Analysis Service is a cutting-edge genomic research solution dedicated to the comprehensive detection, characterization, and interpretation of structural variants within DNA sequences. Structural variants, encompassing a broad range of genomic alterations such as insertions, deletions, inversions, translocations, and duplications, are key drivers of genetic diversity, disease pathogenesis, evolutionary processes, and phenotypic variation. These variants can span from a few base pairs to several megabases, significantly impacting gene function, regulatory networks, and chromosomal architecture. By systematically mapping and analyzing SVs across genomes, our service provides profound insights into genomic plasticity, disease mechanisms, and population genetics dynamics. It synergizes advanced molecular techniques, high-performance computing, and bioinformatics tools to accurately quantify SVs, evaluate their functional consequences, and explore their biological and biomedical significance.

Our SV Analysis Service Enhances Your Research with:

• High-Resolution SV Detection: Utilize state-of-the-art next-generation sequencing (NGS) and long-read sequencing platforms (e.g., PacBio SMRT, Oxford Nanopore Technologies) to achieve unparalleled accuracy in SV identification, even in challenging genomic regions characterized by repeats or complex rearrangements.
• Comprehensive SV Characterization: Employ a suite of algorithms and software tools (e.g., Manta, Delly, Sniffles) to classify SVs into distinct categories, determine their exact breakpoints, and estimate their sizes, providing a detailed portrait of genomic structural heterogeneity.
• Functional Impact Assessment: Predict the biological effects of SVs on gene expression, splicing patterns, protein function, and non-coding regulatory elements using in silico prediction tools (e.g., Ensembl Variant Effect Predictor, REMM) and integrate these findings with functional genomics data sets for a holistic understanding.
• Pathway and Network Analysis: Link detected SVs to biological pathways and interaction networks using databases like KEGG, Reactome, and STRING, revealing potential roles in disease pathways, cellular processes, or developmental programs.
• Population Genetics and Evolutionary Studies: Analyze SV frequencies and distributions across diverse populations or species to uncover patterns of genetic variation, signatures of natural selection, demographic history, or adaptive evolution, employing statistical methods such as PCA, admixture analysis, and selection scans.
• Disease-Associated SV Discovery: Conduct targeted or genome-wide association studies (GWAS) to identify SVs linked to complex traits or genetic disorders, leveraging case-control designs, burden tests, or meta-analyses to enhance statistical power and discover novel disease loci.
• Cancer Genomics Applications: Specialized pipelines for cancer SV analysis, including the detection of fusion genes, chromothripsis events, and kataegis patterns, facilitating the discovery of driver mutations and the understanding of tumor heterogeneity and evolution.
• Customized Reporting and Visualization: Generate detailed, user-friendly reports summarizing SV findings, along with interactive visualizations (e.g., Circos plots, IGV tracks) to facilitate data interpretation, hypothesis generation, and presentation of results.

What Is InDel Analysis

SV Analysis integrates multi-omics datasets, such as transcriptomics, epigenomics, and proteomics, to gain a holistic understanding of how structural variants affect gene expression, regulatory networks, and protein function. This integrative approach enables researchers to uncover the molecular pathways and biological processes disrupted by SVs, providing insights into their contributions to phenotypic diversity and disease pathogenesis. By mapping the distribution and frequency of structural variants across different genomes, SV Analysis Service reveals how these genomic alterations contribute to genetic variation within and between populations, shaping the genetic landscape of species over time. It explores the role of SVs in driving speciation events, promoting adaptive evolution in response to environmental pressures, and influencing the susceptibility to complex diseases, including cancer, neurological disorders, and congenital abnormalities.

How to Measure

1. Sample Collection and Study Design

Target Populations/Groups

• Define Biological Questions:
• Human disease genetics: Case-control cohorts for rare disease-associated SV discovery.
• Evolutionary Biology: Geographically isolated populations or hybrid zones to study adaptive SVs.
• Agriculture: Crop varieties or livestock breeds for trait-associated SV screening.
• Sample Types:
• Humans: Blood, saliva, or buccal swabs (for population studies).
• Animals/Plants: Tissue biopsies (e.g., fin clips, leaves), non-invasive samples (e.g., feces, hair), or environmental DNA (eDNA).
• Microorganisms: Metagenomic samples (soil, water) or cultured isolates (for horizontal gene transfer analysis).

Sampling Strategy

• Spatial Replication: Include multiple sampling sites to capture population structure.
• Temporal Replication: Resample populations over time to track SV frequency changes (e.g., post-environmental stress).
• Replication Depth: Aim for ≥10 samples per group to ensure statistical power.

Genomic Data Generation

Table 1: SV-Oriented Sequencing Approaches

Sample-Level QC

• Remove low-quality samples:
  • Coverage threshold: <10x for WGS; <30x for targeted panels.
  • Missing data: Exclude samples with >20% missing genotypes.
  • Relatedness: Filter closely related individuals (e.g., PI_HAT > 0.125 in PLINK).
• Variant-Level QC
  • Filter SVs by:
    • Allele Frequency: Exclude singletons/doubletons (MAF < 1% for small cohorts).
    • Genotype Quality: Remove calls with Phred score <30.
    • Functional Impact: Prioritize SVs in coding regions or splice sites.

2. SV Detection and Genotyping

Variant Calling Pipelines

• Short Reads (Illumina):
  • Tools: GATK HaplotypeCaller, FreeBayes, DeepVariant.
  • Output: VCF files with SV calls, genotypes, and quality metrics.
• Long Reads (PacBio/Nanopore):
  • Tools: Sniffles, CuteSV, NGMLR.
  • Advantage: Detect large SVs (>50 bp) and complex rearrangements.
• Multi-Sample Genotyping
  • Use GATK GenotypeGVCFs or GLnexus to jointly call SVs across cohorts, improving accuracy for low-frequency variants.

3. Functional Annotation and Impact Prediction

Annotation Tools

• Coding Regions:
  • SnpEff/VEP: Predict frameshifts, in-frame insertions/deletions.
  • PolyPhen-2/SIFT: Assess protein damage likelihood.
• Non-Coding Regions:
  • RegulomeDB: Link SVs to transcription factor binding sites.
  • CADD: Score deleteriousness for regulatory variants.

Pathway Enrichment Analysis

• Tools: DAVID, g:Profiler, or clusterProfiler (R).
• Objective: Identify overrepresented pathways (e.g., DNA repair, immune signaling) disrupted by SVs.

4. Population Genetics and Evolutionary Analysis

Genetic Diversity Metrics

• Calculate π (nucleotide diversity), θw (Watterson's theta), and Tajima's D to assess selection pressures.

Population Structure Analysis

• Tools: PCA (PLINK/EIGENSOFT), ADMIXTURE, STRUCTURE.
• Output: Visualize clusters and admixture events (e.g., bar plots, PCA scatterplots).

Selection Scans

• FST Outliers: Identify SVs with extreme differentiation between populations (e.g., using VCFtools or scikit-allel).
• iHS/XP-EHH: Detect ongoing positive selection in sliding windows.

5. Biomedical and Agricultural Research Interpretation

Disease Association Studies

• Case-control analysis: Test for SV enrichment in affected individuals (e.g., Fisher's exact test).
• Pharmacogenomics: Correlate SVs in drug-metabolizing genes (e.g., CYP2C19) with drug-response phenotypes.

Crop/Livestock Breeding

• GWAS: Link SVs to agronomic traits (e.g., yield, drought tolerance).
• Haplotype Analysis: Track favorable SV-linked haplotypes across breeding generations.

6. Visualization and Reporting

Key Visualizations

• Circos Plots: Display SV distribution across chromosomes.
• Manhattan Plots: Highlight loci under selection (e.g., FST outliers).
• Lollipop Plots: Show SV positions in protein domains (e.g., using MutationMapper).

Interactive Reports

• Deliverables:
  • Annotated VCF files with functional predictions.
  • Custom R/Shiny dashboards for exploratory analysis.
  • Publication-ready figures (e.g., PCA, admixture, selection scans).

Figure 1: SV Analysis

What Can We do

Comprehensive Genomic Coverage: We combine whole-genome sequencing (WGS) data with exome sequencing or reduced-representation libraries (e.g., RAD-seq, GBS) to capture a wide spectrum of structural variants across the genome. This multi-layered approach ensures that no variant goes undetected, from large insertions and deletions to complex rearrangements.

Our Advantages

• RegulomeDB & Epigenomic Data Integration
  For SVs located in non-coding regions, we utilize databases like RegulomeDB to assess their potential impact on regulatory elements such as enhancers, promoters, and insulators. By integrating epigenomic data, we can determine whether an SV might disrupt or create regulatory interactions, thereby influencing gene expression patterns.
• Selective Sweep Detection
  Using statistical methods and population genetic models, we scan genomes for signatures of selective sweeps—regions where a beneficial SV has rapidly increased in frequency, leaving a characteristic pattern of reduced genetic diversity. Identifying such sweeps provides evidence for the adaptive significance of specific SVs.
• Haplotype Network & Phylogenetic Analysis
  By constructing haplotype networks and phylogenetic trees, we can trace the evolutionary relationships between different alleles or SV-containing genomic regions. This allows us to infer the timing and geographic spread of SVs, as well as their role in speciation events or population divergence.
• Functional & Evolutionary Interpretation
  At the heart of our SV Analysis service lies a profound commitment to not just identifying structural variants (SVs) but also to unraveling their biological significance and evolutionary implications. We recognize that SVs are not merely genomic curiosities; they are dynamic elements that can profoundly influence gene expression, protein function, and the overall architecture of the genome.

Applications

1. Conservation of Endangered Species

SV analysis, much like InDel analysis, is instrumental in safeguarding the genetic diversity of endangered species and mitigating the risks of inbreeding in fragmented populations. By identifying unique SV markers, researchers can gain a deeper understanding of the genetic distinctiveness and connectivity between isolated groups. For instance, in the case of the critically endangered Javan rhinoceros, SV profiling uncovered specific genomic rearrangements that may contribute to its limited adaptability and vulnerability to environmental changes. This knowledge prompted the establishment of genetic corridors to facilitate gene flow between subpopulations. These insights are crucial for guiding habitat conservation efforts and implementing genetic rescue strategies to maintain the evolutionary resilience of endangered species.

2. Invasive Species Management

SVs play a significant role in the adaptive potential of invasive species, driving their ability to thrive in new environments. By comparing the SV landscapes of invasive and native taxa, scientists can pinpoint genomic regions that underlie traits such as pesticide resistance, drought tolerance, or competitive advantage. For example, in the case of the European green crab, an invasive species wreaking havoc on coastal ecosystems, SV analysis revealed genetic variants associated with rapid shell hardening, enabling it to evade predators more effectively. Targeting these SVs through gene editing or biocontrol strategies offers a promising avenue for curbing invasions while preserving native biodiversity and agricultural productivity.

3. Human Migration and Health Disparities

SVs serve as molecular signatures that trace the ancient migrations of human populations and their associated health impacts. For instance, SV variants in genes related to immune function reflect population-specific selective pressures during migration, influencing susceptibility to infectious diseases. In Indigenous populations of the Amazon rainforest, SVs in genes involved in detoxification pathways reveal adaptations to a diet rich in plant toxins, offering insights into the etiology of certain metabolic disorders in modernized communities. Furthermore, SV-informed ancestry analyses in admixed populations, such as Latino communities, highlights genetic variants associated with differential risk of complex diseases such as diabetes and cardiovascular conditions.

Demo

Figure 2: Overall structures of the Omicron S-trimer. (Zhen, 2022)

Case Study

FAQs