Structural variation

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Nanopore technology overcomes the limitations of standard short-read sequencing techniques in SV characterization

Zamora-Cánovas, A. et al. J. Thromb. Haemost. (2024)

  • Icon displaying a graphic of any length nanopore reads
    Accurately characterise structural variants using long nanopore sequencing reads
  • Icon displaying a graphic of epigenetic base modification detection
    Use amplification-free whole-genome or targeted sequencing approaches, and detect base modifications as standard
  • Blue icon showing many people
    Scale to any project size, including large population-scale studies
Intro

Unlock the biology of your samples with complete SV characterisation

Structural variants (SVs) are of high importance in both normal and aberrant phenotypes; however, their detection using legacy short-read sequencing technologies is limited by their size, complexity, and position in the genome. Long and ultra-long nanopore reads can span SVs end-to-end enabling unprecedented resolution of even highly complex variants — in any genomic context. Amplification is not required, avoiding PCR bias and allowing SVs to be identified across the genome, including in repetitive or GC-rich regions, such as repeat expansions, which are inaccessible to other methods. This also enables the sequencing of intact modified bases, so that SVs and their epigenetic effects can be revealed in a single experiment.

Technology comparison

Oxford Nanopore sequencing

Legacy short-read sequencing

Any read length (20 bp to >4 Mb)

Short read length (50–300 bp)

  • Assembly contiguity is reduced and complex computational analyses are required to infer results
  • Complex genomic regions such as SVs and repeat elements typically cannot be sequenced in single reads (e.g. transposons, gene duplications, and prophage sequences)
  • Transcript analysis is limited to gene-level expression data
  • Important genetic information is missed

Direct sequencing of native DNA/RNA

Amplification required

  • Eliminate amplification- and GC-bias, along with read length limitations, and access genomic regions that are difficult to amplify
  • Detect epigenetic modifications, such as methylation, as standard — no additional, time-consuming sample prep required
  • Create cost-effective, amplification-free, targeted panels with adaptive sampling to detect SVs, repeats, SNVs, and methylation in a single assay
  • Amplification is often required and can introduce bias
  • Base modifications are removed, necessitating additional sample prep, sequencing runs, and expense
  • Uniformity of coverage is reduced, resulting in assembly gaps

Real-time data streaming

Fixed run time with bulk data delivery

  • Analyse data as it is generated for immediate access to actionable results
  • Stop sequencing when sufficient data is obtained — wash and reuse flow cell
  • Combine real-time data streaming with intuitive, real-time EPI2ME data analysis workflows for deeper insights
  • Time to result is increased
  • Workflow errors cannot be identified until it is too late
  • Additional complexities of handling large volumes of bulk data

Accessible and affordable sequencing

Constrained to centralised labs

  • Sequence on demand with flexible end-to-end workflows that suit your throughput needs
  • Sequence at sample source, even in the most extreme or remote environments, with the portable MinION device — minimise potential sample degradation caused by storage and shipping
  • Scale up with modular GridION and PromethION devices — suitable for high-output, high-throughput sequencing to generate ultra-rich data
  • Perform cost-effective targeted analyses with single-use Flongle Flow Cells
  • Sequence as and when needed using low-cost, independently addressable flow cells — no sample batching needed
  • Use sample barcodes to multiplex samples on a single flow cell
  • Bulky, expensive devices that require substantial site infrastructure — use is restricted to well-resourced, centralised locations, limiting global accessibility
  • High sample batching is required for optimal efficiency, delaying time to results

Streamlined, automatable workflows

Laborious workflows

  • Lengthy sample prep is required
  • Long sequencing run times
  • Workflow efficiency is reduced, and time to result is increased