long-read sequencing
Definition
Long-read sequencing refers to next-generation sequencing technologies that generate reads spanning thousands to millions of base pairs, compared to the 150-300bp typical of short-read methods. Key platforms include Pacific Biosciences (PacBio) SMRT sequencing and Oxford Nanopore Technologies. Long reads enable superior resolution of structural variants, repetitive genomic regions, full-length transcript isoforms, and complex genomic rearrangements that short reads cannot adequately resolve. This technology is particularly valuable for de novo genome assembly, phasing haplotypes, detecting large insertions/deletions, and characterizing alternative splicing patterns. While historically limited by higher error rates, recent advances have dramatically improved accuracy, making long-read sequencing increasingly essential for comprehensive genomic and transcriptomic characterization in both research and clinical applications.
Visualize long-read sequencing in Nodes Bio
Researchers can visualize long-read sequencing data as networks connecting splice variants, isoforms, and structural variants to phenotypes or disease states. Network graphs can map full-length transcript isoforms to their regulatory elements, reveal complex gene fusion events in cancer, or display haplotype-resolved gene networks. This enables identification of previously undetectable connections between genomic structural variations and downstream biological pathways.
Visualization Ideas:
- Isoform-to-pathway networks showing how alternative splicing variants connect to different biological functions
- Structural variant networks linking chromosomal rearrangements to affected genes and downstream pathways
- Haplotype-resolved gene regulatory networks displaying allele-specific expression patterns and their phenotypic consequences
Example Use Case
A cancer genomics team uses long-read sequencing to characterize complex chromosomal rearrangements in glioblastoma samples. They identify novel gene fusion events and cryptic structural variants missed by short-read sequencing. By mapping these variants into a network with known oncogenic pathways, drug targets, and patient outcomes, they discover that a specific fusion involving EGFR creates a constitutively active signaling node. This network-based analysis reveals potential therapeutic vulnerabilities and explains resistance mechanisms to standard EGFR inhibitors in a subset of patients.