Structural Variant Detection
Structural Variant Detection - Related Videos
14:06
Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
0 Views •
Cited by 10 •
2012
Pooled DNA sequencing is a fast and cost-effective strategy to detect rare variants associated with complex phenotypes in large cohorts. Here we describe the computational analysis of pooled, next-generation sequencing of 32 cancer-related genes using the SPLINTER software package. This method is scalable, and applicable to any phenotype of interest.
07:50
Chip-Based Digital PCR to Detect Rare Transcript Variants Using a Nanofluidic Chip
0 Views •
2025
This video demonstrates chip-based digital PCR — a variation of the digital PCR technique that is useful in detecting rare transcript variants. The PCR reaction is partitioned into the chambers of a nanofluidic chip, each of which acts as an independent reaction. The detection of fluorescence signals from the chambers with amplified targets confirms the presence of rare transcript variants in the sample.
04:52
Following the Dynamics of Structural Variants in Experimentally Evolved Populations
0 Views •
Cited by 1 •
2023
We developed a cost-effective method to follow non-single nucleotide polymorphism allele dynamics that can easily be adapted to experimental evolution frozen archives. A triplet PCR technique was coupled with automated parallel capillary electrophoresis to quantify the relative frequency of an insertion allele over the course of experimental evolution.
09:16
Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants
0 Views •
Cited by 7 •
2015
Array CGH for the detection of genomic copy number variants has replaced G-banded karyotype analysis. This paper describes the technology and its application in a diagnostic service laboratory.
09:58
Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models
0 Views •
Cited by 25 •
2016
Here we describe a protocol aimed at investigating the impact of aberrant splicing on drug resistance in solid tumors and hematological malignancies. To this goal, we analyzed the transcriptomic profiles of parental and resistant in vitro models through RNA-seq and established a qRT-PCR based method to validate candidate genes.
FAQs
Structural variant detection gains confidence by combining distinct sequencing signatures rather than relying on one signal. Abnormal sequencing depth can indicate a change in copy number, while discordant paired-end reads suggest altered relationships between genomic segments. Split reads can pinpoint a breakpoint, and long-read alignments can span complex rearrangements. Together, these patterns help characterize the event and its genomic context.
Each signal contributes different structural information. Sequencing depth reflects how much genomic material is represented, paired-end read discordance indicates an unexpected arrangement of read ends, and split reads show that one read crosses a breakpoint. Long-read alignments provide broader sequence continuity. Interpreting these patterns together helps distinguish the location and form of a rearrangement.
Long-read alignments and optical mapping provide evidence that can extend beyond the local patterns detected by short-read analysis. Long reads may connect sequence across a rearranged region, whereas optical mapping supplies complementary information about larger-scale genome organization. These approaches are particularly useful when complex rearrangements cannot be adequately characterized from sequencing depth, paired-end relationships, or split reads alone.
Single-nucleotide analyses focus on individual base changes, whereas larger rearrangements can modify gene function and genome organization without being represented as isolated substitutions. Detecting deletions, duplications, inversions, insertions, and translocations therefore adds a different layer of genetic information. This broader view helps investigators examine how genome structure may relate to developmental disorders, disease mechanisms, or phenotypic variation.
A basic workflow examines sequencing data for abnormal depth, discordant paired-end reads, split reads, and long-read alignment patterns. Candidate changes are then characterized by considering the evidence together and assessing whether complementary approaches, such as optical mapping or other assays, support complex rearrangements. The resulting interpretation describes both the structural change and its genomic context.
Researchers apply structural variant detection when the biological question involves large-scale genome changes or variation that single-nucleotide analyses may overlook. The approach supports studies of disease mechanisms, cancer genomes, population diversity, genome evolution, and developmental disorders. It can also help connect a detected rearrangement with a phenotypic outcome, provided the structural evidence and biological context are considered together.
Structural variant analyses can reveal rearrangements that affect genome organization, supplying information useful for improving genome assemblies. The same evidence helps researchers interpret whether a deletion, duplication, inversion, insertion, or translocation may influence gene function. In this way, detection contributes both to a more accurate representation of genome structure and to investigations linking genomic changes with observable traits.