Genomic Variant Detection
Genomic Variant Detection - Related Videos
14:06
Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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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.
09:16
Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants
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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
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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.
07:50
Chip-Based Digital PCR to Detect Rare Transcript Variants Using a Nanofluidic Chip
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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.
08:31
Isolation and Genome Analysis of Single Virions using 'Single Virus Genomics'
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Cited by 2 •
2013
Single Virus Genomics (SVG) is a method to isolate and amplify the genomes of single virons. Viral suspensions of a mixed assemblage are sorted using flow cytometry onto a microscope slide with discrete wells containing agarose, thereby capturing the virion and reducing genome shearing during downstream processing. Whole genome amplification is achieved using multiple displacement amplification (MDA) resulting in genomic material that is suitable for sequencing.
FAQs
Sequencing reads are first aligned to a reference genome so their observed bases can be compared with an expected sequence. Variant-calling algorithms then assess evidence such as read depth and base quality before accepting a difference. These checks help separate consistent sequence changes from technical errors, improving confidence that a reported alteration reflects the tumor genome.
Comparing tumor DNA with matched normal DNA helps distinguish alterations associated with the tumor from inherited differences present throughout the body. This comparison also supports evaluation of technical artifacts that might otherwise appear as mutations. As a result, researchers can focus interpretation on genomic changes more plausibly related to cancer development or tumor behavior.
Single-nucleotide variants, insertions, deletions, and copy-number changes represent different forms of DNA alteration. Their effects may differ because they change sequence content or the amount of genomic material in distinct ways. Considering these classes together gives researchers a broader view of how genomic changes may alter biological function and contribute to disease behavior.
A typical analysis begins by aligning sequencing reads to a reference genome. Variant-calling algorithms then evaluate read depth, base quality, and other supporting evidence to identify credible alterations. When matched samples are available, tumor-normal comparison further refines interpretation. The resulting variant set can be examined for patterns relevant to tumor biology, biomarkers, or treatment decisions.
The resulting variant profiles help characterize tumor genomes and identify mutations associated with cancer development. Researchers can use these findings to investigate candidate biomarkers, stratify patients into biologically meaningful groups, and support targeted treatment selection. Variant data therefore connects molecular features of tumors with research questions about diagnosis, therapeutic response, and disease behavior.
Analyzing genomic alterations across tumors, or among regions and samples when available, can reveal genetic diversity within and between tumors. These differences may help researchers examine why tumor populations behave differently or change during treatment. Variant detection therefore provides a foundation for investigating therapy resistance and for relating resistant behavior to tumor genomic characteristics.