Rare Mutation Detection
Rare Mutation Detection - Related Videos
11:11
Detection of Rare Mutations in CtDNA Using Next Generation Sequencing
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Cited by 23 •
2017
This manuscript describes a technique for detecting mutations of low frequency in ctDNA, ER-Seq. This method is differentiated by its unique use of two-directional error correction, a special background filter, and efficient molecular acquirement.
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.
06:43
Robotic Duodenal Sleeve Resection for Gastrointestinal Stromal Tumor with Rare Exon 8 KIT Mutation Following Neoadjuvant Imatinib
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2026
This video case report demonstrates the unique management of a duodenal gastrointestinal stromal tumor with a rare, understudied, exon 8 KIT mutation. Neoadjuvant therapy resulted in a 30% reduction in tumor size, making the patient a candidate for a robotic-assisted duodenal sleeve resection instead of the more morbid pancreaticoduodenectomy.
11:15
Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors
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Cited by 8 •
2016
This manuscript describes clinical protocols for two next-generation sequencing panels. One panel interrogates hematologic malignancies while the other panel targets genes commonly mutated in solid tumors. Molecular classification of driver mutations in human malignancies offers valuable prognostic and predictive information.
04:07
Droplet Digital Polymerase Chain Reaction: A Method to Generate Nanodroplet PCR Reactions for Detecting Rare Tumor Mutations
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2025
In this video, we present a step-by-step procedure to generate emulsified nano-droplets to detect target nucleic acids by digital polymerase chain reaction or dPCR.
FAQs
Molecular barcodes and error correction address different parts of the same reliability problem. Barcodes add molecular information that can be tracked during analysis, whereas error-correction procedures help suppress or recognize technical errors. Together, they make it less likely that an artifact will be reported as a genuine rare variant, especially when the signal occurs at very low frequency.
Carefully designed controls provide the reference needed to judge whether a low-frequency signal is credible. In rare mutation detection, controls help reveal technical artifacts that could otherwise resemble authentic variants. Their role is especially important when interpreting results from small cancer cell populations, where a misleading signal could distort conclusions about disease progression or treatment response.
Deep sequencing and allele-specific PCR are both presented as sensitive options for rare mutation detection, but they represent different assay formats. A study can select the approach that fits its analytical design, then strengthen interpretation with molecular barcodes, error correction, and controls. The central requirement is not simply sensitivity, but reliable separation of true variants from artifacts.
A supported workflow begins by choosing a sensitive assay, such as deep sequencing or allele-specific PCR, and then incorporating molecular barcodes, error correction, and carefully designed controls. The resulting data are interpreted for evidence of authentic low-frequency variants rather than technical artifacts. This layered design connects detection to a defensible biological conclusion.
In cancer research, rare mutation detection can reveal tumor heterogeneity by identifying genetically distinct cell populations within a sample. It also supports analysis of circulating tumor DNA and minimal residual disease, where disease-related genetic signals may be present at low levels. These applications help investigators follow changes that could be missed by approaches less suited to rare events.
Tracking rare variants can help researchers investigate emerging drug resistance and changes associated with treatment response. In studies of targeted therapies, the approach can evaluate disease-related genetic changes associated with small cancer cell populations. This makes it useful for monitoring how tumor biology may shift as therapy is assessed.