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NEW YORK (GenomeWeb) — High-resolution melt curve analysis is a robust and simple way of querying the genotypes present in heterogeneous samples, since curve shapes and melting temperatures are related to nucleic acid sequence.

Now, researchers at Johns Hopkins and Stanford Universities have developed a method to pluck quantitative information about all sequence variants in a mixture. The method uses an algorithm relying on the iterative process of machine learning to automatically classify HRM curves, and can enable large-scale genotype assessment in unknown samples.

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Swift Biosciences

Sequencing workflows require library quantification and normalization to ensure data quality and reduce cost. 

May
08
Sponsored by
Sysmex Inostics

This webinar will present recent evidence that demonstrates how incorporating circulating tumor DNA (ctDNA) assessments into real-world patient management can influence patient care decisions, alter radiographic interpretations, and impact clinical outcomes.