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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Customers might want to consider what they might learn about their risk of diseases like Alzheimer's before snagging the genetic testing kits that are on many gift guides this year, NJ.com writes.

The Wall Street Journal reports there is uncertainty surrounding whether He Jiankui's embryo editing did what he said it did.

Stat News reports that the pause on procuring fetal tissue for intramural US National Institutes of Health research will soon affect additional labs there.

In Nature this week: genomic analysis of the invasive fall webworm, amp of constrained coding regions within the human genome, and more.

Jan
30
Sponsored by
Loop Genomics

This webinar will provide a comparison of several next-generation sequencing (NGS) approaches — including short-read 16S, whole-genome sequencing (WGS), and synthetic long-read sequencing technology — for use in microbiome research studies.

Jan
30
Sponsored by
Loop Genomics

This webinar will provide a comparison of several next-generation sequencing (NGS) approaches — including short-read 16S, whole-genome sequencing (WGS), and synthetic long-read sequencing technology — for use in microbiome research studies.