Why personalized gene editing, genetic resurrections and embryo scoring made our list. Earlier this week, published its annual list of Ten Breakthrough Technologies. As always, it features technologies that made the news last year, and which--for better or worse--stand to make waves in the coming years. They're the technologies you should really be paying attention to. This year's list includes tech that's set to transform the energy industry, artificial intelligence, space travel --and of course biotech and health. Our breakthrough biotechnologies for 2026 involve editing a baby's genes and, separately, resurrecting genes from ancient species.

Sequencing a genome to determine an individual's DNA produces an enormous number of short nucleotide subsequences known as reads, which must be reassembled to reconstruct the full genome. We present a method for analyzing this type of data using contrastive learning, in which an encoder model is trained to produce embeddings that cluster together sequences from the same genomic region. The sequential nature of genomic regions is preserved in the form of trajectories through this embedding space. Trained solely to reflect the structure of the genome, the resulting model provides a general representation of $k$-mer sequences, suitable for a range of downstream tasks involving read data. We apply our framework to learn the structure of the $E.\ coli$ genome, and demonstrate its use in simulated ancient DNA (aDNA) read mapping and identification of structural variations. Furthermore, we illustrate the potential of using this type of model for metagenomic species identification. We show how incorporating a domain-specific noise model can enhance embedding robustness, and how a supervised contrastive learning setting can be adopted when a linear reference genome is available, by introducing a distance thresholding parameter $Γ$. The model can also be trained fully self-supervised on read data, enabling analysis without the need to construct a full genome assembly using specialized algorithms. Small prediction heads based on a pre-trained embedding are shown to perform on par with BWA-aln, the current gold standard approach for aDNA mapping, in terms of accuracy and runtime for short genomes. Given the method's favorable scaling properties with respect to total genome size, inference using our approach is highly promising for metagenomic applications and for mapping to genomes comparable in size to the human genome.

An ancient-DNA revolution is turning the high-speed equipment used to study the DNA of living things on to specimens from the past. The technology is being used to create genetic maps of saber-toothed cats, cave bears, and thousands of ancient humans, including Vikings, Polynesian navigators, and numerous Neanderthals. The total number of ancient humans studied is more than 10,000 and rising fast. The old genes have already revealed remarkable stories of human migrations around the globe. But researchers are hoping ancient DNA will be more than a telescope on the past--they hope it will have concrete practical use in the present. Many artists worry about the encroachment of artificial intelligence on artistic creation.

Many people around the world have more Denisovan DNA than previously thought, which has contributed to their sense of smell and ability to thrive at high altitudes, according to a study released Monday. Researchers know that modern humans with ancestry outside of Africa inherited up to 2.1 percent of their DNA from Neanderthals. But far less was known about Denisovans, who are believed to have shared origins with Neanderthals and account for up to 5 percent of DNA in some present day populations. The latest work, from a research team at Harvard Medical School and UCLA, developed a world map of ancient DNA. In doing so, they found that populations in Oceania populations had the highest percentage of ancient DNA – 2 percent Neanderthal and 5 percent Denisovan - while South Asians had more Denisovan DNA – 0.1 percent in Sherpas - than expected.