A landmark study, titled “How error correction affects polymerase chain reaction deduplication: A survey based on unique molecular identifier datasets of short reads” recently published in Quantitative Biology reveals critical flaws in widely-used computational tools for next-generation sequencing (NGS) data analysis.

A new artificial intelligence model could help design antibodies which better protect the body against viruses and disease, a new study from the University of Surrey and University College London reports. The AI model, known as ImmunoMatch, can predict and identify correct protein pairings within antibodies, potentially helping to strengthen the immune system.

The researchers focused on the structure and dynamics of kinetoplast DNA, the mitochondrial genome of Trypanosomes, with an experimental study led by Edinburgh and a computational analysis that involved simulations conducted by UniTrento. Trypanosomes are a family of parasites (flagellated protozoa) that pose a risk to humans and animals because they are responsible for a number of tropical diseases, such as leishmaniasis and sleeping sickness, which are transmitted by the bite of flies, mosquitoes or other insects.

Quantifying the spatial organization and dynamics of the different regions of the mitochondrial genome of this blood parasite is a major challenge in biology. And this particular genome stands out for characteristics that can be useful for the development of new materials.