A new tool to predict the chances of successfully inserting a gene-edited sequence of DNA into the genome of a cell, using a technique known as prime editing, has been developed by researchers at the Wellcome Sanger Institute. An evolution of CRISPR-Cas9 gene editing technology, prime editing has huge potential to treat genetic disease in humans, from cancer to cystic fibrosis. But thus far, the factors determining the success of edits are not well understood. The study, published in Nature Biotechnology, assessed thousands of different DNA sequences introduced into the genome using prime editors. These data were then used to train a machine learning algorithm to help researchers design the best fix for a given genetic flaw, which promises to speed up efforts to bring prime editing into the clinic.

A new study published in the journal Nature Biotechnology has used machine learning to accelerate the development of prime editing, a promising gene-editing technology. The study analyzed thousands of DNA sequences introduced into the genome using prime editors, and used the data to train a machine learning algorithm to design the best fix for a given genetic flaw. By using machine learning to streamline the process of designing genetic fixes, this research could help speed up efforts to bring prime editing into clinical use. Researchers at the Wellcome Sanger Institute have developed a new tool to predict the chances of successfully inserting a gene-edited sequence of DNA into the genome of a cell, using a technique known as prime editing. An evolution of CRISPR-Cas9 gene editing technology, prime editing has huge potential to treat genetic diseases in humans, from cancer to cystic fibrosis.

A new tool to predict the chances of successfully inserting a gene-edited sequence of DNA into the genome of a cell, using a technique known as prime editing, has been developed by researchers at the Wellcome Sanger Institute. An evolution of CRISPR-Cas9 gene editing technology, prime editing has huge potential to treat genetic disease in humans, from cancer to cystic fibrosis. But thus far, the factors determining the success of edits are not well understood. The study, published today (February 16) in Nature Biotechnology, assessed thousands of different DNA sequences introduced into the genome using prime editors. These data were then used to train a machine learning algorithm to help researchers design the best fix for a given genetic flaw, which promises to speed up efforts to bring prime editing into the clinic.

Researchers at EMBL's European Bioinformatics Institute (EMBL-EBI), the Wellcome Sanger Institute, Addenbrooke's Hospital in Cambridge, UK, and collaborators have developed an artificial intelligence (AI) algorithm that uses computer vision to analyse tissue samples from cancer patients. They have shown that the algorithm can distinguish between healthy and cancerous tissues, and can also identify patterns of more than 160 DNA and thousands of RNA changes in tumours. The study, published today in Nature Cancer, highlights the potential of AI for improving cancer diagnosis, prognosis, and treatment. Cancer diagnosis and prognosis are largely based on two main approaches. In one, histopathologists examine the appearance of cancer tissue under the microscope.

IMAGE: A mosaic of tumour microscopy images forming broken DNA molecules. Researchers at EMBL's European Bioinformatics Institute (EMBL-EBI), the Wellcome Sanger Institute, Addenbrooke's Hospital in Cambridge, UK, and collaborators have developed an artificial intelligence (AI) algorithm that uses computer vision to analyse tissue samples from cancer patients. They have shown that the algorithm can distinguish between healthy and cancerous tissues, and can also identify patterns of more than 160 DNA and thousands of RNA changes in tumours. The study, published today in Nature Cancer, highlights the potential of AI for improving cancer diagnosis, prognosis, and treatment. Cancer diagnosis and prognosis are largely based on two main approaches.

A new machine learning tool that can detect whether emerging strains of the bacterium, Salmonella are more likely to cause dangerous bloodstream infections rather than food poisoning has been developed. The tool, created by a scientist at the Wellcome Sanger Institute and her collaborators at the University of Otago, New Zealand and the Helmholtz Institute for RNA-based Infection Research, a site of the Helmholtz Centre for Infection Research, Germany, greatly speeds up the process for identifying the genetic changes underlying new invasive types of Salmonella that are of public health concern. Reported today (8 May) in PLOS Genetics, the machine learning tool could be useful for flagging dangerous bacteria before they cause an outbreak, from hospital wards to a global scale. As the cost of genomic sequencing falls, scientists around the world are using genetics to better understand the bacteria causing infections, how diseases spread, how bacteria gain resistance to drugs, and which strains of bacteria may cause outbreaks. However, current methods to identify the genetic adaptations in emerging strains of bacteria behind an outbreak are time-consuming and often involve manually comparing the new strain to an older reference collection.

A new machine learning tool that can detect whether emerging strains of the bacterium, Salmonella are more likely to cause dangerous bloodstream infections rather than food poisoning has been developed. The tool, created by a scientist at the Wellcome Sanger Institute and her collaborators at the University of Otago, New Zealand and the Helmholtz Institute for RNA-based Infection Research, a site of the Helmholtz Centre for Infection Research, Germany, greatly speeds up the process for identifying the genetic changes underlying new invasive types of Salmonella that are of public health concern. Reported today (8 May) in PLOS Genetics, the machine learning tool could be useful for flagging dangerous bacteria before they cause an outbreak, from hospital wards to a global scale. As the cost of genomic sequencing falls, scientists around the world are using genetics to better understand the bacteria causing infections, how diseases spread, how bacteria gain resistance to drugs, and which strains of bacteria may cause outbreaks. However, current methods to identify the genetic adaptations in emerging strains of bacteria behind an outbreak are time-consuming and often involve manually comparing the new strain to an older reference collection.

A new machine learning tool that can detect whether emerging strains of the bacterium, Salmonella are more likely to cause dangerous bloodstream infections rather than food poisoning has been developed. The tool, created by a scientist at the Wellcome Sanger Institute and her collaborators at the University of Otago, New Zealand and the Helmholtz Institute for RNA-based Infection Research, a site of the Helmholtz Centre for Infection Research, Germany, greatly speeds up the process for identifying the genetic changes underlying new invasive types of Salmonella that are of public health concern. Reported today (8 May) in PLOS Genetics, the machine learning tool could be useful for flagging dangerous bacteria before they cause an outbreak, from hospital wards to a global scale. As the cost of genomic sequencing falls, scientists around the world are using genetics to better understand the bacteria causing infections, how diseases spread, how bacteria gain resistance to drugs, and which strains of bacteria may cause outbreaks. However, current methods to identify the genetic adaptations in emerging strains of bacteria behind an outbreak are time-consuming and often involve manually comparing the new strain to an older reference collection.

The HDCA programme will create genomic reference maps of all the cells that are important for human development, which will revolutionise our understanding of health and disease, from miscarriages and children's developmental disorders, through to cancer and ageing. The HDCA is one part of the ambitious Human Cell Atlas (HCA), a global consortium that aims to transform biological research and medicine by mapping every cell in the human body. Progress on the HDCA and other aspects of the Human Cell Atlas were discussed at the international HCA meeting at the Wellcome Genome Campus, Cambridge, this week. Many diseases have their origin in early human development, and a detailed understanding of development is key to explaining human health and disease. Researchers at the Wellcome Sanger Institute and Newcastle University have collected genomic data from over 250 thousand cells from a range of donated developing human tissues including liver, skin, kidney and placenta.