Researchers confirmed that sperm accumulate mutations over the years, increasing the risk of transmitting diseases to offspring. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. Human semen not only accumulates genetic mutations with age; as the percentage of sperm carrying potentially serious mutations increases, so does the risk of developing diseases in offspring. This is according to a new study by researchers at the Sanger Institute and King's College London.

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.

Genomics is set to become the biggest source of data on the planet, overtaking the current leading heavyweights – astronomy, YouTube and Twitter. Genome sequencing currently produces a staggering 25 petabytes of digital information per year. A petabyte is 1015 bytes, or about 1,000 times the average storage on a personal computer. And there is no sign of a slowdown. The amount of DNA sequencing data produced around the world is doubling approximately every seven months.

She is, in her own words, an "old-school biologist", brought up on the skills of pipettes and Petri dishes and protective goggles, the science of experiments with glassware on benches – what's known as "wet lab" work. "I knew what a gene looked like on a gel," she says, thinking back to her early career. These days that skill set is not enough. "When I started hiring PhD students 15 years ago, they were entirely wet lab," Corcoran says. "Now when we recruit them, the first thing we look for is if they can cope with complex bioinformatic analysis." To be a biologist, nowadays, you need to be a statistician, or even a programmer. You need to be able to work with algorithms. An algorithm, essentially, is a set of instructions – a series of predefined steps. A recipe could be seen as an algorithm, although a more obvious example is a computer program.

New biomedical techniques, such as next-generation genome sequencing, are creating vast amounts of data and transforming the scientific landscape. They're leading to unimaginable breakthroughs -- but leaving researchers racing to keep up. "This is when I start feeling my age," Anne Corcoran says. Corcoran leads a group that looks at how our genomes -- the DNA coiled in almost every cell in our bodies -- relate to our immune systems, and specifically to the antibodies we make to defend against infection. She is, in her own words, an "old-school biologist," brought up on the skills of pipettes and Petri dishes and protective goggles, the science of experiments with glassware on benches -- what's known as'wet lab' work. "I knew what a gene looked like on a gel," she says, thinking back to her early career. These days, that skill set is not enough. "When I started hiring Ph.D. students 15 years ago, they were entirely wet lab," Corcoran says. "Now when we recruit them, the first thing we look for is if they can cope with complex bioinformatic analysis."

"This is when I start feeling my age," says Anne Corcoran. Corcoran leads a group that looks at how our genomes – the DNA coiled in almost every cell in our bodies – relate to our immune systems, and specifically to the antibodies we make to defend against infection. She is, in her own words, an "old-school biologist," brought up on the skills of pipettes and Petri dishes and protective goggles, the science of experiments with glassware on benches – what's known as "wet lab" work. "I knew what a gene looked like on a gel," she says, thinking back to her early career. These days that skill set is not enough. "When I started hiring Ph.D. students 15 years ago, they were entirely wet lab," Corcoran says. "Now when we recruit them, the first thing we look for is if they can cope with complex bioinformatic analysis." To be a biologist, nowadays, you need to be a statistician, or even a programmer.

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.