The programme of the International Conference on Machine Learning (ICML) featured an invited talk by Jennifer Doudna entitled "The future of ML in biology: CRISPR for health and climate". Jennifer Doudna and Emmanuelle Charpentier won the 2020 Nobel Prize in Chemistry for "the development of a method for genome editing". The method in question is often referred to as CRISPR/Cas9 genetic scissors. Using this technique, researchers can change the DNA of animals, plants and microorganisms with extremely high precision. This technology has already had a huge impact on the biological sciences.

From gene editing to protein-structure determination to quantum computing, here are seven technologies that are likely to have an impact on science in the year ahead. Roughly one-tenth of the human genome remained uncharted when genomics researchers Karen Miga at the University of California, Santa Cruz, and Adam Phillippy at the National Human Genome Research Institute in Bethesda, Maryland, launched the Telomere-to-Telomere (T2T) consortium in 2019. Now, that number has dropped to zero. In a preprint published in May last year, the consortium reported the first end-to-end sequence of the human genome, adding nearly 200 million new base pairs to the widely used human consensus genome sequence known as GRCh38, and writing the final chapter of the Human Genome Project1. First released in 2013, GRCh38 has been a valuable tool -- a scaffold on which to map sequencing reads. This is largely because the widely used sequencing technology developed by Illumina, in San Diego, California, produces reads that are accurate, but short.

Russian bioinformaticians have proposed a new neural network architecture capable of evaluating how well a guide RNA has been chosen for a gene editing experiment. Their approach will facilitate more efficient DNA modification with the popular CRISPR/Cas method and therefore will help develop new strategies for creating genetically modified organisms and find ways of treating grave hereditary disorders. The study, supported by a Russian Science Foundation grant, was published in the Nucleic Acids Research journal. Genomic editing, and the CRISPR/Cas method in particular, is widely used in various areas of experimental biology, as well as in agriculture and biotechnology. CRISPR/Cas is one of the many weapons bacteria use to combat viruses.

Machine Learning has made great advances in pharma and biotech efficiency. Correctly diagnosing diseases takes years of medical training. Even then, diagnostics is often an arduous, time-consuming process. In many fields, the demand for experts far exceeds the available supply. This puts doctors under strain and often delays life-saving patient diagnostics.

F. Allen et al., "Predicting the mutations generated by repair of Cas9-induced double-strand breaks," Nat Biotechnol, 37:64–72, 2019. During gene editing with CRISPR technology, the Cas9 scissors that cut DNA home in on the right spot to snip with the help of guide RNA. The way the genetic material is stitched back together afterward isn't terribly precise, though; in fact, scientists have long thought that without a template, the process is random. However, "there's been anecdotal evidence that cells don't repair DNA randomly," geneticist Richard Sherwood of Brigham and Women's Hospital tells The Scientist. A 2016 paper also suggested patterns in the repairs. Sherwood wondered if artificial intelligence could predict these outcomes.

Machine Learning has made great advances in pharma and biotech efficiency. Correctly diagnosing diseases takes years of medical training. Even then, diagnostics is often an arduous, time-consuming process. In many fields, the demand for experts far exceeds the available supply. This puts doctors under strain and often delays life-saving patient diagnostics.

The paper in Nature Communications is here: https://go.nature.com/2rKKfAQ During my postdoc in the laboratory of Luciano Marraffini at the Rockefeller University I had the chance to work on the early developments of CRISPR-Cas9 technologies and in particular their application to modify the genome of bacteria or control gene expression. The catalytically dead variant of Cas9 known as dCas9 can be programmed to bind almost any gene of interest, but it just sits on the DNA instead of introducing a break as Cas9 would. Binding of dCas9 to DNA is strong enough to block the RNA polymerase when in the proper orientation (the guide must bind to the coding strand of the target gene), and the ease with which it can be reprogrammed makes it a fantastic tool to study the effect of silencing genes. When I started my group at the Institut Pasteur 4 years ago, one of my first goal was to setup a genome-wide screen to exploit the properties of dCas9 to investigate the function of genes in E. coli in a systematic way.