(19.09.2023)
Die Suche nach den Ursachen für genetische Erkrankungen ist ein großes Unterfangen, bei dem vermehrt auch künstliche Intelligenz (KI) zum Einsatz kommt. Ein neues KI-Werkzeug von Google DeepMind kann das Krankheitsrisiko abschätzen, das von bestimmten Genmutationen ausgeht. Die entstandene Datenbank soll künftige Untersuchungen zur Entstehung der Krankheiten deutlich erleichtern.
(19.09.2023)
Google DeepMind has developed a new tool called AlphaMissense, which uses the AlphaFold human protein structure models to predict whether a sequence variant which changes one amino acid in a protein is likely to be tolerated or to impact protein function.
The Ensembl Variant Effect Predictor now integrates Google DeepMind’s new AlphaMissense Catalogue.
Google DeepMind has developed an AI system that predicts the chances that tens of millions of genetic variants will cause disease.
(10 May 2023)
The work was led by the international Human Pangenome Reference Consortium (HPRC), a group funded by the National Human Genome Research Institute (NHGRI), part of the National Institutes of Health (NIH) and consisting of 14 institutes, including EMBL’s European Bioinformatics Institute (EMBL-EBI). (…)
The majority of the genomes used to create the human pangenome reference were collected as part of the 1000 genomes project, the largest public catalogue of human variation and genotype data from a wide range of populations. (…)
In order to understand the differences in the genes present across the individual genomes represented in the human pangenome, researchers in EMBL-EBI’s Ensembl team needed to map the high-quality annotations on the reference human genome generated as part of the GENCODE project, across the pangenome.
(25 April 2023)
EMBL-EBI Director Ewan Birney was a graduate student in the 90s, when the Human Genome Project was in full swing. On the project’s 20th anniversary, he shares his memories of the slow but incredibly accurate analysis software he developed, how the private initiative sped up the public one, and how a betting book highlighted how little we knew about the human genome at the time.
(22 July 2021)
For those scientists who rely on experimental protein structure determination, AlphaFold’s predictions have helped accelerate their research. For example, a team at the University of Colorado Boulder is finding promise in using AlphaFold predictions to study antibiotic resistance, while a group at the University of California San Francisco has used them to increase their understanding of SARS-CoV-2 biology.
The AlphaFold Protein Structure Database builds on many contributions from the international scientific community, as well as AlphaFold’s sophisticated algorithmic innovations and EMBL-EBI’s decades of experience in sharing the world’s biological data. DeepMind and EMBL’s European Bioinformatics Institute (EMBL-EBI) are providing access to AlphaFold’s predictions so that others can use the system as a tool to enable and accelerate research and open up completely new avenues of scientific discovery.