(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.
Some genomic differences may not affect fitness. Neutral variation, previously thought to be “junk” DNA, is unaffected by natural selection resulting in higher genetic variation at such sites when compared to sites where variation does influence fitness.[14]
It is not fully clear how natural selection has shaped population differences; however, genetic candidate regions under selection have been identified recently. (…)
It was found that on average, each person carries around 250–300 loss-of-function variants in annotated genes and 50-100 variants previously implicated in inherited disorders. Based on the two trios, it is estimated that the rate of de novo germline mutation is approximately 10−8 per base per generation.
(Dec 2015)
“Now this is not the end… But it is, perhaps, the end of the beginning” as Winston Churchill said. Large-scale sequencing projects will continue for more regional or ethnic groups, in order to extend the global coverage. Much effort will focus on a better understanding of the relationship between genetic variation and common disorders. The translation of this massive genetic information to human health will benefit from the development of complex databases gathering genetic, clinical, and biological data, such as multi-omics profiles, while maintaining protection of potentially sensitive personal information (3). Efforts are also underway to increase genetic awareness in the public and to educate health professionals
The goal of the GENCODE project is to identify and classify all gene features in the human and mouse genomes with high accuracy based on biological evidence, and to release these annotations for the benefit of biomedical research and genome interpretation.
(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.