🚀News from DeepMind AI: Enformer Architecture for Genetic Research
The Enformer architecture, powered by Transformers, advances genetic research to accurately predict how DNA sequence affects gene expression. In early October 2021, Nature Methods published an article by DeepMind and Calico researchers about the new Enformer neural network architecture, which greatly improves the accuracy of predicting gene expression from a DNA sequence. The developers have made this model and its initial predictions of common genetic variants publicly available.
Enformer builds on transformers common in natural language processing to use self-attention mechanisms for greater coverage of the DNA context. By efficiently processing sequences to account for interactions at distances more than 5 times (i.e. 200,000 base pairs) longer than previous methods, the new architecture can simulate the influence of important regulatory elements on the expression of genes found in the DNA sequence.
AI can be used to explore new possibilities for finding patterns in the genome and to put forward mechanistic hypotheses about sequence changes. Like a spell checker, Enformer partially understands a DNA sequence dictionary and can highlight changes that could alter gene expression.
The main application of this new model is to predict which changes in DNA letters, also called genetic variants, will affect gene expression. Compared to previous models, Enformer is much more accurate in predicting the effect of variants on gene expression, both in the case of natural genetic variants and synthetic variants that alter important regulatory sequences. This property is useful for interpreting the growing number of disease-related variants derived from genome-wide associative studies. Variants associated with complex genetic diseases are predominantly located in the non-coding region of the genome, likely causing disease by altering gene expression. But because of the intrinsic correlation between options, many of these disease-related options are only falsely correlated and not causal. Computing tools help distinguish true associations from false positives.
https://deepmind.com/blog/article/enformer
https://www.nature.com/articles/s41592-021-01252-x
https://github.com/deepmind/deepmind-research/tree/master/enformer