MIT chemists develop a generative AI model that can predict thousands of 3D genome structures in minutes, significantly accelerating research in understanding gene expression and cellular function.
Chemists at MIT have developed a groundbreaking technique using generative AI to predict the 3D structure of genomes, a process that was previously time-consuming and complex. This new method can analyze thousands of genome structure s in minutes, significantly accelerating research in this field. The 3D organization of our genetic material, known as chromatin, plays a crucial role in determining which genes are expressed in different cells.
While every cell in our body contains the same DNA sequence, only a subset of genes is active in each cell, resulting in the specialized functions of various tissues and organs. The 3D arrangement of chromatin influences the accessibility of genes, impacting their expression levels.Traditionally, scientists relied on experimental techniques like Hi-C to map chromatin structures. However, these methods were labor-intensive and required several days to generate data from a single cell. The MIT team's AI-powered approach overcomes these limitations by analyzing DNA sequences and predicting possible chromatin conformations. Their model, named ChromoGen, consists of two components: a deep learning model that deciphers information encoded in DNA sequences and chromatin accessibility data, and a generative AI model trained on millions of experimentally derived chromatin structures.ChromoGen's deep learning component analyzes the specific modifications and tags on DNA, known as epigenetic marks, which vary between cell types and influence chromatin folding. This information guides the generative model to predict physically accurate chromatin conformations specific to a given cell type. The model generates multiple possible structures for each DNA sequence because DNA is a highly disordered molecule capable of adopting various conformations. This ability to predict a range of possible structures reflects the complexity of chromatin organization and its impact on gene expression
GENETICS AI MACHINE LEARNING CHROMATIN GENOME STRUCTURE GENE EXPRESSION
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