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b, Enformer outperforms Basenji2 in gene expression prediction from sequence both across genes and across CAGE experiments for protein-coding genes. By using transformer modules instead of dilated convolutions, it achieves a five times larger receptive field able to detect sequence elements 100 kb away, compared with 20 kb for Basenji2 (ref. This increase in the receptive field is important because it greatly expands the number of relevant enhancers seen by the model from 47% (<20 kb) to 84% (<100 kb) as estimated from the proportions of high-confidence enhancer–gene pairs 9.Ī, Enformer is trained to predict human and mouse genomic tracks at 128-bp resolution from 200 kb of input DNA sequence. By contrast, previous state-of-the-art models Basenji2 or ExPecto only reach elements up to 20 kb away (Extended Data Fig. Using transformer layers allowed us to substantially increase the receptive field, reaching distal regulatory elements up to 100 kb away while still being able to effectively integrate their information. By contrast, convolutional layers require many successive layers to reach distal elements due to their local receptive field. Since each position directly attends to all other positions in the sequence, they allow for a much better information flow between distal elements.

This allows the model, for example, to refine the prediction at a TSS by gathering information from all relevant regions, such as enhancers regulating the gene. Attention weight between any two positions depends on the embeddings of their current representation vectors and the distance between them. They consist of attention layers that transform each position in the input sequence by computing a weighted sum across the representations of all other positions in the sequence. Transformers are a class of deep learning models that have achieved substantial breakthroughs in natural language processing (NLP) 6, 7 and were also recently applied to model short DNA sequences 8.

We developed a new model architecture named Enformer (a portmanteau of enhancer and transformer) to predict gene expression and chromatin states in humans and mice from DNA sequences (Fig. The model also produces more accurate predictions of mutation effects, as measured by direct mutagenesis assays and population eQTL studies.Įnformer improves gene expression prediction We demonstrate more effective use of long-range information, as benchmarked by CRISPRi enhancer assays. Training on most of the human and mouse genomes and testing on held out sequences, we observed improved correlation between predictions and measured data relative to previous state-of-the-art models without self-attention. We frame the machine learning problem as predicting thousands of epigenetic and transcriptional datasets in a multitask setting across long DNA sequences. In this work, we introduce a neural network architecture based on self-attention towards this goal. Thus, increasing information flow between distal elements is a promising path to increase predictive accuracy. Many well-studied regulatory elements, including enhancers, repressors, and insulators, can influence gene expression from far greater than 20 kb away 5. However, to make predictions, these models are only able to consider sequence elements up to 20 kb away from the transcription start site (TSS) because the locality of convolutions limits information flow in the network between distal elements. Although sequence-based computational models can in principle overcome these challenges, their accuracy is still limited 1, 2, 3, 4, making expression prediction from sequence a critical unsolved problem.ĭeep convolutional neural networks (CNNs) achieve the current state of the art at predicting gene expression from DNA sequences for the human and mouse genomes 1, 2, 3, 4. Additionally, experimental validation of human genetic variants is laborious and limited to cell types or tissues that can be recapitulated in the laboratory, making it intractable to test all variants of interest in the relevant biological contexts. These models complement population-based association studies, which are often limited to common variants and struggle to disentangle causality from association due to linkage disequilibrium (LD).

Models that predict gene expression and chromatin states from DNA sequences hold the promise to better understand transcriptional regulation and how it is affected by the many noncoding genetic variants associated with human diseases and traits.

Pdf search engine agarwal quantitative aptitude