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. 2024 Sep;34(9):1411–1420. doi: 10.1101/gr.279142.124

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© 2024 Lal et al.; Published by Cold Spring Harbor Laboratory Press

This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.

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Figure 1. — Schematic of regLM. (A,B) DNA sequences are prefixed with a sequence of prompt tokens representing functional labels. (C) A HyenaDNA model is trained or fine-tuned to perform next token prediction on the labeled sequences. (D) The trained model is prompted with a sequence of prompt tokens to generate sequences with desired properties. (E,F) A sequence-to-function regression model trained on the same data set is used to check and filter the generated sequences. (G) The regulatory content of generated sequences is evaluated.