Domain-adaptive neural networks improve cross-species prediction of transcription factor binding
- Kelly Cochran1,2,
- Divyanshi Srivastava1,3,
- Avanti Shrikumar2,
- Akshay Balsubramani4,
- Ross C. Hardison1,3,
- Anshul Kundaje2,4 and
- Shaun Mahony1,3
- 1Center for Eukaryotic Gene Regulation, Pennsylvania State University, University Park, Pennsylvania 16802, USA;
- 2Department of Computer Science, Stanford University, Stanford, California 94305, USA;
- 3Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, Pennsylvania 16802, USA;
- 4Department of Genetics, Stanford University, Stanford, California 94305, USA
Abstract
The intrinsic DNA sequence preferences and cell type–specific cooperative partners of transcription factors (TFs) are typically highly conserved. Hence, despite the rapid evolutionary turnover of individual TF binding sites, predictive sequence models of cell type–specific genomic occupancy of a TF in one species should generalize to closely matched cell types in a related species. To assess the viability of cross-species TF binding prediction, we train neural networks to discriminate ChIP-seq peak locations from genomic background and evaluate their performance within and across species. Cross-species predictive performance is consistently worse than within-species performance, which we show is caused in part by species-specific repeats. To account for this domain shift, we use an augmented network architecture to automatically discourage learning of training species–specific sequence features. This domain adaptation approach corrects for prediction errors on species-specific repeats and improves overall cross-species model performance. Our results show that cross-species TF binding prediction is feasible when models account for domain shifts driven by species-specific repeats.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.275394.121.
- Received February 15, 2021.
- Accepted January 10, 2022.
This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see https://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.
