RT Journal
A1 Cochran, Kelly
A1 Srivastava, Divyanshi
A1 Shrikumar, Avanti
A1 Balsubramani, Akshay
A1 Hardison, Ross C.
A1 Kundaje, Anshul
A1 Mahony, Shaun
T1 Domain adaptive neural networks improve cross-species prediction of transcription factor binding
JF Genome Research 
JO Genome Research 
YR 2022 
FD January 18 
DO 10.1101/gr.275394.121 
SP gr.275394.121 
UL http://genome.cshlp.org/content/early/2022/01/18/gr.275394.121.abstract 
AB 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 demonstrate that cross-species TF binding prediction is feasible when models account for domain shifts driven by species-specific repeats.