RT Journal Article
SR Electronic
T1 A cross-organism framework for supervised enhancer prediction with epigenetic pattern recognition and targeted validation
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 385237
DO 10.1101/385237
A1 Anurag Sethi
A1 Mengting Gu
A1 Emrah Gumusgoz
A1 Landon Chan
A1 Koon-Kiu Yan
A1 Joel Rozowsky
A1 Iros Barozzi
A1 Veena Afzal
A1 Jennifer Akiyama
A1 Ingrid Plajzer-Frick
A1 Chengfei Yan
A1 Catherine Pickle
A1 Momoe Kato
A1 Tyler Garvin
A1 Quan Pham
A1 Anne Harrington
A1 Brandon Mannion
A1 Elizabeth Lee
A1 Yoko Fukuda-Yuzawa
A1 Axel Visel
A1 Diane E. Dickel
A1 Kevin Yip
A1 Richard Sutton
A1 Len A. Pennacchio
A1 Mark Gerstein
YR 2018
UL http://biorxiv.org/content/early/2018/08/05/385237.abstract
AB Enhancers are important noncoding elements, but they have been traditionally hard to characterize experimentally. Only a few mammalian enhancers have been validated, making it difficult to train statistical models for their identification properly. Instead, postulated patterns of genomic features have been used heuristically for identification. The development of massively parallel assays allows for the characterization of large numbers of enhancers for the first time. Here, we developed a framework that uses Drosophila STARR-seq data to create shape-matching filters based on enhancer-associated meta-profiles of epigenetic features. We combined these features with supervised machine learning algorithms (e.g., support vector machines) to predict enhancers. We demonstrated that our model could be applied to predict enhancers in mammalian species (i.e., mouse and human). We comprehensively validated the predictions using a combination of in vivo and in vitro approaches, involving transgenic assays in mouse and transduction-based reporter assays in human cell lines. Overall, the validations involved 153 enhancers in 6 mouse tissues and 4 human cell lines. The results confirmed that our model can accurately predict enhancers in different species without re-parameterization. Finally, we examined the transcription-factor binding patterns at predicted enhancers and promoters in human cell lines. We demonstrated that these patterns enable the construction of a secondary model effectively discriminating between enhancers and promoters.