Year: 2018
Type: article
Abstract: Genomics has revolutionized biology, enabling the interrogation of whole transcriptomes, genome-wide binding sites for proteins, and many other molecular processes. However, individual genomic assays measure elements that interact in vivo as components of larger molecular machines. Understanding how these high-order interactions drive gene expression presents a substantial statistical challenge. Building on Random Forests (RF), Random Intersection Trees (RITs), and through extensive, biologically inspired simulations, we developed the iterative Random Forest algorithm (iRF). iRF trains a feature-weighted ensemble of decision trees to detect stable, high-order interactions with same order of computational cost as RF. We demonstrate the utility of iRF for high-order interaction discovery in two prediction problems: enhancer activity in the early Drosophila embryo and alternative splicing of primary transcripts in human derived cell lines. In Drosophila , among the 20 pairwise transcription factor interactions iRF identifies as stable (returned in more than half of bootstrap replicates), 80% have been previously reported as physical interactions. Moreover, novel third-order interactions, e.g. between Zelda (Zld), Giant (Gt), and Twist (Twi), suggest high-order relationships that are candidates for follow-up experiments. In human-derived cells, iRF re-discovered a central role of H3K36me3 in chromatin-mediated splicing regulation, and identified novel 5th and 6th order interactions, indicative of multi-valent nucleosomes with specific roles in splicing regulation. By decoupling the order of interactions from the computational cost of identification, iRF opens new avenues of inquiry into the molecular mechanisms underlying genome biology.
Authors: Sumanta Basu, Karl Kumbier, James B. Brown, Bin Yu
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Funders: National Science Foundation, U.S. Department of Energy, Adolph C. and Mary Sprague Miller Institute for Basic Research in Science, University of California Berkeley, National Institutes of Health, U.S. Army, U.S. Navy, Office of Naval Research, National Human Genome Research Institute, U.S. National Library of Medicine, Army Research Office, Lawrence Berkeley National Laboratory
Awards: G1023166358, G1079818582, G1143651532, G1176459611, G1286236842, G1314494723, G1490606517, G1513559549, G1765399003, G1863003419, G216544311, Removing statistical bottle-necks in data analysis for the ENCODE Consortium, Nonparametric methods for functional and translational genomics, G3083819904, G318653391, G382641700, G42916606, G4353487559, Canonical Linear Methods and Hierarchical Non-Linear Methods in High-Dimensional Statistics, G5261056217, G5382857288, Emerging Frontiers of Science of Information, ENABLING SCIENTIFIC DISCOVERY FROM COMPLEX DATA AT EXTREME
SCALES, G7354628648, G8178880842, Biomedical Big Data Training Program at UC Berkeley, G8876996369, G969889393
OpenAlex ID: W2731142262