Pounds, S. B. | bioRxiv Science

bioRxiv2024

Bootstrap Evaluation of Association Matrices (BEAM) for Integrating Multiple Omics Profiles with Multiple Outcomes

MotivationLarge datasets containing multiple clinical and omics measurements for each subject motivate the development of new statistical methods to integrate these data to advance scientific discovery. ModelWe propose bootstrap evaluation of association matrices (BEAM), which integrates multiple omics profiles with multiple clinical endpoints. BEAM associates a set omic features with clinical endpoints via regression models and then uses bootstrap resampling to determine statistical significance of the set. Unlike existing methods, BEAM uniquely accommodates an arbitrary number of omic profiles and endpoints. ResultsIn simulations, BEAM performed similarly to the theoretically best simple test and outperformed other integrated analysis methods. In an example pediatric leukemia application, BEAM identified several genes with biological relevance established by a CRISPR assay that had been missed by univariate screens and other integrated analysis methods. Thus, BEAM is a powerful, flexible, and robust tool to identify genes for further laboratory and/or clinical research evaluation. AvailabilitySource code, documentation, and a vignette for BEAM are available on GitHub at: https://github.com/annaSeffernick/BEAMR. The R package is available from CRAN at: https://cran.r-project.org/package=BEAMR. ContactStanley.Pounds@stjude.org Supplementary InformationSupplementary data are available at the journals website.

bioinformatics↗

bioRxiv2024

Topology-informed regulatory element collections coordinate cell identity gene expression programs

Transcription proteins are concentrated at nuclear transcriptional condensates. These condensates contain cis-regulatory elements (CREs), including enhancers and promoters, that are thought to regulate genes in the same condensate. The roles of condensates are of great current interest, but research into their function is limited by an inability to comprehensively identify their associated CREs. Here, we present a conceptual framework and algorithm, BOUQUET, for integrating genome topology, chromatin occupancy, and graph theory to associate CREs and transcription protein machinery with target genes and identify exceptionally protein-rich communities that interact with condensates. BOUQUET uncovers surprising quantitative correlations between community protein accumulation and gene expression phenotypes by combining accurate CRE-gene assignment with co-activator binding profiles. A small subset of communities, which we call "3D-super-enhancers," is exceptionally protein-rich. BOUQUET-predicted 3D-SEs are comparable in number to co-activator nuclear puncta, and all genes known to interact with co-activator condensates in embryonic stem cells are within 3D-SEs. 3D-SEs are enriched for association with cell identity genes across mammalian tissues. Microscopy analyses show frequent co-localization and co-expression of genes from the same 3D-SE within a single co-activator punctum, suggesting 3D-SE components interact with co-activator condensates. Thus 3D-SEs correspond to co-activator puncta, which nominates additional condensate-associated genes and CREs.

molecular biology↗