Hong, L. K. | bioRxiv Science

bioRxiv2020

Quantitative occupancy of myriad transcription factors from one DNase experiment enables efficient comparisons across conditions

Over a thousand different transcription factors (TFs) bind with varying occupancy across the human genome. Chromatin immunoprecipitation (ChIP) can assay occupancy genome-wide, but only one TF at a time, limiting our ability to comprehensively observe the TF occupancy landscape, let alone quantify how it changes across conditions. We developed TOP, a Bayesian hierarchical regression framework, to profile genome-wide quantitative occupancy of numerous TFs using data from a single DNase-seq experiment. TOP is supervised, and its hierarchical structure allows it to predict the occupancy of any sequence-specific TF, even those never assayed with ChIP. We used TOP to profile the quantitative occupancy of nearly 1500 human TF motifs, and examined how their occupancies changed genome-wide in multiple contexts: across 178 cell types, over 12 hours of exposure to different hormones, and across the genetic backgrounds of 70 individuals. TOP enables cost-effective exploration of quantitative changes in the landscape of TF binding.

bioinformatics↗

bioRxiv2019

Causal Network Inference from Gene Transcriptional Time Series Response to Glucocorticoids

Gene regulatory network inference is essential to uncover complex relationships among gene pathways and inform downstream experiments, ultimately paving the way for regulatory network re-engineering. Network inference from transcriptional time series data requires accurate, interpretable, and efficient determination of causal relationships among thousands of genes. Here, we develop Bootstrap Elastic net regression from Time Series (BETS), a statistical framework based on Granger causality for the recovery of a directed gene network from transcriptional time series data. BETS uses elastic net regression and stability selection from bootstrapped samples to infer causal relationships among genes. BETS is highly parallelized, enabling efficient analysis of large transcriptional data sets. We show competitive accuracy on a community benchmark, the DREAM4 100-gene network inference challenge, where BETS is one of the fastest among methods of similar performance but additionally infers whether the causal effects are activating or inhibitory. We apply BETS to transcriptional time series data of 2, 768 differentially-expressed genes from A549 cells exposed to glucocorticoids over a period of 12 hours. We identify a network of 2, 768 genes and 31, 945 directed edges (FDR [≤] 0.2). We validate inferred causal network edges using two external data sources: overexpression experiments on the same glucocorticoid system, and genetic variants associated with inferred edges in primary lung tissue in the Genotype-Tissue Expression (GTEx) v6 project. BETS is freely available as an open source software package at https://github.com/lujonathanh/BETS.

bioinformatics↗