MARADONER: Motif Activity Response Analysis Done Right

TL;DR

MARADONER is a novel statistical framework that improves the accuracy of transcription factor activity inference from high-throughput sequencing data by addressing bias, variance, and heteroscedasticity issues.

Contribution

It introduces an enhanced MARA method with unbiased variance estimation and bias correction, improving activity inference accuracy from promoter motif and activity data.

Findings

Enhanced goodness-of-fit over classic MARA

More accurate activity estimates due to bias adjustment

Capable of modeling heteroscedasticity in data

Abstract

Inferring the activities of transcription factors from high-throughput transcriptomic or open chromatin profiling, such as RNA-/CAGE-/ATAC-Seq, is a long-standing challenge in systems biology. Identification of highly active master regulators enables mechanistic interpretation of differential gene expression, chromatin state changes, or perturbation responses across conditions, cell types, and diseases. Here, we describe MARADONER, a statistical framework and its software implementation for motif activity response analysis (MARA), utilizing the sequence-level features obtained with pattern matching (motif scanning) of individual promoters and promoter- or gene-level activity or expression estimates. Compared to the classic MARA, MARADONER (MARA-done-right) employs an unbiased variance parameter estimation and a bias-adjusted likelihood estimation of fixed effects, thereby enhancing goodness-of-fit and the accuracy of activity estimation. Further, MARADONER is capable of accounting for heteroscedasticity of motif scores and activity estimates.