Incorporating Sequence-Dependent DNA Shape and Dynamics into Transcriptome Data Analysis

Differentially expressed genes in a cellular context may be co-regulated by the same transcription factor. However, in the absence of a concurrent transcription factor binding data, such interactions are difficult to detect, especially at the single cell expression level. Motif enrichments in such genes can be used to gain insight into differential expressions caused by the shared upstream TFs. However, it is now established that many genes are co-regulated by the same TF due to a shared DNA shape or sequence-dependent conformational dynamics instead of sequence motif. In this work, we demonstrate how, starting from a gene expression data, such DNA shape and dynamics signatures can be potentially detected using publicly available tools, including DynaSeq, developed in our group for predicting the sequence-dependent components of these DNA shape features.