Accepted_test

To date, RNA-seq remains the most popular technology for the whole transcriptome analysis identifying differentially expressed genes (DEGs). An important challenge in the analysis of RNA-seq data is the search for transcription factors (TFs) associated with changes in gene expression levels in response to external stimuli. A straightforward approach to solve this issue is to define the enriched motifs of known TFs in promoters of DEGs. We previously proposed an ESDEG tool that implemented this approach, but its efficiency may be further improved. While the number of known motifs is approached the number of known TFs (above 1400 and 1600 for human), the number of structurally different motifs is substantially smaller, and structurally-related TFs often have very similar motifs. Hence, a number of distinct enriched motifs can reach several hundreds, and each of them often respects at least several TFs. Thus, now we are still too far from a reliable definition of key TFs associated with the change of the whole transcriptome. Here we have developed an approach that applied additional information on TF classification and their expression levels to deduce TF regulators from RNA-seq data. Our approach allowed us to identify the most reliable TFs involved in DEG regulation among a large number of TFs whose motifs can be enriched in DEG promoters.