Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany; Collaboration for joint PhD degree between EMBL and Heidelberg University, Faculty of Biosciences, Heidelberg, Germany.
Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
Cell Rep. 2019 Dec 3;29(10):3147-3159.e12. doi: 10.1016/j.celrep.2019.10.106.
Transcription factors (TFs) regulate many cellular processes and can therefore serve as readouts of the signaling and regulatory state. Yet for many TFs, the mode of action-repressing or activating transcription of target genes-is unclear. Here, we present diffTF (https://git.embl.de/grp-zaugg/diffTF) to calculate differential TF activity (basic mode) and classify TFs into putative transcriptional activators or repressors (classification mode). In basic mode, it combines genome-wide chromatin accessibility/activity with putative TF binding sites that, in classification mode, are integrated with RNA-seq. We apply diffTF to compare (1) mutated and unmutated chronic lymphocytic leukemia patients and (2) two hematopoietic progenitor cell types. In both datasets, diffTF recovers most known biology and finds many previously unreported TFs. It classifies almost 40% of TFs based on their mode of action, which we validate experimentally. Overall, we demonstrate that diffTF recovers known biology, identifies less well-characterized TFs, and classifies TFs into transcriptional activators or repressors.
转录因子（TFs）调节许多细胞过程，因此可以作为信号和调节状态的读数。然而，对于许多 TFs 来说，其作用模式——抑制或激活靶基因的转录——尚不清楚。在这里，我们介绍了 diffTF（https://git.embl.de/grp-zaugg/diffTF），以计算差异 TF 活性（基本模式）并将 TFs 分类为假定的转录激活剂或抑制剂（分类模式）。在基本模式中，它将全基因组染色质可及性/活性与假定的 TF 结合位点相结合，而在分类模式中，它与 RNA-seq 相结合。我们应用 diffTF 来比较（1）突变和未突变的慢性淋巴细胞白血病患者，以及（2）两种造血祖细胞类型。在这两个数据集，diffTF 都恢复了大部分已知的生物学知识，并发现了许多以前未报道的 TFs。它根据作用模式对近 40%的 TFs 进行了分类，我们通过实验验证了这些分类。总的来说，我们证明了 diffTF 能够恢复已知的生物学知识，识别出功能不太明确的 TFs，并将 TFs 分类为转录激活剂或抑制剂。
