Supplementary data for the article "funMotifs: Tissue-specific transcription factor motifs"
We built a framework to identify tissue-specific functional motifs (funMotifs) across the genome based on thousands of annotation tracks obtained from large-scale genomics projects including ENCODE, RoadMap Epigenomics and FANTOM. The annotations were weighted using a logistic regression model trained on regulatory elements obtained from massively parallel reporter assays. Overall, genome-wide predicted motifs of 519 TFs were characterized across fifteen tissue types. funMotifs summarizes the weighted annotations into a functional activity score for each of the predicted motifs. Please read the article the data contributed to for further information: https://doi.org/10.1101/683722. The dataset was originally published in DiVA and moved to SND in 2024.
Husen M. Umer, Karolina Smolinska-Garbulowska, Nour-al-dain Marzouka, Zeeshan Khaliq, Claes Wadelius, Jan Komorowski bioRxiv 683722; doi: https://doi.org/10.1101/683722
Department of Cell and Molecular Biology, Computational Biology and Bioinformatics
Uppsala University / Karolinska Institutet
Department of Cell and Molecular Biology, Computational Biology and Bioinformatics / Department of Oncology-Pathology
Department of Immunology, Genetics and Pathology