Supplementary data for the article "funMotifs: Tissue-specific transcription factor motifs"

Karolina Smolinska - Uppsala University, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics

Husen Muhammad Umer - Uppsala University / Karolinska Institutet, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics / Department of Oncology-Pathology

Nour-al-dain Marzouka - Lund University, Department of Clinical Sciences

Claes Wadelius - Uppsala University, Department of Immunology, Genetics and Pathology

Jan Komorowski - Uppsala University, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics

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.

English