Supplementary data for the article "funMotifs: Tissue-specific transcription factor motifs"
https://doi.org/10.57804/y6dd-4f83
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.
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- Husen Muhammad Umer – Uppsala universitet / Karolinska Institutet - Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik / Department of Oncology-Pathology
- Nour-al-dain Marzouka – Lunds universitet - Institutionen för kliniska vetenskaper
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- Zeeshan Khaliq – Uppsala universitet - Science for Life Laboratory, SciLifeLab
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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
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