DOI: 10.5061/dryad.41ns1rnqt
title: Data from: Short activation domains control chromatin association of transcription factors

description: Transcription factors regulate gene expression with DNA-binding domains
 (DBDs) and activation domains. Despite mounting evidence to the contrary,
 it is frequently assumed that DBDs are solely responsible for interacting
 with DNA and chromatin. Here, we used single-molecule tracking of
 transcription factors in living cells to show that short activation
 domains can control the fraction of molecules bound to chromatin. Stronger
 activation domains have higher bound fractions and longer residence times
 on chromatin. Furthermore, mutations that increase activation domain
 strength also increase chromatin binding. This trend was consistent in
 four different activation domains and their mutants. This effect further
 held for activation domains appended to three different structural classes
 of DBDs. Stronger activation domains with high chromatin-bound fractions
 also exhibited increased binding to the p300 coactivator in
 proximity-assisted photoactivation experiments. Taken together, these
 results suggest that activation domains play a major role in tethering
 transcription factors to chromatin, challenging the traditional view that
 the DBD is the sole driver of genome binding.
description: # Data from: Short activation domains control chromatin association of
 transcription factors
 [https://doi.org/10.5061/dryad.41ns1rnqt](https://doi.org/10.5061/dryad.41ns1rnqt) ## Description of the data and file structure Pre-processed SMT data ("trajectories") are organized by Figure when uncompressed from "SMT_trajectories.zip". (We could not deposit raw SMT movies due to storage limitations.) Raw FRAP movies are in "FRAP_movies.zip". Processing steps are described in detail in the Methods section of the linked manuscript. Briefly, SMT movies are subjected to detection (which pixels have a spot?), sub-pixel localization (where is the spot center?), and tracking (which spots connect to which others in subsequent frames?). The "trajs.csv" files describe localizations, one spot per line, and how they are connected. Data from many cells corresponding to one condition have been aggregated and are tabulated here as single CSVs. A Bayesian mixture model computes diffusion spectra based on these data. For FRAP movies, normalized spot recovery is quantified after accounting for spot drift, changes in nuclear intensity, and background intensity. #### "trajs.csv" files CSV files where each line describes a localization that has passed a spot detection threshold. Columns: * y: sub-pixel y-coordinate of the spot * x: sub-pixel x-coordinate of the spot * y_detect: y-pixel where this spot was detected * x_detect: x-pixel where this spot was detected * frame: frame in which this spot was detected * trajectory: index that assigns spots to trajectories. Spots with the same "trajectory" value are connected to each other. #### CZI files ZEISS file format for microscopy data. Contains metadata and can be read by open-source libraries, e.g. Python's `czifile`. #### Changelog August 20, 2025: Added data to SMT_trajectories folder and re-indexed folder to be consistent with the re-submitted paper. Removed superfluous columns from trajs.csv files. Reorganized folder structure to explicitly split out FRAP movies.