Description

We implemented an empirical Bayes framework that estimates the posterior probability that a given nucleotide is footprinted by incorporating a prior on the presence of a footprint (determined by footprints independently identified within individual datasets) and a likelihood model of cleavage rates for both occupied and unoccupied sites.

We applied this approach to all DHSs detected within one or more of the 243 biosamples, and used a consensus approach (Meuleman et al. 2020) to collate overlapping footprinted regions across individual biosamples into distinct high-resolution footprints (ie., consensus footprints).

The track "Consensus DNaseI fotoprints" delineates genomic footprints found in one or more of the 243 biosamples analyzed.

To aid interpretation, we provide a second track, "Consensus footprinted motifs", which displays motifs (clustered representation) that overlap consensus footprints. Please see this page for more information about the motif clusterings.

Display and configuration conventions

Data availability

Footprint data is publically available at https://resources.altius.org/~jvierstra/projects/footprinting.2020 or ZENODO (DOI: 10.5281/zenodo.3603548).

Code and documentation available at GitHub and Read the docs.

Credits

This work was supported by NHGRI grant U54HG007010.

Please direct any questions/comments/inquiries to Jeff Vierstra (jvierstra@altius.org).

References

Vierstra J et al.. Global reference mapping and dynamics of human transcription factor footprints. (2020) bioRxiv.