Discovering novel calcineurin inhibitors through quantitative mapping of protein-peptide affinity landscapes.  Nguyen, H.Q., Roy, J.*, Harink, B.*, Damle, N.*, Baxter, B., Brower, K., Kortemme, T., Thorn, K., Cyert, M., and Fordyce, P.M.  Preprint in  BioRxiv.  (4/23/18); doi:  10.1101/306779   ( web )
  Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding.  Le, D.D., Shimko, T.C., Aditham, A.K., Keys, A.M., Orenstein, Y., and Fordyce, P.M.  Preprint in  BioRxiv.  (9/26/17); doi:  10.1101/193904   ( web )  Now published at  PNAS!  (3/27,8); doi:  10.1073/pnas.1715888115   ( web )
  High-throughput chromatin accessibility profiling at single-cell resolution.  Mezger, A., Klemm, S., Mann, I., Brower, K., Mir, A., Bostick, M., Farmer, A., Fordyce, P., Linnarsson, S., & Greenleaf, W.  Preprint on bioRXiv (4/28/2018) ;  doi:    10.1101/310284   ( web )
  Discovering epistatic feature interactions from neural network models of regulatory DNA sequences.  Greenside, P.G., Shimko, T., Fordyce, P., & Kundaje, A.  Preprint on bioRXiv (04/17/2018); doi:  10.1101/302711   ( web )

Discovering novel calcineurin inhibitors through quantitative mapping of protein-peptide affinity landscapes. Nguyen, H.Q., Roy, J.*, Harink, B.*, Damle, N.*, Baxter, B., Brower, K., Kortemme, T., Thorn, K., Cyert, M., and Fordyce, P.M.

Preprint in BioRxiv. (4/23/18); doi: 10.1101/306779

(web)

Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding. Le, D.D., Shimko, T.C., Aditham, A.K., Keys, A.M., Orenstein, Y., and Fordyce, P.M.

Preprint in BioRxiv. (9/26/17); doi: 10.1101/193904

(web)

Now published at PNAS! (3/27,8); doi: 10.1073/pnas.1715888115

(web)

High-throughput chromatin accessibility profiling at single-cell resolution. Mezger, A., Klemm, S., Mann, I., Brower, K., Mir, A., Bostick, M., Farmer, A., Fordyce, P., Linnarsson, S., & Greenleaf, W.

Preprint on bioRXiv (4/28/2018); doi: 10.1101/310284

(web)

Discovering epistatic feature interactions from neural network models of regulatory DNA sequences. Greenside, P.G., Shimko, T., Fordyce, P., & Kundaje, A.

Preprint on bioRXiv (04/17/2018); doi: 10.1101/302711

(web)