Channel:  ENCODE

bioRxiv posts many COVID19-related papers. A reminder: they have not been formally peer-reviewed and should not guide health-related behavior or be reported in the press as conclusive.

141 Articles

Integrative analysis of 10,000 epigenomic maps across 800 samples for regulatory genomics and disease dissection
Adsera, C. B., Park, Y., Meuleman, W., Kellis, M.
10.1101/810291 — Posted: 2019-10-19
Principles of RNA processing from analysis of enhanced CLIP maps for 150 RNA binding proteins.
Van Nostrand, E. L., Pratt, G. A., Yee, B. A., Wheeler, E. C., Blue, S. M., Mueller, J., Park, S. S., Garcia, K. E., Gelboin-Burkhart, C., Nguyen, T. B., Rabano, I., Stanton, R., Sundararaman, B., Wang, R., Fu, X.-D., Graveley, B. R., Yeo, G. W.
10.1101/807008 — Posted: 2019-10-18
ESCO1 and CTCF enable formation of long chromatin loops by protecting cohesinSTAG1 from WAPL
Wutz, G., St. Hilaire, B. T. G., Ladurner, R., Stocsits, R., Nagasaka, K., Pignard, B., Sanborn, A., Tang, W., Varnai, C., Ivanov, M., Schoenfelder, S., van der Lelij, P., Huang, X., Duernberger, G., Roitinger, E., Mechtler, K., Davidson, I. F., Fraser, P., Aiden, E. L., Peters, J. M.
10.1101/779058 — Posted: 2019-09-23
Chromatin is frequently unknotted at the megabase scale
Goundaroulis, D., Aiden, E. L., Stasiak, A.
10.1101/762872 — Posted: 2019-09-09
A curated benchmark of enhancer-gene interactions for evaluating enhancer-target gene prediction methods
Moore, J. E., Pratt, H., Purcaro, M., Weng, Z.
10.1101/745844 — Posted: 2019-08-24
Prioritizing transcriptomic and epigenomic experiments by using an optimization strategy that leverages imputed data
Schreiber, J., Bilmes, J., Noble, W. S.
10.1101/708107 — Posted: 2019-07-19
An integrative ENCODE resource for cancer genomics
Zhang, J., Lee, D., Dhiman, V., Jiang, P., Xu, J., McGillivray, P., Yang, H., Liu, J., Meyerson, W., Clarke, D., Gu, M., Li, S., Lou, S., Xu, J., Lochovsky, L., Ung, M., Ma, L., Yu, S., Cao, Q., Harmanci, A., Yan, K.-K., Sethi, A., Gursoy, G., Schoenberg, M. R., Rozowsky, J., Warrell, J., Emani, P., Yang, Y. T., Galeev, T., Kong, X., Liu, S., Li, X., Krishnan, J., Feng, Y., Rivera-Mulia, J. C., Adrian, J., Broach, J. R., Bolt, M., Moran, J., Fitzgerald, D., Dileep, V., Liu, T., Mei, S., Sasaki, T., Trevilla-Garcia, C., Wang, S., Wang, Y., Zang, C., Wang, D., Klein, R., Snyder, M., Gilbert, D.
10.1101/706424 — Posted: 2019-07-18
A unified encyclopedia of human functional DNA elements through fully automated annotation of 164 human cell types
Libbrecht, M. W., Rodriguez, O., Weng, Z., Hoffman, M., Bilmes, J. A., Noble, W. S.
10.1101/086025 — Posted: 2019-07-01
A pitfall for machine learning methods aiming to predict across cell types
Schreiber, J., Singh, R., Bilmes, J., Noble, W. S.
10.1101/512434 — Posted: 2019-04-26
Multi-scale deep tensor factorization learns a latent representation of the human epigenome
Schreiber, J., Durham, T. J., Bilmes, J., Noble, W. S.
10.1101/364976 — Posted: 2019-04-11

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