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https://biorxiv.org/cgi/content/short/708107v1?rss=1"
Schreiber, J.Bilmes, J.Noble, W. S.2019-07-19doi:10.1101/708107Cold Spring Harbor Laboratory Press2019-07-19
https://biorxiv.org/cgi/content/short/818849v1?rss=1"
Tippens, N. D.Liang, J.Leung, K. Y.Ozer, A.Booth, J. G.Lis, J.Yu, H.2019-11-07doi:10.1101/818849Cold Spring Harbor Laboratory Press2019-11-07
https://biorxiv.org/cgi/content/short/166744v1?rss=1"
He, Y.Hariharan, M.Gorkin, D. U.Dickel, D. E.Luo, C.Castanon, R. G.Nery, J. R.Lee, A. Y.Williams, B. A.Trout, D.Amrhein, H.Fang, R.Chen, H.Li, B.Visel, A.Pennacchio, L.Ren, B.Ecker, J.2017-07-21doi:10.1101/166744Cold Spring Harbor Laboratory Press2017-07-21
https://biorxiv.org/cgi/content/short/166652v1?rss=1"
Gorkin, D.Barozzi, I.Zhang, Y.Lee, A. Y.Lee, B.Zhao, Y.Wildberg, A.Ding, B.Zhang, B.Wang, M.Strattan, J. S.Davidson, J. M.Qiu, Y.Afzal, V.Akiyama, J. A.Plajzer-Frick, I.Pickle, C. S.Kato, M.Garvin, T. H.Pham, Q. T.Harrington, A. N.Mannion, B. J.Lee, E. A.Fukuda-Yuzawa, Y.He, Y.Preissl, S.Chee, S.Williams, B. A.Trout, D.Amrhein, H.Yang, H.Cherry, J. M.Shen, Y.Ecker, J. R.Wang, W.Dickel, D. E.Visel, A.Pennacchio, L. A.Ren, B.2017-07-21doi:10.1101/166652Cold Spring Harbor Laboratory Press2017-07-21
https://biorxiv.org/cgi/content/short/731729v1?rss=1"
Xiang, G.Keller, C. A.Heuston, E. F.Giardine, B. M.An, L.Wixom, A. Q.Miller, A.Cockburn, A.Lichtenberg, J.Gottgens, B.Li, Q.Bodine, D.Mahony, S.Taylor, J.Blobel, G. A.Weiss, M. J.Cheng, Y.Yue, F.Hughes, J.Higgs, D. R.Zhang, Y.Hardison, R. C.2019-08-10doi:10.1101/731729Cold Spring Harbor Laboratory Press2019-08-10
https://biorxiv.org/cgi/content/short/179648v1?rss=1"
Van Nostrand, E. L.Freese, P.Pratt, G. A.Wang, X.Wei, X.Blue, S. M.Dominguez, D.Cody, N. A. L.Olson, S.Sundararaman, B.Xiao, R.Zhan, L.Bazile, C.Benoit Bouvrette, L. P.Chen, J.Duff, M. O.Garcia, K.Gelboin-Burkhart, C.Hochman, A.Lambert, N. J.Li, H.Nguyen, T. B.Palden, T.Rabano, I.Sathe, S.Stanton, R.Louie, A. L.Aigner, S.Bergalet, J.Zhou, B.Su, A.Wang, R.Yee, B. A.Fu, X.-D.Lecuyer, E.Burge, C. B.Graveley, B.Yeo, G. W.2017-08-23doi:10.1101/179648Cold Spring Harbor Laboratory Press2017-08-23
https://biorxiv.org/cgi/content/short/857169v1?rss=1"
Breschi, A.Munoz-Aguirre, M.Wucher, V.Davis, C. A.Garrido-Martin, D.Djebali, S.Gillis, J.Pervouchine, D. D.Vlasova, A.Dobin, A.Zaleski, C.Drenkow, J.Danyko, C.Scavelli, A.Reverter, F.Snyder, M. P.Gingeras, T. R.Guigo, R.2019-11-27doi:10.1101/857169Cold Spring Harbor Laboratory Press2019-11-27
https://biorxiv.org/cgi/content/short/464800v1?rss=1"
1,600 transcription factors (TFs) encoded in the human genome has been assayed. Here we present data and analyses of ChIP-seq experiments for 208 DNA-associated proteins (DAPs) in the HepG2 hepatocellular carcinoma line, spanning nearly a quarter of its expressed TFs, transcriptional co-factors, and chromatin regulator proteins. The DAP binding profiles classify into major groups associated predominantly with promoters or enhancers, or with both. We confirm and expand the current catalog of DNA sequence motifs; 77 factors showed similar motifs to those previously described using in vivo and/or in vitro methods, and 17 yielded novel motifs. We also describe motifs corresponding to other TFs that co-enrich with the primary ChIP target. FOX family motifs are, for example, significantly enriched in ChIP-seq peaks of 37 other DAPs. We show that promoters and enhancers can be discriminated based on motif content and occupancy patterns. This large catalog reveals High Occupancy Target (HOT) regions at which many DAPs associate, although each contains motifs for only a minority of the numerous associated DAPs. These analyses provide a deeper and more complete overview of the gene regulatory networks that define this cell type.
]]>Partridge, E. C.Chhetri, S. B.Prokop, J. W.Ramaker, R. C.Jansen, C. S.Goh, S.-T.Mackiewicz, M.Newberry, K. M.Brandsmeier, L. A.Meadows, S. K.Messer, C. L.Hardigan, A. A.Dean, E. C.Jiang, S.Savic, D.Mortazavi, A.Wold, B. J.Myers, R. M.Mendenhall, E. M.2018-11-07doi:10.1101/464800Cold Spring Harbor Laboratory Press2018-11-07
https://biorxiv.org/cgi/content/short/385237v1?rss=1"
Sethi, A.Gu, M.Gumusgoz, E.Chan, L.Yan, K.-K.Rozowsky, J. S.Barozzi, I.Afzal, V.Akiyama, J.Plajzer-Frick, I.Yan, C.Pickle, C.Kato, M.Garvin, T.Pham, Q.Harrington, A.Mannion, B.Lee, E.Fukuda-Yuzawa, Y.Visel, A.Dickle, D. E.Yip, K.Sutton, R.Pennacchio, L. A.Gerstein, M.2018-08-05doi:10.1101/385237Cold Spring Harbor Laboratory Press2018-08-05
https://biorxiv.org/cgi/content/short/745844v1?rss=1"
Moore, J. E.Pratt, H.Purcaro, M.Weng, Z.2019-08-24doi:10.1101/745844Cold Spring Harbor Laboratory Press2019-08-24
https://biorxiv.org/cgi/content/short/810291v1?rss=1"
Adsera, C. B.Park, Y.Meuleman, W.Kellis, M.2019-10-18doi:10.1101/810291Cold Spring Harbor Laboratory Press2019-10-18
https://biorxiv.org/cgi/content/short/730549v1?rss=1"
Yao, D. W.O'Connor, L. J.Price, A. L.Gusev, A.2019-08-09doi:10.1101/730549Cold Spring Harbor Laboratory Press2019-08-09
https://biorxiv.org/cgi/content/short/803452v1?rss=1"
Shi, H.Gazal, S.Kanai, M.Koch, E. M.Schoech, A. P.Kim, S. S.Luo, Y.Amariuta, T.Okada, Y.Raychaudhuri, S.Sunyaev, S. R.Price, A. L.2019-10-15doi:10.1101/803452Cold Spring Harbor Laboratory Press2019-10-15
https://biorxiv.org/cgi/content/short/2020.01.02.890657v1?rss=1"
Kim, S. S.Dey, K. K.Weissbrod, O.Marquez-Luna, C.Gazal, S.Price, A. L.2020-01-03doi:10.1101/2020.01.02.890657Cold Spring Harbor Laboratory Press2020-01-03
https://biorxiv.org/cgi/content/short/807792v1?rss=1"
20% more variants with posterior causal probability >0.95 than their non-functionally informed counterparts (and >33% more fine-mapped variants than previous functionally-informed fine-mapping methods). In simulations with mismatched reference LD, PolyFun + SuSiE remained well-calibrated when reducing the maximum number of assumed causal SNPs per locus, which reduces absolute power but still produces large relative improvements. In analyses of 49 UK Biobank traits (average N=318K) with in-sample LD, PolyFun + SuSiE identified 3,025 fine-mapped variant-trait pairs with posterior causal probability >0.95, a >32% improvement vs. SuSiE; 223 variants were fine-mapped for multiple genetically uncorrelated traits, indicating pervasive pleiotropy. We used posterior mean per-SNP heritabilities from PolyFun + SuSiE to perform polygenic localization, constructing minimal sets of common SNPs causally explaining 50% of common SNP heritability; these sets ranged in size from 28 (hair color) to 3,400 (height) to 2 million (number of children). In conclusion, PolyFun prioritizes variants for functional follow-up and provides insights into complex trait architectures.
]]>Weissbrod, O.Hormozdiari, F.Benner, C.Cui, R.Ulirsch, J.Gazal, S.Schoech, A. P.van de Geijn, B.Reshef, Y.Marquez-Luna, C.O'Connor, L. J.Pirinen, M.Finucane, H. K.Price, A. L.2019-10-17doi:10.1101/807792Cold Spring Harbor Laboratory Press2019-10-17
https://biorxiv.org/cgi/content/short/784439v1?rss=1"
Dey, K. K.van de Geijn, B. K.Kim, S. S.Hormozdiari, F.Kelley, D. R.Price, A.2019-09-26doi:10.1101/784439Cold Spring Harbor Laboratory Press2019-09-26
https://biorxiv.org/cgi/content/short/375337v1?rss=1"
Marquez-Luna, C.Gazal, S.Loh, P.-R.Furlotte, N.Auton, A.23andMe Research Team,Price, A. L.2018-07-24doi:10.1101/375337Cold Spring Harbor Laboratory Press2018-07-24
https://biorxiv.org/cgi/content/short/512434v1?rss=1"
Schreiber, J.Singh, R.Bilmes, J.Noble, W. S.2019-01-04doi:10.1101/512434Cold Spring Harbor Laboratory Press2019-01-04
https://biorxiv.org/cgi/content/short/2020.01.31.927798v1?rss=1"
Vierstra, J.Lazar, J.Sandstrom, R.Halow, J.Lee, K.Bates, D.Diegel, M.Dunn, D.Neri, F.Haugen, E.Rynes, E.Reynolds, A.Nelson, J.Johnson, A.Frerker, M.Buckley, M.Kaul, R.Meuleman, W.Stamatoyannopoulos, J. A.2020-02-01doi:10.1101/2020.01.31.927798Cold Spring Harbor Laboratory Press2020-02-01
https://biorxiv.org/cgi/content/short/396275v1?rss=1"
Yang, E.-W.Bahn, J. H.Hsiao, E. Y.-H.Tan, B. X.Sun, Y.Fu, T.Zhou, B.Van Nostrand, E. L.Pratt, G. A.Freese, P.Wei, X.Quinones-Valdez, G.Urban, A. E.Graveley, B. R.Burge, C. B.Yeo, G. W.Xiao, X.2018-08-20doi:10.1101/396275Cold Spring Harbor Laboratory Press2018-08-20
https://biorxiv.org/cgi/content/short/446625v1?rss=1"
Tran, S.Jun, H.-I.Bahn, J. H.Azghadi, A.Ramaswami, G.Van Nostrand, E. L.Nguyen, T. B.Hsiao, Y.-H. E.Lee, C.Pratt, G. A.Yeo, G. W.Geschwind, D. H.Xiao, X.2018-10-17doi:10.1101/446625Cold Spring Harbor Laboratory Press2018-10-17
https://biorxiv.org/cgi/content/short/301267v1?rss=1"
Harvey, S.Xu, Y.Lin, X.Gao, X. D.Qiu, Y.Ahn, J.Xiao, X.Cheng, C.2018-04-13doi:10.1101/301267Cold Spring Harbor Laboratory Press2018-04-13
https://biorxiv.org/cgi/content/short/807008v1?rss=1"
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.2019-10-16doi:10.1101/807008Cold Spring Harbor Laboratory Press2019-10-16
https://biorxiv.org/cgi/content/short/086025v1?rss=1"
Libbrecht, M. W.Rodriguez, O.Weng, Z.Hoffman, M.Bilmes, J. A.Noble, W. S.2016-11-07doi:10.1101/086025Cold Spring Harbor Laboratory Press2016-11-07
https://biorxiv.org/cgi/content/short/009209v1?rss=1"
Maxwell W LibbrechtFerhat AyMichael M HoffmanDavid M GilbertJeffrey A BilmesWilliam Stafford Noble2014-09-16doi:10.1101/009209Cold Spring Harbor Laboratory Press2014-09-16
https://biorxiv.org/cgi/content/short/036137v1?rss=1"
Kai WeiMaxwell W LibbrechtJeffrey A BilmesWilliam Noble2016-01-07doi:10.1101/036137Cold Spring Harbor Laboratory Press2016-01-07
https://biorxiv.org/cgi/content/short/051201v1?rss=1"
Maxwell W LibbrechtJeffrey A BilmesWilliam Stafford Noble2016-05-02doi:10.1101/051201Cold Spring Harbor Laboratory Press2016-05-02
https://biorxiv.org/cgi/content/short/123927v1?rss=1"
Durham, T. J.Libbrecht, M. W.Howbert, J. J.Bilmes, J.Noble, W. S.2017-04-04doi:10.1101/123927Cold Spring Harbor Laboratory Press2017-04-04
https://biorxiv.org/cgi/content/short/147470v1?rss=1"
Chan, R. C. W.Libbrecht, M. W.Roberts, E. G.Noble, W. S.Hoffman, M. M.2017-06-08doi:10.1101/147470Cold Spring Harbor Laboratory Press2017-06-08
https://biorxiv.org/cgi/content/short/082594v1?rss=1"
van der Velde, A. G.Purcaro, M.Noble, W. S.Weng, Z.2016-10-24doi:10.1101/082594Cold Spring Harbor Laboratory Press2016-10-24
https://biorxiv.org/cgi/content/short/181842v1?rss=1"
Ursu, O.Boley, N.Taranova, M.Wang, Y. X. R.Yardimci, G. G.Noble, W. S.Kundaje, A.2017-08-29doi:10.1101/181842Cold Spring Harbor Laboratory Press2017-08-29
https://biorxiv.org/cgi/content/short/481069v1?rss=1"
Chen, W.McKenna, A.Schreiber, J.Yin, Y.Agarwal, V.Noble, W. S.Shendure, J.2018-11-28doi:10.1101/481069Cold Spring Harbor Laboratory Press2018-11-28
https://biorxiv.org/cgi/content/short/822510v1?rss=1"
Meuleman, W.Muratov, A.Rynes, E.Vierstra, J.Teodosiadis, A.Reynolds, A.Haugen, E.Sandstrom, R.Kaul, R.Stamatoyannopoulos, J. A.2019-10-29doi:10.1101/822510Cold Spring Harbor Laboratory Press2019-10-29
https://biorxiv.org/cgi/content/short/801183v1?rss=1"
Schreiber, J.Hedge, D.Noble, W. S.2019-10-11doi:10.1101/801183Cold Spring Harbor Laboratory Press2019-10-11
https://biorxiv.org/cgi/content/short/576405v1?rss=1"
Klein, J. C.Agarwal, V.Inoue, F.Keith, A.Martin, B.Kircher, M.Ahituv, N.Shendure, J.2019-03-13doi:10.1101/576405Cold Spring Harbor Laboratory Press2019-03-13
https://biorxiv.org/cgi/content/short/827071v1?rss=1"
Wainberg, M.Kamber, R. A.Balsubramani, A.Meyers, R. M.Sinnott-Armstrong, N.Hornburg, D.Jiang, L.Chan, J.Jian, R.Gu, M.Shcherbina, A.Dubreuil, M. M.Spees, K.Snyder, M. P.Kundaje, A.Bassik, M. C.2019-11-01doi:10.1101/827071Cold Spring Harbor Laboratory Press2019-11-01
https://biorxiv.org/cgi/content/short/474072v1?rss=1"
Zhang, J.Liu, J.Lee, D.Feng, J.-J.Lochovsky, L.Lou, S.Rutenberg-Schoenberg, M.Gerstein, M.2018-11-19doi:10.1101/474072Cold Spring Harbor Laboratory Press2018-11-19
https://biorxiv.org/cgi/content/short/2020.03.24.006866v1?rss=1"
Spitale, R.Chan, D.Feng, C.England, W.Wyman, D.Flynn, R.Wang, X.Shi, Y.Mortazavi, A.2020-03-25doi:10.1101/2020.03.24.006866Cold Spring Harbor Laboratory Press2020-03-25
https://biorxiv.org/cgi/content/short/2020.02.24.963652v1?rss=1"
Song, M.Pebworth, M.-P.Yang, X.Abnousi, A.Fan, C.Wen, J.Rosen, J.Choudhary, M.Cui, X.Jones, I.Bergenholtz, S.Eze, U.Juric, I.Li, B.Maliskova, L.Liu, W.Pollen, A.Li, Y.Wang, T.Hu, M.Kriegstein, A.Shen, Y.2020-02-25doi:10.1101/2020.02.24.963652Cold Spring Harbor Laboratory Press2020-02-25
https://biorxiv.org/cgi/content/short/2020.04.28.066498v1?rss=1"
Granja, J. M.Corces, M. R.Pierce, S. E.Bagdatli, S. T.Choudhry, H.Chang, H.Greenleaf, W.2020-04-29doi:10.1101/2020.04.28.066498Cold Spring Harbor Laboratory Press2020-04-29
https://biorxiv.org/cgi/content/short/2020.05.04.077255v1?rss=1"
1000 functional variants, many of which may alter RNA-protein interactions. Lastly, 72% of GMAS-associated SNPs were in linkage disequilibrium with GWAS-reported SNPs, and such association was enriched in tissues of relevance for specific traits/diseases. Our study enables a comprehensive view of genetically driven splicing variations in human tissues.
]]>Amoah, K.Hsiao, Y.-H. E.Bahn, J. H.Sun, Y.Burghard, C.Tan, B. X.Yang, E.-W.Xiao, X.2020-05-05doi:10.1101/2020.05.04.077255Cold Spring Harbor Laboratory Press2020-05-05
https://biorxiv.org/cgi/content/short/2020.03.06.981191v1?rss=1"
Chan, T.Fu, T.Bahn, J. H.Jun, H.-I.Lee, J.-H.Quinones-Valdez, G.Cheng, C.Xiao, X.2020-03-08doi:10.1101/2020.03.06.981191Cold Spring Harbor Laboratory Press2020-03-08
https://biorxiv.org/cgi/content/short/2020.03.24.006551v1?rss=1"
Koyano, K.Bahn, J.Xiao, X.2020-03-25doi:10.1101/2020.03.24.006551Cold Spring Harbor Laboratory Press2020-03-25
https://biorxiv.org/cgi/content/short/2020.05.11.078675v1?rss=1"
270,000 perturbations, we identified CREs for GATA1, HDAC6, ERP29, LMO2, MEF2C, CD164, NMU, FEN1 and the FADS gene cluster. Our methods detect subtle gene expression changes and identify CREs regulating multiple genes, sometimes at different magnitudes and directions. We demonstrate the power of HCR-FlowFISH to parse genome-wide association signals by nominating causal variants and target genes.
]]>Reilly, S. K.Gosai, S. J.Guiterrez, A.Ulirsch, J. C.Kanai, M.Berenzy, D.Kales, S.Butler, G. B.Gladden-Young, A.Finucane, H. K.Sabeti, P. C.Tewhey, R.2020-05-12doi:10.1101/2020.05.11.078675Cold Spring Harbor Laboratory Press2020-05-12
https://biorxiv.org/cgi/content/short/694869v1?rss=1"
Lee, D.Shi, M.Moran, J.Wall, M.Zhang, J.Liu, J.Fitzgerald, D.Kyono, Y.Ma, L.White, K. P.Gerstein, M.2019-07-08doi:10.1101/694869Cold Spring Harbor Laboratory Press2019-07-08
https://biorxiv.org/cgi/content/short/706424v1?rss=1"
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.2019-07-18doi:10.1101/706424Cold Spring Harbor Laboratory Press2019-07-18
https://biorxiv.org/cgi/content/short/533273v1?rss=1"
Schreiber, J.Bilmes, J.Noble, W.2019-01-29doi:10.1101/533273Cold Spring Harbor Laboratory Press2019-01-29
https://biorxiv.org/cgi/content/short/364976v1?rss=1"
Schreiber, J.Durham, T. J.Bilmes, J.Noble, W. S.2018-07-08doi:10.1101/364976Cold Spring Harbor Laboratory Press2018-07-08
https://biorxiv.org/cgi/content/short/386656v1?rss=1"
Sisu, C.Muir, P.Frankish, A.Fiddes, I.Diekhans, M.Thybert, D.Odom, D.Flicek, P.Keane, T.Hubbard, T.Harrow, J.Gerstein, M.2018-08-07doi:10.1101/386656Cold Spring Harbor Laboratory Press2018-08-07
https://biorxiv.org/cgi/content/short/2020.03.15.992750v1?rss=1"
Javed, N. M.Farjoun, Y.Fennell, T.Epstein, C. B.Bernstein, B. E.Shoresh, N.2020-03-17doi:10.1101/2020.03.15.992750Cold Spring Harbor Laboratory Press2020-03-17
https://biorxiv.org/cgi/content/short/188755v1?rss=1"
Yardimci, G.Ozadam, H.Sauria, M. E. G.Ursu, O.Yan, K.-K.Yang, T.Chakraborty, A.Kaul, A.Lajoie, B. R.Song, F.Zhan, Y.Ay, F.Gerstein, M.Kundaje, A.Li, Q.Taylor, J.Yue, F.Dekker, J.Noble, W. S.2017-09-14doi:10.1101/188755Cold Spring Harbor Laboratory Press2017-09-14
https://biorxiv.org/cgi/content/short/103614v1?rss=1"
Schreiber, J.Libbrecht, M.Bilmes, J.Noble, W.2017-01-27doi:10.1101/103614Cold Spring Harbor Laboratory Press2017-01-27
https://biorxiv.org/cgi/content/short/101386v1?rss=1"
Yang, T.Zhang, F.Yardimci, G. G.Hardison, R. C.Noble, W. S.Yue, F.Li, Q.2017-01-18doi:10.1101/101386Cold Spring Harbor Laboratory Press2017-01-18
https://biorxiv.org/cgi/content/short/119651v1?rss=1"
Dixon, J.Xu, J.Dileep, V.Zhan, Y.Song, F.Le, V. T.Yardimci, G. G.Chakraborty, A.Bann, D. V.Wang, Y.Clark, R.Zhang, L.Yang, H.Liu, T.Iyyanki, S.An, L.Pool, C.Sasaki, T.Mulia, J. C. R.Ozadam, H.Lajoie, B. R.Kaul, R.Buckley, M.Lee, K.Diegel, M.Pezic, D.Ernst, C.Hadjur, S.Odom, D. T.Stamatoyannopoulos, J. A.Broach, J. R.Hardison, R.Ay, F.Noble, W. S.Dekker, J.Gilbert, D. M.Yue, F.2017-03-28doi:10.1101/119651Cold Spring Harbor Laboratory Press2017-03-28
https://biorxiv.org/cgi/content/short/862177v1?rss=1"
Lou, S.Li, T.Kong, X.Zhang, J.Liu, J.Lee, D.Gerstein, M.2019-12-02doi:10.1101/862177Cold Spring Harbor Laboratory Press2019-12-02
https://biorxiv.org/cgi/content/short/2020.05.29.124164v1?rss=1"
Zhang, J.Liu, J.Lee, D.Lou, S.Chen, Z.Gursoy, G.Gerstein, M.2020-05-30doi:10.1101/2020.05.29.124164Cold Spring Harbor Laboratory Press2020-05-30
https://biorxiv.org/cgi/content/short/2020.02.03.932251v1?rss=1"
80% precision-recall curve metric), we explored the derived weights of the latent factors, finding they highlight the importance of the asymmetric time-direction of chromatin context during transcription.
Author SummaryIn humans, only about 2% of the genome is comprised of so-called coding regions and can give rise to protein products. However, the human transcriptome is much more diverse than the number of genes found in these coding regions. Each gene can give rise to multiple transcripts through a process during transcription called alternative splicing. There is a limited understanding of the regulation of splicing and the underlying splicing code that determines cell-type-specific splicing. Here, we studied epigenetic features that characterize splicing regulation in humans using a recurrent neural network model. Unlike feedforward neural networks, this method contains an internal memory state that learns from spatiotemporal patterns - like the context in language - from a sequence of genomic and epigenetic information, making it better suited for characterizing splicing. We demonstrated that our method improves the prediction of spicing outcomes compared to previous methods. Furthermore, we applied our method to 49 cell types in ENCODE to investigate splicing regulation and found that not only spatial but also temporal epigenomic context can influence splicing regulation during transcription.
]]>Lee, D.Zhang, J.Liu, J.Gerstein, M.2020-02-03doi:10.1101/2020.02.03.932251Cold Spring Harbor Laboratory Press2020-02-03
https://biorxiv.org/cgi/content/short/2020.06.14.150599v1?rss=1"
He, P.Williams, B. A.Trout, D.Marinov, G. K.Amrhein, H.Berghella, L.Goh, S.-T.Plajzer-Frick, I.Afzal, V.Pennacchio, L. A.Dickel, D. E.Visel, A.Ren, B.Hardison, R. C.Zhang, Y.Wold, B. J.2020-06-14doi:10.1101/2020.06.14.150599Cold Spring Harbor Laboratory Press2020-06-14
https://biorxiv.org/cgi/content/short/2020.06.26.172718v1?rss=1"
1.8 million DNase I hypersensitive sites (DHSs), with the vast majority displaying temporal and tissue-selective patterning. Here we show that tissue regulatory DNA compartments show sharp embryonic-to-fetal transitions characterized by wholesale turnover of DHSs and progressive domination by a diminishing number of transcription factors. We show further that aligning mouse and human fetal development on a regulatory axis exposes disease-associated variation enriched in early intervals lacking human samples. Our results provide an expansive new resource for decoding mammalian developmental regulatory programs.
]]>Breeze, C. E.Lazar, J.Mercer, T.Halow, J.Washington, I.Lee, K.Ibarrientos, S.Castillo, A.Neri, F.Haugen, E.Rynes, E.Reynolds, A.Bates, D.Diegel, M.Dunn, D.Kaul, R.Sandstrom, R.Meuleman, W.Bender, M. A.Groudine, M.Stamatoyannopoulos, J. A.2020-06-27doi:10.1101/2020.06.26.172718Cold Spring Harbor Laboratory Press2020-06-27
https://biorxiv.org/cgi/content/short/2020.07.02.185389v1?rss=1"
Xi, W.Beer, M. A.2020-07-03doi:10.1101/2020.07.02.185389Cold Spring Harbor Laboratory Press2020-07-03
https://biorxiv.org/cgi/content/short/2019.12.21.885830v1?rss=1"
Ramaker, R. C.Hardigan, A. A.Goh, S.-T.Partridge, E. C.Wold, B.Cooper, S. J.Myers, R. M.2019-12-23doi:10.1101/2019.12.21.885830Cold Spring Harbor Laboratory Press2019-12-23
https://biorxiv.org/cgi/content/short/2020.05.01.073296v1?rss=1"
Rahmanian, S.Balderrama-Gutierrez, G.Wyman, D.McGill, C. J.Nguyen, K.Spitale, R.Mortazavi, A.2020-05-02doi:10.1101/2020.05.01.073296Cold Spring Harbor Laboratory Press2020-05-02
https://biorxiv.org/cgi/content/short/2020.06.09.143024v1?rss=1"
Reese, F.Mortazavi, A.2020-06-10doi:10.1101/2020.06.09.143024Cold Spring Harbor Laboratory Press2020-06-10
https://biorxiv.org/cgi/content/short/2020.08.07.241901v1?rss=1"
Kim, Y.-S.Jonhson, G. D.Seo, J.Barrera, A.Majoros, W. H.Ochoa, A.Allen, A. S.Reddy, T. E.2020-08-07doi:10.1101/2020.08.07.241901Cold Spring Harbor Laboratory Press2020-08-07
https://biorxiv.org/cgi/content/short/2020.02.21.959510v1?rss=1"
Amariuta, T.Ishigaki, K.Sugishita, H.Ohta, T.Matsuda, K.Murakami, Y.Price, A. L.Kawakami, E.Terao, C.Raychaudhuri, S.2020-02-25doi:10.1101/2020.02.21.959510Cold Spring Harbor Laboratory Press2020-02-25
https://biorxiv.org/cgi/content/short/2020.09.02.279059v1?rss=1"
2x stronger conditional signal (maximum standardized SNP annotation effect size ({tau}*) = 2.0 (s.e. 0.3) vs. 0.91 (s.e. 0.21)), and >2x stronger gene-level enrichment for approved autoimmune disease drug targets (5.3x vs. 2.1x), as compared to the recently proposed Enhancer Domain Score (EDS). In each case, using functionally informed S2G strategies to link genes to SNPs that may regulate them produced much stronger disease signals (4.1x-13x larger{tau} * values) than conventional window-based S2G strategies. We conclude that our characterizations of enhancer-related and candidate master-regulator genes identify gene sets that are important for autoimmune disease, and that combining those gene sets with functionally informed S2G strategies enables us to identify SNP annotations in which disease heritability is concentrated.
]]>Dey, K. K.Gazal, S. K.van de Geijn, B.Kim, S. S.Nasser, J.Engreitz, J. M.Price, A.2020-09-03doi:10.1101/2020.09.02.279059Cold Spring Harbor Laboratory Press2020-09-03
https://biorxiv.org/cgi/content/short/2020.09.08.288563v1?rss=1"
Dey, K. K.Kim, S. S.Gazal, S.Nasser, J.Engreitz, J. M.Price, A.2020-09-09doi:10.1101/2020.09.08.288563Cold Spring Harbor Laboratory Press2020-09-09
https://biorxiv.org/cgi/content/short/2020.11.02.364869v1?rss=1"
Fan, K.Moore, J. E.Zhang, X.-o.Weng, Z.2020-11-02doi:10.1101/2020.11.02.364869Cold Spring Harbor Laboratory Press2020-11-02
https://biorxiv.org/cgi/content/short/2020.10.11.335273v1?rss=1"
Sahin, M.Wong, W.Zhan, Y.Van Deynze, K.Koche, R.Leslie, C. S.2020-10-11doi:10.1101/2020.10.11.335273Cold Spring Harbor Laboratory Press2020-10-11
https://biorxiv.org/cgi/content/short/2020.11.14.382606v1?rss=1"
Zhang, S.Cooper-Knock, J.Weimer, A. K.Shi, M.Moll, T.Harvey, C.Nezhad, H. G.Franklin, J.Souza, C. d. S.Wang, C.Li, J.Eitan, C.Hornstein, E.Kenna, K. P.Project MinE Sequencing Consortium,Veldink, J.Ferraiuolo, L.Shaw, P. J.Snyder, M. P.2020-11-15doi:10.1101/2020.11.14.382606Cold Spring Harbor Laboratory Press2020-11-15
https://biorxiv.org/cgi/content/short/684712v1?rss=1"
Cai, Y.Zhang, Y.Loh, Y. P.Tng, J. Q.Lim, M. C.Cao, Z.Raju, A.Li, S.Manikandan, L.Tergaonkar, V.Tucker-Kellogg, G.Fullwood, M. J.2019-06-28doi:10.1101/684712Cold Spring Harbor Laboratory Press2019-06-28
https://biorxiv.org/cgi/content/short/2020.08.05.238360v1?rss=1"
300,000 immune cells from COVID-19 and 5 inflammatory diseases including rheumatoid arthritis (RA), Crohns disease (CD), ulcerative colitis (UC), lupus, and interstitial lung disease. Our cross-disease analysis revealed that an FCN1+ inflammatory macrophage state is common to COVID-19 bronchoalveolar lavage samples, RA synovium, CD ileum, and UC colon. We also observed that a CXCL10+ CCL2+ inflammatory macrophage state is abundant in severe COVID-19, inflamed CD and RA, and expresses inflammatory genes such as GBP1, STAT1, and IL1B. We found that the CXCL10+ CCL2+ macrophages are transcriptionally similar to blood-derived macrophages stimulated with TNF- and IFN-{gamma} ex vivo. Our findings suggest that IFN-{gamma}, alongside TNF-, might be a key driver of this abundant inflammatory macrophage phenotype in severe COVID-19 and other inflammatory diseases, which may be targeted by existing immunomodulatory therapies.
]]>Zhang, F.Mears, J. R.Shakib, L.Beynor, J. I.Shanaj, S.Korsunsky, I.Nathan, A.Accelerating Medicines Partnership Rheumatoid Arthritis and Systemic Lupus Erythematosus,Donlin, L. T.Raychaudhuri, S.2020-08-05doi:10.1101/2020.08.05.238360Cold Spring Harbor Laboratory Press2020-08-05
https://biorxiv.org/cgi/content/short/2020.11.18.389189v1?rss=1"
Kang, J. B.Nathan, A.Millard, N.Rumker, L.Moody, D. B.Korsunsky, I.Raychaudhuri, S.2020-11-20doi:10.1101/2020.11.18.389189Cold Spring Harbor Laboratory Press2020-11-20
https://biorxiv.org/cgi/content/short/2020.11.23.390682v1?rss=1"
Millard, N.Korsunsky, I.Weinand, K.Fonseka, C. Y.Nathan, A.Kang, J. B.Raychaudhuri, S.2020-11-23doi:10.1101/2020.11.23.390682Cold Spring Harbor Laboratory Press2020-11-23
https://biorxiv.org/cgi/content/short/2020.04.23.057828v1?rss=1"
4.7 years after they had either latent M.tb infection or active disease and defined 31 distinct memory T cell states, including a CD4+CD26+CD161+CCR6+ effector memory state that was significantly reduced in patients who had developed active TB (OR = 0.80, 95% CI: 0.73-0.87, p = 1.21 x 10-6). This state was also polyfunctional; in ex vivo stimulation, it was enriched for IL-17 and IL-22 production, consistent with a Th17-skewed phenotype, but also had more capacity to produce IFN{gamma} than other CD161+CCR6+ Th17 cells. Additionally, in progressors, IL-17 and IL-22 production in this cell state was significantly lower than in non-progressors. Reduced abundance and function of this state may be an important factor in failure to control M.tb infection.
]]>Nathan, A.Beynor, J. I.Baglaenko, Y.Suliman, S.Ishigaki, K.Asgari, S.Huang, C.-C.Luo, Y.Zhang, Z.Lopez Tamara, K.Jimenez, J.Calderon, R. I.Lecca, L.van Rhijn, I.Moody, B.Murray, M. B.Raychaudhuri, S.2020-04-25doi:10.1101/2020.04.23.057828Cold Spring Harbor Laboratory Press2020-04-25
https://biorxiv.org/cgi/content/short/173088v1?rss=1"
Di Pierro, M.Cheng, R. R.Lieberman Aiden, E.Wolynes, P. G.Onuchic, J. N.2017-08-07doi:10.1101/173088Cold Spring Harbor Laboratory Press2017-08-07
https://biorxiv.org/cgi/content/short/374058v1?rss=1"
Nir, G.Farabella, I.Perez Estrada, C.Ebeling, C. G.Beliveau, B. J.Sasaki, H. M.Lee, S. H.Nguyen, S. C.McCole, R. B.Chattoraj, S.Erceg, J.AlHaj Abed, J.Martins, N. M. C.Nguyen, H. Q.Hannan, M. A.Russell, S.Durand, N. C.Rao, S. S. P.Kishi, J. Y.Soler-Vila, P.Di Pierro, M.Onuchic, J. N.Callahan, S.Schreiner, J.Stuckey, J.Yin, P.Lieberman Aiden, E.Marti-Renom, M. A.Wu, C.- t.2018-07-28doi:10.1101/374058Cold Spring Harbor Laboratory Press2018-07-28
https://biorxiv.org/cgi/content/short/529990v1?rss=1"
3,000 potential regulatory enhancer-gene connections across multiple genomic loci. A simple equation based on a mechanistic model for enhancer function performed remarkably well at predicting the complex patterns of regulatory connections we observe in our CRISPR dataset. This Activity-by-Contact (ABC) model involves multiplying measures of enhancer activity and enhancer-promoter 3D contacts, and can predict enhancer-gene connections in a given cell type based on chromatin state maps. Together, CRISPRi-FlowFISH and the ABC model provide a systematic approach to map and predict which enhancers regulate which genes, and will help to interpret the functions of the thousands of disease risk variants in the noncoding genome.
]]>Fulco, C. P.Nasser, J.Jones, T. R.Munson, G.Bergman, D. T.Subramanian, V.Grossman, S. R.Anyoha, R.Patwardhan, T. A.Nguyen, T. H.Kane, M.Doughty, B.Perez, E. M.Durand, N. C.Stamenova, E. K.Lieberman Aiden, E.Lander, E. S.Engreitz, J. M.2019-01-26doi:10.1101/529990Cold Spring Harbor Laboratory Press2019-01-26
https://biorxiv.org/cgi/content/short/2020.03.21.001917v1?rss=1"
Cheng, R. R.Contessoto, V.Aiden, E. L.Wolynes, P. G.Di Pierro, M.Onuchic, J. N.2020-03-22doi:10.1101/2020.03.21.001917Cold Spring Harbor Laboratory Press2020-03-22
https://biorxiv.org/cgi/content/short/779058v1?rss=1"
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.2019-09-23doi:10.1101/779058Cold Spring Harbor Laboratory Press2019-09-23
https://biorxiv.org/cgi/content/short/867341v1?rss=1"
Humble, E.Dobrynin, P.Senn, H.Chuven, J.Scott, A. F.Mohr, D. W.Dudchenko, O.Omer, A. D.Colaric, Z.Lieberman Aiden, E.Wildt, D.Oliagi, S.Tamazian, G.Pukazhenthi, B.Ogden, R.Koepfli, K.-P.2019-12-08doi:10.1101/867341Cold Spring Harbor Laboratory Press2019-12-08
https://biorxiv.org/cgi/content/short/2020.01.08.887828v1?rss=1"
Kenny, N. J.McCarthy, S. A.Dudchenko, O.James, K.Betteridge, E.Corton, C.Dolucan, J.Mead, D.Oliver, K.Omer, A. D.Pelan, S.Ryan, Y.Sims, Y.Skelton, J.Smith, M.Torrance, J.Weisz, D.Wipat, A.Aiden, E. L.Howe, K.Williams, S. T.2020-01-09doi:10.1101/2020.01.08.887828Cold Spring Harbor Laboratory Press2020-01-09
https://biorxiv.org/cgi/content/short/762872v1?rss=1"
Goundaroulis, D.Aiden, E. L.Stasiak, A.2019-09-09doi:10.1101/762872Cold Spring Harbor Laboratory Press2019-09-09
https://biorxiv.org/cgi/content/short/2020.12.18.423551v1?rss=1"
Sanborn, A. L.Yeh, B. T.Feigerle, J. T.Hao, C. V.Townshend, R. J. L.Aiden, E. L.Dror, R. O.Kornberg, R. D.2020-12-18doi:10.1101/2020.12.18.423551Cold Spring Harbor Laboratory Press2020-12-18
https://biorxiv.org/cgi/content/short/816611v1?rss=1"
Cremer, M.Brandstetter, K.Maiser, A.Rao, S. S.Schmid, V.Mitra, N.Mamberti, S.Klein, K. N.Gilbert, D. M.Leonhardt, H.Cardoso, M. C.Lieberman Aiden, E.Harz, H.Cremer, T.2019-10-24doi:10.1101/816611Cold Spring Harbor Laboratory Press2019-10-24
https://biorxiv.org/cgi/content/short/205740v1?rss=1"
Robinson, J.Turner, D.Durand, N. C.Thorvaldsdottir, H.Mesirov, J. P.Aiden, E. L.2017-10-19doi:10.1101/205740Cold Spring Harbor Laboratory Press2017-10-19
https://biorxiv.org/cgi/content/short/139782v1?rss=1"
Rao, S.Huang, S.-C.Glenn St. Hilaire, B.Engreitz, J. M.Perez, E. M.Kieffer-Kwon, K.-R.Sanborn, A. L.Johnstone, S. E.Bochkov, I. D.Huang, X.Shamim, M. S.Omer, A. D.Bernstein, B. E.Casellas, R.Lander, E. S.Lieberman Aiden, E.2017-05-18doi:10.1101/139782Cold Spring Harbor Laboratory Press2017-05-18
https://biorxiv.org/cgi/content/short/142026v1?rss=1"
10 billion total reads)nC_LIO_LIMulti-loop interaction communities identified surrounding key macrophage genes.nC_LIO_LIMulti-loop communities connect dynamic enhancers through both static and newly acquired DNA loops, forming hubs of activationnC_LIO_LIMacrophage activation hubs are enriched for AP-1 bound long-range enhancer interactions, suggesting cell-type specific TFs drive changes in 3D structure and transcription through regulatory DNA loopsnC_LI
]]>Phanstiel, D. H.Van Bortle, K.Spacek, D. V.Hess, G. T.Saad Shamim, M.Machol, I.Love, M. I.Lieberman Aiden, E.Bassik, M. C.Snyder, M. P.2017-05-25doi:10.1101/142026Cold Spring Harbor Laboratory Press2017-05-25
https://biorxiv.org/cgi/content/short/481283v1?rss=1"
Stamenova, E. K.Durand, N.Dudchenko, O.Shamim, M. S.Huang, S.-C.Jiang, Y.Bochkov, I. D.Rao, S. S. P.Lander, E. S.Gnirke, A.Aiden, E. L.2018-11-29doi:10.1101/481283Cold Spring Harbor Laboratory Press2018-11-29
https://biorxiv.org/cgi/content/short/2021.02.19.431931v1?rss=1"
Ren, X.Wang, M.Li, B.Jamieson, K.Zheng, L.Jones, I. R.Li, B.Takagi, M. A.Lee, J.Maliskova, L.Tam, T. W.Yu, M.Hu, R.Lee, L.Abnousi, A.Li, G.Li, Y.Hu, M.Ren, B.Wang, W.Shen, Y.2021-02-19doi:10.1101/2021.02.19.431931Cold Spring Harbor Laboratory Press2021-02-19
https://biorxiv.org/cgi/content/short/2020.12.30.424817v1?rss=1"
Cao, F.Zhang, Y.Cai, Y.Animesh, S.Zhang, Y.Akincilar, S.Loh, Y. P.Chng, W. J.Tergaonkar, V.Kwoh, C. K.Fullwood, M.2020-12-31doi:10.1101/2020.12.30.424817Cold Spring Harbor Laboratory Press2020-12-31
https://biorxiv.org/cgi/content/short/2020.04.18.047738v1?rss=1"
Wang, B.Kong, L.BABU, D.Choudhary, R.Fam, W.Tng, J. Q.Goh, Y.Liu, X.Song, F. F.Chia, P.Chan, M. C.An, O.Tham, C. Y.Benoukraf, T.Yang, H.Wang, W.Chng, W. J.Tenen, D.Fullwood, M. J.2020-04-18doi:10.1101/2020.04.18.047738Cold Spring Harbor Laboratory Press2020-04-18
https://biorxiv.org/cgi/content/short/2021.01.04.425344v1?rss=1"
See, Y. X.Chen, K.Fullwood, M. J.2021-01-05doi:10.1101/2021.01.04.425344Cold Spring Harbor Laboratory Press2021-01-05
https://biorxiv.org/cgi/content/short/2021.01.11.426176v1?rss=1"
Fullwood, M.Animesh, S.Choudhary, R.Goh, B. C.Tay, J.chong, w.-q.Ng, X. Y.2021-01-11doi:10.1101/2021.01.11.426176Cold Spring Harbor Laboratory Press2021-01-11
https://biorxiv.org/cgi/content/short/2021.02.17.431699v1?rss=1"
Zhang, K.Hocker, J. D.Miller, M.Hou, X.Chiou, J.Poirion, O. B.Qiu, Y.Li, Y. E.Gaulton, K. J.Wang, A.Preissl, S.Ren, B.2021-02-17doi:10.1101/2021.02.17.431699Cold Spring Harbor Laboratory Press2021-02-17
https://biorxiv.org/cgi/content/short/2021.02.04.429675v1?rss=1"
Chen, P.Fiaux, P.Li, B.Zhang, K.Kubo, N.Jiang, S.Hu, R.Wu, S.Wang, M.Wang, W.McVicker, G. P.Mischel, P.Ren, B.2021-02-05doi:10.1101/2021.02.04.429675Cold Spring Harbor Laboratory Press2021-02-05
https://biorxiv.org/cgi/content/short/2021.03.08.434430v1?rss=1"
Gemberling, M.Siklenka, K.Rodriguez, E.Eisinger, K.Barrera, A.Liu, F.Kantor, A.Li, L.Cigliola, V.Hazlett, M.Williams, C.Bartelt, L.Bodle, J.Daniels, H.Rouse, C.Hilton, I.Madigan, V.Asokan, A.Ciofani, M.Poss, K.Reddy, T. E.West, A.Gersbach, C.2021-03-08doi:10.1101/2021.03.08.434430Cold Spring Harbor Laboratory Press2021-03-08
https://biorxiv.org/cgi/content/short/2021.03.08.434470v1?rss=1"
100,000 putative non-coding regulatory elements defined by open chromatin sites in human K562 leukemia cells for their role in regulating essential cellular processes. In an initial screen containing more than 1 million gRNAs, we discovered approximately 12,000 regulatory elements with evidence of impact on cell fitness. We validated many of the screen hits in K562 cells, evaluated cell-type specificity in a second cancer cell line, and identified target genes of regulatory elements using CERES perturbations combined with single cell RNA-seq. This comprehensive and quantitative genome-wide map of essential regulatory elements represents a framework for extensive characterization of noncoding regulatory elements that drive complex cell phenotypes and for prioritizing non-coding genetic variants that likely contribute to common traits and disease risk.
]]>Klann, T.Barrera, A.Ettyreddy, A.Rickels, R.Bryois, J.Jiang, S.Adkar, S.Iglesias, N.Sullivan, P.Reddy, T. E.Crawford, G. E.Gersbach, C.2021-03-09doi:10.1101/2021.03.08.434470Cold Spring Harbor Laboratory Press2021-03-09
https://biorxiv.org/cgi/content/short/2021.03.19.436212v1?rss=1"
Jagadeesh, K. A.Dey, K. K.Montoro, D. T.Gazal, S.Engreitz, J. M.Xavier, R. J.Price, A. L.Regev, A.2021-03-19doi:10.1101/2021.03.19.436212Cold Spring Harbor Laboratory Press2021-03-19
https://biorxiv.org/cgi/content/short/2021.01.13.424697v1?rss=1"
Griesemer, D.Xue, J. R.Reilly, S. K.Ulirsch, J. C.Kukreja, K.Davis, J.Kanai, M.Yang, D. K.Montgomery, S. B.Novina, C. D.Tewhey, R.Sabeti, P. C.2021-01-13doi:10.1101/2021.01.13.424697Cold Spring Harbor Laboratory Press2021-01-13
https://biorxiv.org/cgi/content/short/2021.04.26.441442v1?rss=1"
10 assays in four donors (>1500 open-access functional genomic and proteomic datasets, in total). Each dataset is mapped to a matched, diploid personal genome, which has long-read phasing and structural variants. The mappings enable us to identify >1 million loci with allele-specific behavior. These loci exhibit coordinated epigenetic activity along haplotypes and less conservation than matched, non-allele-specific loci, in a fashion broadly paralleling tissue-specificity. Surprisingly, they can be accurately modelled just based on local nucleotide-sequence context. Combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci and enables models for transferring known eQTLs to difficult-to-profile tissues. Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.
]]>Rozowsky, J.Drenkow, J.Yang, Y.Gursoy, G.Galeev, T.Borsari, B.Epstein, C.Xiong, K.Xu, J.Gao, J.Yu, K.Berthel, A.Chen, Z.Navarro, F.Liu, J.Sun, M.Wright, J.Chang, J.Cameron, C.Shoresh, N.Gaskell, E.Adrian, J.Aganezov, S.Balderrama-Gutierrez, G.Banskota, S.Corona, G.Chee, S.Chhetri, S.Martins, G.Danyko, C.Davis, C.Farid, D.Farrell, N.Gabdank, I.Gofin, Y.Gorkin, D.Gu, M.Hecht, V.Hitz, B.Issner, R.Kirsche, M.Kong, X.Lam, B.Li, S.Li, B.Li, T.Li, X.Lin, K.Luo, R.Mackiewicz, M.Moore, J.Mudge, J.Nel2021-04-26doi:10.1101/2021.04.26.441442Cold Spring Harbor Laboratory Press2021-04-26
https://biorxiv.org/cgi/content/short/2021.04.26.441522v1?rss=1"
Rebboah, E.Reese, F.Williams, K.Balderrama-Gutierrez, G.McGill, C.Trout, D.Rodriguez, I. M.Liang, H.Wold, B. J.Mortazavi, A.2021-04-27doi:10.1101/2021.04.26.441522Cold Spring Harbor Laboratory Press2021-04-27
https://biorxiv.org/cgi/content/short/2021.04.19.440534v1?rss=1"
Reshef, Y. A.Rumker, L.Kang, J. B.Nathan, A.Murray, M. B.Moody, D. B.Raychaudhuri, S.2021-04-20doi:10.1101/2021.04.19.440534Cold Spring Harbor Laboratory Press2021-04-20
https://biorxiv.org/cgi/content/short/2021.05.06.443037v1?rss=1"
Rajderkar, S.Barozzi, I.Zhu, Y.Hu, R.Zhang, Y.Li, B.Fukuda-Yuzawa, Y.Kelman, G.Akeza, A.Blow, M. J.Pham, Q.Harrington, A. N.Godoy, J.Meky, E. M.von Maydell, K.Novak, C. S.Plajzer-Frick, I.Afzal, V.Tran, S.Talkowski, M. E.Llyod, K. C. K.Ren, B.Dickel, D. E.Visel, A.Pennacchio, L. A.2021-05-07doi:10.1101/2021.05.06.443037Cold Spring Harbor Laboratory Press2021-05-07
https://biorxiv.org/cgi/content/short/2020.11.02.364265v1?rss=1"
Pierce, S. E.Granja, J. M.Greenleaf, W. J.2020-11-02doi:10.1101/2020.11.02.364265Cold Spring Harbor Laboratory Press2020-11-02
https://biorxiv.org/cgi/content/short/2021.02.25.430130v1?rss=1"
Delorey, T. M.Ziegler, C. G. K.Heimberg, G.Normand, R.Yang, Y.Segerstolpe, A.Abbondanza, D.Fleming, S. J.Subramanian, A.Montoro, D. T.Jagadeesh, K. A.Dey, K.Sen, P.Slyper, M.Pita-Juarez, Y.Phillips, D.Bloom-Ackermann, Z.Barkas, N.Ganna, A.Gomez, J.Normandin, E.Naderi, P.Popov, Y. V.Raju, S. S.Niezen, S.Tsai, L. T.- Y.Siddle, K. J.Sud, M.Tran, V. M.Karuthedath Vellarikkal, S.Amir-Zilberstein, L.Atri, D. S.Beechem, J. M.Brook, O. R.Chen, J.Divakar, P.Dorceus, P.Engreitz, J. M.Essene, A.Fitzgerald, D. M.Fropf, R.Gaz2021-02-25doi:10.1101/2021.02.25.430130Cold Spring Harbor Laboratory Press2021-02-25
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Pratt, H. E.Andrews, G. R.Phalke, N.Purcaro, M. J.van der Velde, A. G.Moore, J. E.Weng, Z.2021-10-12doi:10.1101/2021.10.11.463518Cold Spring Harbor Laboratory Press2021-10-12
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Bergman, D. T.Jones, T. R.Liu, V.Siraj, L.Kang, H. Y.Nasser, J.Nguyen, T. H.Grossman, S. R.Fulco, C. P.Lander, E. S.Engreitz, J. M.2021-10-24doi:10.1101/2021.10.23.462170Cold Spring Harbor Laboratory Press2021-10-24
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Du, A. Y.Zhuo, X.Sundaram, V.Jensen, N. O.Chaudhari, H. G.Saccone, N. L.Cohen, B. A.Wang, T.2022-03-17doi:10.1101/2022.03.16.483999Cold Spring Harbor Laboratory Press2022-03-17
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Zheng, S.Thakkar, N.Harris, H. L.Zhang, M.Liu, S.Gerstein, M.Aiden, E. L.Rowley, J.Noble, W. S.Gursoy, G.Singh, R.2022-04-19doi:10.1101/2022.04.19.488754Cold Spring Harbor Laboratory Press2022-04-19
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Mannion, B. J.Osterwalder, M.Tran, S.Plajzer-Frick, I.Novak, C. S.Afzal, V.Akiyama, J. A.Barton, S.Beckman, E.Garvin, T. H.Godfrey, P.Godoy, J.Hunter, R. D.Kato, M.Kosicki, M.Kronshage, A. H.Lee, E. A.Meky, E. M.Pham, Q. T.von Maydell, K.Zhu, Y.Lopez-Rios, J.Dickel, D. E.Visel, A.Pennacchio, L. A.2022-05-30doi:10.1101/2022.05.29.493901Cold Spring Harbor Laboratory Press2022-05-30
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Hitz, B. C.Lee, J.-W.Jolanki, O.Kagda, M. S.Graham, K.Sud, P.Gabdank, I.Strattan, J. S.Sloan, C. A.Dreszer, T.Rowe, L. D.Podduturi, N. R.Malladi, V. S.Chan, E. T.Davidson, J. M.Ho, M.Miyasato, S.Simison, M.Tanaka, F.Luo, Y.Whaling, I.Lin, K.Jou, J.Hong, E. L.Lee, B. T.Sandstrom, R.Rynes, E.Nelson, J.Nishida, A.Ingersoll, A.Buckley, M.Frerker, M.Kim, D. S.Boley, N.Trout, D.Dobin, A.Rahmanian, S.Wyman, D.Balderrama-Gutierrez, G.Reese, F.Durand, N. C.Dudchenko, O.Weisz, D.Rao, S. S. P.Blackburn, A.Gkountarou2023-04-06doi:10.1101/2023.04.04.535623Cold Spring Harbor Laboratory Press2023-04-06
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Zhang, Y.Chen, K.Tang, S. C.Cai, Y.Nambu, A.See, Y. X.Fu, C.Raju, A.Lebeau, B.Ling, Z.Mutwil, M.Lakshmanan, M.Osato, M.Tergaonkar, V.Fullwood, M. J.2023-08-30doi:10.1101/2023.08.29.555291Cold Spring Harbor Laboratory Press2023-08-30
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13 million enhancer-gene regulatory interactions across 352 cell types and tissues, by integrating predictive models, measurements of chromatin state and 3D contacts, and large-scale genetic perturbations generated by the ENCODE Consortium7. We first create a systematic benchmarking pipeline to compare predictive models, assembling a dataset of 10,411 element-gene pairs measured in CRISPR perturbation experiments, >30,000 fine-mapped eQTLs, and 569 fine-mapped GWAS variants linked to a likely causal gene. Using this framework, we develop a new predictive model, ENCODE-rE2G, that achieves state-of-the-art performance across multiple prediction tasks, demonstrating a strategy involving iterative perturbations and supervised machine learning to build increasingly accurate predictive models of enhancer regulation. Using the ENCODE-rE2G model, we build an encyclopedia of enhancer-gene regulatory interactions in the human genome, which reveals global properties of enhancer networks, identifies differences in the functions of genes that have more or less complex regulatory landscapes, and improves analyses to link noncoding variants to target genes and cell types for common, complex diseases. By interpreting the model, we find evidence that, beyond enhancer activity and 3D enhancer-promoter contacts, additional features guide enhancer-promoter communication including promoter class and enhancer-enhancer synergy. Altogether, these genome-wide maps of enhancer-gene regulatory interactions, benchmarking software, predictive models, and insights about enhancer function provide a valuable resource for future studies of gene regulation and human genetics.
]]>Gschwind, A. R.Mualim, K. S.Karbalayghareh, A.Sheth, M. U.Dey, K. K.Jagoda, E.Nurtdinov, R. N.Xi, W.Tan, A. S.Jones, H.Ma, X. R.Yao, D.Nasser, J.Avsec, Z.James, B. T.Shamim, M. S.Durand, N. C.Rao, S. S. P.Mahajan, R.Doughty, B. R.Andreeva, K.Ulirsch, J. C.Fan, K.Perez, E. M.Nguyen, T. C.Kelley, D. R.Finucane, H. K.Moore, J. E.Weng, Z.Kellis, M.Bassik, M. C.Price, A. L.Beer, M. A.Guigo, R.Stamatoyannopoulos, J. A.Aiden, E. L.Greenleaf, W. J.Leslie, C. S.Steinmetz, L. M.Kundaje, A.Engreitz, J. M.2023-11-13doi:10.1101/2023.11.09.563812Cold Spring Harbor Laboratory Press2023-11-13