bioRxiv Channel: ENCODE
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This feed contains articles for bioRxiv Channel "ENCODE"
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Prioritizing transcriptomic and epigenomic experiments by using an optimization strategy that leverages imputed data
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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
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Transcription imparts architecture, function, and logic to enhancer units
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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
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Spatiotemporal DNA Methylome Dynamics of the Developing Mammalian Fetus
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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
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Systematic mapping of chromatin state landscapes during mouse development
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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
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An integrative view of the regulatory and transcriptional landscapes in mouse hematopoiesis
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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
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A Large-Scale Binding and Functional Map of Human RNA Binding Proteins
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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
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A limited set of transcriptional programs define major histological types and provide the molecular basis for a cellular taxonomy of the human body
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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
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Occupancy patterns of 208 DNA-associated proteins in a single human cell type
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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
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A cross-organism framework for supervised enhancer prediction with epigenetic pattern recognition and targeted validation
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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
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A curated benchmark of enhancer-gene interactions for evaluating enhancer-target gene prediction methods
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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
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Integrative analysis of 10,000 epigenomic maps across 800 samples for regulatory genomics and disease dissection
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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
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Quantifying genetic effects on disease mediated by assayed gene expression levels
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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
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Population-specific causal disease effect sizes in functionally important regions impacted by selection
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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
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Improving the informativeness of Mendelian disease pathogenicity scores for common disease
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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
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Functionally-informed fine-mapping and polygenic localization of complex trait heritability
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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
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Evaluating the informativeness of deep learning annotations for human complex diseases
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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
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Modeling functional enrichment improves polygenic prediction accuracy in UK Biobank and 23andMe data sets
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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
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A pitfall for machine learning methods aiming to predict across cell types
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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
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Global reference mapping and dynamics of human transcription factor footprints
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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
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Allele-specific binding of RNA-binding proteins reveals functional genetic variants in the RNA
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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
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Widespread RNA editing dysregulation in Autism Spectrum Disorders
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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
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Co-regulation of alternative splicing by hnRNPM and ESRP1 during EMT
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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
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Principles of RNA processing from analysis of enhanced CLIP maps for 150 RNA binding proteins.
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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
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A unified encyclopedia of human functional DNA elements through fully automated annotation of 164 human cell types
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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
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Joint annotation of chromatin state and chromatin conformation reveals relationships among domain types and identifies domains of cell type-specific expression
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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
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Choosing panels of genomics assays using submodular optimization
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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
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Eliminating redundancy among protein sequences using submodular optimization
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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
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PREDICTD: PaRallel Epigenomics Data Imputation With Cloud-based Tensor Decomposition
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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
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Segway 2.0: Gaussian mixture models and minibatch training
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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
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LR-DNase: Predicting TF binding from DNase-seq data
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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
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GenomeDISCO: A concordance score for chromosome conformation capture experiments using random walks on contact map graphs
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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
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Massively parallel profiling and predictive modeling of the outcomes of CRISPR/Cas9-mediated double-strand break repair
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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
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Index and biological spectrum of accessible DNA elements in the human genome
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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
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Zero-shot imputations across species are enabled through joint modeling of human and mouse epigenomics
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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
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A systematic evaluation of the design, orientation, and sequence context dependencies of massively parallel reporter assays
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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
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A genome-wide almanac of co-essential modules assigns function to uncharacterized genes
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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
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RADAR: annotation and prioritization of variants in the post-transcriptional regulome of RNA-binding proteins
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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
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Transcriptome-Wide Combinatorial RNA Structure Probing in Living Cells
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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
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3D Epigenomic Characterization Reveals Insights Into Gene Regulation and Lineage Specification During Corticogenesis
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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
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ArchR: An integrative and scalable software package for single-cell chromatin accessibility analysis
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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
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Allele-specific alternative splicing in human tissues
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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
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Differential RNA editing between epithelial and mesenchymal tumors impacts mRNA abundance in immune response pathways
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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
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Extracellular microRNA 3' end modification across diverse body fluids
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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
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HCR-FlowFISH: A flexible CRISPR screening method to identify cis-regulatory elements and their target genes
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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
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STARRPeaker: Uniform processing and accurate identification of whole human STARR-seq active regions
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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
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An integrative ENCODE resource for cancer genomics
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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
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Completing the ENCODE3 compendium yields accurate imputations across a variety of assays and human biosamples
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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
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Multi-scale deep tensor factorization learns a latent representation of the human epigenome
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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
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Pseudogenes in the mouse lineage: transcriptional activity and strain-specific history
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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
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Detecting sample swaps in diverse NGS data types using linkage disequilibrium
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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
<![CDATA[
Measuring the reproducibility and quality of Hi-C data
]]>
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
<![CDATA[
Nucleotide sequence and DNaseI sensitivity are predictive of 3D chromatin architecture
]]>
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
<![CDATA[
HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient
]]>
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
<![CDATA[
An Integrative Framework For Detecting Structural Variations In Cancer Genomes
]]>
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
<![CDATA[
TopicNet: a framework for measuring transcriptional regulatory network change
]]>
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
<![CDATA[
DiNeR: a Differential Graphical Model for analysis of co-regulation Network Rewiring
]]>
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
<![CDATA[
Epigenome-based Splicing Prediction using a Recurrent Neural Network
]]>
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
<![CDATA[
The changing mouse embryo transcriptome at whole tissue and single-cell resolution
]]>
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
<![CDATA[
Atlas and developmental dynamics of mouse DNase I hypersensitive sites
]]>
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
<![CDATA[
Loop extrusion model predicts CTCF interaction specificity
]]>
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
<![CDATA[
Dissecting the regulatory activity and key sequence elements of loci with exceptional numbers of transcription factor associations
]]>
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
<![CDATA[
Long-TUC-seq is a robust method for quantification of metabolically labeled full-length isoforms
]]>
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
<![CDATA[
Swan: a library for the analysis and visualization of long-read transcriptomes
]]>
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
<![CDATA[
Detecting regulatory elements in high-throughput reporter assays
]]>
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
<![CDATA[
In silico integration of thousands of epigenetic datasets into 707 cell type regulatory annotations improves the trans-ethnic portability of polygenic risk scores
]]>
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
<![CDATA[
Unique contribution of enhancer-driven and master-regulator genes to autoimmune disease revealed using functionally informed SNP-to-gene linking strategies
]]>
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
<![CDATA[
Integrative approaches to improve the informativeness of deep learning models for human complex diseases
]]>
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
<![CDATA[
Genetic and Epigenetic Features of Promoters with Ubiquitous Chromatin Accessibility Support Ubiquitous Transcription of Cell-essential Genes
]]>
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
<![CDATA[
HiC-DC+: systematic 3D interaction calls and differential analysis for Hi-C and HiChIP
]]>
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
<![CDATA[
Genome-wide Identification of the Genetic Basis of Amyotrophic Lateral Sclerosis
]]>
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
<![CDATA[
H3K27me3-rich genomic regions can function as silencers to repress gene expression via chromatin interactions
]]>
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
<![CDATA[
IFN-γ and TNF-α drive a CXCL10+ CCL2+ macrophage phenotype expanded in severe COVID-19 and other diseases with tissue inflammation
]]>
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
<![CDATA[
Efficient and precise single-cell reference atlas mapping with Symphony
]]>
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
<![CDATA[
Maximizing statistical power to detect clinically associated cell states with scPOST
]]>
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
<![CDATA[
Multimodal memory T cell profiling identifies a reduction in a polyfunctional Th17 state associated with tuberculosis progression
]]>
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
<![CDATA[
De Novo Prediction of Human Chromosome Structures: Epigenetic Marking Patterns Encode Genome Architecture
]]>
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
<![CDATA[
Walking along chromosomes with super-resolution imaging, contact maps, and integrative modeling
]]>
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
<![CDATA[
Activity-by-Contact model of enhancer specificity from thousands of CRISPR perturbations
]]>
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
<![CDATA[
Exploring Chromosomal Structural Heterogeneity AcrossMultiple Cell Lines
]]>
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
<![CDATA[
ESCO1 and CTCF enable formation of long chromatin loops by protecting cohesinSTAG1 from WAPL
]]>
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
<![CDATA[
Chromosomal-level genome assembly of the scimitar-horned oryx: insights into diversity and demography of a species extinct in the wild
]]>
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
<![CDATA[
The Gene-Rich Genome of the Scallop Pecten maximus
]]>
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
<![CDATA[
Chromatin is frequently unknotted at the megabase scale
]]>
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
<![CDATA[
Simple biochemical features underlie transcriptional activation domain diversity and dynamic, fuzzy binding to Mediator
]]>
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
<![CDATA[
Cohesin depleted cells pass through mitosis and reconstitute a functional nuclear architecture
]]>
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
<![CDATA[
Juicebox.js provides a cloud-based visualization system for Hi-C data
]]>
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
<![CDATA[
Cohesin Loss Eliminates All Loop Domains, Leading To Links Among Superenhancers And Downregulation Of Nearby Genes
]]>
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
<![CDATA[
Static And Dynamic DNA Loops Form AP-1 Bound Activation Hubs During Macrophage Development
]]>
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
<![CDATA[
The Hi-Culfite assay reveals relationships between chromatin contacts and DNA methylation state
]]>
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
<![CDATA[
Parallel Characterization of cis-Regulatory Elements for Multiple Genes UsingCRISPRpath
]]>
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
<![CDATA[
Chromatin Interaction Neural Network (ChINN): A machine learning-based method for predicting chromatin interactions from DNA sequences
]]>
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
<![CDATA[
Three-dimensional Genome Organization Maps in Normal Haematopoietic Stem Cells and Acute Myeloid Leukemia
]]>
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
<![CDATA[
MYC overexpression leads to increased chromatin interactions at superenhancers and c-Myc binding sites
]]>
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
<![CDATA[
Biop-C: A Method for Chromatin Interactome Analysis of Solid Cancer Needle Biopsy Samples
]]>
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
<![CDATA[
A cell atlas of chromatin accessibility across 25 adult human tissues
]]>
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
<![CDATA[
Discovery and Functional Characterization of Pro-growth Enhancers in Human Cancer Cells
]]>
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
<![CDATA[
Transgenic mice for in vivo epigenome editing with CRISPR-based systems
]]>
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
<![CDATA[
Genome-wide annotation of gene regulatory elements linked to cell fitness
]]>
https://biorxiv.org/cgi/content/short/2021.03.08.434470v1?rss=1"
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
<![CDATA[
Identifying disease-critical cell types and cellular processes across the human body by integration of single-cell profiles and human genetics
]]>
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
<![CDATA[
Genome-wide functional screen of 3'UTR variants uncovers causal variants for human disease and evolution
]]>
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
<![CDATA[
Multi-tissue integrative analysis of personal epigenomes
]]>
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
<![CDATA[
Mapping and modeling the genomic basis of differential RNA isoform expression at single-cell resolution with LR-Split-seq
]]>
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
<![CDATA[
Axes of inter-sample variability among transcriptional neighborhoods reveal disease associated cell states in single-cell data
]]>
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
<![CDATA[
Topologically Associating Domain Boundaries are Commonly Required for Normal Genome Function
]]>
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
<![CDATA[
High-throughput single-cell chromatin accessibility CRISPR screens enable unbiased identification of regulatory networks in cancer
]]>
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
<![CDATA[
A single-cell and spatial atlas of autopsy tissues reveals pathology and cellular targets of SARS-CoV-2
]]>
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
<![CDATA[
A catalog of transcription start sites across 115 human tissue and cell types
]]>
https://biorxiv.org/cgi/content/short/2021.05.12.443890v1?rss=1"
Moore, J. E.Zhang, X.-O.Elhajjajy, S. I.Fan, K.Reese, F.Mortazavi, A.Weng, Z.2021-05-13doi:10.1101/2021.05.12.443890Cold Spring Harbor Laboratory Press2021-05-13
<![CDATA[
Systematic comparison of experimental assays and analytical pipelines for identification of active enhancers genome-wide
]]>
https://biorxiv.org/cgi/content/short/2021.06.02.446833v1?rss=1"
Yao, L.Liang, J.Ozer, A.Leung, A. K.-Y.ENCODE Consortium,Lis, J. T.Yu, H.2021-06-03doi:10.1101/2021.06.02.446833Cold Spring Harbor Laboratory Press2021-06-03
<![CDATA[
Glucocorticoid receptor collaborates with pioneer factors and AP-1 to execute genome-wide regulation
]]>
https://biorxiv.org/cgi/content/short/2021.06.01.444518v1?rss=1"
Wissink, E. M.Martinez, D. M.Ehmsen, K. T.Yamamoto, K. R.Lis, J. T.2021-06-01doi:10.1101/2021.06.01.444518Cold Spring Harbor Laboratory Press2021-06-01
<![CDATA[
Factorbook: an Updated Catalog of Transcription Factor Motifs and Candidate Regulatory Motif Sites
]]>
https://biorxiv.org/cgi/content/short/2021.10.11.463518v1?rss=1"
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
<![CDATA[
Widespread contribution of transposable elements to the rewiring of mammalian 3D genomes and gene regulation
]]>
https://biorxiv.org/cgi/content/short/2022.02.01.475239v1?rss=1"
Choudhary, M. N.Quaid, K.Xing, X.Schmidt, H.Wang, T.2022-02-03doi:10.1101/2022.02.01.475239Cold Spring Harbor Laboratory Press2022-02-03
<![CDATA[
Leveraging single-cell ATAC-seq to identify disease-critical fetal and adult brain cell types
]]>
https://biorxiv.org/cgi/content/short/2021.05.20.445067v1?rss=1"
Kim, S. S.Jagadeesh, K.Dey, K. K.Shen, A. Z.Raychaudhuri, S.Kellis, M.Price, A. L.2021-05-21doi:10.1101/2021.05.20.445067Cold Spring Harbor Laboratory Press2021-05-21
<![CDATA[
Compatibility logic of human enhancer and promoter sequences
]]>
https://biorxiv.org/cgi/content/short/2021.10.23.462170v1?rss=1"
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
<![CDATA[
Chromatin interaction aware gene regulatory modeling with graph attention networks
]]>
https://biorxiv.org/cgi/content/short/2021.03.31.437978v1?rss=1"
Karbalayghareh, A.Sahin, M.Leslie, C. S.2021-04-02doi:10.1101/2021.03.31.437978Cold Spring Harbor Laboratory Press2021-04-02
<![CDATA[
Evolution of transposable element-derived enhancer activity
]]>
https://biorxiv.org/cgi/content/short/2022.03.16.483999v1?rss=1"
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
<![CDATA[
Predicting A/B compartments from histone modifications using deep learning
]]>
https://biorxiv.org/cgi/content/short/2022.04.19.488754v1?rss=1"
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
<![CDATA[
Uncovering Hidden Enhancers Through Unbiased In Vivo Testing
]]>
https://biorxiv.org/cgi/content/short/2022.05.29.493901v1?rss=1"
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
<![CDATA[
The ENCODE Imputation Challenge: A critical assessment of methods for cross-cell type imputation of epigenomic profiles
]]>
https://biorxiv.org/cgi/content/short/2022.07.30.502157v1?rss=1"
Schreiber, J. M.Boix, C. A.Lee, J. w.Li, H.Guan, Y.Chang, C.-C.Chang, J.-C.Hawkins-Hooker, A.Schoelkopf, B.Schweikert, G.Rojas Carulla, M.Canakoglu, A.Guzzo, F.Nanni, L.Masseroli, M.Carman, M. J.Pinoli, P.Hong, C.Yip, K. Y.Spence, J. P.Batra, S. S.Song, Y. S.Mahony, S.Zhang, Z.Tan, W.Shen, Y.Sun, Y.Shi, M.Adrian, J.Sandstrom, R. S.Farrell, N.Halow, J. M.Lee, K.Jiang, L.Yang, X.Epstein, C. B.Strattan, J. S.Snyder, M. P.Kellis, M.Noble, W. S.Kundaje, A. B.2022-08-02doi:10.1101/2022.07.30.502157Cold Spring Harbor Laboratory Press2022-08-02
<![CDATA[
Genome-wide CRISPR guide RNA design and specificity analysis with GuideScan2
]]>
https://biorxiv.org/cgi/content/short/2022.05.02.490368v1?rss=1"
Schmidt, H.Zhang, M.Mourelatos, H.Sanchez-Rivera, F. J.Lowe, S. W.Ventura, A.Leslie, C. S.Pritykin, Y.2022-05-03doi:10.1101/2022.05.02.490368Cold Spring Harbor Laboratory Press2022-05-03
<![CDATA[
Scalable sequence-informed embedding of single-cell ATAC-seq data with CellSpace
]]>
https://biorxiv.org/cgi/content/short/2022.05.02.490310v1?rss=1"
Tayyebi, Z.Pine, A. R.Leslie, C. S.2022-05-02doi:10.1101/2022.05.02.490310Cold Spring Harbor Laboratory Press2022-05-02
<![CDATA[
Epiphany: predicting Hi-C contact maps from 1D epigenomic signals
]]>
https://biorxiv.org/cgi/content/short/2021.12.02.470663v1?rss=1"
Yang, R.Das, A.Gao, V. R.Karbalayghareh, A.Noble, W. S.Bilmes, J. A.Leslie, C. S.2021-12-03doi:10.1101/2021.12.02.470663Cold Spring Harbor Laboratory Press2021-12-03
<![CDATA[
Three linked opposing regulatory variants under selection associate with IVD
]]>
https://biorxiv.org/cgi/content/short/2022.12.22.521605v1?rss=1"
Brown, E. A.Kales, S.Boyle, M. J.Vitti, J.Kotliar, D.Schaffner, S. F.Tewhey, R. S.Sabeti, P. C.2022-12-22doi:10.1101/2022.12.22.521605Cold Spring Harbor Laboratory Press2022-12-22
<![CDATA[
Multiplex genomic recording of enhancer and signal transduction activity in mammalian cells
]]>
https://biorxiv.org/cgi/content/short/2021.11.05.467434v1?rss=1"
Chen, W.Choi, J.Nathans, J. F.Agarwal, V.Martin, B.Nichols, E.Leith, A.Lee, C.Shendure, J.2021-11-05doi:10.1101/2021.11.05.467434Cold Spring Harbor Laboratory Press2021-11-05
<![CDATA[
Massively parallel characterization of transcriptional regulatory elements in three diverse human cell types
]]>
https://biorxiv.org/cgi/content/short/2023.03.05.531189v1?rss=1"
Agarwal, V.Inoue, F.Schubach, M.Martin, B.Dash, P.Zhang, Z.Sohota, A.Noble, W.Yardimci, G.Kircher, M.Shendure, J.Ahituv, N.2023-03-06doi:10.1101/2023.03.05.531189Cold Spring Harbor Laboratory Press2023-03-06
<![CDATA[
Orthogonal CRISPR screens to identify transcriptional and epigenetic regulators of human CD8 T cell function
]]>
https://biorxiv.org/cgi/content/short/2023.05.01.538906v1?rss=1"
McCutcheon, S.Swartz, A.Brown, M.Barrera, A.McRoberts Amador, C.Siklenka, K.Humayun, L.Isaacs, J.Reddy, T. E.Nair, S.Antonia, S.Gersbach, C. A.2023-05-01doi:10.1101/2023.05.01.538906Cold Spring Harbor Laboratory Press2023-05-01
<![CDATA[
Multi-center integrated analysis of non-coding CRISPR screens
]]>
https://biorxiv.org/cgi/content/short/2022.12.21.520137v1?rss=1"
Yao, D.Tycko, J.Oh, W.Bounds, L. R.Gosai, S. J.Lataniotis, L.Mackay-Smith, A.Doughty, B. R.Gabdank, I.Schmidt, H.Youngworth, I.Andreeva, K.Ren, X.Barrera, A.Luo, Y.Siklenka, K.Yardimci, G. G.The ENCODE4 Consortium,Tewhey, R.Kundaje, A.Greenleaf, W. J.Sabeti, P. C.Leslie, C.Pritykin, Y.Moore, J. E.Beer, M. A.Gersbach, C.Reddy, T. E.Shen, Y.Engreitz, J. M.Bassik, M. C.Reilly, S. K.2022-12-22doi:10.1101/2022.12.21.520137Cold Spring Harbor Laboratory Press2022-12-22
<![CDATA[
Functional characterization of gene regulatory elements and neuropsychiatric disease-associated risk loci in iPSCs and iPSC-derived neurons
]]>
https://biorxiv.org/cgi/content/short/2023.08.30.555359v1?rss=1"
Yang, X.Jones, I. R.Chen, P. B.Yang, H.Ren, X.Zheng, L.Li, B.Li, Y. E.Sun, Q.Wen, J.Beaman, C.Cui, X.Li, Y.Wang, W.Hu, M.Ren, B.Shen, Y.2023-08-30doi:10.1101/2023.08.30.555359Cold Spring Harbor Laboratory Press2023-08-30
<![CDATA[
The ENCODE Uniform Analysis Pipelines
]]>
https://biorxiv.org/cgi/content/short/2023.04.04.535623v1?rss=1"
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
<![CDATA[
Super-silencer perturbation by EZH2 and REST inhibition leads to large loss of chromatin interactions and reduction in cancer growth
]]>
https://biorxiv.org/cgi/content/short/2023.08.29.555291v1?rss=1"
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
<![CDATA[
Regulatory Transposable Elements in the Encyclopedia of DNA Elements
]]>
https://biorxiv.org/cgi/content/short/2023.09.05.556380v1?rss=1"
Du, A. Y.Chobirko, J. D.Zhuo, X.Feschotte, C.Wang, T.2023-09-06doi:10.1101/2023.09.05.556380Cold Spring Harbor Laboratory Press2023-09-06
<![CDATA[
Gapped-kmer sequence modeling robustly identifies regulatory vocabularies and distal enhancers conserved between evolutionarily distant mammals
]]>
https://biorxiv.org/cgi/content/short/2023.10.06.561128v1?rss=1"
Oh, J. W.Beer, M. A.2023-10-06doi:10.1101/2023.10.06.561128Cold Spring Harbor Laboratory Press2023-10-06
<![CDATA[
An encyclopedia of enhancer-gene regulatory interactions in the human genome
]]>
https://biorxiv.org/cgi/content/short/2023.11.09.563812v1?rss=1"
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
<![CDATA[
Isoform and pathway-specific post-transcriptional RNA processing in human cells
]]>
https://biorxiv.org/cgi/content/short/2024.06.12.598705v1?rss=1"
Bedi, K.Magnuson, B.Narayanan, I. V.McShane, A.Ashaka, M.Paulsen, M. T.Wilson, T. E.Ljungman, M.2024-06-12doi:10.1101/2024.06.12.598705Cold Spring Harbor Laboratory Press2024-06-12
<![CDATA[
Characterizing nascent transcription patterns of PROMPTs, eRNAs, and readthrough transcripts in the ENCODE4 deeply profiled cell lines
]]>
https://biorxiv.org/cgi/content/short/2024.04.09.588612v1?rss=1"
McShane, A.Venkata Narayanan, I.Paulsen, M. T.Ashaka, M.Blinkiewicz, H.Yang, N. T.Magnuson, B.Bedi, K.Wilson, T. E.Ljungman, M.2024-04-09doi:10.1101/2024.04.09.588612Cold Spring Harbor Laboratory Press2024-04-09
<![CDATA[
CRISPR tiling deletion screens reveal functional enhancers of neuropsychiatric risk genes and allelic compensation effects (ACE) on transcription
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https://biorxiv.org/cgi/content/short/2024.10.08.616922v1?rss=1"
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Quantifying Functional Conservation of Human and Mouse Regulatory Elements via FUNCODE
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https://biorxiv.org/cgi/content/short/2024.10.31.620766v1?rss=1"
Fang, W.Chen, C.Zhang, B.Wang, Y.Zhao, R.Zhou, W.Ji, H.2024-11-03doi:10.1101/2024.10.31.620766Cold Spring Harbor Laboratory Press2024-11-03
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Directionality of Transcriptional Regulatory Elements
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https://biorxiv.org/cgi/content/short/2024.12.01.621925v1?rss=1"
Chen, Y.Shah, S. R.Leung, A.Paramo, M. I.Cochran, K.Kundaje, A.Clark, A. G.Lis, J. T.Yu, H.2024-12-02doi:10.1101/2024.12.01.621925Cold Spring Harbor Laboratory Press2024-12-02
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An Expanded Registry of Candidate cis-Regulatory Elements for Studying Transcriptional Regulation
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https://biorxiv.org/cgi/content/short/2024.12.26.629296v1?rss=1"
Moore, J. E.Pratt, H. E.Fan, K.Phalke, N.Fisher, J.Elhajjajy, S. I.Andrews, G.Gao, M.Shedd, N.Fu, Y.Lacadie, M. C.Meza, J.Ganna, M.Choudhury, E.Swofford, R.Farrell, N. P.Pampari, A.Ramalingam, V.Reese, F.Borsari, B.Yu, X.Wattenberg, E. S.Ruiz-Romero, M.Razavi-Mohseni, M.Xu, J.Galeev, T.Beer, M. A.Guigo, R.Gerstein, M.Engreitz, J. M.Ljungman, M.Reddy, T. E.Snyder, M.Epstein, C. B.Gaskell, E.Bernstein, B. E.Dickel, D. E.Visel, A.Pennacchio, L. A.Mortazavi, A.Kundaje, A.Weng, Z.2024-12-26doi:10.1101/2024.12.26.629296Cold Spring Harbor Laboratory Press2024-12-26