Hold out the genome: a roadmap to solving the cis-regulatory code

Highly accurate protein structure prediction with AlphaFold

Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works

Initial sequencing and analysis of the human genome

International Human Genome Sequencing Consortium, Whitehead Institute for Biomedical Research, C. F. G. R., Lander, E. S., Linton, L. M., Birren, B., Nusbaum, C., Zody, M. C., Baldwin, J., Devon, K., Dewar, K., et al. (2001) · Nature · doi:10.1038/35057062 · Cited by 24,216 · see citing works

The sequence of the human genome

Venter, J. C., Adams, M. D., Myers, E. W., Li, P. W., Mural, R. J., Sutton, G. G., Smith, H. O., Yandell, M., Evans, C. A., Holt, R. A., et al. (2001) · Science · doi:10.1126/science.1058040 · Cited by 13,909 · see citing works

Comprehensive Mapping of Long-Range Interactions Reveals Folding Principles of the Human Genome

Lieberman-Aiden, E., van Berkum, N. L., Williams, L., Imakaev, M., Ragoczy, T., Telling, A., Amit, I., Lajoie, B. R., Sabo, P. J., Dorschner, M. O., et al. (2009) · Science · doi:10.1126/science.1181369 · Cited by 8,898 · see citing works

Protein complex prediction with AlphaFold-Multimer

Evans, R., O’Neill, M., Pritzel, A., Antropova, N., Senior, A., Green, T., Žídek, A., Bates, R., Blackwell, S., Yim, J., et al. (2021) · doi:10.1101/2021.10.04.463034 · Cited by 3,808 · see citing works

Evolutionary Rate at the Molecular Level

KIMURA, M. (1968) · Nature · doi:10.1038/217624a0 · Cited by 3,747 · see citing works

Systematic Localization of Common Disease-Associated Variation in Regulatory DNA

Maurano, M. T., Humbert, R., Rynes, E., Thurman, R. E., Haugen, E., Wang, H., Reynolds, A. P., Sandstrom, R., Qu, H., Brody, J., et al. (2012) · Science · doi:10.1126/science.1222794 · Cited by 3,716 · see citing works

The Human Transcription Factors

Lambert, S. A., Jolma, A., Campitelli, L. F., Das, P. K., Yin, Y., Albu, M., Chen, X., Taipale, J., Hughes, T. R., & Weirauch, M. T. (2018) · Cell · doi:10.1016/j.cell.2018.01.029 · Cited by 3,399 · see citing works

The complete sequence of a human genome

Nurk, S., Koren, S., Rhie, A., Rautiainen, M., Bzikadze, A. V., Mikheenko, A., Vollger, M. R., Altemose, N., Uralsky, L., Gershman, A., et al. (2022) · Science · doi:10.1126/science.abj6987 · Cited by 3,136 · see citing works

Predicting effects of noncoding variants with deep learning–based sequence model

Zhou, J., & Troyanskaya, O. G. (2015) · Nature Methods · doi:10.1038/nmeth.3547 · Cited by 2,338 · see citing works

Human organoids: model systems for human biology and medicine

Kim, J., Koo, B.-K., & Knoblich, J. A. (2020) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-020-0259-3 · Cited by 2,187 · see citing works

Function and information content of DNA methylation

Schübeler, D. (2015) · Nature · doi:10.1038/nature14192 · Cited by 2,060 · see citing works

Genetic and epigenetic fine mapping of causal autoimmune disease variants

Farh, K. K.-H., Marson, A., Zhu, J., Kleinewietfeld, M., Housley, W. J., Beik, S., Shoresh, N., Whitton, H., Ryan, R. J. H., Shishkin, A. A., et al. (2015) · Nature · doi:10.1038/nature13835 · Cited by 1,956 · see citing works

A Phase Separation Model for Transcriptional Control

Hnisz, D., Shrinivas, K., Young, R. A., Chakraborty, A. K., & Sharp, P. A. (2017) · Cell · doi:10.1016/j.cell.2017.02.007 · Cited by 1,753 · see citing works

The evolutionary significance of cis-regulatory mutations

Wray, G. A. (2007) · Nature Reviews Genetics · doi:10.1038/nrg2063 · Cited by 1,521 · see citing works

A genomic code for nucleosome positioning

Segal, E., Fondufe‐Mittendorf, Y. N., Chen, L., Thåström, A., Field, Y., Moore, I. K., Wang, J.-P. Z., & Widom, J. (2006) · Nature · doi:10.1038/nature04979 · Cited by 1,492 · see citing works

Effective gene expression prediction from sequence by integrating long-range interactions

Avsec, Ž., Agarwal, V., Visentin, D., Ledsam, J. R., Grabska-Barwinska, A., Taylor, K. R., Assael, Y., Jumper, J., Kohli, P., & Kelley, D. R. (2021) · Nature Methods · doi:10.1038/s41592-021-01252-x · Cited by 1,326 · see citing works

Impact of cytosine methylation on DNA binding specificities of human transcription factors

Yin, Y., Morgunova, E., Jolma, A., Kaasinen, E., Sahu, B., Khund-Sayeed, S., Das, P. K., Kivioja, T., Dave, K., Zhong, F., et al. (2017) · Science · doi:10.1126/science.aaj2239 · Cited by 1,257 · see citing works

Cis-regulatory elements: Molecular mechanisms and evolutionary processes underlying divergence

Wittkopp, P. J., & Kalay, G. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3095 · Cited by 1,231 · see citing works

Deep learning: new computational modelling techniques for genomics

Eraslan, G., Avsec, Ž., Gagneur, J., & Theis, F. J. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0122-6 · Cited by 1,229 · see citing works

DNA-Binding Specificities of Human Transcription Factors

Jolma, A., Yan, J., Whitington, T., Toivonen, J., Nitta, K. R., Rastas, P. M. A., Morgunova, E. Y., Enge, M., Taipale, M., Wei, G., et al. (2013) · Cell · doi:10.1016/j.cell.2012.12.009 · Cited by 1,228 · see citing works

In Vitro Selection of Functional Nucleic Acids

Wilson, D. S., & Szostak, J. W. (1999) · Annual Review of Biochemistry · doi:10.1146/annurev.biochem.68.1.611 · Cited by 1,209 · see citing works

Toward Causal Representation Learning

Scholkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., & Bengio, Y. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3058954 · Cited by 1,170 · see citing works

Evolution of the mutation rate

Lynch, M. (2010) · Trends in Genetics · doi:10.1016/j.tig.2010.05.003 · Cited by 1,138 · see citing works

Genome-Wide Quantitative Enhancer Activity Maps Identified by STARR-seq

Arnold, C. D., Gerlach, D., Stelzer, C., Boryń, Ł. M., Rath, M., & Stark, A. (2013) · Science · doi:10.1126/science.1232542 · Cited by 1,125 · see citing works

Repetitive Elements May Comprise Over Two-Thirds of the Human Genome

de Koning, A. P. J., Gu, W., Castoe, T. A., Batzer, M. A., & Pollock, D. D. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1002384 · Cited by 1,099 · see citing works

Regulatory evolution of innate immunity through co-option of endogenous retroviruses

Chuong, E. B., Elde, N. C., & Feschotte, C. (2016) · Science · doi:10.1126/science.aad5497 · Cited by 1,008 · see citing works

Enhancer Evolution across 20 Mammalian Species

Villar, D., Berthelot, C., Aldridge, S., Rayner, T. F., Lukk, M., Pignatelli, M., Park, T. J., Deaville, R., Erichsen, J. T., Jasinska, A. J., et al. (2015) · Cell · doi:10.1016/j.cell.2015.01.006 · Cited by 825 · see citing works

Comprehensive Genome-wide Protein-DNA Interactions Detected at Single-Nucleotide Resolution

Rhee, H. S., & Pugh, B. F. (2011) · Cell · doi:10.1016/j.cell.2011.11.013 · Cited by 760 · see citing works

Organization and regulation of gene transcription

Cramer, P. (2019) · Nature · doi:10.1038/s41586-019-1517-4 · Cited by 732 · see citing works

The evolutionary dynamics of eukaryotic gene order

Hurst, L. D., Pál, C., & Lercher, M. J. (2004) · Nature Reviews Genetics · doi:10.1038/nrg1319 · Cited by 719 · see citing works

Systematic dissection and optimization of inducible enhancers in human cells using a massively parallel reporter assay

Melnikov, A., Murugan, A., Zhang, X., Tesileanu, T., Wang, L., Rogov, P., Feizi, S., Gnirke, A., Callan, C. G., Kinney, J. B., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2137 · Cited by 711 · see citing works

Systematic mapping of functional enhancer–promoter connections with CRISPR interference

Fulco, C. P., Munschauer, M., Anyoha, R., Munson, G., Grossman, S. R., Perez, E. M., Kane, M., Cleary, B., Lander, E. S., & Engreitz, J. M. (2016) · Science · doi:10.1126/science.aag2445 · Cited by 643 · see citing works

Developmental enhancers and chromosome topology

Furlong, E. E. M., & Levine, M. (2018) · Science · doi:10.1126/science.aau0320 · Cited by 641 · see citing works

An Atomic Model of the Interferon-β Enhanceosome

Panne, D., Maniatis, T., & Harrison, S. C. (2007) · Cell · doi:10.1016/j.cell.2007.05.019 · Cited by 625 · see citing works

Base-resolution models of transcription-factor binding reveal soft motif syntax

Avsec, Ž., Weilert, M., Shrikumar, A., Krueger, S., Alexandari, A., Dalal, K., Fropf, R., McAnany, C., Gagneur, J., Kundaje, A., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00782-6 · Cited by 610 · see citing works

Predicting Gene Expression from Sequence

Beer, M., & Tavazoie, S. (2004) · Cell · doi:10.1016/s0092-8674(04)00304-6 · Cited by 601 · see citing works

The resolution of inflammation

Buckley, C. D., Gilroy, D. W., Serhan, C. N., Stockinger, B., & Tak, P. P. (2012) · Nature reviews. Immunology · doi:10.1038/nri3362 · Cited by 580 · see citing works

DNA-dependent formation of transcription factor pairs alters their binding specificity

Jolma, A., Yin, Y., Nitta, K. R., Dave, K., Popov, A. N., Taipale, M., Enge, M., Kivioja, T., Morgunova, E. Y., & Taipale, J. (2015) · Nature · doi:10.1038/nature15518 · Cited by 555 · see citing works

Clustering of housekeeping genes provides a unified model of gene order in the human genome

Lercher, M. J., Urrutia, A. O., & Hurst, L. D. (2002) · Nature Genetics · doi:10.1038/ng887 · Cited by 548 · see citing works

Functional proteins from a random-sequence library

Keefe, A. D., & Szostak, J. W. (2001) · Nature · doi:10.1038/35070613 · Cited by 540 · see citing works

Structure of the DNA-binding domains from NFAT, Fos and Jun bound specifically to DNA

Chen, L., Glover, J. N. M., Hogan, P. G., Rao, A., & Harrison, S. C. (1998) · Nature · doi:10.1038/32100 · Cited by 521 · see citing works

A single-cell atlas of chromatin accessibility in the human genome

Zhang, K., Hocker, J. D., Miller, M., Hou, X., Chiou, J., Poirion, O. B., Qiu, Y., Li, Y. E., Gaulton, K. J., Wang, A., et al. (2021) · Cell · doi:10.1016/j.cell.2021.10.024 · Cited by 502 · see citing works

A cooperative interaction between NF‐kappa B and Sp1 is required for HIV‐1 enhancer activation.

Perkins, N. D., Edwards, N. L., Duckett, C. S., Agranoff, A. B., Schmid, R. M., & Nabel, G. J. (1993) · The EMBO Journal · doi:10.1002/j.1460-2075.1993.tb06029.x · Cited by 484 · see citing works

Histone modification: cause or cog?

Henikoff, S., & Shilatifard, A. (2011) · Trends in Genetics · doi:10.1016/j.tig.2011.06.006 · Cited by 482 · see citing works

Shadow enhancers as a source of evolutionary novelty

Hong, J.-W., Hendrix, D. A., & Levine, M. S. (2008) · Science · doi:10.1126/science.1160631 · Cited by 474 · see citing works

Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters

Sharon, E., Kalma, Y., Sharp, A., Raveh-Sadka, T., Levo, M., Zeevi, D., Keren, L., Yakhini, Z., Weinberger, A., & Segal, E. (2012) · Nature Biotechnology · doi:10.1038/nbt.2205 · Cited by 457 · see citing works

CRISPR–Cas9 epigenome editing enables high-throughput screening for functional regulatory elements in the human genome

Klann, T. S., Black, J. B., Chellappan, M., Safi, A., Song, L., Hilton, I. B., Crawford, G. E., Reddy, T. E., & Gersbach, C. A. (2017) · Nature Biotechnology · doi:10.1038/nbt.3853 · Cited by 447 · see citing works

Human 5′ UTR design and variant effect prediction from a massively parallel translation assay

Sample, P. J., Wang, B., Reid, D. W., Presnyak, V., McFadyen, I. J., Morris, D. R., & Seelig, G. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0164-5 · Cited by 442 · see citing works

Species-specific endogenous retroviruses shape the transcriptional network of the human tumor suppressor protein p53

Wang, T., Zeng, J., Lowe, C. B., Sellers, R. G., Salama, S. R., Yang, M., Burgess, S. M., Brachmann, R. K., & Haussler, D. H. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0703637104 · Cited by 389 · see citing works

Global reference mapping of human transcription factor footprints

Vierstra, J., Lazar, J., Sandstrom, R., Halow, J., Lee, K., Bates, D., Diegel, M., Dunn, D., Neri, F., Haugen, E., et al. (2020) · Nature · doi:10.1038/s41586-020-2528-x · Cited by 386 · see citing works

Evaluation of methods for modeling transcription factor sequence specificity

DREAM5 Consortium, Weirauch, M. T., Cote, A., Norel, R., Annala, M., Zhao, Y., Riley, T. R., Saez-Rodriguez, J., Cokelaer, T., Vedenko, A., et al. (2013) · Nature Biotechnology · doi:10.1038/nbt.2486 · Cited by 381 · see citing works

High-resolution analysis of DNA regulatory elements by synthetic saturation mutagenesis

Patwardhan, R. P., Lee, C., Litvin, O., Young, D. L., Pe'er, D., & Shendure, J. (2009) · Nature Biotechnology · doi:10.1038/nbt.1589 · Cited by 377 · see citing works

Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA

Baek, M., McHugh, R., Anishchenko, I., Jiang, H., Baker, D., & DiMaio, F. (2024) · Nature Methods · doi:10.1038/s41592-023-02086-5 · Cited by 372 · see citing works

Structure and Function of the Interferon- EnhanceosomePreprint

Maniatis, T., Falvo, J. V., Kim, T. H., KIM, T. K., Lin, C. H., Parekh, B., & WATHELET, M. G. (1998) · Cold Spring Harbor Symposia on Quantitative Biology · doi:10.1101/sqb.1998.63.609 · Cited by 358 · see citing works

Combinatorial function of transcription factors and cofactors

Reiter, F., Wienerroither, S., & Stark, A. (2017) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2016.12.007 · Cited by 354 · see citing works

Activation with CpG-A and CpG-B Oligonucleotides Reveals Two Distinct Regulatory Pathways of Type I IFN Synthesis in Human Plasmacytoid Dendritic Cells

Kerkmann, M., Rothenfußer, S., Hornung, V., Towarowski, A., Wagner, M., Sarris, A., Giese, T., Endres, S., & Hartmann, G. (2003) · The Journal of Immunology · doi:10.4049/jimmunol.170.9.4465 · Cited by 351 · see citing works

Nucleosome-mediated cooperativity between transcription factors

Mirny, L. A. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0913805107 · Cited by 349 · see citing works

Can AlphaFold2 predict the impact of missense mutations on structure?

Buel, G. R., & Walters, K. J. (2022) · Nature Structural & Molecular Biology · doi:10.1038/s41594-021-00714-2 · Cited by 332 · see citing works

Genomic Gene Clustering Analysis of Pathways in EukaryotesPreprint

Lee, J. M., & Sonnhammer, E. L. L. (2003) · Genome Research · doi:10.1101/gr.737703 · Cited by 329 · see citing works

Using AlphaFold to predict the impact of single mutations on protein stability and function

Pak, M. A., Markhieva, K. A., Novikova, M. S., Petrov, D. S., Vorobyev, I. S., Maksimova, E. S., Kondrashov, F. A., & Ivankov, D. N. (2023) · PLOS One · doi:10.1371/journal.pone.0282689 · Cited by 322 · cited 1× · see citing works

Transcriptional enhancers: Intelligent enhanceosomes or flexible billboards?

Arnosti, D. N., & Kulkarni, M. M. (2005) · Journal of Cellular Biochemistry · doi:10.1002/jcb.20352 · Cited by 309 · see citing works

Analysis of combinatorial cis-regulation in synthetic and genomic promoters

Gertz, J., Siggia, E. D., & Cohen, B. A. (2009) · Nature · doi:10.1038/nature07521 · Cited by 304 · see citing works

Where Are the Disease-Associated eQTLs?

Umans, B. D., Battle, A., & Gilad, Y. (2021) · Trends in Genetics · doi:10.1016/j.tig.2020.08.009 · Cited by 296 · see citing works

DeepSTARR predicts enhancer activity from DNA sequence and enables the <i>de novo</i> design of enhancers

de Almeida, B. P., Reiter, F., Pagani, M., & Stark, A. (2021) · doi:10.1101/2021.10.05.463203 · Cited by 284

Deciphering eukaryotic gene-regulatory logic with 100 million random promoters

de Boer, C. G., Vaishnav, E. D., Sadeh, R., Abeyta, E. L., Friedman, N., & Regev, A. (2020) · Nature Biotechnology · doi:10.1038/s41587-019-0315-8 · Cited by 283 · see citing works

DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers

de Almeida, B. P., Reiter, F., Pagani, M., & Stark, A. (2022) · Nature Genetics · doi:10.1038/s41588-022-01048-5 · Cited by 283 · see citing works

Different gene regulation strategies revealed by analysis of binding motifs

Wunderlich, Z., & Mirny, L. A. (2009) · Trends in Genetics · doi:10.1016/j.tig.2009.08.003 · Cited by 282 · see citing works

Deciphering the multi-scale, quantitative cis-regulatory code

Kim, S., & Wysocka, J. (2023) · Molecular Cell · doi:10.1016/j.molcel.2022.12.032 · Cited by 282 · see citing works

The evolution, evolvability and engineering of gene regulatory DNA

Vaishnav, E. D., de Boer, C. G., Molinet, J., Yassour, M., Fan, L., Adiconis, X., Thompson, D. A., Levin, J. Z., Cubillos, F. A., & Regev, A. (2022) · Nature · doi:10.1038/s41586-022-04506-6 · Cited by 279 · see citing works

Navigating the pitfalls of applying machine learning in genomics

Whalen, S., Schreiber, J. M., Noble, W. S., & Pollard, K. S. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00434-9 · Cited by 278 · see citing works

A sequence-based global map of regulatory activity for deciphering human genetics

Chen, K. M., Wong, A. K., Troyanskaya, O. G., & Zhou, J. (2022) · Nature Genetics · doi:10.1038/s41588-022-01102-2 · Cited by 269 · see citing works

ChIP-nexus enables improved detection of in vivo transcription factor binding footprints

He, Q., Johnston, J., & Zeitlinger, J. (2015) · Nature Biotechnology · doi:10.1038/nbt.3121 · Cited by 263 · see citing works

Single-molecule regulatory architectures captured by chromatin fiber sequencing

Stergachis, A. B., Debo, B. M., Haugen, E., Churchman, L. S., & Stamatoyannopoulos, J. A. (2020) · Science · doi:10.1126/science.aaz1646 · Cited by 257 · see citing works

Learning the Sequence Determinants of Alternative Splicing from Millions of Random Sequences

Rosenberg, A. B., Patwardhan, R. P., Shendure, J., & Seelig, G. (2015) · Cell · doi:10.1016/j.cell.2015.09.054 · Cited by 254 · see citing works

Pioneer factors as master regulators of the epigenome and cell fate

Balsalobre, A., & Drouin, J. (2022) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-022-00464-z · Cited by 251 · see citing works

Homotypic clusters of transcription factor binding sites are a key component of human promoters and enhancers

Gotea, V., Visel, A., Westlund, J. M., Nobrega, M. A., Pennacchio, L. A., & Ovcharenko, I. (2010) · Genome Research · doi:10.1101/gr.104471.109 · Cited by 248 · see citing works

50+ years of eukaryotic transcription: an expanding universe of factors and mechanisms

Roeder, R. G. (2019) · Nature Structural & Molecular Biology · doi:10.1038/s41594-019-0287-x · Cited by 246 · see citing works

Predicting mRNA Abundance Directly from Genomic Sequence Using Deep Convolutional Neural Networks

Agarwal, V., & Shendure, J. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107663 · Cited by 243 · see citing works

Structural perspective of cooperative transcription factor binding

Morgunova, E. Y., & Taipale, J. (2017) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2017.03.006 · Cited by 242 · see citing works

A comprehensive library of human transcription factors for cell fate engineering

Ng, A. H. M., Khoshakhlagh, P., Arias, J. E. R., Pasquini, G., Wang, K., Swiersy, A., Shipman, S. L., Appleton, E., Kiaee, K., Kohman, R. E., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0742-6 · Cited by 235 · see citing works

Shadow Enhancers Are Pervasive Features of Developmental Regulatory Networks

Cannavò, E., Khoueiry, P., Garfield, D. A., Geeleher, P., Zichner, T., Gustafson, E. H., Ciglar, L., Korbel, J. O., & Furlong, E. E. M. (2016) · Current Biology · doi:10.1016/j.cub.2015.11.034 · Cited by 235 · see citing works

Saturation mutagenesis of twenty disease-associated regulatory elements at single base-pair resolution

Kircher, M., Xiong, C., Martin, B., Schubach, M., Inoue, F., Bell, R. J. A., Costello, J. F., Shendure, J., & Ahituv, N. (2019) · Nature Communications · doi:10.1038/s41467-019-11526-w · Cited by 234 · see citing works

A transcription factor atlas of directed differentiation

Joung, J., Ma, S., Tay, T., Geiger-Schuller, K., Kirchgatterer, P. C., Verdine, V. K., Guo, B., Arias-García, M. A., Allen, W. E., Singh, A., et al. (2023) · Cell · doi:10.1016/j.cell.2022.11.026 · Cited by 223 · see citing works

Causal role for inheritance of H3K27me3 in maintaining the OFF state of a <i>Drosophila</i> HOX gene

Coleman, R. T., & Struhl, G. (2017) · Science · doi:10.1126/science.aai8236 · Cited by 221 · see citing works

The Genome Project-Write

Boeke, J. D., Church, G. M., Hessel, A., Kelley, N. J., Arkin, A. P., Cai, Y., Carlson, R. H., Chakravarti, A., Cornish, V. W., Holt, L. J., et al. (2016) · Science · doi:10.1126/science.aaf6850 · Cited by 219 · see citing works

Evaluating Enhancer Function and Transcription

Field, A. R., & Adelman, K. (2020) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-011420-095916 · Cited by 218 · see citing works

A Deep Neural Network for Predicting and Engineering Alternative Polyadenylation

Bogard, N., Linder, J., Rosenberg, A. B., & Seelig, G. (2019) · Cell · doi:10.1016/j.cell.2019.04.046 · Cited by 215 · see citing works

The Evolution of Lineage-Specific Regulatory Activities in the Human Embryonic Limb

Cotney, J. L., Leng, J., Yin, J., Reilly, S. K., DeMare, L. E., Emera, D., Ayoub, A. E., RAKIC, P. T., & Noonan, J. P. (2013) · Cell · doi:10.1016/j.cell.2013.05.056 · Cited by 208 · see citing works

Resolving systematic errors in widely used enhancer activity assays in human cells

Muerdter, F., Boryń, Ł. M., Woodfin, A. R., Neumayr, C., Rath, M., Zabidi, M. A., Pagani, M., Haberle, V., Kazmar, T., Catarino, R. R., et al. (2018) · Nature Methods · doi:10.1038/nmeth.4534 · Cited by 208 · see citing works

Complex Interdependence Regulates Heterotypic Transcription Factor Distribution and Coordinates Cardiogenesis

Luna‐Zurita, L., Stirnimann, C. U., Glatt, S., Kaynak, B. L., Thomas, S., Baudin, F., Samee, M. A. H., He, D. X., Small, E. M., Mileikovsky, M., et al. (2016) · Cell · doi:10.1016/j.cell.2016.01.004 · Cited by 207 · see citing works

Deep learning of the regulatory grammar of yeast 5′ untranslated regions from 500,000 random sequences

Cuperus, J. T., Groves, B., Kuchina, A., Rosenberg, A. B., Jojic, N., Fields, S., & Seelig, G. (2017) · Genome Research · doi:10.1101/gr.224964.117 · Cited by 205 · see citing works

Cross-talk between Site-specific Transcription Factors and DNA Methylation States

Blattler, A., & Farnham, P. (2013) · Journal of Biological Chemistry · doi:10.1074/jbc.r113.512517 · Cited by 200 · see citing works

Sequence determinants of human gene regulatory elements

Sahu, B., Hartonen, T., Pihlajamaa, P., Wei, B., Dave, K., Zhu, F., Kaasinen, E., Lidschreiber, K., Lidschreiber, M., Daub, C. O., et al. (2022) · Nature Genetics · doi:10.1038/s41588-021-01009-4 · Cited by 198 · see citing works

Propagation of Polycomb-repressed chromatin requires sequence-specific recruitment to DNA

Laprell, F., Finkl, K., & Müller, J. (2017) · Science · doi:10.1126/science.aai8266 · Cited by 188 · see citing works

Molecular Roadblocks for Cellular Reprogramming

Vierbuchen, T., & Wernig, M. (2012) · Molecular Cell · doi:10.1016/j.molcel.2012.09.008 · Cited by 187 · see citing works

Defining genome architecture at base-pair resolution

Peng, H., Badat, M., Hanssen, L. L. P., Hentges, L. D., Crump, N. T., Downes, D. J., Jeziorska, D. M., Oudelaar, A. M., Schweßinger, R., Taylor, S., et al. (2021) · Nature · doi:10.1038/s41586-021-03639-4 · Cited by 176 · see citing works

Deep conservation of the enhancer regulatory code in animals

Wong, E. S., Zheng, D., Tan, S. Z., Bower, N. I., Garside, V., Vanwalleghem, G., Gaiti, F., Scott, E., Hogan, B. M., Kikuchi, K., et al. (2020) · Science · doi:10.1126/science.aax8137 · Cited by 176 · see citing works

Quantitative genome-wide enhancer activity maps for five Drosophila species show functional enhancer conservation and turnover during cis-regulatory evolution

Arnold, C. D., Gerlach, D., Spies, D., Matts, J. A., Sytnikova, Y. A., Pagani, M., Lau, N. C., & Stark, A. (2014) · Nature Genetics · doi:10.1038/ng.3009 · Cited by 175 · see citing works

Decoding gene regulation in the fly brain

Janssens, J., Aibar, S., Taskiran, I. I., Ismail, J. N., Gomez, A. E., Aughey, G., Spanier, K. I., De Rop, F. V., González-Blas, C. B., Dionne, M., et al. (2022) · Nature · doi:10.1038/s41586-021-04262-z · Cited by 170 · see citing works

Decoding gene regulation in the fly brain

Janssens, J., Aibar, S., Taskiran, I. I., Ismail, J. N., Spanier, K. I., Bravo González-Blas, C., Quan, X. J., Papasokrati, D., Hulselmans, G., Makhzami, S., et al. (2021) · doi:10.1101/2021.08.11.454937 · Cited by 170 · see citing works

High-Throughput Discovery and Characterization of Human Transcriptional Effectors

Tycko, J., DelRosso, N. V., Hess, G. T., Aradhana, Banerjee, A., Mukund, A. X., Van, M. V., Ego, B. K., Yao, D., Spees, K., et al. (2020) · Cell · doi:10.1016/j.cell.2020.11.024 · Cited by 152 · see citing works

Massively Parallel Assays and Quantitative Sequence–Function Relationships

Kinney, J. B., & McCandlish, D. M. (2019) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-083118-014845 · Cited by 148 · see citing works

Enhancer grammar in development, evolution, and disease: dependencies and interplay

Jindal, G. A., & Farley, E. K. (2021) · Developmental Cell · doi:10.1016/j.devcel.2021.02.016 · Cited by 147 · see citing works

Conserved expression without conserved regulatory sequence: the more things change, the more they stay the same

Weirauch, M. T., & Hughes, T. R. (2010) · Trends in Genetics · doi:10.1016/j.tig.2009.12.002 · Cited by 146 · see citing works

Liquid–Liquid Phase Separation in ChromatinPreprint

Rippe, K. (2021) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a040683 · Cited by 140 · see citing works

Benchmarking of deep neural networks for predicting personal gene expression from DNA sequence highlights shortcomings

Sasse, A., Ng, B., Spiro, A. E., Tasaki, S., Bennett, D. A., Gaiteri, C., De Jager, P. L., Chikina, M., & Mostafavi, S. (2023) · Nature Genetics · doi:10.1038/s41588-023-01524-6 · Cited by 139 · see citing works

Shadow enhancers: Frequently asked questions about distributed <i>cis</i>‐regulatory information and enhancer redundancy

Barolo, S. (2012) · BioEssays · doi:10.1002/bies.201100121 · Cited by 136 · see citing works

Automated design of thousands of nonrepetitive parts for engineering stable genetic systems

Hossain, A., Lopez, E., Halper, S. M., Cetnar, D. P., Reis, A. C., Strickland, D., Klavins, E., & Salis, H. M. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0584-2 · Cited by 133 · see citing works

Current sequence-based models capture gene expression determinants in promoters but mostly ignore distal enhancers

Karollus, A., Mauermeier, T., & Gagneur, J. (2023) · Genome Biology · doi:10.1186/s13059-023-02899-9 · Cited by 128 · see citing works

Synthetic chromosomes, genomes, viruses, and cells

Venter, J. C., Glass, J. I., Hutchison, C. A., & Vashee, S. (2022) · Cell · doi:10.1016/j.cell.2022.06.046 · Cited by 127 · see citing works

An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models

Tu, L., Lalwani, G., Gella, S., & He, H. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00335 · Cited by 125 · see citing works

Multiplexed gene synthesis in emulsions for exploring protein functional landscapes

Plesa, C., Sidore, A. M., Lubock, N. B., Zhang, D., & Kosuri, S. (2018) · Science · doi:10.1126/science.aao5167 · Cited by 124 · see citing works

Splendor and misery of adaptation, or the importance of neutral null for understanding evolution

Koonin, E. V. (2016) · BMC Biology · doi:10.1186/s12915-016-0338-2 · Cited by 120 · see citing works

Principles of Chromosome Architecture Revealed by Hi-C

Eagen, K. P. (2018) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2018.03.006 · Cited by 120 · see citing works

Identification and functional characterization of transcriptional activators in human cells

Alerasool, N., Leng, H., Lin, Z.-Y., Gingras, A.-C., & Taipale, M. (2022) · Molecular Cell · doi:10.1016/j.molcel.2021.12.008 · Cited by 120 · see citing works

Debugging and consolidating multiple synthetic chromosomes reveals combinatorial genetic interactions

Zhao, Y., Coelho, C., Hughes, A. L., Lazar‐Stefanita, L., Yang, S., Brooks, A. N., Walker, R. S. K., Zhang, W., Lauer, S. L., Hernandez, C., et al. (2023) · Cell · doi:10.1016/j.cell.2023.09.025 · Cited by 115 · see citing works

Core promoter sequence in yeast is a major determinant of expression levelPreprint

Lubliner, S., Regev, I., Lotan-Pompan, M., Edelheit, S., Weinberger, A., & Segal, E. (2015) · Genome Research · doi:10.1101/gr.188193.114 · Cited by 111 · see citing works

The Replication Fork: Understanding the Eukaryotic Replication Machinery and the Challenges to Genome Duplication

Leman, A. R., & Noguchi, E. (2013) · Genes · doi:10.3390/genes4010001 · Cited by 102 · see citing works

Seven myths of how transcription factors read the cis-regulatory code

Zeitlinger, J. (2020) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2020.08.002 · Cited by 101 · see citing works

Recycling of modified H2A-H2B provides short-term memory of chromatin states

Flury, V., Reverón-Gómez, N., Alcaraz, N., Stewart-Morgan, K. R., Wenger, A., Klose, R. J., & Groth, A. (2023) · Cell · doi:10.1016/j.cell.2023.01.007 · Cited by 97 · see citing works

Single-molecule footprinting identifies context-dependent regulation of enhancers by DNA methylation

Kreibich, E., Kleinendorst, R., Barzaghi, G., Kaspar, S., & Krebs, A. R. (2023) · Molecular Cell · doi:10.1016/j.molcel.2023.01.017 · Cited by 97 · see citing works

The ENCODE project: Missteps overshadowing a success

Eddy, S. R. (2013) · Current Biology · doi:10.1016/j.cub.2013.03.023 · Cited by 96 · see citing works

FIREWACh: high-throughput functional detection of transcriptional regulatory modules in mammalian cells

Murtha, M. J., Keskin, Z. T., Tang, Z., Strino, F., Chen, X. X., Wang, Y., Xi, X., Basilico, C., Brown, S. M., Bonneau, R., et al. (2014) · Nature Methods · doi:10.1038/nmeth.2885 · Cited by 90 · see citing works

DNMTs and Impact of CpG Content, Transcription Factors, Consensus Motifs, lncRNAs, and Histone Marks on DNA Methylation

Loaeza‐Loaeza, J., Beltrán, A. S., & Hernández‐Sotelo, D. (2020) · Genes · doi:10.3390/genes11111336 · Cited by 89 · see citing works

Positional specificity of different transcription factor classes within enhancers

Grossman, S. R., Engreitz, J., Ray, J. P., Nguyen, T. H., Hacohen, N., & Lander, E. S. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1804663115 · Cited by 88 · see citing works

CG Methylation

Vinson, C., & Chatterjee, R. (2012) · Epigenomics · doi:10.2217/epi.12.55 · Cited by 85 · see citing works

Signal transduction controls heterogeneous NF-κB dynamics and target gene expression through cytokine-specific refractory states

Adamson, A. D., Boddington, C., Downton, P. J., Rowe, W., Bagnall, J., Lam, C. W., Maya‐Mendoza, A., Schmidt, L., Harper, C. V., Spiller, D. G., et al. (2016) · Nature Communications · doi:10.1038/ncomms12057 · Cited by 83 · see citing works

Origin and evolution of developmental enhancers in the mammalian neocortex

Emera, D., Yin, J., Reilly, S. K., Gockley, J., & Noonan, J. P. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1603718113 · Cited by 82 · see citing works

A single-cell massively parallel reporter assay detects cell-type-specific gene regulation

Zhao, S., Hong, C. K., Myers, C. A., Granas, D. M., White, M. A., Corbo, J. C., & Cohen, B. A. (2023) · Nature Genetics · doi:10.1038/s41588-022-01278-7 · Cited by 77 · see citing works

Synthetic and genomic regulatory elements reveal aspects of cis-regulatory grammar in mouse embryonic stem cells

King, D., Hong, C. K., Shepherdson, J. L., Granas, D. M., Maricque, B. B., & Cohen, B. A. (2020) · eLife · doi:10.7554/elife.41279 · Cited by 76 · see citing works

Improving representations of genomic sequence motifs in convolutional networks with exponential activations

Koo, P. K., & Ploenzke, M. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-020-00291-x · Cited by 75 · see citing works

Reading the chromatinized genome

Michael, A. K., & Thomä, N. H. (2021) · Cell · doi:10.1016/j.cell.2021.05.029 · Cited by 74 · see citing works

STARR‐seq and UMI‐STARR‐seq: Assessing Enhancer Activities for Genome‐Wide‐, High‐, and Low‐Complexity Candidate Libraries

Neumayr, C., Pagani, M., Stark, A., & Arnold, C. D. (2019) · Current Protocols in Molecular Biology · doi:10.1002/cpmb.105 · Cited by 73 · see citing works

Ribosome dynamics and mRNA turnover, a complex relationship under constant cellular scrutiny

Morris, C., Cluet, D., & Ricci, E. (2021) · Wiley Interdisciplinary Reviews - RNA · doi:10.1002/wrna.1658 · Cited by 71 · see citing works

The splicing code

Baralle, M., & Baralle, F. E. (2017) · Biosystems · doi:10.1016/j.biosystems.2017.11.002 · Cited by 69 · see citing works

Predicting Gene Expression from Sequence: A Reexamination

Yuan, Y., Guo, L., Shen, L., & Liu, J. S. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030243 · Cited by 69 · see citing works

Local epigenomic state cannot discriminate interacting and non-interacting enhancer–promoter pairs with high accuracy

Xi, W., & Beer, M. A. (2018) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006625 · Cited by 68 · see citing works

High-Throughput SELEX Determination of DNA Sequences Bound by Transcription Factors In Vitro

Ogawa, N., & Biggin, M. D. (2011) · Methods in molecular biology · doi:10.1007/978-1-61779-292-2_3 · Cited by 65 · see citing works

Long-range phasing of dynamic, tissue-specific and allele-specific regulatory elements

Battaglia, S., Dong, K., Wu, J., Chen, Z., Najm, F. J., Zhang, Y., Moore, M. M., Hecht, V. C., Shoresh, N., & Bernstein, B. (2022) · Nature Genetics · doi:10.1038/s41588-022-01188-8 · Cited by 64 · see citing works

De Novo Genesis of Enhancers in Vertebrates

Eichenlaub, M., & Ettwiller, L. M. (2011) · PLoS Biology · doi:10.1371/journal.pbio.1001188 · Cited by 62 · see citing works

Adaptive Evolution and the Birth of CTCF Binding Sites in the Drosophila Genome

Ni, X., Zhang, Y. E., Nègre, N., Chen, S., Long, M., & White, K. P. (2012) · PLoS Biology · doi:10.1371/journal.pbio.1001420 · Cited by 60 · see citing works

Accurate prediction of nucleic acid and protein-nucleic acid complexes using RoseTTAFoldNAPreprint

Baek, M., McHugh, R., Anishchenko, I. V., Baker, D. A., & DiMaio, F. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.09.507333 · Cited by 60 · see citing works

Inflated performance measures in enhancer–promoter interaction-prediction methods

Cao, F., & Fullwood, M. J. (2019) · Nature Genetics · doi:10.1038/s41588-019-0434-7 · Cited by 56 · see citing works

tDNA insulators and the emerging role of TFIIIC in genome organization

Bortle, K. V., & Corces, V. G. (2012) · Transcription · doi:10.4161/trns.21579 · Cited by 55 · see citing works

The Ongoing Quest to Crack the Genetic Code for Protein Production

Nieuwkoop, T., Finger‐Bou, M., John van der, O. O. S. T., & Claassens, N. J. (2020) · Molecular Cell · doi:10.1016/j.molcel.2020.09.014 · Cited by 55 · see citing works

Synthetic regulatory reconstitution reveals principles of mammalian Hox cluster regulation

Pinglay, S., Bulajić, M., Rahe, D. P., Huang, E., Brosh, R., Mamrak, N., King, B. R., German, S. V., Cadley, J. A., Rieber, L., et al. (2022) · Science · doi:10.1126/science.abk2820 · Cited by 54 · see citing works

How should novelty be valued in science?

Cohen, B. A. (2017) · eLife · doi:10.7554/elife.28699 · Cited by 52 · see citing works

A community effort to optimize sequence-based deep learning models of gene regulation

Rafi, A. M., Nogina, D., Penzar, D., Lee, D., Lee, D., KIM, N. S., Kim, S., Kim, D., Shin, Y., Kwak, I., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02414-w · Cited by 51 · see citing works

Enhancer architecture and chromatin accessibility constrain phenotypic space during Drosophila development

Galupa, R., Álvarez-Canales, G., Borst, N. O., Fuqua, T., Gándara, L., Misunou, N., Richter, K., Alves, M. R. P., Karumbi, E., Perkins, M. L., et al. (2022) · Developmental Cell · doi:10.1016/j.devcel.2022.12.003 · Cited by 51 · see citing works

Systematic Investigation of Transcription Factor Activity in the Context of Chromatin Using Massively Parallel Binding and Expression Assays.

Levo, M., Avnit-Sagi, T., Lotan-Pompan, M., Kalma, Y., Weinberger, A., Yakhini, Z., & Segal, E. (2017) · Molecules and Cells · doi:10.1016/j.molcel.2017.01.007 · Cited by 49 · see citing works

p53 pulse modulation differentially regulates target gene promoters to regulate cell fate decisions

Harton, M. D., Koh, W. S., Bunker, A. D., Singh, A., & Batchelor, E. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188685 · Cited by 45 · see citing works

Cytoplasmic mRNA turnover and ageing

Borbolis, F., & Syntichaki, P. (2015) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2015.09.006 · Cited by 43 · see citing works

Computational Identification of Diverse Mechanisms Underlying Transcription Factor-DNA Occupancy

Cheng, Q., Kazemian, M., Pham, H. N., Blatti, C. A., Celniker, S., Wolfe, S. A., Brodsky, M. H., & Sinha, S. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003571 · Cited by 43 · see citing works

Cooperative Transcription Factor Complexes in Control

Martínez, G., & Rao, A. (2012) · Science · doi:10.1126/science.1231310 · Cited by 39 · see citing works

DropSynth 2.0: high-fidelity multiplexed gene synthesis in emulsions

Sidore, A. M., Plesa, C., Samson, J., Lubock, N. B., & Kosuri, S. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa600 · Cited by 38 · see citing works

MAUDE: inferring expression changes in sorting-based CRISPR screens

de Boer, C. G., Ray, J. P., Hacohen, N., & Regev, A. (2020) · Genome biology · doi:10.1186/s13059-020-02046-8 · Cited by 35 · see citing works

A protein activity assay to measure global transcription factor activity reveals determinants of chromatin accessibility

Wei, B., Jolma, A., Sahu, B., Orre, L. M., Zhong, F., Zhu, F., Kivioja, T., Sur, I., Lehtiö, J., Taipale, M., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4138 · Cited by 33 · see citing works

Regulatory activity is the default DNA state in eukaryotes

Luthra, I., Jensen, C., Chen, X. E., Salaudeen, A. L., Rafi, A. M., & de Boer, C. G. (2024) · Nature Structural & Molecular Biology · doi:10.1038/s41594-024-01235-4 · Cited by 29 · see citing works

Studying transcription factor function in the genome at molecular resolution

Krebs, A. R. (2021) · Trends in Genetics · doi:10.1016/j.tig.2021.03.008 · Cited by 27 · see citing works

Functional analysis of a random-sequence chromosome reveals a high level and the molecular nature of transcriptional noise in yeast cells

Gvozdenov, Z., Barcutean, Z., & Struhl, K. (2023) · Molecular Cell · doi:10.1016/j.molcel.2023.04.010 · Cited by 23 · see citing works

Short tandem repeats bind transcription factors to tune eukaryotic gene expressionPreprint

Horton, C. A., Alexandari, A. M., Hayes, M. G. B., Marklund, E., Schaepe, J. M., Aditham, A. K., Shah, N., Shrikumar, A., Afek, A., Greenleaf, W. J., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.24.493321 · Cited by 20 · see citing works

Genomic Machine Learning Meta-regression: Insights on Associations of Study Features With Reported Model Performance

Barnett, E. J., Onete, D. G., Salekin, A., & Faraone, S. V. (2023) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2023.3343808 · Cited by 20 · see citing works

Towards More Realistic Simulated Datasets for Benchmarking Deep Learning Models in Regulatory GenomicsPreprint

Prakash, E., Shrikumar, A., & Kundaje, A. (2021) · bioRxiv · doi:10.1101/2021.12.26.474224 · Cited by 19 · see citing works

Active learning of enhancers and silencers in the developing neural retina

Friedman, R. Z., Ramu, A., Lichtarge, S., Wu, Y., Tripp, L., Lyon, D., Myers, C. A., Granas, D. M., Gause, M., Corbo, J. C., et al. (2025) · Cell Systems · doi:10.1016/j.cels.2024.12.004 · Cited by 17 · see citing works

Inferring gene expression from ribosomal promoter sequences, a crowdsourcing approachPreprint

Meyer, P., Siwo, G., Zeevi, D., Sharon, E., Norel, R., Segal, E., & Stolovitzky, G. (2013) · Genome Research · doi:10.1101/gr.157420.113 · Cited by 15 · see citing works

Current sequence-based models capture gene expression determinants in promoters but mostly ignore distal enhancersPreprint

Karollus, A., Mauermeier, T., & Gagneur, J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.15.508087 · Cited by 14 · see citing works

Multiplex profiling of developmental enhancers with quantitative, single-cell expression reportersPreprint

Lalanne, J.-B., Regalado, S. G., Domcke, S., Calderon, D., Martin, B., Li, T., Suiter, C. C., Lee, C., Trapnell, C., & Shendure, J. (2022) · bioRxiv · doi:10.1101/2022.12.10.519236 · Cited by 14 · see citing works

Trans MPRA: A framework for assaying the role of many trans -acting factors at many enhancersPreprint

Calderon, D., Ellis, A., Daza, R. M., Martin, B., Tome, J. M., Chen, W., Chardon, F. M., Leith, A., Lee, C., Trapnell, C., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.30.321323 · Cited by 13 · see citing works

Modeling the Evolutionary Architectures of Transcribed Human Enhancer Sequences Reveals Distinct Origins, Functions, and Associations with Human Trait Variation

Fong, S., & Capra, J. A. (2021) · Molecular Biology and Evolution · doi:10.1093/molbev/msab138 · Cited by 11 · see citing works

Reply to ‘Inflated performance measures in enhancer–promoter interaction-prediction methods’

Whalen, S., & Pollard, K. S. (2019) · Nature Genetics · doi:10.1038/s41588-019-0473-0 · Cited by 8 · see citing works

Genomic analysis of a synthetic reversed sequence reveals default chromatin states in yeast and mammalian cellsPreprint

Camellato, B. R., Brosh, R., Maurano, M. T., & Boeke, J. D. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.22.496726 · Cited by 7 · see citing works

Enhancer architecture and chromatin accessibility constrain phenotypic space during developmentPreprint

Galupa, R., Álvarez-Canales, G., Borst, N. O., Fuqua, T., Gándara, L., Misunou, N., Richter, K., Alves, M. R. P., Karumbi, E., Perkins, M. L., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.02.494376 · Cited by 6 · see citing works

Enhancer jungles establish robust tissue-specific regulatory control in the human genome

Li, S., & Ovcharenko, I. (2019) · Genomics · doi:10.1016/j.ygeno.2019.12.022 · Cited by 5 · see citing works

Synthetic genomic reconstitution reveals principles of mammalian Hox cluster regulationPreprint

Pinglay, S., Bulajić, M., Rahe, D. P., Huang, E., Brosh, R., German, S. V., Cadley, J. A., Rieber, L., Easo, N., Mahony, S., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.07.07.451065 · Cited by 5 · see citing works

Machine learning for discovery: deciphering RNA splicing logicPreprint

Liao, S. E., Sudarshan, M., & Regev, O. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.01.510472 · Cited by 5 · see citing works

Machine-guided design of cell-type-targeting cis-regulatory elements

Gosai, S. J., Castro, R. I., Fuentes, N., Butts, J. C., Mouri, K., Alasoadura, M., Kales, S., Nguyen, T. T. L., Noche, R. R., Rao, A. S., et al. (2024) · Nature · doi:10.1038/s41586-024-08070-z · Cited by 142 · see citing works

Toward a foundation model of causal cell and tissue biology with a Perturbation Cell and Tissue Atlas

Rood, J. E., Hupalowska, A., & Regev, A. (2024) · Cell · doi:10.1016/j.cell.2024.07.035 · Cited by 139 · see citing works

Deciphering the impact of genomic variation on function

Writing group (ordered by contribution), Engreitz, J., Lawson, H. A., Singh, H., Starita, L. M., Hon, G. C., Carter, H. K., Sahni, N., Reddy, T. E., Lin, X., et al. (2024) · Nature · doi:10.1038/s41586-024-07510-0 · Cited by 91 · see citing works

Predicting gene expression from DNA sequence using deep learning models

Barbadilla-Martínez, L., Klaassen, N. H. M., van Steensel, B., & de Ridder, J. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-025-00841-2 · Cited by 83 · see citing works

DNA-guided transcription factor interactions extend human gene regulatory code

Xie, Z., Sokolov, I., Osmala, M., Xue, Y., Bower, G., Pett, J. P., Chen, Y., Wang, K., Cavga, A. D., Popov, A. N., et al. (2025) · Nature · doi:10.1038/s41586-025-08844-z · Cited by 62 · see citing works

A community effort to optimize sequence-based deep learning models of gene regulation

Rafi, A. M., Nogina, D., Penzar, D., Lee, D., Lee, D., KIM, N. S., Kim, S., Kim, D., Shin, Y., Kwak, I., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02414-w · Cited by 51 · see citing works

Genome access is transcription factor-specific and defined by nucleosome position

Grand, R. S., Pregnolato, M., Baumgartner, L., Hoerner, L., Burger, L., & Schübeler, D. (2024) · Molecular Cell · doi:10.1016/j.molcel.2024.08.009 · Cited by 44 · see citing works

From computational models of the splicing code to regulatory mechanisms and therapeutic implications

Capitanchik, C., Wilkins, O. G., Wagner, N., Gagneur, J., & Ule, J. (2024) · Nature Reviews Genetics · doi:10.1038/s41576-024-00774-2 · Cited by 42 · see citing works

Unlocking gene regulation with sequence-to-function models

Sasse, A., Chikina, M. D., & Mostafavi, S. (2024) · Nature Methods · doi:10.1038/s41592-024-02331-5 · Cited by 34 · see citing works

Epigenomic insights into common human disease pathology

Bell, C. G. (2024) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-024-05206-2 · Cited by 32 · see citing works

Using machine learning to enhance and accelerate synthetic biology

Rai, K., Wang, Y., O’Connell, R. W., Patel, A. B., & Bashor, C. J. (2024) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2024.100553 · Cited by 31 · see citing works

Regulatory activity is the default DNA state in eukaryotes

Luthra, I., Jensen, C., Chen, X. E., Salaudeen, A. L., Rafi, A. M., & de Boer, C. G. (2024) · Nature Structural & Molecular Biology · doi:10.1038/s41594-024-01235-4 · Cited by 29 · see citing works

Designing realistic regulatory DNA with autoregressive language modelsPreprint

Lal, A., Garfield, D., Biancalani, T., & Eraslan, G. (2024) · Genome Research · doi:10.1101/gr.279142.124 · Cited by 30 · see citing works

Design principles of cell-state-specific enhancers in hematopoiesis

Frömel, R., Rühle, J., Bernal Martinez, A., Szu-Tu, C., Pacheco Pastor, F., Martinez-Corral, R., & Velten, L. (2025) · Cell · doi:10.1016/j.cell.2025.04.017 · Cited by 29 · see citing works

Deep-learning prediction of gene expression from personal genomes

Drusinsky, S., Whalen, S., & Pollard, K. S. (2026) · Genome biology · doi:10.1186/s13059-025-03926-7 · Cited by 29 · see citing works

Decoding biology with massively parallel reporter assays and machine learningPreprint

Fleur, A. L., Shi, Y., & Seelig, G. (2024) · Genes & Development · doi:10.1101/gad.351800.124 · Cited by 26 · see citing works

Chromatin landscape at cis-regulatory elements orchestrates cell fate decisions in early embryogenesis

Cardamone, F., Piva, A., Löser, E., Eichenberger, B., Romero-Mulero, M. C., Zenk, F. L., Shields, E. J., Cabezas‐Wallscheid, N., Bonasio, R., Tiana, G., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-57719-4 · Cited by 26 · see citing works

Discovering the hidden function in fungal genomes

Gervais, N. C., & Shapiro, R. S. (2024) · Nature Communications · doi:10.1038/s41467-024-52568-z · Cited by 23 · see citing works

Modelling and design of transcriptional enhancers

Winter, S. D., Konstantakos, V., & Aerts, S. (2025) · Nature Reviews Bioengineering · doi:10.1038/s44222-025-00280-y · Cited by 20 · see citing works

High-throughput data and modeling reveal insights into the mechanisms of cooperative DNA-binding by transcription factor proteins

Martin, V., Zhuang, F., Zhang, Y., Pinheiro, K., & Gordân, R. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad872 · Cited by 20 · see citing works

Active learning of enhancers and silencers in the developing neural retina

Friedman, R. Z., Ramu, A., Lichtarge, S., Wu, Y., Tripp, L., Lyon, D., Myers, C. A., Granas, D. M., Gause, M., Corbo, J. C., et al. (2025) · Cell Systems · doi:10.1016/j.cels.2024.12.004 · Cited by 17 · see citing works

Machine-guided design of synthetic cell type-specific cis -regulatory elementsPreprint

Gosai, S. J., Castro, R. I., Fuentes, N., Butts, J. C., Kales, S., Noche, R. R., Mouri, K., Sabeti, P. C., Reilly, S. K., & Tewhey, R. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.08.552077 · Cited by 18 · see citing works

One versus many independent assemblies of symbiotic nitrogen fixation in flowering plants

Doyle, J. J., Ren, J., Pawlowski, K., James, E. K., & Gao, Y. (2025) · Nature Communications · doi:10.1038/s41467-025-60433-w · Cited by 17 · see citing works

Artificial intelligence-guided strategies for next-generation biological sequence design

Zhang, P., Wei, L., Li, J., & Wang, X. (2024) · National Science Review · doi:10.1093/nsr/nwae343 · Cited by 20 · see citing works

Genomic convergence in hibernating mammals elucidates the genetics of metabolic regulation in the hypothalamus

Ferris, E., Murcia, J. D. G., Rodriguez, A. C., Steinwand, S., Hörndli, C. S., Tränkner, D., Maldonado-Catala, P. J., & Gregg, C. J. (2025) · Science · doi:10.1126/science.adp4025 · Cited by 15 · see citing works

Gene regulatory networks: from correlative models to causal explanations

Maizels, R. J., & Briscoe, J. (2026) · Nature Reviews Genetics · doi:10.1038/s41576-026-00939-1 · Cited by 18 · see citing works

Improving the performance of supervised deep learning for regulatory genomics using phylogenetic augmentation

Duncan, A. G., Mitchell, J. A., & Moses, A. M. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae190 · Cited by 15 · see citing works

Engineering genetic elements for microbial protein expression systems: Advances, challenges, applications, and prospects

Li, X., Ye, C., Liu, T., Li, S., Zhang, M., Zhao, Y., Jin, Y., Cheng, J., Yang, G., & Li, P. (2025) · Synthetic and Systems Biotechnology · doi:10.1016/j.synbio.2025.10.008 · Cited by 15 · see citing works

A genome-wide screen identifies silencers with distinct chromatin properties and mechanisms of repression

Hofbauer, L., Pleyer, L., Reiter, F., Schleiffer, A., Vlasova, A. V., Serebreni, L., Huang, A., & Stark, A. (2024) · Molecular Cell · doi:10.1016/j.molcel.2024.10.041 · Cited by 14 · see citing works

Perspective on recent developments and challenges in regulatory and systems genomics

Zeitlinger, J., Roy, S., Ay, F., Mathelier, A., Medina-Rivera, A., Mahony, S., Sinha, S. K., & Ernst, J. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbaf106 · Cited by 14 · see citing works

GHT-SELEX demonstrates unexpectedly high intrinsic sequence specificity and complex DNA binding of many human transcription factors

Jolma, A., Hernández-Corchado, A., Yang, A. W. H., Fathi, A., Laverty, K. U., Brechalov, A. V., Razavi, R., Albu, M., Zheng, H., Bücher, P., et al. (2026) · Nature Methods · doi:10.1038/s41592-026-03177-9 · Cited by 13 · see citing works

EPInformer: A Scalable Deep Learning Framework for Gene Expression Prediction by Integrating Promoter-enhancer Sequences with Multimodal Epigenomic DataPreprint

Lin, J., Luo, R., & Pinello, L. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.01.606099 · Cited by 13 · see citing works

Characterizing homology-induced data leakage and memorization in genome-trained sequence modelsPreprint

Rafi, A. M., Kiyota, B., Yachie, N., & de Boer, C. G. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.22.634321 · Cited by 11 · see citing works

Codes across (life)sciences

Prinz, R., Bücher, P., Kun, Á., Paredes, O., Aragno, A., Shelby, C. L., Gumbel, M., Fimmel, E., & Strüngmann, L. (2025) · Biosystems · doi:10.1016/j.biosystems.2025.105515 · Cited by 9 · see citing works

A scalable approach to investigating sequence-to-function predictions from personal genomes

Spiro, A. E., Tu, X., Sheng, Y., Sasse, A., Hosseini, R., Chikina, M. D., & Mostafavi, S. (2026) · Nature Methods · doi:10.1038/s41592-026-03124-8 · Cited by 10 · see citing works

Plant Synthetic Promoters

Szymczyk, P., & Majewska, M. (2024) · Applied Sciences · doi:10.3390/app14114877 · Cited by 10 · see citing works

Harnessing omics to decode the mechanisms of symbiotic nitrogen fixation

Ye, K., Zheng, J., Dong, Z., Wang, S., & Huang, S. (2025) · aBIOTECH · doi:10.1007/s42994-025-00208-5 · Cited by 9 · see citing works

Mammalian genome writing: Unlocking new length scales for genome engineering

Pinglay, S., Atwater, J. T., Brosh, R., Shendure, J., Maurano, M. T., & Boeke, J. D. (2026) · Cell · doi:10.1016/j.cell.2025.12.034 · Cited by 11 · see citing works

Codebook: sequence specificity and genomic binding of poorly-characterized human transcription factorsPreprint

Jolma, A., Laverty, K. U., Fathi, A., Yang, A. W. H., Yellan, I., Vorontsov, I. E., Inukai, S., Kribelbauer-Swietek, J. F., Gralak, A. J., Razavi, R., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.11.622097 · Cited by 9 · see citing works

GUANinE v1.0: Benchmark Datasets for Genomic AI Sequence-to-Function ModelsPreprint

Robson, E. S., & Ioannidis, N. M. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.12.562113 · Cited by 8 · see citing works

Massively parallel approaches for characterizing noncoding functional variation in human evolution

Rong, S., Root, E., & Reilly, S. K. (2024) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2024.102256 · Cited by 6 · see citing works

Characterizing uncertainty in predictions of genomic sequence-to-activity modelsPreprint

Bajwa, A., Rastogi, R., Kathail, P., Shuai, R. W., & Ioannidis, N. M. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.21.572730 · Cited by 7 · see citing works

regLM: Designing realistic regulatory DNA with autoregressive language models

Lal, A., Garfield, D., Biancalani, T., & Eraslan, G. (2024) · doi:10.1101/2024.02.14.580373 · Cited by 6 · see citing works

Integrated Single-Cell Analysis Dissects Regulatory Mechanisms Underlying Tumor-Associated Macrophage Plasticity in Hepatocellular Carcinoma

Gu, Y., Zhu, W., Zhang, Z., Shu, H., Huang, H., & Sun, X. S. (2025) · Genes · doi:10.3390/genes16070817 · Cited by 6 · see citing works

Mechanisms for DNA Interplay in Eukaryotic Transcription Factors

Muñoz, V., Goluguri, R. R., Ghosh, C., Tanielian, B., & Sadqi, M. (2025) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-071524-111008 · Cited by 6 · see citing works

Diverse short tandem repeat sequences influence gene regulation in human populations

Mitina, A., Engchuan, W., Trost, B., Pellecchia, G., Scherer, S. W., & Yuen, R. K. C. (2025) · Genome biology · doi:10.1186/s13059-025-03754-9 · Cited by 5 · see citing works

Generative AI for synthetic biology: Designing biological parts, circuits, and genomes

Kim, N., Carluccio, G. D., Zhang, K., & Collins, J. J. (2026) · Cell Systems · doi:10.1016/j.cels.2026.101533 · Cited by 5 · see citing works

A parts list of promoters and gRNA scaffolds for mammalian genome engineering and molecular recording

McDiarmid, T. A., Taylor, M. L., Chen, W., Chardon, F. M., Choi, J., Liao, H., Li, X., Kim, H., Lalanne, J., Li, T. Y., et al. (2025) · Nature Biotechnology · doi:10.1038/s41587-025-02896-2 · Cited by 7 · see citing works

Omnireg-gpt: a high-efficiency foundation model for comprehensive genomic sequence understanding

Wang, A., Li, J., Dong, H., Xu, B., Yin, Q., Xu, Y., Fu, J., & Zhao, J. (2025) · Nature Communications · doi:10.1038/s41467-025-65066-7 · Cited by 4 · see citing works

EPInformer: scalable and integrative prediction of gene expression from promoter-enhancer sequences with multimodal epigenomic profiles

Lin, J., Li, Z., Zhao, Y., Luo, R., & Pinello, L. (2026) · Nature Communications · doi:10.1038/s41467-026-70535-8 · Cited by 4 · see citing works

PLANeT: Understanding and leveraging the genome of land plants for a sustainable future

Wang, L., Amar, M., Arbizu, C. I., Bowman, J. L., Cannon, C. H., Cantrill, D. J., Cao, X., Chen, S., Chen, X., Chen, X., et al. (2026) · Cell · doi:10.1016/j.cell.2026.01.026 · Cited by 4 · see citing works

Predictable Engineering of Signal-Dependent Cis-Regulatory ElementsPreprint

Cornwall-Scoones, J., Benzinger, D., Yu, T., Pezzotta, A., Sagner, A., Gerontogianni, L., Bernadet, S., Finnie, E., Boezio, G. L. M., Stuart, H. T., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.07.642002 · Cited by 4 · see citing works

Genome-wide investigation of transcription factor footprints and dynamics using cFOOT-seq

Wang, H., Wu, A., Yang, M., Zhou, D., Chen, X., Shi, Z., Zhang, Y., Liu, Y.-X., Chen, K., Wang, X., et al. (2025) · Protein & Cell · doi:10.1093/procel/pwaf071 · Cited by 3 · see citing works

Phenotypic signatures of incomplete lineage sorting in hominids

Blom, S. K., Hipsley, C. A., & Zhang, G. (2025) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2025.10.009 · Cited by 3 · see citing works

Toward generalizable and interpretable AI in regulatory genomics

NAGAI, M., Murphy, A. E., Rizzo, K., & Koo, P. K. (2026) · Nature Genetics · doi:10.1038/s41588-026-02670-3 · Cited by 6 · see citing works

Deep learning on chromatin profiles reveals the cis-regulatory sequence code of the rice genome

Zhou, X., Ruan, Z., Zhang, C., Kaufmann, K., & Chen, D. (2024) · Journal of genetics and genomics/Journal of Genetics and Genomics · doi:10.1016/j.jgg.2024.12.007 · Cited by 3 · see citing works

Cardiovascular disease-associated non-coding variants disrupt GATA4-DNA binding and regulatory functions

Peña-Martínez, E. G., Messon-Bird, J. L., Rodríguez-Ríos, J. M., Velázquez-Roig, R., Pomales-Matos, D. A., Rivera-Madera, A., Sanabria-Alberto, L., Barreiro-Rosario, A. C., Figueroa-Rosado, J. A., Valle, J. R.-D., et al. (2025) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2025.100415 · Cited by 3 · see citing works

Beyond AlphaFold: how AI is decoding the grammar of the genome

Perkel, J. M. (2025) · Nature · doi:10.1038/d41586-025-02621-8 · Cited by 3 · see citing works

FoxP3 forms a head-to-head dimer in vivo and stabilizes its multimerization on adjacent microsatellites

Leng, F., Clark, R., Zhang, W., Viennet, T., Wang, C., Arthanari, H., Wang, X., & Hur, S. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.116633 · Cited by 2 · see citing works

Genome-wide modelling of plant transcription factor binding captures regulatory variants associated with phenotypic traits

Peleke, F. F., Zumkeller, S. M., Schirmer, D., Schmitz, G., Hartwig, T., Engelhorn, J., Weizel, S., Schmitt, A. O., Jores, T., & Szymański, J. (2026) · Nature Communications · doi:10.1038/s41467-026-73634-8 · Cited by 2 · see citing works

Diversified, miniaturized and ancestral parts for mammalian genome engineering and molecular recordingPreprint

McDiarmid, T. A., Taylor, M. L., Chen, W., Chardon, F. M., Choi, J., Liao, H., Li, X., Kim, H., Lalanne, J., Li, T. W., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.30.615957 · Cited by 2 · see citing works

Interpretable Distillation Reveals that Deep-learning-based Splicing Models Suffer from Pervasive Confounders and Blind SpotsPreprint

Liu, S., Zhang, W., & Regev, O. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.28.691252 · Cited by 2 · see citing works

Lentiviral single-cell MPRA of synthetic enhancers reveals motif affinity-based encoding of cell type specificityPreprint

Rühle, J., Frömel, R., Bernal, A., Szu‐Tu, C., Bowness, J. S., & Velten, L. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.27.708495 · Cited by 2 · see citing works

Lineage specifying transcription factors determine cell function by direct control of metabolismPreprint

Bott, A. J., Youngs, J. C., Cunningham, C. N., Vranken, J. G. V., Wei, P., Cluntun, A. A., Gygi, S. P., Ayer, D. E., & Rutter, J. P. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.02.735862 · Cited by 2 · see citing works

Generating information-dense promoter sequences with optimal string packing

Andreani, V., South, E. J., & Dunlop, M. J. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012276 · Cited by 1 · see citing works

Initiators counteract Polycomb repression and stimulate long-range contacts between enhancers and the Abdominal-B promoter in Drosophila

Kyrchanova, O., Kudryashova, K. S., Dubrovskaya, V. A., Ibragimov, A. N., Schedl, P. D., & Georgiev, P. G. (2025) · Open Biology · doi:10.1098/rsob.250192 · Cited by 1 · see citing works

Quantifying transcription factor specificity with advanced DNA universal microarrays featuring long and modified binding sites

Bayer, Y., O’Hagan, M., Miodownik, I., & Afek, A. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf1382 · Cited by 1 · see citing works

Decoding RNA–protein interactions using high-throughput methods

Régis, M., Pulcina, P., & Kretov, D. A. (2026) · RNA Biology · doi:10.1080/15476286.2026.2623240 · Cited by 3 · see citing works

How to build the regulatory genome: a constructionist guide to the cis-regulatory code

Bell, C. C., de Boer, C. G., Camellato, B. R., Delàs, M. J., Ibrahim, D. M., & Montgomery, M. T. (2026) · Development · doi:10.1242/dev.205516 · Cited by 1 · see citing works

Interpretable distillation reveals that deep learning splicing models suffer from pervasive confounders and blind spots

Liu, S., Zhang, W., & Regev, O. (2026) · Genome biology · doi:10.1186/s13059-026-04124-9 · Cited by 1 · see citing works

ExoShorkie: predicting RNA-seq coverage of exogenous genomes in yeast by transfer learning

Mandl, J., & Orenstein, Y. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag369 · Cited by 1 · see citing works

Regulation of stereotypic chromatin conformations at enhancers by CLOCK:BMAL1Preprint

Nie, X. Y., Ahuja, N., Hsieh, A., Abbi, G., Jiang, Y., & Menet, J. S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.24.590818 · Cited by 1 · see citing works

Iterative improvement of deep learning models using synthetic regulatory genomicsPreprint

Ribeiro-dos-Santos, A. M., & Maurano, M. T. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.04.636130 · Cited by 1 · see citing works

Identifying non-coding variant effects at scale via machine learning models of cis-regulatory reporter assaysPreprint

Butts, J. C., Rong, S., Gosai, S. J., Castro, R. I., Noon, M., Adeniran, K., Ghosh, R., Sabeti, P. C., Tewhey, R., & Reilly, S. K. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.16.648420 · Cited by 1 · see citing works

A systematic delineation of 3′UTR regulatory elements and their contextual associations

Liang, N., Li, J., Ding, Y., Wang, Y., Champer, J., Ren, L., & Gao, G. (2026) · Cell Systems · doi:10.1101/2025.06.09.658412 · Cited by 1 · see citing works

Multi-scale dissection, compaction and derivatization of mammalian developmental enhancersPreprint

Lalanne, J., Li, T., Kajiwara, E. A. N., Huynh, C., Do, T. V., Martin, B. K., Regalado, S. G., & Shendure, J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.20.719625 · Cited by 1 · see citing works

Retracing and rewriting the evolutionary trajectories of mammalian developmental enhancersPreprint

Li, T., Lalanne, J., Kajiwara, E. A. N., Jain, S., Li, X., Do, T. V., Martin, B. K., Regalado, S. G., Daza, R. M., & Shendure, J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.20.719714 · Cited by 1 · see citing works

OpenSplice: the impact of half a million mutations on the alternative splicing of 600 human exonsPreprint

Quarantani, G., Clarke, J., Thompson, M. J., Sang, F., Valcárcel, J., & Lehner, B. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.22.727141 · Cited by 3 · see citing works

Transcription Factors and Signal Transduction

Carlberg, C. (2024) · Springer eBooks · doi:10.1007/978-3-031-68730-3_4

Investigating Data Size, Sequence Diversity, and Model Complexity in MPRA-based Sequence-to-Function PredictionPreprint

Sheng, Y., Tu, X., & Mostafavi, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.11.642630

Transcriptomics for modern biotechnology

Buzdin, A. A., Kuzmin, D., Garazha, A., & Belalov, I. S. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-19110-7.00006-9

QED Eukaryote Genetic Code and Principle of Information Flow and Biological Protein Synthesis

(2025) · Acta Scientific Medical Sciences · doi:10.31080/asms.2025.09.2097

DNA Conformational Flexibility Descriptors Improve Transcription Factor Binding Prediction Across the Protein FamiliesPreprint

Dey, U., Yella, V. R., & Kumar, A. A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.20.665724

Learning causal regulatory motifs and grammars using deep learning models and massively parallel reporter assaysPreprint

Thompson, M., & Lehner, B. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.25.666754 · Cited by 1 · see citing works

Visualization and Prediction of in vivo Phosphate Dynamics via Auto-Glowing Plant SensorsPreprint

Chiu, C.-W., Chan, Y.-F., Lu, T.-Y., Chiu, L.-J., Li, Y., Lu, H., Huang, H.-J., Mishin, A. S., Sarkisyan, K. S., Hsiao, C., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.30.667770

Personalized Synthetic Electrocardiograms with OutcomesPreprint

Isaksen, J. L., Thambawita, V. L., Nørregaard, M., Ellervik, C., Hansen, T. F., Linneberg, A. R., Linz, D., Graff, C., Riegler, M. A., & Kanters, J. K. (2025) · medRxiv · doi:10.1101/2025.08.01.25332716

Introduction to Noncoding Regions

Shakir, S., Mishra, B. N., Mishra, M. K., & Verma, N. (2025) · Concepts and strategies in plant sciences · doi:10.1007/978-981-96-9859-2_1

Inferring fungal cis-regulatory networks from genome sequences via unsupervised and interpretable representation learning

Moses, A. M., Stajich, J., Gasch, A. P., & Knowles, D. A. (2025) · Genetics · doi:10.1093/genetics/iyaf209

PETRA: Prime editing of transcribed regulatory elements to assay expressionPreprint

Armas, M., Herger, M., Cubitt, L., & Findlay, G. M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.18.689114

RegEvol: detection of directional selection in regulatory sequences through phenotypic predictions and phenotype-to-fitness functionsPreprint

Laverré, A., Latrille, T., & Robinson‐Rechavi, M. (2025) · eLife · doi:10.1101/2025.11.26.690685

Chromosome-naïve global networks of initiators of hybrid assembly pathways of endogenous multiprotein complexesPreprint

Glinsky, G. V. (2026) · Research Square · doi:10.21203/rs.3.rs-8494003/v1

ExoShorkie: Predicting RNA-seq coverage of exogenous genomes in yeast by transfer learningPreprint

Mandl, J., & Orenstein, Y. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.25.701486

A Cytosine Deaminase–Based Genomic Footprinting Assay (cFOOT-seq) for Detecting Transcription Factor Occupancy

Yang, M., Wu, A., Wang, H., Liu, X., Shi, J., Gao, S., & Zhang, J. (2026) · BIO-PROTOCOL · doi:10.21769/bioprotoc.5637

Theoretical and empirical study of the evolution of gene regulatory networks

Petit, A. J. R. (2024) · doi:10.70675/0487bca1z3a98z4bc8z9fdezfa82d0e76a32

Comprehensive mapping of human genetic variation at Epromoters

Wan, J. (2024) · theses.fr (ABES) · doi:10.70675/a2e4bbffze012z41baz8c31z2f84f271295e

Sequence design for three-dimensional genome folding using Akita Semifreddo

Smaruj, P. N., Kelley, D. R., & Fudenberg, G. (2026) · Genome biology · doi:10.64898/2026.04.28.721368

Long-term evolution of regulatory DNA sequences. Part 1: simulations on global, biophysically-realistic genotype–phenotype maps

Mascolo, E., Borbély, R., Herrera‐Álvarez, S., Guet, C. C., Crocker, J., & Tkačik, G. (2026) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2026.102483

A fine-tuned genomic language model captures nucleotide-level information overlooked by missense variant impact predictorsPreprint

Su, Y., & Lin, Y.-J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.06.723362

DNA conformational flexibility descriptors improve transcription factor binding prediction across diverse transcription factor familiesPreprint

Dey, U., Yella, V. R., & Kumar, A. (2026) · Genome Research · doi:10.1101/gr.281357.125

Evaluation of Active Learning Selection Strategies and Characterization of Informative Sequences for Sequence-to-Expression ModelsPreprint

Qian, J., Rafi, A. M., Cazottes, E., & de Boer, C. G. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.21.727038

Guiding future genomics research through machine learning : functional DNA analysis and benchmarking

Luthra, I. (2025) · cIRcle (University of British Columbia) · doi:10.14288/1.0447747

Seven myths of how transcription factors read the cis-regulatory code

Zeitlinger, J. (2020) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2020.08.002 · Cited by 101 · see citing works

Deciphering eukaryotic gene-regulatory logic with 100 million random promoters

de Boer, C. G., Vaishnav, E. D., Sadeh, R., Abeyta, E. L., Friedman, N., & Regev, A. (2020) · Nature Biotechnology · doi:10.1038/s41587-019-0315-8 · Cited by 283 · see citing works

Deciphering genetic regulatory codes: A challenge for functional genomics

Michelson, A. M. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.032685999 · Cited by 60 · see citing works

Deciphering eukaryotic cis -regulatory logic with 100 million random promotersPreprint

de Boer, C. G., Vaishnav, E. D., Sadeh, R., Abeyta, E. L., Friedman, N., & Regev, A. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/224907 · Cited by 14 · see citing works