Systematic dissection and optimization of inducible enhancers in human cells using a massively parallel reporter assay
Abstract
Learning to read and write the transcriptional regulatory code is of central importance to progress in genetic analysis and engineering. Here we describe a massively parallel reporter assay (MPRA) that facilitates the systematic dissection of transcriptional regulatory elements. In MPRA, microarray-synthesized DNA regulatory elements and unique sequence tags are cloned into plasmids to generate a library of reporter constructs. These constructs are transfected into cells and tag expression is assayed by high-throughput sequencing. We apply MPRA to compare >27,000 variants of two inducible enhancers in human cells: a synthetic cAMP-regulated enhancer and the virus-inducible interferon-β enhancer. We first show that the resulting data define accurate maps of functional transcription factor binding sites in both enhancers at single-nucleotide resolution. We then use the data to train quantitative sequence-activity models (QSAMs) of the two enhancers. We show that QSAMs from two cellular states can be combined to design enhancer variants that optimize potentially conflicting objectives, such as maximizing induced activity while minimizing basal activity.
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A major advantage of MPRAs is that sequences to be tested can simply be synthesized, enabling straightforward saturation mutagenesis and programming synthetic enhancers.
“A major advantage of MPRAs is that sequences to be tested can simply be synthesized, enabling straightforward saturation mutagenesis [G] of enhancers67,184,185 as well as programming synthetic enhancers to inform modelling of their properties186,187.”
From Towards a comprehensive catalogue of validated and target-linked human enhancers (2020) · cites this paper as [184]
Current CRISPR sgRNA libraries can be decoded directly via the CROP-seq vector, while other perturbations, such as ORFs or non-coding elements, can be tagged with short barcodes.
“Existing CRISPR sgRNA libraries can be read out directly (using the CROP-seq vector) while other perturbations (e.g., ORFs, non-coding sequences) can be paired with short barcodes (Melnikov et al., 2012; Yang et al., 2011).”
From Optical Pooled Screens in Human Cells (2019) · cites this paper as Melnikov et al., 2012
Massively parallel reporter assays and CRISPR genome editing have matured rapidly to enable functional characterization of targeted sequence perturbations at scale.
“To address this, there has been rapid maturation of technologies, such as massively parallel reporter assays121–123 and CRISPR genome editing, to support functional characterization of targeted sequence perturbations at scale.”
From A brief history of human disease genetics (2020) · cites this paper as [122]
The PESCA platform extends previous paralleled reporter assays carried out using bulk tissue or sorted cells by including a single-cell RNA-seq-based readout to evaluate the cell-type-specificity of gene expression.
“The PESCA platform extends previous paralleled reporter assays (Hartl et al., 2017; Inoue et al., 2017; Melnikov et al., 2012; Murtha et al., 2014; Patwardhan et al., 2012; Shen et al., 2016) carried out using bulk tissue or sorted cells by including a single-cell RNA-seq-based readout (Cao et al., 2017; Hrvatin et al., 2018; Klein et al., 2015; Macosko et al., 2015; Rosenberg et al., 2018; Stroud”
From A scalable platform for the development of cell-type-specific viral drivers (2019) · cites this paper as Melnikov et al., 2012
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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works
Adaptation in Natural and Artificial Systems
Holland, J. H. (1992) · The MIT Press eBooks · doi:10.7551/mitpress/1090.001.0001 · Cited by 47,340 · see citing works
The ENCODE Project Consortium (2007) · Nature · doi:10.1038/nature05874 · Cited by 5,223 · see citing works
Transcriptional regulation by the phosphorylation-dependent factor CREB
Mayr, B., & Montminy, M. (2001) · Nature Reviews Molecular Cell Biology · doi:10.1038/35085068 · Cited by 2,587 · see citing works
The NIH Roadmap Epigenomics Mapping Consortium
Bernstein, B. E., Stamatoyannopoulos, J. A., Costello, J. F., Ren, B., Milosavljevic, A., Meissner, A., Kellis, M., Marra, M. A., Beaudet, A. L., Ecker, J. R., et al. (2010) · Nature Biotechnology · doi:10.1038/nbt1010-1045 · Cited by 1,892 · see citing works
Self-citationInitial impact of the sequencing of the human genome
Lander, E. S. (2011) · Nature · doi:10.1038/nature09792 · Cited by 1,044 · see citing works
Self-citationBook Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 636 · 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
KUNSCH, C. A., Ruben, S. M., & Rosen, C. A. (1992) · Molecular and Cellular Biology · doi:10.1128/mcb.12.10.4412-4421.1992 · Cited by 488 · see citing works
Bioluminescent Assays for High-Throughput Screening
Fan, F., & Wood, K. V. (2007) · Assay and Drug Development Technologies · doi:10.1089/adt.2006.053 · Cited by 449 · 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
The Upward Bias in Measures of Information Derived from Limited Data Samples
Treves, A., & Panzeri, S. (1995) · Neural Computation · doi:10.1162/neco.1995.7.2.399 · Cited by 352 · see citing works
Kinney, J. B., Murugan, A., Callan, C. G., & Cox, E. C. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1004290107 · Cited by 334 · see citing works
Self-citationLeProust, E. M., Peck, B. J., Spirin, K., McCuen, H. B., Moore, B., Namsaraev, E., & Caruthers, M. H. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq163 · Cited by 326 · 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
Kunsch, C., Ruben, S. M., & Rosen, C. A. (1992) · Molecular and Cellular Biology · doi:10.1128/mcb.12.10.4412 · Cited by 295 · see citing works
Chromatin Immunoprecipitation (ChIP)Preprint
Carey, M., Peterson, C. L., & Smale, S. T. (2009) · Cold Spring Harbor Protocols · doi:10.1101/pdb.prot5279 · Cited by 263 · see citing works
Transcriptional Regulation in Eukaryotes: Concepts, Strategies and Techniques
Carey, M., & Smale, S. T. (2000) · Cited by 253 · see citing works
Improved Tet-responsive promoters with minimized background expression
Loew, R., Heinz, N., Hampf, M., Bujard, H., & Gossen, M. (2010) · BMC Biotechnology · doi:10.1186/1472-6750-10-81 · Cited by 246 · see citing works
Mey, M. D., Maertens, J., Lequeux, G. J., et al. (2007) · BMC Biotechnology · doi:10.1186/1472-6750-7-34 · Cited by 197 · see citing works
Fink, J. S., Verhave, M., Kasper, S. F., et al. (1988) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.85.18.6662 · Cited by 180 · see citing works
Quantitative analysis of the relationship between nucleotide sequence and functional activity
Stormo, G. D., Schneider, T. D., & Gold, L. (1986) · Nucleic Acids Research · doi:10.1093/nar/14.16.6661 · Cited by 158 · see citing works
Parallel on-chip gene synthesis and application to optimization of protein expression
Quan, J., Saaem, I., Tang, N. C., Ma, S., Negre, N. N., GONG, H. R., White, K. P., & Tian, J. (2011) · Nature Biotechnology · doi:10.1038/nbt.1847 · Cited by 152 · see citing works
Targeting cancer by transcriptional control in cancer gene therapy and viral oncolysis
Dorer, D. E., & Nettelbeck, D. M. (2009) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2009.03.013 · Cited by 150 · see citing works
Falvo, J. V., Parekh, B. S., Lin, C. H., Fraenkel, E., & Maniatis, T. (2000) · Molecular and Cellular Biology · doi:10.1128/mcb.20.13.4814-4825.2000 · Cited by 140 · see citing works
Different binding specificities and transactivation of variant CRE's by CREB complexes
Benbrook, D. M., & Jones, N. C. (1994) · Nucleic Acids Research · doi:10.1093/nar/22.8.1463 · Cited by 140 · see citing works
Synthetic design of strong promoters
Schlabach, M. R., Hu, J. K., Li, M. Z., & Elledge, S. J. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0914803107 · Cited by 135 · see citing works
Matzas, M., Stähler, P. F., Kefer, N., Siebelt, N., Boisguérin, V., Leonard, J. T., Keller, A., Stähler, C., Häberle, P., Gharizadeh, B., et al. (2010) · Nature Biotechnology · doi:10.1038/nbt.1710 · Cited by 96 · see citing works
Excess information at bacteriophage T7 genomic promoters detected by a random cloning technique
Schneider, T. D., & Stormo, G. D. (1989) · Nucleic Acids Research · doi:10.1093/nar/17.2.659 · Cited by 67 · see citing works
Quantitative sequence-activity models (QSAM)—tools for sequence design
Jonsson, J., Norberg, T., Carlsson, L. M. S., et al. (1993) · Nucleic Acids Research · doi:10.1093/nar/21.3.733 · Cited by 59 · see citing works
Synthetic promoter elements obtained by nucleotide sequence variation and selection for activity
Edelman, G. M., Meech, R., Owens, G. C., & Jones, F. S. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.7.3038 · Cited by 56 · see citing works
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Research works printed but not linked: 1
- [20]Bishop CM Pattern recognition and machine learning 2006 Springer
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Sorted by citations
Transcriptional enhancers: from properties to genome-wide predictions
Shlyueva, D., Stampfel, G., & Stark, A. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3682 · Cited by 1,356 · 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
BCL11A enhancer dissection by Cas9-mediated in situ saturating mutagenesis
Canver, M. C., Smith, E. C., Sher, F., Pinello, L., Sanjana, N. E., Shalem, O., Chen, D. D., Schupp, P. G., Vinjamur, D. S., Garcia, S. P., et al. (2015) · Nature · doi:10.1038/nature15521 · Cited by 886 · see citing works
Large-scale de novo DNA synthesis: technologies and applications
Kosuri, S., & Church, G. M. (2014) · Nature Methods · doi:10.1038/nmeth.2918 · Cited by 869 · see citing works
A brief history of human disease genetics
Claussnitzer, M., Cho, J. H., Collins, R., Cox, N. J., Dermitzakis, E. T., Hurles, M. E., Kathiresan, S., Kenny, E. E., Lindgren, C. M., MacArthur, D. G., et al. (2020) · Nature · doi:10.1038/s41586-019-1879-7 · Cited by 697 · see citing works
Bauer, D. E., Kamran, S. C., Lessard, S., Xu, J., Fujiwara, Y., Lin, C., Shao, Z., Canver, M. C., Smith, E. C., Pinello, L., et al. (2013) · Science · doi:10.1126/science.1242088 · Cited by 653 · see citing works
Tewhey, R., Kotliar, D., Park, D. S., Liu, B., Winnicki, S., Reilly, S. K., Andersen, K. G., Mikkelsen, T. S., Lander, E. S., Schaffner, S. F., et al. (2016) · Cell · doi:10.1016/j.cell.2016.04.027 · Cited by 480 · see citing works
Enhancer-promoter interactions are encoded by complex genomic signatures on looping chromatin
Whalen, S., Truty, R. M., & Pollard, K. S. (2016) · Nature Genetics · doi:10.1038/ng.3539 · Cited by 456 · see citing works
Towards a comprehensive catalogue of validated and target-linked human enhancers
Gasperini, M., Tome, J. M., & Shendure, J. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-019-0209-0 · Cited by 396 · see citing works
Inoue, F., Kircher, M., Martin, B., Cooper, G. M., Witten, D. M., McManus, M. T., Ahituv, N., & Shendure, J. (2017) · Genome Research · doi:10.1101/gr.212092.116 · Cited by 331 · see citing works
Werling, D. M., Brand, H., An, J.-Y., Stone, M. R., Zhu, L., Glessner, J. T., Collins, R. L., Dong, S., Layer, R. M., Markenscoff-Papadimitriou, E., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0107-y · Cited by 293 · see citing works
Optical Pooled Screens in Human Cells
Feldman, D., Singh, A., Schmid-Burgk, J. L., Carlson, R. J., Mezger, A., Garrity, A. J., Zhang, F., & Blainey, P. C. (2019) · Cell · doi:10.1016/j.cell.2019.09.016 · Cited by 321 · see citing works
Systematic Functional Dissection of Common Genetic Variation Affecting Red Blood Cell Traits
Ulirsch, J. C., Nandakumar, S. K., Wang, L., Giani, F. C., Zhang, X., Rogov, P., Melnikov, A., McDonel, P., Do, R., Mikkelsen, T. S., et al. (2016) · Cell · doi:10.1016/j.cell.2016.04.048 · Cited by 328 · see citing works
Self-citationDecoding enhancers using massively parallel reporter assays
Inoue, F., & Ahituv, N. (2015) · Genomics · doi:10.1016/j.ygeno.2015.06.005 · Cited by 272 · see citing works
Many human accelerated regions are developmental enhancers
Capra, J. A., Erwin, G. D., McKinsey, G., Rubenstein, J. L. R., & Pollard, K. S. (2013) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2013.0025 · Cited by 235 · see citing works
A systematic evaluation of the design and context dependencies of massively parallel reporter assays
Klein, J. C., Agarwal, V., Inoue, F., Keith, A., Martin, B., Kircher, M., Ahituv, N., & Shendure, J. (2020) · Nature Methods · doi:10.1038/s41592-020-0965-y · Cited by 226 · see citing works
Wang, X., He, L., Goggin, S. M., Saadat, A., Wang, L., Sinnott-Armstrong, N., Claussnitzer, M., & Kellis, M. (2018) · Nature Communications · doi:10.1038/s41467-018-07746-1 · Cited by 159 · see citing works
Self-citationDirect GR Binding Sites Potentiate Clusters of TF Binding across the Human Genome
Vockley, C. M., D’Ippolito, A. M., McDowell, I. C., Majoros, W. H., Safi, A., Song, L., Crawford, G. E., & Reddy, T. E. (2016) · Cell · doi:10.1016/j.cell.2016.07.049 · Cited by 182 · see citing works
A new era for functional labeling of neurons: activity-dependent promoters have come of age
Kawashima, T., Okuno, H., & Bito, H. (2014) · Frontiers in Neural Circuits · doi:10.3389/fncir.2014.00037 · Cited by 165 · see citing works
Genome-scale high-resolution mapping of activating and repressive nucleotides in regulatory regions
Ernst, J., Melnikov, A., Zhang, X., Wang, L., Rogov, P., Mikkelsen, T. S., & Kellis, M. (2016) · Nature Biotechnology · doi:10.1038/nbt.3678 · Cited by 162 · see citing works
Self-citationGrossman, S. R., Zhang, X., Wang, L., Engreitz, J., Melnikov, A., Rogov, P., Tewhey, R., Isakova, A., Deplancke, B., Bernstein, B. E., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1621150114 · Cited by 165 · see citing works
Self-citationThe power of multiplexed functional analysis of genetic variants
Gasperini, M., Starita, L., & Shendure, J. (2016) · Nature Protocols · doi:10.1038/nprot.2016.135 · Cited by 160 · see citing works
Jacobs, J., Atkins, M., Davie, K., Imrichova, H., Romanelli, L., Christiaens, V., Hulselmans, G., Potier, D., Wouters, J., Taskiran, I. I., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0140-x · Cited by 155 · see citing works
A scalable platform for the development of cell-type-specific viral drivers
Hrvatin, S., Tzeng, C. P., Nagy, M. A., Stroud, H., Koutsioumpa, C., Wilcox, O. F., Assad, E. G., Green, J., Harvey, C. D., Griffith, E. C., et al. (2019) · eLife · doi:10.7554/elife.48089 · Cited by 118 · see citing works
A new class of temporarily phenotypic enhancers identified by CRISPR/Cas9-mediated genetic screening
Diao, Y., Li, B., Meng, Z., Jung, I., Lee, A. Y., Dixon, J., Maliskova, L., Guan, K.-L., Shen, Y., & Ren, B. (2016) · Genome Research · doi:10.1101/gr.197152.115 · Cited by 122 · see citing works
Andersson, R. (2015) · BioEssays · doi:10.1002/bies.201400162 · Cited by 127 · see citing works
From trans to cis: transcriptional regulatory networks in neocortical development
Shibata, M., Gulden, F. O., & Sestan, N. (2015) · Trends in Genetics · doi:10.1016/j.tig.2014.12.004 · Cited by 109 · see citing works
Systematic interrogation of human promoters
Weingarten-Gabbay, S., Nir, R., Lubliner, S., Sharon, E., Kalma, Y., Weinberger, A., & Segal, E. (2019) · Genome Research · doi:10.1101/gr.236075.118 · Cited by 110 · see citing works
“Computational and high-throughput experimental approaches had characterized binding specificity (Jolma et al. 2013) and mapped the positions of TF binding sites in the human genome (Xie et al. 2005; Gerstein et al. 2012; Wang et al. 2012).”
Supervised enhancer prediction with epigenetic pattern recognition and targeted validation
Sethi, A., Gu, M., Gumusgoz, E., Chan, L., Yan, K.-K., Rozowsky, J., Barozzi, I., Afzal, V., Akiyama, J. A., Plajzer-Frick, I., et al. (2020) · Nature Methods · doi:10.1038/s41592-020-0907-8 · Cited by 109 · see citing works
Hold out the genome: a roadmap to solving the cis-regulatory code
de Boer, C. G., & Taipale, J. (2024) · Nature · doi:10.1038/s41586-023-06661-w · Cited by 107 · see citing works
Vockley, C. M., Guo, C., Majoros, W. H., Nodzenski, M., Scholtens, D. M., Hayes, M. G., Lowe, W. L., & Reddy, T. E. (2015) · Genome Research · doi:10.1101/gr.190090.115 · Cited by 99 · see citing works
Multiplex enhancer-reporter assays uncover unsophisticated TP53 enhancer logic
Verfaillie, A., Svetlichnyy, D., Imrichova, H., Davie, K., Fiers, M., Kalender Atak, Z., Hulselmans, G., Christiaens, V., & Aerts, S. (2016) · Genome Research · doi:10.1101/gr.204149.116 · Cited by 82 · see citing works
Human evolution: the non-coding revolution
Franchini, L. F., & Pollard, K. S. (2017) · BMC Biology · doi:10.1186/s12915-017-0428-9 · Cited by 90 · see citing works
Maricque, B. B., Chaudhari, H. G., & Cohen, B. A. (2019) · Nature Biotechnology · doi:10.1038/nbt.4285 · Cited by 83 · see citing works
Krebs, A. R., Dessus-Babus, S., Burger, L., & Schübeler, D. (2014) · eLife · doi:10.7554/elife.04094 · Cited by 81 · see citing works
Functional characterization of enhancer evolution in the primate lineage
Klein, J. C., Keith, A., Agarwal, V., Durham, T., & Shendure, J. (2018) · Genome Biology · doi:10.1186/s13059-018-1473-6 · Cited by 63 · see citing works
The Molecular Basis of Human Brain Evolution
Enard, W. (2016) · Current Biology · doi:10.1016/j.cub.2016.09.030 · Cited by 55 · see citing works
Mah, W., & Won, H. (2020) · Schizophrenia Research · doi:10.1016/j.schres.2019.03.007 · Cited by 38 · see citing works
Cis-regulatory landscapes of four cell types of the retina
Hartl, D., Krebs, A. R., Jüttner, J., Roska, B., & Schübeler, D. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx923 · Cited by 37 · see citing works
A signature of Neanderthal introgression on molecular mechanisms of environmental responses
Findley, A. S., Zhang, X., Boye, C., Lin, Y. L., Kalita, C. A., Barreiro, L., Lohmueller, K. E., Pique-Regi, R., & Luca, F. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1009493 · Cited by 14 · see citing works
Self-citationApplication of Array-Based Oligonucleotides for Synthesis of Genetic Designs
Sinyakov, A. N., Ryabinin, V. A., & Kostina, E. V. (2021) · Molecular Biology · doi:10.1134/s0026893321030109 · Cited by 9 · see citing works
Interpreting noncoding genetic variation in complex traits and human disease
Ward, L. D., & Kellis, M. (2012) · Nature Biotechnology · doi:10.1038/nbt.2422 · Cited by 6 · see citing works
Self-citation
- Authors
- Melnikov, A., Murugan, A., Zhang, X., Tesileanu, T., Wang, L., Rogov, P., Feizi, S., Gnirke, A., Callan, C. G., Kinney, J. B., Kellis, M., Lander, E. S., & Mikkelsen, T. S.
- Affiliations
- Broad Institute (US)
- Princeton University (US)
- Cold Spring Harbor Laboratory (US)
- Corresponding author
- Mikkelsen, Tarjei S
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2012-02-26
- Volume
- 30
- Issue
- 3
- Pages
- 271-277
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC3297981
- OpenAlex
- W2023386301
- Cited by
- 711
- References
- 31
- Citations by year
- 2026: 52 · 2025: 56 · 2024: 47 · 2023: 40 · 2022: 40 · 2021: 55 · 2020: 55 · 2019: 62 · 2018: 53 · 2017: 48 · 2016: 56 · 2015: 49 · 2014: 44 · 2013: 31 · 2012: 22
- FWCI
- 18.76
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) (acceptedVersion)
- Funders
- National Human Genome Research Institute (R01HG004037)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
- Keywords
- enhancers, massively parallel reporter assay, synthetic enhancers, transcription factor binding sites, transcriptional regulation, human cells
- MeSH terms
- Base Sequence, Binding Sites, Biological Assay, Enhancer Elements, Genetic, Humans, Models, Genetic, Molecular Sequence Data, Transcription Factors, Transcription, Genetic, Mutagenesis, Sequence Alignment, Genes, Reporter
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