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A systematic evaluation of the design and context dependencies of massively parallel reporter assays
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Abstract
Massively parallel reporter assays (MPRAs) functionally screen thousands of sequences for regulatory activity in parallel. To date, there are limited studies that systematically compare differences in MPRA design. Here, we screen a library of 2,440 candidate liver enhancers and controls for regulatory activity in HepG2 cells using nine different MPRA designs. We identify subtle but significant differences that correlate with epigenetic and sequence-level features, as well as differences in dynamic range and reproducibility. We also validate that enhancer activity is largely independent of orientation, at least for our library and designs. Finally, we assemble and test the same enhancers as 192-mers, 354-mers and 678-mers and observe sizable differences. This work provides a framework for the experimental design of high-throughput reporter assays, suggesting that the extended sequence context of tested elements and to a lesser degree the precise assay, influence MPRA results.
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“The strength of the regularization was controlled by a single λ parameter, which was optimized using tenfold crossvalidation for each training set using the cv.glmnet function of the glmnet library in R.”
From Effective gene expression prediction from sequence by integrating long-range interactions (2021) · cites this paper as [45]
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“whereas the activity-CNN added ETS and AP-1 sites underlying higher enhancer activity, in agreement with previous MPRA studies in the liver60.”
From Single-cell spatial multi-omics and deep learning dissect enhancer-driven gene regulatory networks in liver zonation (2024) · cites this paper as [60]
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“Moreover, we included 795 shuffled regions as a negative control, and 360 positive controls that previously showed activity in HepG2, and for which a mouse orthologous region can be found that is also accessible in the mouse liver.”
From Single-cell spatial multi-omics and deep learning dissect enhancer-driven gene regulatory networks in liver zonation (2024) · cites this paper as [60]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“The zonation-CNN added Tbx3 and Tcf7l1 motifs as crucial sequence features to the hepatocyte enhancers; while the activity-CNN added ETS and AP-1 sites underlying higher enhancer activity, in agreement with previous MPRA studies in the liver17.”
From Enhancer grammar of liver cell types and hepatocyte zonation states (2022) · cites this paper as [17]
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“new experimental approaches, such as Massively Parallel Reporter Assays (MPRAs), have been established to directly test and identify active enhancers14–18.”
From Enhancer grammar of liver cell types and hepatocyte zonation states (2022) · cites this paper as [17]
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Cites
BEDTools: a flexible suite of utilities for comparing genomic features
Quinlan, A. R., & Hall, I. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq033 · Cited by 27,437
An integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072
Integrative analysis of 111 reference human epigenomes
Roadmap Epigenomics Consortium, Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., et al. (2015) · Nature · doi:10.1038/nature14248 · Cited by 6,368
The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans
The GTEx Consortium, Ardlie, K. G., Deluca, D. S., Segrè, A. V., Sullivan, T. J., Young, T. R., Gelfand, E. T., Trowbridge, C. A., Maller, J. B., Tukiainen, T., et al. (2015) · Science · doi:10.1126/science.1262110 · Cited by 5,062
PEAR: a fast and accurate Illumina Paired-End reAd mergeR
Zhang, J., Kobert, K., Flouri, T., & Stamatakis, A. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt593 · Cited by 4,497
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
Alipanahi, B., Delong, A., Weirauch, M. T., & Frey, B. J. (2015) · Nature Biotechnology · doi:10.1038/nbt.3300 · Cited by 2,873
An atlas of active enhancers across human cell types and tissues
The FANTOM Consortium, Andersson, R., Gebhard, C., Miguel-Escalada, I., Hoof, I., Bornholdt, J., Boyd, M., Chen, Y., Zhao, X., Schmidl, C., et al. (2014) · Nature · doi:10.1038/nature12787 · Cited by 2,550
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
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
Expression of a β-globin gene is enhanced by remote SV40 DNA sequences
Banerji, J., Rusconi, S., & Schaffner, W. (1981) · Cell · doi:10.1016/0092-8674(81)90413-x · Cited by 1,724
Research works printed but not linked: 1
- Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at arXiv https://arxiv.org/abs/1303.3997 (2013). · arXiv
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Cooper, Y. A., Teyssier, N., Dräger, N. M., Guo, Q., Davis, J. E., Sattler, S. M., Yang, Z., Patel, A. M., Wu, S. J., Kosuri, S., et al. (2022) · Science · doi:10.1126/science.abi8654 · Cited by 121
Massively parallel characterization of transcriptional regulatory elements
Agarwal, V., Inoue, F., Schubach, M., Penzar, D., Martin, B. K., Dash, P. M., Keukeleire, P., Zhang, Z., Sohota, A., Zhao, J., et al. (2025) · Nature · doi:10.1038/s41586-024-08430-9 · Cited by 146
Adaptive sequence divergence forged new neurodevelopmental enhancers in humans
Mangan, R. J., Alsina, F. C., Mosti, F., Sotelo-Fonseca, J. E., Snellings, D. A., Au, E. H., Carvalho, J., Sathyan, L., Johnson, G. D., Reddy, T. E., et al. (2022) · Cell · doi:10.1016/j.cell.2022.10.016 · Cited by 119
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Tippens, N. D., Liang, J. H., Leung, A. K.-Y., Wierbowski, S. D., Ozer, A., Booth, J. G., Lis, J. T., & Yu, H. (2020) · Nature Genetics · doi:10.1038/s41588-020-0686-2 · Cited by 104
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Tabet, D. R., Parikh, V. N., Mali, P., Roth, F. P., & Claussnitzer, M. (2022) · Annual Review of Genetics · doi:10.1146/annurev-genet-072920-032107 · Cited by 84
Georgakopoulos-Soares, I., Deng, C., Agarwal, V., Chan, C. S. Y., Zhao, J., Inoue, F., & Ahituv, N. (2023) · Nature Communications · doi:10.1038/s41467-023-37960-5 · Cited by 82
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 89
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Weiss, C. V., Harshman, L., Inoue, F., Fraser, H. B., Petrov, D. A., Ahituv, N., & Gokhman, D. (2021) · eLife · doi:10.7554/elife.63713 · Cited by 70
Mouri, K., Guo, M. H., de Boer, C. G., Lissner, M. M., Harten, I. A., Newby, G. A., DeBerg, H. A., Platt, W. F., Gentili, M., Liu, D. R., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01056-5 · Cited by 67
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Hansen, T. J., & Hodges, E. C. (2022) · Genome Research · doi:10.1101/gr.276766.122 · Cited by 71
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Chen, W., Choi, J., Li, X., Nathans, J. F., Martin, B., Yang, W., Hamazaki, N., Qiu, C., Lalanne, J., Regalado, S. G., et al. (2024) · Nature · doi:10.1038/s41586-024-07706-4 · Cited by 65
Kundu, K., Tardáguila, M., Mann, A., Watt, S., Ponstingl, H., Vasquez, L. J., Schiller, D. V., Morrell, N. W., Stegle, O., Pastinen, T., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01025-y · Cited by 66
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Lalanne, J., Regalado, S. G., Domcke, S., Calderon, D., Martin, B., Li, X., Li, T., Suiter, C. C., Lee, C., Trapnell, C., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02260-3 · Cited by 57
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Lambert, J. T., Su-Feher, L., Cichewicz, K., Warren, T. L., Zdilar, I., Wang, Y., Lim, K. J., Haigh, J., Morse, S. J., Canales, C. P., et al. (2021) · eLife · doi:10.7554/elife.69479 · Cited by 39
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Mulvey, B., Lagunas, T., & Dougherty, J. D. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.06.011 · Cited by 41
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Davis, J. E., Insigne, K. D., Jones, E. M., Hastings, Q. B., Boldridge, W. C., & Kosuri, S. (2020) · Cell Systems · doi:10.1016/j.cels.2020.05.011 · Cited by 34
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Feng, Y., Xie, N., Inoue, F., Fan, S., Saskin, J., Zhang, C., Zhang, F., Hansen, M. E. B., Nyambo, T., Mpoloka, S. W., et al. (2024) · Nature Genetics · doi:10.1038/s41588-023-01626-1 · Cited by 33
Fu, T., Amoah, K., Chan, T. W., Bahn, J. H., Lee, J., Terrazas, S., Chong, R., Kosuri, S., & Xiao, X. G. (2024) · Nature Communications · doi:10.1038/s41467-024-46795-7 · Cited by 31
Yin, C., Castillo-Hair, S. M., Byeon, G. W., Bromley, P., Meuleman, W. R. A., & Seelig, G. (2025) · Cell Systems · doi:10.1016/j.cels.2025.101302 · Cited by 35
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Kent, D., Marchetti, L., Mikulášová, A., Russell, L. J., & Rico, D. (2023) · BioEssays · doi:10.1002/bies.202200239 · Cited by 31
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Hecker, D., Lauber, M., Ardakani, F. B., Ashrafiyan, S., Manz, Q., Kersting, J., Hoffmann, M., Schulz, M. H., & List, M. (2023) · PROTEOMICS · doi:10.1002/pmic.202200462 · Cited by 27
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Trauernicht, M., Martinez-Ara, M., & van Steensel, B. (2019) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2019.10.006 · Cited by 23
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Characterization of sequence determinants of enhancer function using natural genetic variation
Yang, M. G., Ling, E., Cowley, C. J., Greenberg, M. E., & Vierbuchen, T. (2022) · eLife · doi:10.7554/elife.76500 · Cited by 25
Chan, Y.-C., Kienle, E., Oti, M., Liddo, A. D., Méndez-Lago, M., Aschauer, D. F., Peter, M., Pagani, M., Arnold, C. D., Vonderheit, A., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-33448-w · Cited by 21
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Ni, P., Moe, J., & Su, Z. (2022) · BMC Biology · doi:10.1186/s12915-022-01426-9 · Cited by 18
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Zhou, W., Shi, H., Wang, Z., Huang, Y., Ni, L., Chen, X., Liu, Y., Li, C., Liu, Y., & Liu, Y. (2024) · Genomics Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzae012 · Cited by 10
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Szymczyk, P., & Majewska, M. (2024) · Applied Sciences · doi:10.3390/app14114877 · Cited by 10
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Datsomor, A. K., Wilberg, R., Torgersen, J. S., Sandve, S. R., & Harvey, T. N. (2023) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkad039 · Cited by 10
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Metadata
- Authors
- Klein, J. C., Agarwal, V., Inoue, F., Keith, A., Martin, B., Kircher, M., Ahituv, N., & Shendure, J.
- Affiliations
- Memorial Sloan Kettering Cancer Center (US)
- University of Washington (US)
- University of California, San Francisco (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Ahituv, Nadav
- Shendure, Jay
- ORCID
- Venue
- Nature Methods
- Published
- 2020-10-12
- Volume
- 17
- Issue
- 11
- Pages
- 1083-1091
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC7727316
- OpenAlex
- W3092421956
- Cited by
- 226
- References
- 51
- Citations by year
- 2026: 34 · 2025: 46 · 2024: 38 · 2023: 35 · 2022: 32 · 2021: 27 · 2020: 10
- FWCI
- 8.44
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Methods (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (1UM1HG009408, 5R01HG009136, 5F30HG009479, 1R21HG010065)
- Howard Hughes Medical Institute
- U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (5T32HL007093)
- Uehara Memorial Foundation
- U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (1R01MH109907, 1U01MH116438)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- massively parallel reporter assay, assay design, enhancer activity, HepG2 cells, sequence context, dynamic range, reproducibility, epigenetic features
- MeSH terms
- Enhancer Elements, Genetic, Humans, Regulatory Sequences, Nucleic Acid, Transcription Factors, Reproducibility of Results, Gene Library, Sequence Analysis, DNA, Genes, Reporter, Hep G2 Cells, High-Throughput Nucleotide Sequencing
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