The DOI matches its Crossref record; 50 of the 51 references it cites were found in a registry or an index.
A systematic evaluation of the design and context dependencies of massively parallel reporter assays
Open accessPublished version of a preprint
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
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.
In the citing paper
“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]
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.
In the citing paper
“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.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Cited by
Sorted by citations
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
Self-citationDeciphering the multi-scale, quantitative cis-regulatory code
Kim, S., & Wysocka, J. (2023) · Molecular Cell · doi:10.1016/j.molcel.2022.12.032 · Cited by 302
Synthetic promoter designs enabled by a comprehensive analysis of plant core promoters
Jores, T., Tonnies, J., Wrightsman, T., Buckler, E., Cuperus, J., Fields, S., & Queitsch, C. (2021) · Nature Plants · doi:10.1038/s41477-021-00932-y · Cited by 200
Durrant, M. G., Fanton, A., Tycko, J., Hinks, M. M., Chandrasekaran, S. S., Perry, N. T., Schaepe, J. M., Du, P. P., Lotfy, P., Bassik, M. C., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01494-w · Cited by 212
Generating specificity in genome regulation through transcription factor sensitivity to chromatin
Isbel, L., Grand, R. S., & Schübeler, D. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00512-6 · Cited by 152
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
