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A systematic evaluation of the design and context dependencies of massively parallel reporter assays.

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.

SUBMITTER: Klein JC

PROVIDER: S-EPMC7727316 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Motivation:The majority of the human genome is composed of non-coding regions containing regulatory elements such as enhancers, which are crucial for controlling gene expression. Many variants associated with complex traits are in these regions, and may disrupt gene regulatory sequences. Consequently, it is important to not only identify true enhancers but also to test if a variant within an enhancer affects gene regulation. Recently, allele-specific analysis in high-throughput reporter assays, such as massively parallel reporter assays (MPRAs), have been used to functionally validate non-coding variants. However, we are still missing high-quality and robust data analysis tools for these datasets. Results:We have further developed our method for allele-specific analysis QuASAR (quantitative allele-specific analysis of reads) to analyze allele-specific signals in barcoded read counts data from MPRA. Using this approach, we can take into account the uncertainty on the original plasmid proportions, over-dispersion, and sequencing errors. The provided allelic skew estimate and its standard error also simplifies meta-analysis of replicate experiments. Additionally, we show that a beta-binomial distribution better models the variability present in the allelic imbalance of these synthetic reporters and results in a test that is statistically well calibrated under the null. Applying this approach to the MPRA data, we found 602 SNPs with significant (false discovery rate 10%) allele-specific regulatory function in LCLs. We also show that we can combine MPRA with QuASAR estimates to validate existing experimental and computational annotations of regulatory variants. Our study shows that with appropriate data analysis tools, we can improve the power to detect allelic effects in high-throughput reporter assays. Availability and implementation:http://github.com/piquelab/QuASAR/tree/master/mpra. Contact:fluca@wayne.edu or rpique@wayne.edu. Supplementary information:Supplementary data are available online at Bioinformatics.

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A systematic evaluation of the design and context dependencies of massively parallel reporter assays.

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