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A systematic evaluation of the design, orientation, and sequence context dependencies of massively parallel reporter assays

ABSTRACT: A systematic evaluation of the design, orientation, and sequence context dependencies of massively parallel reporter assays

PROVIDER: PRJNA598033 | ENA |

REPOSITORIES: ENA

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			Action	DRS
		Other
	SRR10800981_1.fastq.gz	Fastqsanger.gz
	SRR10800981_2.fastq.gz	Fastqsanger.gz
	SRR10800982_1.fastq.gz	Fastqsanger.gz
	SRR10800982_2.fastq.gz	Fastqsanger.gz

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A systematic evaluation of the design, orientation, and sequence context dependencies of massively parallel reporter assays

Project description:Enhancers play important roles in evolution and disease. However, traditional assays to test enhancers are low throughput and not scalable to the >100,000 enhancers in the human genome. To better prioritize variants associated with disease and to study the role of enhancers, our group and others developed massively parallel reporter assays (MPRAs), which functionally screen thousands of sequences for regulatory activity in parallel. Although MPRAs have been applied to address diverse questions in gene regulation, there has been no systematic comparison of how differences in experimental design influence findings, making it difficult to interpret results and compare between groups. Here, we screen a library of 2,440 sequences, representing candidate liver enhancers and controls, in HepG2 cells for regulatory activity using nine different approaches (including conventional episomal, STARR-seq, and lentiviral MPRA designs). We identify subtle but significant differences in the resulting measurements that correlate with epigenetic and sequence-level features. We also test this library in both orientations with respect to the promoter, validating en masse that enhancer activity is robustly independent of orientation. Finally, we develop and apply a novel method to assemble and functionally test libraries of the same putative enhancers as 192-mers, 354-mers, and 678-mers, and observe surprisingly large differences in functional activity. 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.

2019-12-30 | GSE142696 | GEO

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

Project description: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.

| S-EPMC7727316 | biostudies-literature

Characterization of de novo promoter variants in autism spectrum disorder with massively parallel reporter assays

Project description:To functionaly test thousands of promoter based Autism associated de novo variants, we established neural progenitor cells from human embryonic stem cells and performed massively parallel reporter assays to collect transcription rates associated with reference and alternate allele sequences

2023-05-24 | GSE216129 | GEO

Transcriptional-Regulatory Convergence Across Functional MDD Risk Variants Identified by Massively Parallel Reporter Assays

Project description:Transcriptional-Regulatory Convergence Across Functional MDD Risk Variants Identified by Massively Parallel Reporter Assays

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Transcriptional-Regulatory Convergence Across Functional MDD Risk Variants Identified by Massively Parallel Reporter Assays [Expt1]

Project description:Using Massively Parallel Reporter Assays (MPRAs), we functionally screened over 1,000 SNPs, prioritized from 39 neuropsychiatric trait/disease GWAS loci based on overlap with predicted regulatory features—including expression quantitative trait loci (eQTL) and histone marks—from human brains and cell cultures. Using mouse neuroblastoma Neuro2a (N2a) cells, we identify over 100 SNPs with allelic effects on expression. Functional SNPs were enriched for binding sequences of retinoic acid-receptive transcription factors (TFs); with additional allelic differences unmasked in cells treated with all-trans retinoic acid (ATRA). Motifs overrepresented at functional SNPs corresponded to a set of TFs highly specific to serotonergic neurons and collectively downregulated in schizophrenia. Our application of MPRAs to screen MDD-associated SNPs suggested a shared transcriptional regulatory program across loci, a subset of which are unmasked by retinoids. Additional code and results corresponding to this GEO submission can be found at https://bitbucket.org/jdlabteam/n2a_atra_mdd_mpra_paper/src

2021-06-30 | GSE167517 | GEO

Dissection of c-AMP Response Element Architecture by Using Genomic and Episomal Massively Parallel Reporter Assays

Project description:We designed and assayed synthetic regulatory elements varying c-amp response element number, affinity, distance to the promoter, spacing between multiple CREs, and surrounding sequence content. Using 5 massively-parallel reporter assays, we measured the expression of 17,406 unique sequences when placed upstream of a minimal promoter using next-generation sequencing of barcodes associated with synthesized elements. We then determine how the varied c-amp response element architectures influence expression when these variants are assayed in an episomal and genomic context across a range of forskolin concentrations, an inducer of c-amp response element activity. Included here are all sequences designed and analyzed in the assay. Assay results from certain library designs were not insightful and not included in the main text but are additionally included here in the barcode-mapping and MPRA sequencing datasets.

2019-09-25 | GSE137922 | GEO

Transcriptional-Regulatory Convergence Across Functional MDD Risk Variants Identified by Massively Parallel Reporter Assays [Expt1]

Project description:Transcriptional-Regulatory Convergence Across Functional MDD Risk Variants Identified by Massively Parallel Reporter Assays [Expt1]

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Transcriptional-Regulatory Convergence Across Functional MDD Risk Variants Identified by Massively Parallel Reporter Assays [Expt2]

Project description:Transcriptional-Regulatory Convergence Across Functional MDD Risk Variants Identified by Massively Parallel Reporter Assays [Expt2]

| PRJNA704847 | ENA

Characterization of de novo promoter variants in autism spectrum disorder with massively parallel reporter assays

Project description:Characterization of de novo promoter variants in autism spectrum disorder with massively parallel reporter assays

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Effective design and inference for cell sorting and sequencing based massively parallel reporter assays.

Project description:MotivationThe ability to measure the phenotype of millions of different genetic designs using Massively Parallel Reporter Assays (MPRAs) has revolutionized our understanding of genotype-to-phenotype relationships and opened avenues for data-centric approaches to biological design. However, our knowledge of how best to design these costly experiments and the effect that our choices have on the quality of the data produced is lacking.ResultsIn this article, we tackle the issues of data quality and experimental design by developing FORECAST, a Python package that supports the accurate simulation of cell-sorting and sequencing-based MPRAs and robust maximum likelihood-based inference of genetic design function from MPRA data. We use FORECAST's capabilities to reveal rules for MPRA experimental design that help ensure accurate genotype-to-phenotype links and show how the simulation of MPRA experiments can help us better understand the limits of prediction accuracy when this data are used for training deep learning-based classifiers. As the scale and scope of MPRAs grows, tools like FORECAST will help ensure we make informed decisions during their development and the most of the data produced.Availability and implementationThe FORECAST package is available at: https://gitlab.com/Pierre-Aurelien/forecast. Code for the deep learning analysis performed in this study is available at: https://gitlab.com/Pierre-Aurelien/rebeca.

| S-EPMC10182853 | biostudies-literature

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