SynthesisHuman mutation2019
Meta-analysis of massively parallel reporter assays enables prediction of regulatory function across cell types.
Synthesis in Human mutation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-analysis of massively parallel reporter assays enables prediction of regulatory function across cell types.Human mutation · 2019Pooled it
- Simultaneous epigenomic profiling and regulatory activity measurement using e2MPRA.Nature communications · 2026Article
- Lineage-specific regulatory evolution: insights from massively parallel reporter assays.Current opinion in genetics & development · 2025Review
- ChromActivity: integrative epigenomic and functional characterization assay based annotation of regulatory activity across diverse human cell types.Genome biology · 2025Article
- Article
- Human gene regulatory evolution is driven by the divergence of regulatory element function in both cis and trans.Cell genomics · 2024Article
- Optimizing sequence design strategies for perturbation MPRAs: a computational evaluation framework.Nucleic acids research · 2024Article
- CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods.Genome biology · 2024Article
- Best practices for perturbation MPRA-a computational evaluation framework of sequence design strategies.bioRxiv : the preprint server for biology · 2023Article
- Review
- Pharmacogenomics: current status and future perspectives.Nature reviews. Genetics · 2023Review
- Characterization of De Novo Promoter Variants in Autism Spectrum Disorder with Massively Parallel Reporter Assays.International journal of molecular sciences · 2023Article
- Functional interrogation of autoimmune disease genetics using CRISPR/Cas9 technologies and massively parallel reporter assays.Seminars in immunopathology · 2022Review
- Creating New β-Globin-Expressing Lentiviral Vectors by High-Resolution Mapping of Locus Control Region Enhancer Sequences.Molecular therapy. Methods & clinical development · 2020Article
- Identification and Massively Parallel Characterization of Regulatory Elements Driving Neural Induction.Cell stem cell · 2019Article
- Predicting functional variants in enhancer and promoter elements using RegulomeDB.Human mutation · 2019Article
- Reports from the fifth edition of CAGI: The Critical Assessment of Genome Interpretation.Human mutation · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Deciphering the potential of noncoding loci to influence gene regulation has been the subject of intense research, with important implications in understanding genetic underpinnings of human diseases. Massively parallel reporter assays (MPRAs) can measure regulatory activity of thousands of DNA sequences and their variants in a single experiment. With increasing number of publically available MPRA data sets, one can now develop data-driven models which, given a DNA sequence, predict its regulatory activity. Here, we performed a comprehensive meta-analysis of several MPRA data sets in a variety of cellular contexts. We first applied an ensemble of methods to predict MPRA output in each context and observed that the most predictive features are consistent across data sets. We then demonstrate that predictive models trained in one cellular context can be used to predict MPRA output in another, with loss of accuracy attributed to cell-type-specific features. Finally, we show that our approach achieves top performance in the Fifth Critical Assessment of Genome Interpretation "Regulation Saturation" Challenge for predicting effects of single-nucleotide variants. Overall, our analysis provides insights into how MPRA data can be leveraged to highlight functional regulatory regions throughout the genome and can guide effective design of future experiments by better prioritizing regions of interest.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.