ArticleHuman mutation2017
Predicting gene expression in massively parallel reporter assays: A comparative study.
Article in Human mutation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 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
34 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
- EnhancerMatcher: comparing cell-type-specific enhancer activity of DNA sequences using deep convolutional neural networks and explainable AI.NAR genomics and bioinformatics · 2025Article
- A review of post-GWAS studies in schizophrenia.Translational psychiatry · 2025Review
- ChromActivity: integrative epigenomic and functional characterization assay based annotation of regulatory activity across diverse human cell types.Genome biology · 2025Article
- Non-local modeling of enhancer-promoter interactions, a correspondence on "LOCO-EPI: Leave-one-chromosome-out (LOCO) as a benchmarking paradigm for deep learning based prediction of enhancer-promoter interactions".Applied intelligence (Dordrecht, Netherlands) · 2025Article
- Optimization of regulatory DNA with active learning.Computational and structural biotechnology journal · 2025Article
- Gapped-kmer sequence modeling robustly identifies regulatory vocabularies and distal enhancers conserved between evolutionarily distant mammals.Nature communications · 2024Article
- Machine learning identifies activation of RUNX/AP-1 as drivers of mesenchymal and fibrotic regulatory programs in gastric cancer.Genome research · 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
- Deep molecular learning of transcriptional control of a synthetic CRE enhancer and its variants.iScience · 2024Article
- MicroRNAs provide negative feedback and stability in gene regulatory network models of cell-state transitions.Frontiers in epigenetics and epigenomics · 2024Article
- Tissue-specific and tissue-agnostic effects of genome sequence variation modulating blood pressure.Cell reports · 2023Article
- Best practices for perturbation MPRA-a computational evaluation framework of sequence design strategies.bioRxiv : the preprint server for biology · 2023Article
- Leveraging massively parallel reporter assays for evolutionary questions.Genome biology · 2023Review
- Exploiting deep transfer learning for the prediction of functional non-coding variants using genomic sequence.Bioinformatics (Oxford, England) · 2022Article
- Indicators of Successful Career Transitions from Physical Sciences and Engineering to Biomedical Research.Current genomics · 2021Article
- WEVar: a novel statistical learning framework for predicting noncoding regulatory variants.Briefings in bioinformatics · 2021Article
- Article
- Statistical considerations for the analysis of massively parallel reporter assays data.Genetic epidemiology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
23 authors.
Funding
Abstract
In many human diseases, associated genetic changes tend to occur within noncoding regions, whose effect might be related to transcriptional control. A central goal in human genetics is to understand the function of such noncoding regions: given a region that is statistically associated with changes in gene expression (expression quantitative trait locus [eQTL]), does it in fact play a regulatory role? And if so, how is this role "coded" in its sequence? These questions were the subject of the Critical Assessment of Genome Interpretation eQTL challenge. Participants were given a set of sequences that flank eQTLs in humans and were asked to predict whether these are capable of regulating transcription (as evaluated by massively parallel reporter assays), and whether this capability changes between alternative alleles. Here, we report lessons learned from this community effort. By inspecting predictive properties in isolation, and conducting meta-analysis over the competing methods, we find that using chromatin accessibility and transcription factor binding as features in an ensemble of classifiers or regression models leads to the most accurate results. We then characterize the loci that are harder to predict, putting the spotlight on areas of weakness, which we expect to be the subject of future studies.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.