ArticleGenomics, proteomics & bioinformatics2023
Computational Assessment of the Expression-modulating Potential for Non-coding Variants.
Article in Genomics, proteomics & bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- Quantifying the regulatory potential of genetic variants via a hybrid sequence-oriented model with SVEN.Nature communications · 2024Article
- Multi-omics analysis in human retina uncovers ultraconserved cis-regulatory elements at rare eye disease loci.Nature communications · 2024Article
- REVA as A Well-curated Database for Human Expression-modulating Variants.Genomics, proteomics & bioinformatics · 2021Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large-scale genome-wide association studies (GWAS) and expression quantitative trait locus (eQTL) studies have identified multiple non-coding variants associated with genetic diseases by affecting gene expression. However, pinpointing causal variants effectively and efficiently remains a serious challenge. Here, we developed CARMEN, a novel algorithm to identify functional non-coding expression-modulating variants. Multiple evaluations demonstrated CARMEN's superior performance over state-of-the-art tools. Applying CARMEN to GWAS and eQTL datasets further pinpointed several causal variants other than the reported lead single-nucleotide polymorphisms (SNPs). CARMEN scales well with the massive datasets, and is available online as a web server at http://carmen.gao-lab.org.
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Registered trials
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