ArticleBioinformatics (Oxford, England)2016
Predicting regulatory variants with composite statistic.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- DeepPTMPred: a multi-modal deep learning framework for accurate prediction of protein post-translational modification sites.Briefings in bioinformatics · 2026Article
- Comparison of missing data handling methods for variant pathogenicity predictors.NAR genomics and bioinformatics · 2025Article
- Multi-omics insights into the molecular signature and prognosis of hypopharyngeal squamous cell carcinoma.Communications biology · 2025Article
- Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors.Human genomics · 2024Article
- Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors.bioRxiv : the preprint server for biology · 2024Article
- Exploiting deep transfer learning for the prediction of functional non-coding variants using genomic sequence.Bioinformatics (Oxford, England) · 2022Article
- RevUP: an online scoring system for regulatory variants implicated in rare diseases.Bioinformatics (Oxford, England) · 2022Article
- A multi-dimensional integrative scoring framework for predicting functional variants in the human genome.American journal of human genetics · 2022Article
- WEVar: a novel statistical learning framework for predicting noncoding regulatory variants.Briefings in bioinformatics · 2021Article
- Triangulating Molecular Evidence to Prioritize Candidate Causal Genes at Established Atopic Dermatitis Loci.The Journal of investigative dermatology · 2021Article
- DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome.Bioinformatics (Oxford, England) · 2021Article
- Systematic Evaluation of DNA Sequence Variations onFrontiers in genetics · 2021Article
- Comprehensive functional annotation of susceptibility variants associated with asthma.Human genetics · 2020Article
- Article
- regBase: whole genome base-wise aggregation and functional prediction for human non-coding regulatory variants.Nucleic acids research · 2019Article
- Regulatory variants: from detection to predicting impact.Briefings in bioinformatics · 2019Review
- Post genome-wide association analysis: dissecting computational pathway/network-based approaches.Briefings in bioinformatics · 2019Review
- Biological relevance of computationally predicted pathogenicity of noncoding variants.Nature communications · 2019Article
- iMEGES: integrated mental-disorder GEnome score by deep neural network for prioritizing the susceptibility genes for mental disorders in personal genomes.BMC bioinformatics · 2018Article
- A semi-supervised approach for predicting cell-type specific functional consequences of non-coding variation using MPRAs.Nature communications · 2018Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
motivationPrediction and prioritization of human non-coding regulatory variants is critical for understanding the regulatory mechanisms of disease pathogenesis and promoting personalized medicine. Existing tools utilize functional genomics data and evolutionary information to evaluate the pathogenicity or regulatory functions of non-coding variants. However, different algorithms lead to inconsistent and even conflicting predictions. Combining multiple methods may increase accuracy in regulatory variant prediction.
resultsHere, we compiled an integrative resource for predictions from eight different tools on functional annotation of non-coding variants. We further developed a composite strategy to integrate multiple predictions and computed the composite likelihood of a given variant being regulatory variant. Benchmarked by multiple independent causal variants datasets, we demonstrated that our composite model significantly improves the prediction performance. AVAILABILITY AND IMPLEMENTATION: We implemented our model and scoring procedure as a tool, named PRVCS, which is freely available to academic and non-profit usage at http://jjwanglab.org/PRVCS CONTACT: wang.junwen@mayo.edu, jliu@stat.harvard.edu, or limx54@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.