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Although several frameworks have been proposed for the evaluation of the functionality of non-coding variants, most of them used \u2018black boxes\u2019 methods that simplify the task as the pathogenicity\/benign classification problem, which ignores the distinct regulatory mechanisms of variants and leads to less desirable performance. In this study, we developed DVAR, an unsupervised framework that leverage various biochemical and evolutionary evidence to distinguish the gene regulatory categories of variants and assess their comprehensive functional impact simultaneously.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>DVAR performed de novo pattern discovery in high-dimensional data and identified five regulatory clusters of non-coding variants. Leveraging the new insights into the multiple functional patterns, it measures both the between-class and the within-class functional implication of the variants to achieve accurate prioritization. Compared to other two-class learning methods, it showed improved performance in identification of clinically significant variants, fine-mapped GWAS variants, eQTLs and expression-modulating variants. Moreover, it has superior performance on disease causal variants verified by genome-editing (like CRISPR-Cas9), which could provide a pre-selection strategy for genome-editing technologies across the whole genome. Finally, evaluated in BioVU and UK Biobank, two large-scale DNA biobanks linked to complete electronic health records, DVAR demonstrated its effectiveness in prioritizing non-coding variants associated with medical phenotypes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>The C++ and Python source codes, the pre-computed DVAR-cluster labels and DVAR-scores across the whole genome are available at https:\/\/www.vumc.org\/cgg\/dvar.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/bty826","type":"journal-article","created":{"date-parts":[[2018,9,25]],"date-time":"2018-09-25T19:11:43Z","timestamp":1537902703000},"page":"1453-1460","source":"Crossref","is-referenced-by-count":20,"title":["<i>De novo<\/i>pattern discovery enables robust assessment of functional consequences of non-coding variants"],"prefix":"10.1093","volume":"35","author":[{"given":"Hai","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Molecular Physiology & Biophysics, Nashville, TN, USA"},{"name":"Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Molecular Physiology & Biophysics, Nashville, TN, USA"},{"name":"Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quan","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Molecular Physiology & Biophysics, Nashville, TN, USA"},{"name":"Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Molecular Physiology & Biophysics, Nashville, TN, USA"},{"name":"Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Ji","sequence":"additional","affiliation":[{"name":"Department of Molecular Physiology & Biophysics, Nashville, TN, USA"},{"name":"Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangze","family":"Zheng","sequence":"additional","affiliation":[{"name":"Department of Molecular Physiology & Biophysics, Nashville, TN, USA"},{"name":"Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xue","family":"Zhong","sequence":"additional","affiliation":[{"name":"Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA"},{"name":"Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nancy J","family":"Cox","sequence":"additional","affiliation":[{"name":"Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA"},{"name":"Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bingshan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Molecular Physiology & Biophysics, Nashville, TN, USA"},{"name":"Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2018,9,26]]},"reference":[{"key":"2023012806480414200_bty826-B1","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1016\/j.ajhg.2018.03.026","article-title":"FUN-LDA: a Latent Dirichlet Allocation Model for Predicting Tissue-Specific Functional Effects of Noncoding Variation: methods and Applications","volume":"102","author":"Backenroth","year":"2018","journal-title":"Am. 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