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New tools for genetic variants interpretation and for Single Nucleotide Polymorphisms (SNPs) prioritization are actually needed. Given a list of the most relevant SNPs statistically associated to a specific pathology as result of a genotype study, a critical issue is the identification of genes that are effectively related to the disease by re-scoring the importance of the identified genetic variations. Vice versa, given a list of genes, it can be of great importance to predict which SNPs can be involved in the onset of a particular disease, in order to focus the research on their effects.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We propose a new bioinformatics approach to support biological data mining in the analysis and interpretation of SNPs associated to pathologies. This system can be employed to design custom genotyping chips for disease-oriented studies and to re-score GWAS results. The proposed method relies (1) on the data integration of public resources using a<jats:italic>gene-centric<\/jats:italic>database design, (2) on the evaluation of a set of static biomolecular annotations, defined as<jats:italic>features<\/jats:italic>, and (3) on the SNP scoring function, which computes SNP scores using parameters and weights set by users. We employed a machine learning classifier to set default feature weights and an ontological annotation layer to enable the enrichment of the input gene set. We implemented our method as a web tool called<jats:italic>SNPranker 2.0<\/jats:italic>(<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"http:\/\/www.itb.cnr.it\/snpranker\" ext-link-type=\"uri\">http:\/\/www.itb.cnr.it\/snpranker<\/jats:ext-link>), improving our first published release of this system. A user-friendly interface allows the input of a list of genes, SNPs or a biological process, and to customize the features set with relative weights. As result, SNPranker 2.0 returns a list of SNPs, localized within input and ontologically enriched genes, combined with their prioritization scores.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Different databases and resources are already available for SNPs annotation, but they do not prioritize or re-score SNPs relying on a-priori biomolecular knowledge. SNPranker 2.0 attempts to fill this gap through a user-friendly integrated web resource. End users, such as researchers in medical genetics and epidemiology, may find in SNPranker 2.0 a new tool for data mining and interpretation able to support SNPs analysis. Possible scenarios are GWAS data re-scoring, SNPs selection for custom genotyping arrays and SNPs\/diseases association studies.<\/jats:p><\/jats:sec>","DOI":"10.1186\/1471-2105-14-s1-s9","type":"journal-article","created":{"date-parts":[[2019,12,11]],"date-time":"2019-12-11T01:59:23Z","timestamp":1576029563000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["SNPranker 2.0: a gene-centric data mining tool for diseases associated SNP prioritization in GWAS"],"prefix":"10.1186","volume":"14","author":[{"given":"Ivan","family":"Merelli","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrea","family":"Calabria","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paolo","family":"Cozzi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Federica","family":"Viti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ettore","family":"Mosca","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luciano","family":"Milanesi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2013,1,14]]},"reference":[{"issue":"11","key":"5583_CR1","doi-asserted-by":"publisher","first-page":"1217","DOI":"10.1038\/ng1669","volume":"37","author":"PIW de Bakker","year":"2005","unstructured":"de Bakker PIW, Yelensky R, Peter I, Gabriel SB, Daly MJ, Altshuler D: Efficiency and power in genetic association studies. 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