title: Guidelines for reporting and using prediction tools for genetic variation analysis.
abstract: Computational prediction methods are widely used for the analysis of human genome sequence variants and their effects on gene/protein function, splice site aberration, pathogenicity, and disease risk. New methods are frequently developed. We believe that guidelines are essential for those writing articles about new prediction methods, as well as for those applying these tools in their research, so that the necessary details are reported. This will enable readers to gain the full picture of technical information, performance, and interpretation of results, and to facilitate comparisons of related methods. Here, we provide instructions on how to describe new methods, report datasets, and assess the performance of predictive tools. We also discuss what details of predictor implementation are essential for authors to understand. Similarly, these guidelines for the use of predictors provide instructions on what needs to be delineated in the text, as well as how researchers can avoid unwarranted conclusions. They are applicable to most prediction methods currently utilized. By applying these guidelines, authors will help reviewers, editors, and readers to more fully comprehend prediction methods and their use.
authors.affiliation: Department of Experimental Medical Science, Lund University, Lund, Sweden. mauno.vihinen@med.lu.se
authors.forename: Mauno
authors.initials: M
authors.lastname: Vihinen
mesh_terms: D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D015894:Genome, Human; D006801:Humans