title: In silico tools for splicing defect prediction: a survey from the viewpoint of end users.
abstract: RNA splicing is the process during which introns are excised and exons are spliced. The precise recognition of splicing signals is critical to this process, and mutations affecting splicing comprise a considerable proportion of genetic disease etiology. Analysis of RNA samples from the patient is the most straightforward and reliable method to detect splicing defects. However, currently, the technical limitation prohibits its use in routine clinical practice. In silico tools that predict potential consequences of splicing mutations may be useful in daily diagnostic activities. In this review, we provide medical geneticists with some basic insights into some of the most popular in silico tools for splicing defect prediction, from the viewpoint of end users. Bioinformaticians in relevant areas who are working on huge data sets may also benefit from this review. Specifically, we focus on those tools whose primary goal is to predict the impact of mutations within the 5' and 3' splicing consensus regions: the algorithms used by different tools as well as their major advantages and disadvantages are briefly introduced; the formats of their input and output are summarized; and the interpretation, evaluation, and prospection are also discussed.
authors.affiliation: Division of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
authors.forename: Xueqiu
authors.initials: X
authors.lastname: Jian
authors.affiliation: 1] Division of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA [2] Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, USA.
authors.forename: Eric
authors.initials: E
authors.lastname: Boerwinkle
authors.affiliation: Division of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
authors.forename: Xiaoming
authors.initials: X
authors.lastname: Liu
mesh_terms: D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D014644:Genetic Variation / Q000235:genetics*; D006801:Humans; D012322:RNA Precursors / Q000235:genetics*; D012326:RNA Splicing / Q000235:genetics*; D012984:Software
chemical_list: D012322:RNA Precursors