In silico tools for splicing defect prediction - A survey from the viewpoint of end-users

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.Genet Med 16 7, 497–503.

[1]  Lise Getoor,et al.  SplicePort—An interactive splice-site analysis tool , 2007, Nucleic Acids Res..

[2]  Christopher B. Burge,et al.  Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals , 2003, RECOMB '03.

[3]  Thilo Dörk,et al.  Nonclassical splicing mutations in the coding and noncoding regions of the ATM Gene: Maximum entropy estimates of splice junction strengths , 2004, Human mutation.

[4]  P Chambon,et al.  Organization and expression of eucaryotic split genes coding for proteins. , 1981, Annual review of biochemistry.

[5]  S. Karlin,et al.  Prediction of complete gene structures in human genomic DNA. , 1997, Journal of molecular biology.

[6]  Peter K Rogan,et al.  Automated splicing mutation analysis by information theory , 2005, Human mutation.

[7]  A. Krainer,et al.  Listening to silence and understanding nonsense: exonic mutations that affect splicing , 2002, Nature Reviews Genetics.

[8]  Benjamin J. Raphael,et al.  Using positional distribution to identify splicing elements and predict pre-mRNA processing defects in human genes , 2011, Proceedings of the National Academy of Sciences.

[9]  Filomena Ficarazzi,et al.  Comparative In Vitro and In Silico Analyses of Variants in Splicing Regions of BRCA1 and BRCA2 Genes and Characterization of Novel Pathogenic Mutations , 2013, PloS one.

[10]  Jørgen Kjems,et al.  A novel approach to describe a U1 snRNA binding site. , 2003, Nucleic acids research.

[11]  P. Sharp,et al.  Spliced segments at the 5′ terminus of adenovirus 2 late mRNA* , 1977, Proceedings of the National Academy of Sciences.

[12]  Kian Huat Lim,et al.  Spliceman - a computational web server that predicts sequence variations in pre-mRNA splicing , 2012, Bioinform..

[13]  Schraga Schwartz,et al.  SROOGLE: webserver for integrative, user-friendly visualization of splicing signals , 2009, Nucleic Acids Res..

[14]  D. Cooper,et al.  The mutational spectrum of single base-pair substitutions in mRNA splice junctions of human genes: Causes and consequences , 1992, Human Genetics.

[15]  Lili Wan,et al.  RNA and Disease , 2009, Cell.

[16]  A. Krainer,et al.  Disruption of an SF2/ASF-dependent exonic splicing enhancer in SMN2 causes spinal muscular atrophy in the absence of SMN1 , 2002, Nature Genetics.

[17]  P. Sharp,et al.  Splicing of precursors to mRNAs by the spliceosomes , 1993 .

[18]  Luciano Milanesi,et al.  Analysis of donor splice sites in different eukaryotic organisms , 1997, Journal of Molecular Evolution.

[19]  J. Weissenbach,et al.  Identification and characterization of a spinal muscular atrophy-determining gene , 1995, Cell.

[20]  Dominique Stoppa-Lyonnet,et al.  Evaluation of in silico splice tools for decision‐making in molecular diagnosis , 2008, Human mutation.

[21]  Sigve Nakken,et al.  Effects of intronic mutations in the LDLR gene on pre-mRNA splicing: Comparison of wet-lab and bioinformatics analyses. , 2009, Molecular genetics and metabolism.

[22]  Dominique Vaur,et al.  Contribution of bioinformatics predictions and functional splicing assays to the interpretation of unclassified variants of the BRCA genes , 2011, European Journal of Human Genetics.

[23]  Petr Divina,et al.  Ab initio prediction of mutation-induced cryptic splice-site activation and exon skipping , 2009, European Journal of Human Genetics.

[24]  Christophe Béroud,et al.  Bioinformatics identification of splice site signals and prediction of mutation effects , 2010 .

[25]  M. King,et al.  Nonsyndromic deafness DFNA1 associated with mutation of a human homolog of the Drosophila gene diaphanous. , 1997, Science.

[26]  Jean-Philippe Vert,et al.  Guidelines for splicing analysis in molecular diagnosis derived from a set of 327 combined in silico/in vitro studies on BRCA1 and BRCA2 variants , 2012, Human mutation.

[27]  R. Roberts,et al.  An amazing sequence arrangement at the 5′ ends of adenovirus 2 messenger RNA , 1977, Cell.

[28]  D. Baralle,et al.  Missed threads , 2009, EMBO reports.

[29]  David Haussler,et al.  Improved splice site detection in Genie , 1997, RECOMB '97.

[30]  Marvin B. Shapiro,et al.  RNA splice junctions of different classes of eukaryotes: sequence statistics and functional implications in gene expression. , 1987, Nucleic acids research.

[31]  S. Knudsen,et al.  Prediction of human mRNA donor and acceptor sites from the DNA sequence. , 1991, Journal of molecular biology.

[32]  K. Lunetta,et al.  Methods in Genetics and Clinical Interpretation Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium Design of Prospective Meta-Analyses of Genome-Wide Association Studies From 5 Cohorts , 2010 .

[33]  Wei Zhu,et al.  Gene structure prediction from consensus spliced alignment of multiple ESTs matching the same genomic locus , 2004, Bioinform..

[34]  Jinhua Wang,et al.  ESEfinder: a web resource to identify exonic splicing enhancers , 2003, Nucleic Acids Res..

[35]  C. Béroud,et al.  Human Splicing Finder: an online bioinformatics tool to predict splicing signals , 2009, Nucleic acids research.

[36]  Guey-Shin Wang,et al.  Splicing in disease: disruption of the splicing code and the decoding machinery , 2007, Nature Reviews Genetics.

[37]  Karl-Heinz Glatting,et al.  Genome-wide prediction of splice-modifying SNPs in human genes using a new analysis pipeline called AASsites , 2011, BMC Bioinformatics.

[38]  D. Cooper,et al.  Loss of exon identity is a common mechanism of human inherited disease. , 2011, Genome research.

[39]  Elisa de Stanchina,et al.  Determinants of exon 7 splicing in the spinal muscular atrophy genes, SMN1 and SMN2. , 2006, American journal of human genetics.

[40]  Peter Devilee,et al.  Intronic variants in BRCA1 and BRCA2 that affect RNA splicing can be reliably selected by splice‐site prediction programs , 2009, Human mutation.

[41]  S. Salzberg,et al.  GeneSplicer: a new computational method for splice site prediction. , 2001, Nucleic acids research.