Literature DB >> 18951448

Prediction and assessment of splicing alterations: implications for clinical testing.

Amanda B Spurdle1, Fergus J Couch, Frans B L Hogervorst, Paolo Radice, Olga M Sinilnikova.   

Abstract

Sequence variants that may result in splicing alterations are a particular class of inherited variants for which consequences can be more readily assessed, using a combination of bioinformatic prediction methods and in vitro assays. There is also a general agreement that a variant would invariably be considered pathogenic on the basis of convincing evidence that it results in transcript(s) carrying a premature stop codon or an in-frame deletion disrupting known functional domain(s). This commentary discusses current practices used to assess the clinical significance of this class of variants, provides suggestions to improve assessment, and highlights the issues involved in routine assessment of potential splicing aberrations. We conclude that classification of sequence variants that may alter splicing is greatly enhanced by supporting in vitro analysis. Additional studies that assess large numbers of variants for induction of splicing aberrations and exon skipping are needed to define the contribution of splicing/exon skipping to cancer and disease. These studies will also provide the impetus for development of algorithms that better predict splicing patterns. To facilitate variant classification and development of more specific bioinformatic tools, we call for the deposition of all laboratory data from splicing analyses into national and international databases. 2008 Wiley-Liss, Inc.

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Year:  2008        PMID: 18951448      PMCID: PMC2832470          DOI: 10.1002/humu.20901

Source DB:  PubMed          Journal:  Hum Mutat        ISSN: 1059-7794            Impact factor:   4.878


  50 in total

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4.  Does nonsense-mediated mRNA decay explain the ovarian cancer cluster region of the BRCA2 gene?

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Journal:  Hum Mutat       Date:  2005-11       Impact factor: 4.878

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Journal:  Science       Date:  2005-09-02       Impact factor: 47.728

9.  In silico and in vivo splicing analysis of MLH1 and MSH2 missense mutations shows exon- and tissue-specific effects.

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  56 in total

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2.  RNA analysis of cancer predisposing genes in formalin-fixed paraffin-embedded tissue determines aberrant splicing.

Authors:  Anne Ml Jansen; Heleen M van der Klift; Marieke Ae Roos; Jaap Dh van Eendenburg; Carli Mj Tops; Juul T Wijnen; Frederik J Hes; Hans Morreau; Tom van Wezel
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3.  Experimental assessment of splicing variants using expression minigenes and comparison with in silico predictions.

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Journal:  Hum Mutat       Date:  2014-09-10       Impact factor: 4.878

4.  RNA splicing meets genetic testing: detection and interpretation of splicing defects in genetic diseases.

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5.  Comparison of mRNA splicing assay protocols across multiple laboratories: recommendations for best practice in standardized clinical testing.

Authors:  Phillip J Whiley; Miguel de la Hoya; Mads Thomassen; Alexandra Becker; Rita Brandão; Inge Sokilde Pedersen; Marco Montagna; Mireia Menéndez; Francisco Quiles; Sara Gutiérrez-Enríquez; Kim De Leeneer; Anna Tenés; Gemma Montalban; Demis Tserpelis; Toshio Yoshimatsu; Carole Tirapo; Michela Raponi; Trinidad Caldes; Ana Blanco; Marta Santamariña; Lucia Guidugli; Gorka Ruiz de Garibay; Ming Wong; Mariella Tancredi; Laura Fachal; Yuan Chun Ding; Torben Kruse; Vanessa Lattimore; Ava Kwong; Tsun Leung Chan; Mara Colombo; Giovanni De Vecchi; Maria Caligo; Diana Baralle; Conxi Lázaro; Fergus Couch; Paolo Radice; Melissa C Southey; Susan Neuhausen; Claude Houdayer; Jim Fackenthal; Thomas Van Overeem Hansen; Ana Vega; Orland Diez; Rien Blok; Kathleen Claes; Barbara Wappenschmidt; Logan Walker; Amanda B Spurdle; Melissa A Brown
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6.  Tumor characteristics as an analytic tool for classifying genetic variants of uncertain clinical significance.

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7.  Assessing pathogenicity: overview of results from the IARC Unclassified Genetic Variants Working Group.

Authors:  Sean V Tavtigian; Marc S Greenblatt; David E Goldgar; Paolo Boffetta
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8.  Genetic evidence and integration of various data sources for classifying uncertain variants into a single model.

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9.  Assessment of functional effects of unclassified genetic variants.

Authors:  Fergus J Couch; Lene Juel Rasmussen; Robert Hofstra; Alvaro N A Monteiro; Marc S Greenblatt; Niels de Wind
Journal:  Hum Mutat       Date:  2008-11       Impact factor: 4.878

10.  Locus-specific databases and recommendations to strengthen their contribution to the classification of variants in cancer susceptibility genes.

Authors:  Marc S Greenblatt; Lawrence C Brody; William D Foulkes; Maurizio Genuardi; Robert M W Hofstra; Magali Olivier; Sharon E Plon; Rolf H Sijmons; Olga Sinilnikova; Amanda B Spurdle
Journal:  Hum Mutat       Date:  2008-11       Impact factor: 4.878

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