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Statistics and Informatics in Molecular Cancer Research$
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Carsten Wiuf and Claus L. Andersen

Print publication date: 2009

Print ISBN-13: 9780199532872

Published to Oxford Scholarship Online: September 2009

DOI: 10.1093/acprof:oso/9780199532872.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 23 September 2021

Bioinformatic approaches to the analysis of alternative splicing variants in cancer biology

Bioinformatic approaches to the analysis of alternative splicing variants in cancer biology

Chapter:
(p.177) 8 Bioinformatic approaches to the analysis of alternative splicing variants in cancer biology
Source:
Statistics and Informatics in Molecular Cancer Research
Author(s):

Lue Ping Zhao

Jessica Andriesen

Wenhong Fan

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199532872.003.0008

This chapter discusses the use of bioinformatic methods to analyze alternative splicing and investigate its role in cancer biology. A brief introduction to alternative splicing is followed by a discussion of methods for alternative splicing analysis. Both historical and current methods for detecting alternative splice variants are presented, including a detailed discussion of the application of microarray technology. Designs for bioinformatic approaches useful in studies of cancer biology are outlined, including those for matched and unmatched studies. These designs incorporate a general framework for alternative splice variant detection, methods for transcript abundance measurement, and limits of current technology. An example is presented from a study of alternative splice variants in medulloblastomas which illustrates the complex yet significant differences that can be detected between tissues when alternative splicing is considered.

Keywords:   alternative splicing, splice variant, exon array, design, transcript abundance, medulloblastoma, exon, intron, estimating equation, linear model

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