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Interpretation of mRNA splicing mutations in genetic disease: review of the literature and guidelines for information-theoretical analysis

The interpretation of genomic variants has become one of the paramount challenges in the post-genome sequencing era. In this review we summarize nearly 20 years of research on the applications of information theory (IT) to interpret coding and non-coding mutations that alter mRNA splicing in rare an...

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Autores principales: Caminsky, Natasha, Mucaki, Eliseos J., Rogan, Peter K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000Research 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4329672/
https://www.ncbi.nlm.nih.gov/pubmed/25717368
http://dx.doi.org/10.12688/f1000research.5654.1
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author Caminsky, Natasha
Mucaki, Eliseos J.
Rogan, Peter K.
author_facet Caminsky, Natasha
Mucaki, Eliseos J.
Rogan, Peter K.
author_sort Caminsky, Natasha
collection PubMed
description The interpretation of genomic variants has become one of the paramount challenges in the post-genome sequencing era. In this review we summarize nearly 20 years of research on the applications of information theory (IT) to interpret coding and non-coding mutations that alter mRNA splicing in rare and common diseases. We compile and summarize the spectrum of published variants analyzed by IT, to provide a broad perspective of the distribution of deleterious natural and cryptic splice site variants detected, as well as those affecting splicing regulatory sequences. Results for natural splice site mutations can be interrogated dynamically with Splicing Mutation Calculator, a companion software program that computes changes in information content for any splice site substitution, linked to corresponding publications containing these mutations. The accuracy of IT-based analysis was assessed in the context of experimentally validated mutations. Because splice site information quantifies binding affinity, IT-based analyses can discern the differences between variants that account for the observed reduced (leaky) versus abolished mRNA splicing. We extend this principle by comparing predicted mutations in natural, cryptic, and regulatory splice sites with observed deleterious phenotypic and benign effects. Our analysis of 1727 variants revealed a number of general principles useful for ensuring portability of these analyses and accurate input and interpretation of mutations. We offer guidelines for optimal use of IT software for interpretation of mRNA splicing mutations.
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spelling pubmed-43296722015-02-24 Interpretation of mRNA splicing mutations in genetic disease: review of the literature and guidelines for information-theoretical analysis Caminsky, Natasha Mucaki, Eliseos J. Rogan, Peter K. F1000Res Review The interpretation of genomic variants has become one of the paramount challenges in the post-genome sequencing era. In this review we summarize nearly 20 years of research on the applications of information theory (IT) to interpret coding and non-coding mutations that alter mRNA splicing in rare and common diseases. We compile and summarize the spectrum of published variants analyzed by IT, to provide a broad perspective of the distribution of deleterious natural and cryptic splice site variants detected, as well as those affecting splicing regulatory sequences. Results for natural splice site mutations can be interrogated dynamically with Splicing Mutation Calculator, a companion software program that computes changes in information content for any splice site substitution, linked to corresponding publications containing these mutations. The accuracy of IT-based analysis was assessed in the context of experimentally validated mutations. Because splice site information quantifies binding affinity, IT-based analyses can discern the differences between variants that account for the observed reduced (leaky) versus abolished mRNA splicing. We extend this principle by comparing predicted mutations in natural, cryptic, and regulatory splice sites with observed deleterious phenotypic and benign effects. Our analysis of 1727 variants revealed a number of general principles useful for ensuring portability of these analyses and accurate input and interpretation of mutations. We offer guidelines for optimal use of IT software for interpretation of mRNA splicing mutations. F1000Research 2014-11-18 /pmc/articles/PMC4329672/ /pubmed/25717368 http://dx.doi.org/10.12688/f1000research.5654.1 Text en Copyright: © 2014 Caminsky N et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. http://creativecommons.org/publicdomain/zero/1.0/ Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).
spellingShingle Review
Caminsky, Natasha
Mucaki, Eliseos J.
Rogan, Peter K.
Interpretation of mRNA splicing mutations in genetic disease: review of the literature and guidelines for information-theoretical analysis
title Interpretation of mRNA splicing mutations in genetic disease: review of the literature and guidelines for information-theoretical analysis
title_full Interpretation of mRNA splicing mutations in genetic disease: review of the literature and guidelines for information-theoretical analysis
title_fullStr Interpretation of mRNA splicing mutations in genetic disease: review of the literature and guidelines for information-theoretical analysis
title_full_unstemmed Interpretation of mRNA splicing mutations in genetic disease: review of the literature and guidelines for information-theoretical analysis
title_short Interpretation of mRNA splicing mutations in genetic disease: review of the literature and guidelines for information-theoretical analysis
title_sort interpretation of mrna splicing mutations in genetic disease: review of the literature and guidelines for information-theoretical analysis
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4329672/
https://www.ncbi.nlm.nih.gov/pubmed/25717368
http://dx.doi.org/10.12688/f1000research.5654.1
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