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A critical analysis of the combined usage of protein localization prediction methods: Increasing the number of independent data sets can reduce the accuracy of predicted mitochondrial localization

Lookup NU author(s): Kieren Lythgow, Professor Gavin Hudson, Dr Peter Andras, Professor Patrick Chinnery

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Abstract

In the absence of a comprehensive experimentally derived mitochondrial proteome, several bioinformatic approaches have been developed to aid the identification of novel mitochondrial disease genes within mapped nuclear genetic loci. Often, many classifiers are combined to increase the sensitivity and specificity of the predictions. Here we show that the greatest sensitivity and specificity are obtained by using a combination of seven carefully selected classifiers. We also show that increasing the number of independent prediction methods can paradoxically decrease the accuracy of predicting mitochondrial localization. This approach will help to accelerate the identification of new mitochondrial disease genes by providing a principled way for the selection for combination of appropriate prediction methods of mitochondrial localization of proteins. (C) 2011 Elsevier B.V. and Mitochondria Research Society. All rights reserved.

Publication metadata

Author(s): Lythgow KT, Hudson G, Andras P, Chinnery PF

Publication type: Article

Publication status: Published

Journal: Mitochondrion

Year: 2011

Volume: 11

Issue: 3

Pages: 444-449

Print publication date: 31/12/2011

Date deposited: 02/07/2013

ISSN (print): 1567-7249

ISSN (electronic): 1872-8278

Publisher: Elsevier BV

URL: http://dx.doi.org/10.1016/j.mito.2010.12.016

DOI: 10.1016/j.mito.2010.12.016

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