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Comparative analysis of nuclear localization signal (NLS) prediction methods

Comparative analysis of nuclear localization signal (NLS) prediction methods

Aim. Comparative analysis of six state-of-the-art nuclear localization signal (NLS) prediction methods (PSORT II, NucPred, cNLSMapper, NLStradamus, NucImport and seqNLS). Methods. Each program was tested for correct predictions using a dataset of 155 experimentally determined NLSs and for false-posi...

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Bibliographic Details
Main Authors: Lisitsyna, O.M., Seplyarskiy, V.B., Sheval, E.V.
Format: Article
Language:English
Published: Інститут молекулярної біології і генетики НАН України 2017
Series:Вiopolymers and Cell
Subjects:
Bioinformatics
Online Access:http://dspace.nbuv.gov.ua/handle/123456789/152918
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Summary:Aim. Comparative analysis of six state-of-the-art nuclear localization signal (NLS) prediction methods (PSORT II, NucPred, cNLSMapper, NLStradamus, NucImport and seqNLS). Methods. Each program was tested for correct predictions using a dataset of 155 experimentally determined NLSs and for false-positives using a dataset of 155 transmembrane proteins, which putatively lack NLS. Results. The most suitable NLS predictors wer fond to be NucPred, NLStradamus and seqNLS; these programs provide the maximum rate of correct to wrong predictions among the tested programs. However, the best results obtained by these programs were only ~ 45 % of the correct predictions. Conclusion. The identification of novel NLSs by predictors still requires experimental verification.

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