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...
Збережено в:
| Дата: | 2017 |
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| Автори: | , , |
| Формат: | Стаття |
| Мова: | English |
| Опубліковано: |
Інститут молекулярної біології і генетики НАН України
2017
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| Назва видання: | Вiopolymers and Cell |
| Теми: | |
| Онлайн доступ: | http://dspace.nbuv.gov.ua/handle/123456789/152918 |
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| Назва журналу: | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| Цитувати: | Comparative analysis of nuclear localization signal (NLS) prediction methods / O.M. Lisitsyna, V.B. Seplyarskiy, E.V. Sheval // Вiopolymers and Cell. — 2017. — Т. 33, № 2. — С. 147-154. — Бібліогр.: 28 назв. — англ. |
Репозитарії
Digital Library of Periodicals of National Academy of Sciences of Ukraine| Резюме: | 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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