DocumentCode :
632546
Title :
The impact of SignalP 4.0 on the prediction of secreted proteins
Author :
Melhem, Hind ; Xiang Jia Min ; Butler, G.
Author_Institution :
Dept. of Comput. Sci., Al-Hashemite Univ., Al-Zarqa, Jordan
fYear :
2013
fDate :
16-19 April 2013
Firstpage :
16
Lastpage :
22
Abstract :
Secreted proteins play important roles in eukaryotes across each of the kingdoms of fungi, animals, plants and protists. The majority of secreted proteins are signal peptide dependent proteins so their prediction relies on the SignalP family of predictors for signal peptides. The recent release of SignalP 4.0 prompts the question of its impact on the prediction of secreted proteins when compared to current approaches utilising SignalP 3.0. We performed comparisons of SignalP 4.0 and SignalP 3.0 when used alone or in combination with other tools. We used a large dataset for each of the four kingdoms. Results are reported for sensitivity, specificity, and Matthews Correlation Coefficient (MCC) in each case. In terms of MCC, the best performance of any combination of tools always involved SignalP 4.0. In particular for the plant and protist kingdoms there were notable gains due to the use of SignalP 4.0.
Keywords :
cellular biophysics; microorganisms; molecular biophysics; proteins; Matthews Correlation Coefficient; SignalP 3.0; SignalP family; eukaryotes; fungi kingdoms; protist kingdoms; secreted protein prediction; signal peptide dependent proteins; signalP 4.0 impact; Amino acids; Animals; Erbium; Fungi; Peptides; Proteins; Tin;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2013 IEEE Symposium on
Conference_Location :
Singapore
Type :
conf
DOI :
10.1109/CIBCB.2013.6595383
Filename :
6595383
Link To Document :
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