Journal of Proteomics & Bioinformatics

Journal of Proteomics & Bioinformatics
Open Access

ISSN: 0974-276X

+44 1223 790975

Xiang Jia Min

Xiang Jia Min

Youngstown, OH 44555

  • Research Article
    Evaluation of Computational Methods for Secreted Protein Prediction in Different Eukaryotes
    Author(s): Xiang Jia MinXiang Jia Min

    Secreted proteins play important biological roles in eukaryotes. Computational identification of all secreted proteins, i. e. the secretome, from predicted proteome of completely sequenced genomes is an essential step in functional annotation. To develop screening methods for secreted proteins in different kingdoms of eukaryotes, we have evaluated the prediction accuracies of SignalP, Phobius, TargetP, and WolfPsort used individually or in combination with TMHMM and PS-Scan. Prediction accuracy was represented by Mathews’ Correlation Coefficient (MCC). The tools show different strength for predicting secreted proteins in different kingdoms of eukaryotes. When individual tools were used, we found that the tools having the highest accuracy were WolfPsort for fungi (73.1%), Phobius for animals (82.8%), SignalP for plants (55.4%), and Phobius for protists (42.. View More»
    DOI: 10.4172/jpb.1000133

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