Cell-PLoc 2.0: an improved package of web-servers for predicting subcellular localization of proteins in various organisms

By Kuo-Chen Chou, Hongbin Shen. 2010

369 citations recorded by the source corpus.

Cell-PLoc 2.0 is a package of web-servers evolved from Cell-PLoc (Chou, K.C. & Shen, H.B., Nature Protocols, 2008, 2:153-162) by a top-down approach to improve the power for predicting subcellular localization of proteins in various organisms. It contains six predictors: Euk-mPLoc 2.0, Hum-mPLoc 2.0, Plant-mPLoc, Gpos-mPLoc, Gneg-mPLoc, and Virus-mPLoc, specialized for eukaryotic, human, plant, Gram- positive bacterial, Gram-negative bacterial, and virus proteins, respectively. Compared with Cell-PLoc, the predictors in the Cell-PLoc 2.0 have the following advantageous features: (1) they all have the capacity to deal with the multiplex proteins that can simultaneiously exist, or move between, two or more subcellular location sites; (2) no accession number is needed for the input of a query protein even if using the “high- level” GO (gene ontology) prediction engine; (3) the functional domain information and sequential evolution information are fused into the “ab initio” sequence-based prediction engine to enhance its accuracy. In this protocol, a step- to-step guide is provided for how to use the web server predictors in the Cell-PLoc 2.0 package, which is freely accessible to the public at http://www.csbio.sjtu.edu.cn/bioinf/Cell-PLoc-2/.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b845d4e4c16e04360e6b318101b6b80ad0470b83

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In Molecular Biology

1. Kuo-Chen Chou
2. Xuan Xiao
3. Hao Lin
4. Ling-Yun Wu
5. Jun Ding
6. Yan Xu
7. Zhi-cheng Wu
8. Pengmian Feng

Citations in the corpus

• iLoc-Euk: A Multi-Label Classifier for Predicting the Subcellular Localization of Singleplex and Multiplex Eukaryotic Proteins (2011, background): “1 of [63] and elucidated in [26], among the three cross-validation methods, the jackknife test is deemed the least arbitrary that can always yield a unique result for a given benchmark dataset and hence has been widely recognized and increasingly used to examine the power of various predictors (see,”
• iSNO-PseAAC: Predict Cysteine S-Nitrosylation Sites in Proteins by Incorporating Position Specific Amino Acid Propensity into Pseudo Amino Acid Composition (2013, background): “However, as elaborated in [57] and demonstrated by Eqs.”
• iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition (2013, methodology): “However, as elaborated by an analysis in (52) and demonstrated by Equations 28–32 of (23), among the three cross-validation methods, the jackknife test is deemed the least arbitrary and most objective because it can always yield a unique result for a given benchmark data set, and hence has been wide”
• Some remarks on protein attribute prediction and pseudo amino acid composition (2010, methodology): “In general, the shortest protein sequence investigated is 50 amino acids (Chou and Shen, 2008a; Chou and Shen, 2010c), hence we can set the maximum value allowed for l is 49.”
• Impacts of bioinformatics to medicinal chemistry. (2015, neutral): “0 [133] at http://www.”