Cell-PLoc 2.0 – Predicting Subcellular localization of Proteins in different Organisms

Cell-PLoc 2.0

:: DESCRIPTION

Cell-PLoc is a package of Web servers for predicting subcellular localization of proteins in various organisms.The package contains the following six predictors: Euk-mPLoc 2.0 , Hum-mPLoc 2.0, Plant-PLoc, Gpos-PLoc, Gneg-PLoc and Virus-PLoc, specialized for eukaryotic, human, plant, Gram-positive bacterial, Gram-negative bacterial and viral proteins, respectively.

::DEVELOPER

Computational Systems Biology GroupShanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Nat Protoc. 2008;3(2):153-62. doi: 10.1038/nprot.2007.494.
Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms.
Chou KC1, Shen HB.

Kuo-Chen Chou, and Hong-Bin Shen,
Cell-PLoc 2.0: an improved package of web-servers for predicting subcellular localization of proteins in various organisms,
Natural Science, 2010, 2: 1090-1103
PLoS One. 2010 Apr 1;5(4):e9931. doi: 10.1371/journal.pone.0009931.
A new method for predicting the subcellular localization of eukaryotic proteins with both single and multiple sites: Euk-mPLoc 2.0.
Chou KC1, Shen HB.

A top-down approach to enhance the power of predicting human protein subcellular localization: Hum-mPLoc 2.0.
Shen HB, Chou KC.
Anal Biochem. 2009 Nov 15;394(2):269-74. doi: 10.1016/j.ab.2009.07.046.

Plant-mPLoc: a top-down strategy to augment the power for predicting plant protein subcellular localization.
Chou KC, Shen HB.
PLoS One. 2010 Jun 28;5(6):e11335. doi: 10.1371/journal.pone.0011335.

Gpos-mPLoc: a top-down approach to improve the quality of predicting subcellular localization of Gram-positive bacterial proteins.
Shen HB, Chou KC.
Protein Pept Lett. 2009;16(12):1478-84.

Gneg-mPLoc: a top-down strategy to enhance the quality of predicting subcellular localization of Gram-negative bacterial proteins.
Shen HB, Chou KC.
J Theor Biol. 2010 May 21;264(2):326-33. doi: 10.1016/j.jtbi.2010.01.018.

J Biomol Struct Dyn. 2010 Oct;28(2):175-86.
Virus-mPLoc: a fusion classifier for viral protein subcellular location prediction by incorporating multiple sites.
Shen HB1, Chou KC.

iLocator – An Image-based Multi-label Human Protein Subcellular Localization Predictor

iLocator

:: DESCRIPTION

iLocator is an image-based multi-label subcellular location predictor, which covers 7 cellular localizations, i.e. cytoplasm, endoplasmic reticulum, Golgi apparatus, lysosome, mitochondria, nucleus, and vesicles. The iLocator incorporates both global and local image descriptors, and uses an ensemble multi-label classifier to generate accurate predictions.

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

iLocator

:: REQUIREMENTS

  • Windows
  • Matlab

:: DOWNLOAD

 iLocator

:: MORE INFORMATION

Citation:

Bioinformatics. 2013 Aug 15;29(16):2032-40. doi: 10.1093/bioinformatics/btt320. Epub 2013 Jun 4.
An image-based multi-label human protein subcellular localization predictor (iLocator) reveals protein mislocalizations in cancer tissues.
Xu YY1, Yang F, Zhang Y, Shen HB.

CellWhere 2019.10 – Graphical Display of Interaction Networks organized on Subcellular Localizations

CellWhere 2019.10

:: DESCRIPTION

CellWhere is a data combining and visualization tool that enables bench researchers to quickly explore the reported subcellular locations of a list of genes/proteins, and to put these subcellular locations into the context of previously identified physical interactions that could be occurring between these proteins and others within the cell.

::DEVELOPER

CellWhere team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • WEb browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

CellWhere: graphical display of interaction networks organized on subcellular localizations.
Zhu L, Malatras A, Thorley M, Aghoghogbe I, Mer A, Duguez S, Butler-Browne G, Voit T, Duddy W.
Nucleic Acids Res. 2015 Apr 16. pii: gkv354.

Mycosub – Predicting Subcellular Localization of Mycobacterial Proteins

Mycosub

:: DESCRIPTION

The web-server MycoSub was used to predict the subcellular localizations of mycobacterial proteins based on optimal tripeptide compositions.

::DEVELOPER

LinDing Group

:: SCREENSHOTS

n/a

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Mol Biosyst. 2014 Dec 1. [Epub ahead of print]
Predicting the subcellular localization of mycobacterial proteins by incorporating the optimal tripeptides into the general form of pseudo amino acid composition.
Zhu PP1, Li WC, Zhong ZJ, Deng EZ, Ding H, Chen W, Lin H.

ESLpred2 – Subcellular Localization of Eukaryotic Proteins

ESLpred2

:: DESCRIPTION

ESLpred is a SVM based method for predicting subcellular localization of Eukaryotic proteins using dipeptide composition and PSIBLAST generated pfofile Using this server user may know the function of their protein based on its location in cell.

ESLpred2” is an improved version of our previous most popular method, ESLpred ,

::DEVELOPER

ESLpred2 Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2008 Nov 28;9:503. doi: 10.1186/1471-2105-9-503.
ESLpred2: improved method for predicting subcellular localization of eukaryotic proteins.
Garg A1, Raghava GP.

Bhasin,M. and Raghava, G.P.S. (2004)
ESLpred: SVM Based Method for Subcellular Localization of Eukaryotic Proteins using Dipeptide Composition and PSI-BLAST.
Nucleic Acids Reasearch 32:W414-9.

PSLpred – SVM based method for the Subcellular Localization of Prokaryotic Proteins

PSLpred

:: DESCRIPTION

 PSLpred is a method for subcellular localization proteins belongs to prokaryotic genomes. The pathogen play an important role in our life.

::DEVELOPER

PSLpred Team

: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Bhasin, M., Garg, A. and Raghava, GPS (2005)
PSLpred: prediction of subcellular localization of bacterial proteins.
Bioinformatics 21(10):2522-4.

RSLpred – Predicting Subcellular Localization of Rice Proteins

RSLpred

:: DESCRIPTION

RSLpred is an effort for genome-scale subcellular prediction of encoded rice proteins.

::DEVELOPER

RSLpredteam

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Proteomics. 2009 May;9(9):2324-42. doi: 10.1002/pmic.200700597.
RSLpred: an integrative system for predicting subcellular localization of rice proteins combining compositional and evolutionary information.
Kaundal R1, Raghava GP.

TBpred – SVM based Subcellular Localization Prediction method for Mycobacterial Proteins

TBpred

:: DESCRIPTION

TBpred is a prediction server that predicts four subcellular localization (cytoplasmic,integral membrane,secretory and membrane attached by lipid anchor) of mycobacterial proteins.

::DEVELOPER

TBpred team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2007 Sep 13;8:337.
Support Vector Machine-based method for predicting subcellular localization of mycobacterial proteins using evolutionary information and motifs.
Rashid M1, Saha S, Raghava GP.

mPLR-Loc – Multi-Label Protein Subcellular Localization Prediction

mPLR-Loc

:: DESCRIPTION

mPLR-Loc is an efficient multi-label predictor based on penalized logistic regression and adaptive decisions for predicting both single- and multi-location proteins.

::DEVELOPER

Dr. Man-Wai Mak

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Anal Biochem. 2015 Mar 15;473:14-27. doi: 10.1016/j.ab.2014.10.014. Epub 2014 Oct 31.
mPLR-Loc: an adaptive decision multi-label classifier based on penalized logistic regression for protein subcellular localization prediction.
Wan S, Mak MW, Kung SY.

R3P-Loc – Multi-Label Protein Subcellular Localization Prediction

R3P-Loc

:: DESCRIPTION

R3P-Loc stands for Ridge Regression and Random Projection for protein subcellular Localization prediction, meaning that this predictor applies random projection to reduce the feature dimensions of an ensemble ridge regression classifier.

::DEVELOPER

Dr. Man-Wai Mak

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

J Theor Biol. 2014 Nov 7;360:34-45. doi: 10.1016/j.jtbi.2014.06.031. Epub 2014 Jul 2.
R3P-Loc: a compact multi-label predictor using ridge regression and random projection for protein subcellular localization.
Wan S, Mak MW, Kung SY.