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.

mGOASVM v2 – Multi-Label Protein Subcellular Localization Prediction

mGOASVM v2

:: DESCRIPTION

mGOASVM is an efficient multi-label predictor for predicting the subcellular localization of multi-location proteins.

::DEVELOPER

Dr. Man-Wai Mak

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2012 Nov 6;13:290. doi: 10.1186/1471-2105-13-290.
mGOASVM: Multi-label protein subcellular localization based on gene ontology and support vector machines.
Wan S1, Mak MW, Kung SY.

mLASSO-Hum – Human Protein Subcellular Localization

mLASSO-Hum

:: DESCRIPTION

mLASSO-Hum is an interpretable multi-label predictor which can yield sparse and interpretable solutions for large-scale prediction of human protein subcellular localization.

::DEVELOPER

Dr. Man-Wai Mak

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

J Theor Biol. 2015 Oct 7;382:223-34. doi: 10.1016/j.jtbi.2015.06.042. Epub 2015 Jul 9.
mLASSO-Hum: A LASSO-based interpretable human-protein subcellular localization predictor.
Wan S, Mak MW, Kung SY.