Tag: Subcellular Localization

Discriminative HMMs – Find Discriminative Motif to Predict Protein Subcellular Localization

Discriminative HMMs

:: DESCRIPTION

Discriminative HMMs (Hidden Markov models) predicts localizations using motifs that are present in a compartment but absent in other, nearby, compartments by utilizing an hierarchical structure that mimics the protein sorting mechanism.

::DEVELOPER

Tien-ho Lin, Robert F. Murphy, and Ziv Bar-Joseph

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 Discriminative HMMs

:: MORE INFORMATION

Citation

IEEE/ACM Trans Comput Biol Bioinform. 2011 Mar-Apr;8(2):441-51.
Discriminative motif finding for predicting protein subcellular localization.
Lin TH, Murphy RF, Bar-Joseph Z.

SCLpredT – Protein Subcellular Localization Prediction

SCLpredT

:: DESCRIPTION

SCLpredT is an enhanced version of SCLpred (Subcellular Localisation), in that: it incorporates homology information to proteins of known localization ; it is trained on a larger dataset; in has one more output class (“organelle”).

::DEVELOPER

Gianluca Pollastri group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Springerplus. 2013 Oct 3;2:502. doi: 10.1186/2193-1801-2-502. eCollection 2013.
SCLpredT: Ab initio and homology-based prediction of subcellular localization by N-to-1 neural networks.
Adelfio A1, Volpato V, Pollastri G.

AAIndexLoc – Predicting Protein Subcellular Localization Using Amino Acid Index

AAIndexLoc

:: DESCRIPTION

AAIndexLoc is a machine-learning-based algorithm that uses amino acid index to predict protein subcellular localization based on its sequence.

::DEVELOPER

Bioinformatics Institute (BII), Singapore.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Amino Acids. 2008 Aug;35(2):345-53. Epub 2007 Dec 28.
AAIndexLoc: predicting subcellular localization of proteins based on a new representation of sequences using amino acid indices.
Tantoso E, Li KB.

DisLocate – Find Disulfide bonds in Eukaryotes with predicted subcellular Localization

DisLocate

:: DESCRIPTION

 DisLocate is a a two-step method based on machine learning models for predicting both the bonding state and the connectivity patterns of cysteine residues in a protein chain.

::DEVELOPER

Bologna Biocomputing Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 No. Only Web Service

:: MORE INFORMATION

Citation

Bioinformatics. 2011 Aug 15;27(16):2224-30. Epub 2011 Jun 29.
Improving the prediction of disulfide bonds in Eukaryotes with machine learning methods and protein subcellular localization.
Savojardo C, Fariselli P, Alhamdoosh M, Martelli PL, Pierleoni A, Casadio R.

SpaPredictor – Interpretable Protein Subcellular Localization

SpaPredictor

:: DESCRIPTION

SpaPredictor includes two interpretable multi-label human-protein predictors, namely mLASSO and mEN, which can yield sparse and interpretable solutions for large-scale prediction of both single-label and multi-label human proteins.

::DEVELOPER

Dr. Man-Wai Mak

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2016 Feb 24;17(1):97. doi: 10.1186/s12859-016-0940-x.
Sparse regressions for predicting and interpreting subcellular localization of multi-label proteins.
Wan S, 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.

AtSubP – Arabidopsis Subcellular Localization Prediction Server

AtSubP

:: DESCRIPTION

AtSubP (for Arabidopsis subcellular localization predictor) is an integrative support vector machine-based localization predictor that is based on the combinatorial presence of diverse protein features, such as its amino acid composition, sequence-order effects, terminal information, Position-Specific Scoring Matrix, and similarity search-based Position-Specific Iterated-Basic Local Alignment Search Tool information.

::DEVELOPER

The Zhao Bioinformaitcs Lab at the Samuel Roberts Noble Foundation

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Plant Physiol. 2010 Sep;154(1):36-54. doi: 10.1104/pp.110.156851. Epub 2010 Jul 20.
Combining machine learning and homology-based approaches to accurately predict subcellular localization in Arabidopsis.
Kaundal R1, Saini R, Zhao PX.

CELLO v.2.5 / CELLO2GO – subCELlular LOcalization predictor

CELLO v.2.5 / CELLO2GO

:: DESCRIPTION

CELLO is a multi-class SVM classification system. CELLO uses 4 types of sequence coding schemes: the amino acid composition, the di-peptide composition, the partitioned amino acid composition and the sequence composition based on the physico-chemical properties of amino acids. We combine votes from these classifiers and use the jury votes to determine the final assignment.

CELLO2GO is a publicly available, web-based system for screening various properties of a targeted protein and its subcellular localization.

::DEVELOPER

CELLO team,  National Chiao Tung University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

CELLO2GO: a web server for protein subCELlular LOcalization prediction with functional gene ontology annotation.
Yu CS, Cheng CW, Su WC, Chang KC, Huang SW, Hwang JK, Lu CH.
PLoS One. 2014 Jun 9;9(6):e99368. doi: 10.1371/journal.pone.0099368.

Protein Sci. 2004 May;13(5):1402-6.
Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions.
Yu CS, Lin CJ, Hwang JK.

PredAlgo 1.0 – Protein Subcellular Localization Prediction in Green Algae

PredAlgo 1.0

:: DESCRIPTION

PredAlgo is a new sequence analysis tool, dedicated to the prediction of protein subcellular localization in green algae. It uses a neural network trained with carefuly curated sets of Chlamydomonas reinhardtii proteins. PredAlgo predicts the localization to one of three compartments: the mitochondrion, the chloroplast, the secretory pathway within the cell.

::DEVELOPER

Nicolas J. Tourasse or Olivier Vallon

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Mol Biol Evol. 2012 Dec;29(12):3625-39. doi: 10.1093/molbev/mss178.
PredAlgo: a new subcellular localization prediction tool dedicated to green algae.
Tardif M, Atteia A, Specht M, Cogne G, Rolland N, Brugière S, Hippler M, Ferro M, Bruley C, Peltier G, Vallon O, Cournac L.

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.