Gram-LocEN – Interpretable prediction of subcellular multi-localization of Gram-positive and Gram-negative bacterial proteins

Gram-LocEN

:: DESCRIPTION

Gram-LocEN is an interpretable multi-label predictor which uses unified features to yield sparse and interpretable solutions for large-scale prediction of both single-label and multi-label proteins of different species, including Gram-positive bacteria and Gram-negative bacteria. Given a query protein sequence in a particular species, a set of GO terms are retrieved from a newly created compact databases, namely ProSeq-GO.

::DEVELOPER

Dr. Man-Wai Mak

:: SCREENSHOTS

N/A

::REQUIREMENTS

Web browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

S. Wan, M. W. Mak, and S. Y. Kung,
“Gram-LocEN: Interpretable prediction of subcellular multi-localization of Gram-positive and Gram-negative bacterial proteins”
2016, submitted.

HybridGO-Loc – Multi-Label Protein Subcellular Localization Prediction

HybridGO-Loc

:: DESCRIPTION

HybridGO-Loc stands for mining Hybrid features on Gene Ontology (GO) for protein subcellular Localization prediction, meaning that this predictor extracts the feature of proteins from different perspectives of GO information (i.e. GO frequency occurrences and GO semantic similarity) and then processes the information by a multi-label multi-class SVM classifier with an adaptive decision scheme.

::DEVELOPER

Dr. Man-Wai Mak

:: SCREENSHOTS

N/A

::REQUIREMENTS

Web browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

Wan S, Mak MW, Kung SY.
HybridGO-Loc: mining hybrid features on gene ontology for predicting subcellular localization of multi-location proteins.
PLoS One. 2014 Mar 19;9(3):e89545. doi: 10.1371/journal.pone.0089545. PMID: 24647341; PMCID: PMC3960097.

PSI-predictor – Plant Subcellular Localization Prediction

PSI-predictor

:: DESCRIPTION

PSI-predictor (Plant Subcellular localization Integrative predictor) is currently the most comprehensive and integrative subcellular location predictor for plants. Based on the wisdom of group-voting and artificial neural network, PSI integrated prediction results from 11 individual predictors to give accurate results on cytosol (cytos), endoplasmic reticulum (ER), extracellular (extra), golgi apparatus (golgi), membrane (membr), mitochondria (mito), nuclear (nucl), peroxisome (pero), plastid (plast) and vacuole (vacu). The community outperformed each individual predictor both on every subcellular location (≥0.8) and overall, with an AUROC~0.932.

::DEVELOPER

Ming Chen’s Bioinformatics Group, Zhejiang University.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web Browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

Lili Liu, Zijun Zhang, Qian Mei, Ming Chen (2013)
PSI: A comprehensive and integrative approach for accurate plant subcellular localization prediction,
PLoS One, DOI:10.1371/journal.pone.0075826

CellOrganizer 2.8.1 – Image-derived Models of Subcellular Organization and Protein Distribution

CellOrganizer 2.8.1

:: DESCRIPTION

The CellOrganizer project provides tools for :learning generative models of cell organization directly from images/ storing and retrieving those models in XML files/ synthesizing cell images (or other representations) from one or more models

::DEVELOPER

CellOrganizer TEam

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Mac / Linux
MatLab

:: DOWNLOAD

CellOrganizer

:: MORE INFORMATION

Citation

Methods Cell Biol. 2012;110:179-93.
CellOrganizer: Image-derived models of subcellular organization and protein distribution.
Murphy RF.

Protein Prowler 1.2 – Subcellular Localisation Predictor

Protein Prowler 1.2

:: DESCRIPTION

The PProwler (Protein Prowler) Subcellular Localisation Predictor accepts amino acid sequences, presented in the FASTA format, and determines the localisation of the protein.

::DEVELOPER

Bioinformatics group,The University of Queensland

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web Browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

Bioinformatics. 2005 May 15;21(10):2279-86. Epub 2005 Mar 3.
Prediction of subcellular localization using sequence-biased recurrent networks.
Bodén M, Hawkins J.

iCluster 1.05 – SubCellular Localisation Image Visualiser

iCluster 1.05

:: DESCRIPTION

iCluster is a complete integrated methodology for testing for difference in fluorescent protein subcellular localisation imaging. iCluster combines components for automated statistics generation, spatial organisation and layout of image sets by similarity to enable patterns of difference in image sets to be seen and browsed, and statistical testing to give rigorous confirmation of differences between experiments.

DEVELOPER

Nicholas Hamilton. @ IMB (The University of Queensland’s Institute for Molecular Bioscience)

:: SCREENSHOTS

:: REQUIREMENTS

Windows / Mac / Linux
Java

:: DOWNLOAD

iCluster

:: MORE INFORMATION

Citation

Statistical and visual differentiation of high throughput subcellular imaging,
N. Hamilton, J. Wang, M.C. Kerr and R.D. Teasdale, B
MC Bioinformatics 2009, 10:94