TrSSP – Transporter Substrate Specificity Prediction Server

TrSSP

:: DESCRIPTION

TrSSP is an integrative Support Vector Machine (SVM) based transporter substrate specificity predictor that is based on primary sequence information such as amino acid composition, AAIndex composition and PSSM profiles.

::DEVELOPER

The Zhao Bioinformatics Laboratory

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Prediction of membrane transport proteins and their substrate specificities using primary sequence information.
Mishra NK, Chang J, Zhao PX.
PLoS One. 2014 Jun 26;9(6):e100278. doi: 10.1371/journal.pone.0100278

CROP 1.33 – Clustering 16S rRNA For OTU Prediction

CROP 1.33

:: DESCRIPTION

CROP is a clustering tool designed mainly for Metagenomics studies, which clusters 16S rRNA sequences into Operational Taxonomic Units (OTU).

::DEVELOPER

Ting Chen

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • GSL
  • C++ Compiler

:: DOWNLOAD

 CROP

:: MORE INFORMATION

Citation:

Xiaolin Hao; Rui Jiang; Ting Chen
Clustering 16S rRNA for OTU prediction: a method of unsupervised Bayesian clustering
Bioinformatics 2011; doi: 10.1093/bioinformatics/btq725

PPSP 1.06 – Prediction of PK-Specific Phosphorylation Site

PPSP 1.06

:: DESCRIPTION

PPSP  is a novel, versatile and comprehensive program which deployed with approach of Bayesian decision theory (BDT). PPSP could predict the potential phosphorylation sites accurately for approximately 70 PK (Protein Kinase) groups.

::DEVELOPER

PPSP team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2006 Mar 20;7:163.
PPSP: prediction of PK-specific phosphorylation site with Bayesian decision theory.
Xue Y, Li A, Wang L, Feng H, Yao X.

KYG – RNA Interface Residue Prediction from Protein 3D Structure

KYG

:: DESCRIPTION

KYG predicts RNA interfaces out of 3D structures of RNA-binding proteins.

::DEVELOPER

Center for Informational Biology, Ochanomizu University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C++ COmpiler

:: DOWNLOAD

 KYG

:: MORE INFORMATION

Citation

Kim, O.T.P., Yura, K., Go, N. (2006)
Amino acid residue doublet propensity in the protein-RNA interface and its application to RNA interface prediction.
Nuc. Acids. Res. 34 (22), 6450-6460.

PhylCRM 1.1 – Cis-regulatory Module (CRM) Prediction

PhylCRM 1.1

:: DESCRIPTION

PhylCRM is a new cis-regulatory module (CRM) prediction algorithm. PhylCRM combines data for individual motif occurances scored on an alignment using previously described MONKEY scoring sheme (Moses et al., Genome Biology 5, R98, 2004) into a single CRM prediction. PhylCRM can scan very long genomic sequences for candidate CRMs by quantifying both motif clustering and conservation across arbitrarily many genomes using an evolutionary model consistent with the phylogeny of the genomes.

::DEVELOPER

The Bulyk Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

  PhylCRM

:: MORE INFORMATION

Citation

Nat Methods. 2008 Apr;5(4):347-53. doi: 10.1038/nmeth.1188. Epub 2008 Mar 2.
Systematic identification of mammalian regulatory motifs’ target genes and functions.
Warner JB, Philippakis AA, Jaeger SA, He FS, Lin J, Bulyk ML.

PSSpred v2 – Multiple Neural Network Training program for Protein Secondary Strucure Prediction

PSSpred v2

:: DESCRIPTION

PSSpred (Protein Secondary Structure PREDiction) is a simple neural network training algorithm for accurate protein secondary structure prediction. It first collects multiple sequence alignments using PSI-BLAST. Amino-acid frequence and log-odds data with Henikoff weights are then used to train secondary structure, separately, based on the Rumelhart error backpropagation method. The final secondary structure prediction result is a combination of 7 neural network predictors from different profile data and parameters.

::DEVELOPER

Yang Zhang’s Research Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 PSSpred

:: MORE INFORMATION

SVMSEQ 1.0 – Protein Contact Prediction

SVMSEQ 1.0

:: DESCRIPTION

SVMSEQ is a new algorithm for protein residue-residue contact prediction using Support Vector Machines.

::DEVELOPER

Yang Zhang’s Research Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
:: DOWNLOAD

 SVMSEQ

:: MORE INFORMATION

Citation

S. Wu, Y. Zhang.
A comprehensive assessment of sequence-based and template-based methods for protein contact prediction.
Bioinformatics, vol 24, 924-931 (2008)

XP-BLUP – Trans-ethnic Trait Prediction

XP-BLUP

:: DESCRIPTION

XP-BLUP aims to improve the genetic prediction of quantitative traits in minority populations by leveraging trans-ethnic information.

:: DEVELOPER

Tang Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

XP-BLUP

:: MORE INFORMATION

Citation

Coram MA, Fang H, Candille SI, Assimes TL, Tang H.
Leveraging Multi-ethnic Evidence for Risk Assessment of Quantitative Traits in Minority Populations.
Am J Hum Genet. 2017 Aug 3;101(2):218-226.

DoGSiteScorer 2.0 – Binding Site Prediction, analysis and Druggability Assessment

DoGSiteScorer 2.0

:: DESCRIPTION

DoGSiteScorer is an automated pocket detection and analysis tool. Size, shape and physico-chemical features of automatically predicted pockets are annotated and incorporated into a support vector machine for druggability estimations.

::DEVELOPER

Center for Bioinformatics (ZBH), University of Hamburg

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux

:: DOWNLOAD

DoGSiteScorer

:: MORE INFORMATION

Citation

Bioinformatics. 2012 Aug 1;28(15):2074-5. doi: 10.1093/bioinformatics/bts310. Epub 2012 May 23.
DoGSiteScorer: a web server for automatic binding site prediction, analysis and druggability assessment.
Volkamer A1, Kuhn D, Rippmann F, Rarey M.

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

 NO

:: 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