ANARCI – Antigen Receptor Numbering And Receptor ClassificatIon

ANARCI

:: DESCRIPTION

ANARCI is a tool for numbering amino-acid sequences of antibody and T-cell receptor variable domains.

::DEVELOPER

Oxford Protein Informatics Group (OPIG)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX
  • Python

:: DOWNLOAD

 ANARCI

:: MORE INFORMATION

Citation:

ANARCI: Antigen receptor numbering and receptor classification.
Dunbar J, Deane CM.
Bioinformatics. 2015 Sep 30. pii: btv552.

MIDClass 2 – Gene Expression Classification

MIDClass 2

:: DESCRIPTION

MIDClass (Microarray Interval Discriminant CLASSifier) is a new classification method for expression profiling data based on association rules.

::DEVELOPER

MIDClass team

:: SCREENSHOTS

MIDClass

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • JRE

:: DOWNLOAD

 MIDClass

:: MORE INFORMATION

Citation:

MIDClass: microarray data classification by association rules and gene expression intervals.
Giugno R, Pulvirenti A, Cascione L, Pigola G, Ferro A.
PLoS One. 2013 Aug 6;8(8):e69873. doi: 10.1371/journal.pone.0069873. Print 2013.

GLaD – A Mixed-Membership Model for Heterogenous Tumor Subtype Classification

GLaD

:: DESCRIPTION

GLAD is a mixed-membership classification model that simultaneously learns a sparse biomarker signature for each subtype as well as a distribution over subtypes for each sample.

::DEVELOPER

Flaherty Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux
  • Python

:: DOWNLOAD

  GLaD

:: MORE INFORMATION

Citation

GLAD: A mixed-membership model for heterogeneous tumor subtype classification.
Saddiki H, McAuliffe J, Flaherty P.
Bioinformatics. 2014 Sep 29. pii: btu618.

MetaBinG 0.4 / MetaBinG2 – ultra-fast Metagenomic Sequence Classification system using GPUs

MetaBinG 0.4 / MetaBinG2

:: DESCRIPTION

MetaBinG  is an ultra-fast metagenomic sequence classification system using graphic processing units (GPUs).

MetaBinG2 is a fast and accurate metagenomic sequence classification system for samples with many unknown organisms

::DEVELOPER

Dr. Chaochun Wei

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 MetaBinG / MetaBinG2

:: MORE INFORMATION

Citation:

Biol Direct. 2018 Aug 22;13(1):15. doi: 10.1186/s13062-018-0220-y.
MetaBinG2: a fast and accurate metagenomic sequence classification system for samples with many unknown organisms.
Qiao Y, Jia B, Hu Z, Sun C, Xiang Y, Wei C.

PLoS One. 2011;6(11):e25353. doi: 10.1371/journal.pone.0025353. Epub 2011 Nov 23.
MetaBinG: using GPUs to accelerate metagenomic sequence classification.
Jia P1, Xuan L, Liu L, Wei C.

PlantMiRNAPred – Classification of Real and Pseudo Plant Pre-miRNAs

PlantMiRNAPred

:: DESCRIPTION

PlantMiRNAPred web server can be used to classify real plant pre-miRNAs and pseudo hairpins.

::DEVELOPER

NClab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Bioinformatics. 2011 May 15;27(10):1368-76. doi: 10.1093/bioinformatics/btr153. Epub 2011 Mar 26.
PlantMiRNAPred: efficient classification of real and pseudo plant pre-miRNAs.
Xuan P, Guo M, Liu X, Huang Y, Li W, Huang Y.

CancerIN – Classification and Designing of Anticancer Compounds

CancerIN

:: DESCRIPTION

CancerIN  is a web server developed for predicting anticancer activity of molecules. Similarity based approach has been used for discrimination or classification of anticancer and non-anticancer molecule.

::DEVELOPER

CancerIN team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux /MacOs
  • Python

:: DOWNLOAD

CancerIN

:: MORE INFORMATION

Citation

Prediction of anticancer molecules using hybrid model developed on molecules screened against NCI-60 cancer cell lines.
Singh H, Kumar R, Singh S, Chaudhary K, Gautam A, Raghava GP.
BMC Cancer. 2016 Feb 9;16:77. doi: 10.1186/s12885-016-2082-y.

PROCLASS – Protein Structure Classification Server

PROCLASS

:: DESCRIPTION

 PROCLASS allows to predict the class of protein from its amino acid sequence. It predict weather protein belong to class Alpha or Beta or Alpha+Beta or Alpha/Beta.

::DEVELOPER

Dr. G P S Raghava,

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Raghava, G P S (1999)
Proclass: A computer program for predicting the protein structural classes.
J. Biosciences 24, 176.

GPCRSclass – SVM based Classification of Amine Type of GPCR

GPCRSclass

:: DESCRIPTION

 GPCRSclass is a dipeptide composition based method for predicting Amine Type of G-protein-coupled receptors. In this method type amine is predicted from dipeptide composition of proteins using SVM.

::DEVELOPER

 GPCRSclass Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Bhasin M, Raghava GPS.
GPCRsclass: a web tool for the classification of amine type of G-protein-coupled receptors.
Nucleic Acids Res.(2005). 33(Web Server issue):W143-7

RNAcon 1.0 – Classification of non-coding RNAs (ncRNA)

RNAcon 1.0

:: DESCRIPTION

RNAcon is a web-server for the prediction and classification of non-coding RNAs. It uses SVM-based model for the discrimination between coding and ncRNAs and RandomForest-based prediction model for the classification of ncRNAs into different classes. The structural information based graph properties were used for the development of prediction model.

::DEVELOPER

RNAcon team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 RNAcon

:: MORE INFORMATION

Citation

Panwar, B; Arora, A. and Raghava GP (2014)
Prediction and classification of ncRNAs using structural information
BMC Genomics 2014, 15:127

EGFRpred – Classification of Active and Inactive Anti-egfr Compounds

EGFRpred

:: DESCRIPTION

EGFRpred is a web service for the predicting and designing of inhibitors against EGFR (a cell surface receptor whose overexpression is known to cause cancer).

::DEVELOPER

EGFRpred Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

QSAR based model for discriminating EGFR inhibitors and non-inhibitors using Random forest.
Singh H, Singh S, Singla D, Agarwal SM, Raghava GP.
Biol Direct. 2015 Mar 25;10(1):10.