Tag: Classification

ClassifyR 2.12.0 – Performance Assessment of Classification with Applications to Transcriptomics

ClassifyR 2.12.0

:: DESCRIPTION

ClassifyR is a framework for two-class classification problems, with applications to differential variability and differential distribution testing.

::DEVELOPER

Dario Strbenac <dario.strbenac at sydney.edu.au>, John Ormerod, Graham Mann, Jean Yang

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • R
  • BioConductor

:: DOWNLOAD

 ClassifyR

:: MORE INFORMATION

Citation

ClassifyR: An R Package for Performance Assessment of Classification with Applications to Transcriptomics.
Strbenac D, Mann GJ, Ormerod JT, Yang JY.
Bioinformatics. 2015 Feb 1. pii: btv066.

ArchKI – Classification of common Functional Loops of Kinase Super-families

ArchKI

:: DESCRIPTION

ArchKI is a structural classification of kinase loops with information of functional residues. We are interested in finding relationships between structure and function of classified kinase-loops using three different sources of functional information: (i) functional residues (or regions) described in the literature (ii) residues described in the SITE records of the pdb files that specify residues comprising catalytic, cofactor, anticodon, regulatory or other important sites and (iii) residues in contact with heteroatoms.

::DEVELOPER

ArchKI Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 No

:: MORE INFORMATION

Citation

Proteins. 2004 Aug 15;56(3):539-55.
Classification of common functional loops of kinase super-families.
Fernandez-Fuentes N, Hermoso A, Espadaler J, Querol E, Aviles FX, Oliva B.

UProC 1.2.0 – Tools for Ultra-fast Protein Sequence Classification

UProC 1.2.0

:: DESCRIPTION

The UProC (ultrafast protein classification) toolbox implements a novel algorithm (“Mosaic Matching”) for large-scale sequence analysis and is now available in terms of an open source C library. UProC is up to three orders of magnitude faster than profile-based methods and achieved up to 80% higher sensitivity on unassembled short reads (100 bp) from simulated metagenomes. UProC does not depend on a multiple alignment of family-specific sequences. Therefore, in addition to the protein domain classfication according to the Pfam database, UProC can, in principle, also provide the detection of KEGG Orthologs

::DEVELOPER

Dr. Peter Meinicke

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • C++ Compiler

:: DOWNLOAD

 UProC

:: MORE INFORMATION

Citation

Bioinformatics. 2014 Dec 23. pii: btu843.
UProC: tools for ultra-fast protein domain classification.
Meinicke P

Kolmogorov – Compression-based Classification of Biological Sequences and Structures

Kolmogorov

:: DESCRIPTION

Kolmogorov is a multistep approach to classify and cluster Biological Sequences and Structures, via Compression.

::DEVELOPER

Raffaele Giancarlo

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOSX / Windows
  • Perl
  • BioPerl

:: DOWNLOAD

 Kolmogorov

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2007 Jul 13;8:252.
Compression-based classification of biological sequences and structures via the Universal Similarity Metric: experimental assessment.
Ferragina P1, Giancarlo R, Greco V, Manzini G, Valiente G.

NBC – Naive Bayes Classification tool

NBC

:: DESCRIPTION

NBC uses a method similar to that used in many email spam filters to score a genetic sample against different genomes, to possibly identify the closest match.

::DEVELOPER

Drexel’s EESI Lab.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 NBC

:: MORE INFORMATION

Citation

NBC: the Naive Bayes Classification tool webserver for taxonomic classification of metagenomic reads.
Rosen GL, Reichenberger ER, Rosenfeld AM.
Bioinformatics. 2011 Jan 1;27(1):127-9. doi: 10.1093/bioinformatics/btq619.

NBC update: The addition of viral and fungal databases to the Naïve Bayes classification tool.
Rosen GL, Lim TY.
BMC Res Notes. 2012 Jan 31;5:81. doi: 10.1186/1756-0500-5-81.

TEclass 2.1.3C – Classification of TE Consensus Sequences

TEclass 2.1.3C

:: DESCRIPTION

TEclass classifies unknown transpsosable element (TE) consensus sequences into four categories, according to their mechanism of transposition: DNA transposons, LTRs, LINEs, SINEs.

::DEVELOPER

Institute of Bioinformatics WWU Muenster

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

  TEclass

:: MORE INFORMATION

Citation

Abrusan G., Grundmann N., DeMeester L., Makalowski W. 2009.
TEclass: a tool for automated classification of unknown eukaryotic transposable elements.
Bioinformatics 25:1329-1330

Jstacs 2.3 – Java Framework for Statistical Analysis and Classification of Biological Sequences

Jstacs 2.3

:: DESCRIPTION

Jstacs is an open source Java library, which focuses on the statistical analysis of biological sequences instead. Jstacs comprises an efficient representation of sequence data and provides implementations of many statistical models with generative and discriminative approaches for parameter learning. Using Jstacs, classifiers can be assessed and compared on test datasets or by cross-validation experiments evaluating several performance measures.

::DEVELOPER

Jstacs Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux/  MacOSX
  • Java

:: DOWNLOAD

 Jstacs

:: MORE INFORMATION

Citation

Michael Seifert, Marc Strickert, Alexander Schliep and Ivo Grosse
Exploiting prior knowledge and gene distances in the analysis of tumor expression profiles with extended Hidden Markov Models
Bioinformatics (2011) 27 (12): 1645-1652.

DRWPClass 1.0 – Pathway-based Disease Classification

DRWPClass 1.0

:: DESCRIPTION

DRWPClass is a pathway-based disease classification method. It incorporates directed pathway topological information to infer reproducible pathway activities by directed random walk (DRW) and uses them for accurate and robust cancer classification.

::DEVELOPER

Wei Liu <freelw@gmail.com> and Chunquan Li <lcqbio@aliyun.com>

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 DRWPClass

:: MORE INFORMATION

Citation

Bioinformatics. 2013 Sep 1;29(17):2169-77. doi: 10.1093/bioinformatics/btt373. Epub 2013 Jul 10.
Topologically inferring risk-active pathways toward precise cancer classification by directed random walk.
Liu W1, Li C, Xu Y, Yang H, Yao Q, Han J, Shang D, Zhang C, Su F, Li X, Xiao Y, Zhang F, Dai M, Li X.

GeneCommittee – Testing the Discriminatory Power of Gene Sets in Microarray data Classification

GeneCommittee

:: DESCRIPTION

GeneCommittee is a web-based interactive tool for giving specific support to the study of the discriminative classification power of custom hypothesis in the form of biological relevant gene sets.

::DEVELOPER

Sistemas Informáticos de Nueva Generación, UA.PT Bioinformatics

:: SCREENSHOTS

 N/A

::REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2014 Jan 30;15:31. doi: 10.1186/1471-2105-15-31.
geneCommittee: a web-based tool for extensively testing the discriminatory power of biologically relevant gene sets in microarray data classification.
Reboiro-Jato M, Arrais JP, Oliveira JL, Fdez-Riverola F1

HaploGrep 2.2.9 – Automatic mtDNA Haplogroup Classification

HaploGrep 2.2.9

:: DESCRIPTION

HaploGrep is a web application for finding the corresponding haplogroup to given mtDNA profiles based on Phylotree (mtDNA classification tree).

::DEVELOPER

GenEpi – Division of Genetic Epidemiology Innsbruck

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux
  • JRE

:: DOWNLOAD

HaploGrep

:: MORE INFORMATION

Citation

HaploGrep 2: mitochondrial haplogroup classification in the era of high-throughput sequencing.
Weissensteiner H, Pacher D, Kloss-Brandstätter A, Forer L, Specht G, Bandelt HJ, Kronenberg F, Salas A, Schönherr S.
Nucleic Acids Res. 2016 Apr 15. pii: gkw233