OMiMa – Identify Functional Motifs in DNA or Protein Sequences

OMiMa

:: DESCRIPTION

The OMiMa (the Optimized Mixture Markov model) System is a computational tool for identifying functional motifs in DNA or protein sequences. OMiMa System is based on the Optimized Mixture of Markov models that are able to incorporate most dependencies within a motif. Most important, OMiMa is capable to adjust model complexity according to motif dependency structures, so it can minimize model complexity without compromising prediction accuracy. OMiMa uses our fast Markov chain optimization method, the Directed Neighbor-Joining (DNJ), which makes OMiMa more computationally efficent.

::DEVELOPER

OMiMa team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 OMiMa

:: MORE INFORMATION

Citation

Weichun Huang, David M Umbach, Uwe Ohler, Leping Li.
Optimized mixed Markov models for motif identification.
BMC Bioinformatics 2006, 7:279

FoldAmyloid – Prediction of Amyloidogenic Regions from Protein Sequence

FoldAmyloid

:: DESCRIPTION

FoldAmyloid is a web server for the prediction of amyloidogenic regions in protein chain

::DEVELOPER

the BioInformatics Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Bioinformatics. 2010 Feb 1;26(3):326-32. doi: 10.1093/bioinformatics/btp691. Epub 2009 Dec 17.
FoldAmyloid: a method of prediction of amyloidogenic regions from protein sequence.
Garbuzynskiy SO1, Lobanov MY, Galzitskaya OV.

pviz 0.1.12 – Dynamic JavaScript & SVG library for Visualization of Protein Sequence Features

pviz 0.1.12

:: DESCRIPTION

pViz.js is a visualization library for displaying protein sequence features in a web browser

::DEVELOPER

pviz team

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • Java

:: DOWNLOAD

 pviz

 :: MORE INFORMATION

Citation

Visualization of protein sequence features using JavaScript and SVG with pViz.js.
Mukhyala K, Masselot A.
Bioinformatics. 2014 Aug 21. pii: btu567.

SimTandem 1.1.96 – Protein Sequence Identification

SimTandem 1.1.96

:: DESCRIPTION

SimTandem is a freely available tool for identification of peptides from LC-MS/MS spectra. It is based on a similarity search of mass spectra in a database of theoretical spectra generated from a database of known protein sequences.

::DEVELOPER

SIRET Research Group

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows/Linux
  • OpenMS

:: DOWNLOAD

 SimTandem

:: MORE INFORMATION

Citation:

Jiri Novak, Timo Sachsenberg, David Hoksza, Tomas Skopal and Oliver Kohlbacher.
On Comparison of SimTandem with State-of-the-Art Peptide Identification Tools, Efficiency of Precursor Mass Filter and Dealing with Variable Modifications.
Journal of Integrative Bioinformatics, 10(3):228, 2013.

Epitopia – Detection of Immunogenic Regions in Protein Sequences

Epitopia

:: DESCRIPTION

 Epitopia is a server for detection of immunogenic regions in protein structures or sequences.Epitopia implements a machine learning scheme to rank individual amino acids in the protein, according to their potential of eliciting a humoral immune response.

::DEVELOPER

Mayrose Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 Epitopia

:: MORE INFORMATION

Citation

Epitopia: a web-server for predicting B-cell epitopes.
Rubinstein ND, Mayrose I, Martz E, Pupko T.
BMC Bioinformatics. 2009 Sep 14;10:287. doi: 10.1186/1471-2105-10-287.

MUTPATH – Map Mutational Paths through Protein Sequence Space

MUTPATH

:: DESCRIPTION

mutpath is a Python package for identifying mutational paths through sequence space generated using the MarkovJumps feature of BEAST.

::DEVELOPER

Bloom Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

 mutpath

:: MORE INFORMATION

Citation

L. Ian Gong and Jesse D. Bloom.
Epistatically interacting substitutions are enriched during adaptive protein evolution.”
PLoS Genetics. 10:e1004328 (2014)

NetCTL 1.2b – Predict CTL Epitopes in Protein Sequence

NetCTL 1.2b

:: DESCRIPTION

NetCTL predicts CTL epitopes in protein sequences. NetCTL expands the MHC class I binding predicition to 12 MHC supertypes including the supertypes A26 and B39. The accuracy of the MHC class I peptide binding affinity is significantly improved compared to the earlier version. Also the prediction of proteasonal cleavage has been improved and is now identical to the predictions obtained by the NetChop-3.0 server. The updated version has been trained on a set of 886 known MHC class I ligands.

::DEVELOPER

DTU Health Tech

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

NetCTL

:: MORE INFORMATION

Citation

Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction.
Larsen MV, Lundegaard C, Lamberth K, Buus S, Lund O, Nielsen M.
BMC Bioinformatics. Oct 31;8:424. 2007

Gibbs Motif Sampler 3.2 – Identify Motifs, Conserved Regions, in DNA or Protein Sequences

Gibbs Motif Sampler 3.2

:: DESCRIPTION

The Gibbs Motif Sampler will allow you to identify motifs, conserved regions, in DNA or protein sequences.

::DEVELOPER

Wadsworth Center

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/Windows with Cygwin/MacOsX

:: DOWNLOAD

 Gibbs Motif Sampler

:: MORE INFORMATION

Citation

Neuwald AF, Liu JS, and Lawrence CE. (1995)
Gibbs motif sampling: detection of bacterial outer membrane protein repeats.
Protein Sci 4(8):1618-1632. PubMed: 8520488.