NLStradamus 1.8 – Hidden Markov Model for Nuclear Localization Signal Prediction

NLStradamus 1.8

:: DESCRIPTION

NLStradamus predicts NLSs in nuclear proteins that are transported by the import machinery of the cell.

::DEVELOPER

Alan Moses’ Computational Biology Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

NLStradamus

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2009 Jun 29;10:202. doi: 10.1186/1471-2105-10-202.
NLStradamus: a simple Hidden Markov Model for nuclear localization signal prediction.
Nguyen Ba AN1, Pogoutse A, Provart N, Moses AM.

SeqNLS – Nuclear Localization Signal Prediction

SeqNLS

:: DESCRIPTION

SeqNLS is a sequential pattern mining algorithmto effectively identify potential NLS patterns without being constrained by the limitation of current knowledge of NLSs.

::DEVELOPER

Machine Learning and Evolution Laboratory (MLEG)

:: SCREENSHOTS

n/a

:: REQUIREMENTS

  • Web Browser
:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

PLoS One. 2013 Oct 29;8(10):e76864. doi: 10.1371/journal.pone.0076864. eCollection 2013.
SeqNLS: nuclear localization signal prediction based on frequent pattern mining and linear motif scoring.
Lin JR1, Hu J.

NucPred 1.1 – Predicting Nuclear Localization of Proteins

NucPred 1.1

:: DESCRIPTION

NucPred (pronounced newk-pred) analyses a eukaryotic protein sequence and predicts if the protein: spends at least some time in the nucleus or spends no time in the nucleus. Don’t forget that proteins can have multiple functions and/or multiple subcellular locations. However, if a protein is already known to be secreted or is an integral membrane protein, a second role as a nuclear protein is not likely. NucPred will make a small number of confident but contradictory predictions like this. So please use all sources of biological information (both real and predicted) when interpreting the results.

::DEVELOPER

Stockholm Bioinformatics Center

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

NucPred

:: MORE INFORMATION

The source code to NucPred is freely available to all under the GNU Public License (GPL)

Citation:

NucPred – Predicting Nuclear Localization of Proteins. Brameier M, Krings A, Maccallum RM. Bioinformatics, 2007. PubMed id: 17332022