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What it does

This calls a Python re-implementation of the PredictNLS tool for prediction of nuclear localization signals (NLSs), which works by looking for matches to a known set of patterns (described using regular expressions).

The input is a FASTA file of protein sequences, and the output is tabular with these columns (multiple rows per protein):

Column	Description
1	Sequence identifier
2	Start of NLS
3	NLS sequence
4	NLS pattern (regular expression)
5	Number of reference proteins with this NLS
6	Percentage of reference proteins with this NLS which are nuclear localized
7	Comma separated list of reference proteins
8	Comma separated list of reference proteins' localizations

If a sequence has no predicted NLS, then there is no line in the output file for it. This is a simplification of the text rich output from the command line tool, to give a tabular file suitable for use within Galaxy.

Information about potential DNA binding (shown in the original predictnls tool) is not given.

Localizations

The following abbreviations are used (derived from SWISS-PROT):

Abbr	Localization
cyt	Cytoplasm
pla	Chloroplast
ret	Eendoplasmic reticululm
ext	Extracellular
gol	Golgi
lys	Lysosomal
mit	Mitochondria
nuc	Nuclear
oxi	Peroxisom
vac	Vacuolar
rip	Periplasmic

References

If you use this Galaxy tool in work leading to a scientific publication please cite the following papers:

Peter J.A. Cock, Björn A. Grüning, Konrad Paszkiewicz and Leighton Pritchard (2013). Galaxy tools and workflows for sequence analysis with applications in molecular plant pathology. PeerJ 1:e167 http://dx.doi.org/10.7717/peerj.167

Murat Cokol, Rajesh Nair, and Burkhard Rost (2000). Finding nuclear localization signals. EMBO reports 1(5), 411–415 http://dx.doi.org/10.1093/embo-reports/kvd092