SigniSite 2.0 Server: Residue level genotype phenotype correlation in protein multiple sequence alignments

SigniSite performs residue level genotype phenotype correlation in protein multiple sequence alignments by identifying amino acid residues significantly associated with the phenotype of the data set. Input is a protein multiple sequence alignment in FASTA format. The phenotype is represented by a real-valued numerical parameter placed white-space separated, last in the identifier of each sequence. For test of server functionality, please see 'Sample data'.

Instructions Output format Article abstract


Sample data

Please note that non-amino acid residue characters, will not be included in the evaluation,
i.e. anything other than 'ARNDCQEGHILKMFPSTWYV' will be excluded.
Sample data for test of server functionality is available. The sample data will reproduce the results used on 'output format' page.
To load the sample data, simply click the 'Load sample data' button below and 'submit':
Paste a multiple alignment in FASTA format into the field below:
Submit a multiple alignment file in FASTA format directly from your local disk:

HIVdb benchmark data set

Click here to download. Please note that the benchmark data set was compiled from the Stanford University HIV drug resistance database, see "How to Cite the HIV Drug Resistance Database".


Instructions on the meaning of the parameters below, can be found here
Significance threshold α =
Method for correction for multiple testing
Choose sorting of numerical values
Unique sequence ID for relative numbering
Type of logo
Please click here for instructions
Should gaps be ignored? By default SigniSite-2.0 consideres gaps as amino acid residue #21

Reference / citation / citing SigniSite

For publication of results, please cite:

Jessen,L.E., Hoof,I., Lund,O. and Nielsen,M. (2013)
SigniSite: Identification of residue-level genotype-phenotype correlations in protein multiple sequence alignments.
Nucl. Acids Res. (1 July 2013) 41(W1): W286-W291. doi: 10.1093/nar/gkt497

View the Abstract or the Open Access Full Text (PDF)


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