NNAlign: a web-based prediction method allowing non-expert end-user discovery of sequence motifs in quantitative peptide data
PLoS ONE 6(11): e26781. doi:10.1371/journal.pone.0026781 (Nov 2011)
1Center for Biological Sequence Analysis,
Technical University of Denmark,
DK-2800 Lyngby, Denmark
2Schafer-N, Copenhagen, Denmark
3Division of Experimental Immunology,
Institute of Medical Microbiology and Immunology,
University of Copenhagen, Denmark
Recent advances in high-throughput technologies have made it possible to generate both
gene and protein sequence data at an unprecedented rate and scale thereby enabling entirely new
"omics"-based approaches towards the analysis of complex biological processes.
However, the amount and complexity of data that even a single experiment can
produce seriously challenges researchers with limited bioinformatics
expertise, who need to handle, analyze and interpret the data before it can
be understood in a biological context. Thus, there is an unmet need for
tools allowing non-bioinformatics users to interpret large data sets.
We have recently developed a method, NNAlign, which is generally applicable to
any biological problem where quantitative peptide data is available.
This method efficiently identifies underlying sequence patterns by simultaneously
aligning peptide sequences and identifying motifs associated with quantitative
readouts. Here, we provide a web-based implementation of NNAlign allowing
non-expert end-users to submit their data (optionally adjusting method parameters),
and in return receive a trained method (including a visual representation of the
identified motif) that subsequently can be used as prediction method and applied to
unknown proteins/peptides. We have successfully applied this method to several different
data sets including peptide microarray-derived sets containing more than 100,000 data points.
NNAlign is available online at http://www.cbs.dtu.dk/services/NNAlign
Citation: Andreatta M., Schafer-Nielsen C., Lund L., Buus S. and Nielsen M. (2011) "NNAlign: a web-based prediction method allowing non-expert end-user discovery of sequence motifs in quantitative peptide data". PLoS ONE 6(11): e26781. doi:10.1371/journal.pone.0026781