Kirsti Paulsen graduated with distinction from UNSW with a Bachelor of Science, majoring in Genetics. She started in the Edwards Lab as an MPhil student in July 2017. Kirsti’s project is evaluating the use of intrinsic disorder predictors for short linear motif discovery.
[LinkedIn]
Palopoli N, Lythgow KT & Edwards RJ (2015). QSLiMFinder: improved short linear motif prediction using specific query protein data. Bioinformatics 31(14): 2284-2293. [PDF]
Motivation: The sensitivity of de novo short linear motif (SLiM) prediction is limited by the number of patterns (the motif space) being assessed for enrichment. QSLiMFinder uses specific query protein information to restrict the motif space and thereby increase the sensitivity and specificity of predictions.
Results: QSLiMFinder was extensively benchmarked using known SLiM-containing proteins and simulated protein interaction datasets of real human proteins. Exploiting prior knowledge of a query protein likely to be involved in a SLiM-mediated interaction increased the proportion of true positives correctly returned and reduced the proportion of datasets returning a false positive prediction. The biggest improvement was seen if a short region of the query protein flanking the interaction site was known.
Availability and Implementation: All the tools and data used in this study, including QSLiMFinder and the SLiMBench benchmarking software, are freely available under a GNU license as part of SLiMSuite, at: http://bioware.soton.ac.uk.
Supplementary information: Supplementary data are available at the journal’s web site and at http://bioware.soton.ac.uk/research/qslimfinder/.
PMID: 25792551
