Please use this identifier to cite or link to this item: https://doi.org/10.1186/1471-2164-11-S1-S15
Title: Parameterization of disorder predictors for large-scale applications requiring high specificity by using an extended benchmark dataset
Authors: Sirota, F.L
Ooi, H.-S
Gattermayer, T
Schneider, G
Eisenhaber, F 
Maurer-Stroh, S 
Keywords: algorithm
amino acid sequence
article
computer prediction
computer program
controlled study
gold standard
human
information processing
intermethod comparison
nonhuman
parametric test
protein database
quality control
sensitivity and specificity
sequence analysis
web browser
algorithm
evaluation
methodology
protein database
proteomics
proteome
Algorithms
Databases, Protein
Humans
Proteome
Proteomics
Issue Date: 2010
Publisher: BioMed Central Ltd.
Citation: Sirota, F.L, Ooi, H.-S, Gattermayer, T, Schneider, G, Eisenhaber, F, Maurer-Stroh, S (2010). Parameterization of disorder predictors for large-scale applications requiring high specificity by using an extended benchmark dataset. BMC Genomics 11 (SUPPL. 1) : S15. ScholarBank@NUS Repository. https://doi.org/10.1186/1471-2164-11-S1-S15
Abstract: Background: Algorithms designed to predict protein disorder play an important role in structural and functional genomics, as disordered regions have been reported to participate in important cellular processes. Consequently, several methods with different underlying principles for disorder prediction have been independently developed by various groups. For assessing their usability in automated workflows, we are interested in identifying parameter settings and threshold selections, under which the performance of these predictors becomes directly comparable.Results: First, we derived a new benchmark set that accounts for different flavours of disorder complemented with a similar amount of order annotation derived for the same protein set. We show that, using the recommended default parameters, the programs tested are producing a wide range of predictions at different levels of specificity and sensitivity. We identify settings, in which the different predictors have the same false positive rate. We assess conditions when sets of predictors can be run together to derive consensus or complementary predictions. This is useful in the framework of proteome-wide applications where high specificity is required such as in our in-house sequence analysis pipeline and the ANNIE webserver.Conclusions: This work identifies parameter settings and thresholds for a selection of disorder predictors to produce comparable results at a desired level of specificity over a newly derived benchmark dataset that accounts equally for ordered and disordered regions of different lengths. © 2010 Sirota et al; licensee BioMed Central Ltd.
Source Title: BMC Genomics
URI: https://scholarbank.nus.edu.sg/handle/10635/174459
ISSN: 14712164
DOI: 10.1186/1471-2164-11-S1-S15
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