Article

Sequence-based feature prediction and annotation of proteins

Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, DK-2800 Lyngby, Denmark.
Genome biology (Impact Factor: 10.81). 03/2009; 10(2):206. DOI: 10.1186/gb-2009-10-2-206
Source: PubMed

ABSTRACT

A recent trend in computational methods for annotation of protein function is that many prediction tools are combined in complex workflows and pipelines to facilitate the analysis of feature combinations, for example, the entire repertoire of kinase-binding motifs in the human proteome.

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Available from: Alfonso Valencia
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    • "Many tools have been developed to mine several databases of biological information to finally predict a protein function based on sequence similarities. Detailed strategies on genomics and proteomics sequence annotation can be found in previous publications [11] [12] [13] [14] [15] [16] [17]. Nevertheless, once the genome and proteome are annotated, one of the most disseminated strategies of proteomics data functional annotation includes the use of ontologies, which can be understood as an explicit specification of a conceptualization [18]. "
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    • "The number of instances for each class should be balanced, as some classifiers like SVM tend to produce reduced accuracies for imbalanced datasets. Appropriate representation of the informative experimental data available and its conversion into datasets relevant to machine learning denotes the critical step of generating an efficient classifier (Juncker et al., 2009). "

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    • "The annotation of most genes and gene products is incomplete with only a sparse set of annotations to generic high-level categories available (Faria et al., 2012). For those annotations that do exist, the overwhelming majority are automatically generated on the basis of sequence or structural similarity without any curatorial review (du Plessis et al., 2011; Juncker et al., 2009). Such automatically generated annotations have known quality issues relative to manually curated annotations, especially those based on published experimental findings (Bell et al., 2012; Dolan et al., 2005; Faria et al., 2012; Park et al., 2011; Schnoes et al., 2009; Skunca et al., 2012). "
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