Identification of deleterious non-synonymous single nucleotide polymorphisms using sequence-derived information

Background: As the number of non-synonymous single nucleotide polymorphisms (nsSNPs), also known as single amino acid polymorphisms (SAPs), increases rapidly, computational methods that can distinguish disease-causing SAPs from neutral SAPs are needed. Many methods have been developed to distinguish disease-causing SAPs based on both structural and sequence features of the mutation point. One limitation of these methods is that they are not applicable to the cases where protein structures are not available. In this study, we explore the feasibility of classifying SAPs into disease-causing and neutral mutations using only information derived from protein sequence. Results: We compiled a set of 686 features that were derived from protein sequence. For each feature, the distance between the wild-type residue and mutant-type residue was computed. Then a greedy approach was used to select the features that were useful for the classification of SAPs. 10 features were selected. Using the sel...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Jing Hu, Changhui Yan
• Keywords: BMCBI 2008, Decision Tree Method, Overall Accuracy, SAPS