Gene classification using codon usage and support vector machines.
A novel approach for gene classification, which adopts codon usage bias as input feature vector for classification by support vector machines (SVM) is proposed. The DNA sequence is first converted to a 59-dimensional feature vector where each element corresponds to the relative synonymous usage frequency of a codon. As the input to the classifier is independent of sequence length and variance, our approach is useful when the sequences to be classified are of different lengths, a condition that homology-based methods tend to fail. The method is demonstrated by using 1,841 Human Leukocyte Antigen (HLA) sequences which are classified into two major classes: HLA-I and HLA-II; each major class is further subdivided into sub-groups of HLA-I and HLA-II molecules. Using codon usage frequencies, binary SVM achieved accuracy rate of 99.3% for HLA major class classification and multi-class SVM achieved accuracy rates of 99.73% and 98.38% for sub-class classification of HLA-I and HLA-II molecules, respectively. The results show that gene classification based on codon usage bias is consistent with the molecular structures and biological functions of HLA molecules.
Ma
Jianmin
J
BioInformatics Research Center, NanyangTechnological University, Singapore 637553. jmma@ntu.edu.sg
Nguyen
Minh N
MN
Rajapakse
Jagath C
JC
D000465:Algorithms; D001185:Artificial Intelligence; D003062:Codon / Q000145:classification* / Q000235:genetics; D030541:Databases, Genetic; D016002:Discriminant Analysis; D005796:Genes; D005805:Genes, MHC Class I; D005802:Genes, MHC Class II; D005815:Genetic Code; D006680:HLA Antigens / Q000145:classification / Q000235:genetics; D006801:Humans; D008285:Major Histocompatibility Complex / Q000235:genetics; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D017422:Sequence Analysis, DNA / Q000379:methods*
D003062:Codon; D006680:HLA Antigens