Optimizing substitution matrices by separating score distributions.
BACKGROUND
Homology search is one of the most fundamental tools in Bioinformatics. Typical alignment algorithms use substitution matrices and gap costs. Thus, the improvement of substitution matrices increases accuracy of homology searches. Generally, substitution matrices are derived from aligned sequences whose relationships are known, and gap costs are determined by trial and error. To discriminate relationships more clearly, we are encouraged to optimize the substitution matrices from statistical viewpoints using both positive and negative examples utilizing Bayesian decision theory.


RESULTS
Using Cluster of Orthologous Group (COG) database, we optimized substitution matrices. The classification accuracy of the obtained matrix is better than that of conventional substitution matrices to COG database. It also achieves good performance in classifying with other databases.
Hourai
Yuichiro
Y
Department of Computer Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan. hourai@is.s.u-tokyo.ac.jp
Akutsu
Tatsuya
T
Akiyama
Yutaka
Y
D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D016000:Cluster Analysis; D030562:Databases, Protein; D008958:Models, Molecular; D015233:Models, Statistical; D008969:Molecular Sequence Data; D011506:Proteins / Q000737:chemistry* / Q000235:genetics*; D011786:Quality Control; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid
D011506:Proteins