Protein sequence comparison based on K-string dictionary.
The current K-string-based protein sequence comparisons require large amounts of computer memory because the dimension of the protein vector representation grows exponentially with K. In this paper, we propose a novel concept, the "K-string dictionary", to solve this high-dimensional problem. It allows us to use a much lower dimensional K-string-based frequency or probability vector to represent a protein, and thus significantly reduce the computer memory requirements for their implementation. Furthermore, based on this new concept, we use Singular Value Decomposition to analyze real protein datasets, and the improved protein vector representation allows us to obtain accurate gene trees.
Department of Mathematics, Statistics and Computer Science, University of Illinois at Chicago, IL 60607-7045,USA.
Chenglong
C
Yu
Rong L
RL
He
Stephen S-T
SS
Yau
D003627:Data Interpretation, Statistical; D030562:Databases, Protein; D010802:Phylogeny; D011506:Proteins / Q000737:chemistry* / Q000235:genetics; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein
D011506:Proteins
Cardinality; Frequency vector; K-string; MSA; NADH dehydrogenase 1; ND1; SVD; Sequence comparison; Singular Value Decomposition; multiple sequence alignment