Title:Assessing the Statistical Significance of Local Sequence Alignment Based on Transformation Score Matrix
VOLUME: 7 ISSUE: 4
Author(s):Juan Li and Huisheng Fang
Affiliation:School of Life Science and Technology, China Pharmaceutical University, Nanjing, China.
Keywords:Local sequence alignment, normal distribution, statistical significance, traditional score matrix, transformation
matrix, Alignment Based, BLAST package of sequence, statistical method, parameters, declumping, amino acid
Abstract:Sequence alignment is a basic field in bioinformatics, especially the sequence alignment of remotely
homologue proteins is a hot spot. In our previous work, we developed a new score matrix named transformation matrix
which can greatly enhance the quality of the alignment of distant protein sequences. Here, by using the transformation
score matrix, we assessed the statistical significance of the local sequence alignment. Compared with the traditional score
matrix, the local sequence alignment method has the following features: (i) The optimal alignment scores approximately
follow a normal distribution. (ii) The distribution is closely related with N, which represents the length of two sequence
alignments but not the lengths of the two sequences being compared. Therefore, for a pair of two aligned protein
sequences, we can calculate the P-value based on the N and the optimal alignment score.
