On the dependence structure of sequence alignment scores calculated with multiple scoring matrices.

Area of Science:

• Bioinformatics
• Computational Biology
• Statistical Modeling

Background:

• Protein sequence alignment is crucial for understanding protein function and evolution.
• Current methods often involve testing multiple scoring matrices, leading to statistical challenges.
• Interpreting p-values and E-values becomes difficult due to the multiple testing problem.

Purpose of the Study:

• To address the multiple testing problem in protein sequence alignment when using various scoring matrices.
• To develop a statistically sound method for interpreting alignment scores derived from different matrices.
• To introduce logistic copula functions for modeling score dependence.

Main Methods:

• Focusing on local alignment algorithms.
• Employing logistic copula functions to explicitly model the dependence structure between scores from different matrices.
• Deriving p-value correction factors for the use of multiple scoring matrices.

Main Results:

• The proposed method provides accurate p-value correction factors when multiple scoring matrices are applied to the same sequences.
• The logistic copula parameter quantifies the dependence between scores from different matrices.
• This offers insights into the relatedness of scoring matrices.

Conclusions:

• Logistic copula functions offer a robust statistical framework for protein sequence alignment.
• Accurate p-value interpretation is achievable even when employing multiple scoring matrices.
• The dependence measure provides valuable information about scoring matrix similarity.