A novel algorithm for detecting multiple covariance and clustering of biological sequences

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Biological sequences accumulate multiple mutations, including compensatory ones, crucial for stability.
• Existing methods struggle to detect non-synchronous multiple mutations at different sequence sites.

Purpose of the Study:

• To develop a novel algorithm, Fastcov, for identifying multiple correlated mutations in biological sequences.
• To enhance the understanding of protein structural stability and co-evolutionary dynamics.

Main Methods:

• Developed Fastcov algorithm using independent pair and tandem models with inter-restriction thinking.
• Employed 10-fold cross-validation on diverse datasets to assess performance.
• Analyzed multiple covariant patterns in relation to phylogenetic trees.

Main Results:

• Fastcov effectively identifies co-pairs and multiple covariant patterns.
• Achieved >98% accuracy in sequence classification using identified patterns.
• Demonstrated that patterns reflect co-evolutionary modes and protein stability.

Conclusions:

• Fastcov offers a reliable method for detecting covariant pairs and multiple covariances.
• The algorithm provides powerful sequence classification capabilities.
• Fastcov advances the study of mutations driven by natural selection, drugs, and environmental factors.