Vector representations and related matrices of DNA primary sequence based on L-tuple

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• DNA sequence analysis is crucial for understanding biological functions and evolutionary relationships.
• Existing methods for sequence comparison have limitations in capturing complex similarities.
• The need for robust mathematical frameworks to analyze DNA primary sequences is evident.

Purpose of the Study:

• To develop a novel mathematical approach for quantifying DNA sequence similarity using L-tuple vectors and related matrices.
• To demonstrate the utility of this method in sequence database searching and phylogenetic analysis.
• To provide a new tool for comparative genomics and evolutionary studies.

Main Methods:

• Construction of 4(L)-component vectors from DNA primary sequences based on L-tuples.
• Generation of L x L related matrices from corresponding DNA sequence vectors.
• Mathematical characterization of these matrices to determine DNA sequence similarity.
• Application of the method for searching similar sequences in a database and constructing phylogenetic trees.

Main Results:

• Successfully constructed L-tuple vectors and related matrices for DNA sequences.
• Demonstrated that matrix characteristics effectively quantify DNA sequence similarity.
• Validated the method's utility through successful identification of similar sequences and H5N1 avian influenza virus phylogenetic tree construction.

Conclusions:

• The proposed related matrix method offers a robust and effective way to measure DNA sequence similarity.
• This novel approach has practical applications in bioinformatics, including sequence searching and phylogenetic analysis.
• The mathematical framework provides a valuable tool for advancing genomic research and understanding viral evolution.