MTRAP: pairwise sequence alignment algorithm by a new measure based on transition probability between two consecutive...

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Sequence alignment is crucial for analyzing biological systems.
• Current methods struggle with homologous sequences exhibiting low similarity.
• Existing alignment measures often overlook correlations between consecutive residues.

Purpose of the Study:

• To develop a more accurate sequence alignment method.
• To address limitations in aligning low-similarity homologous sequences.
• To incorporate inter-residue correlations into alignment metrics.

Main Methods:

• Introduced MTRAP, a novel pairwise alignment method.
• Developed a new metric based on compound systems of two sequences.
• Modified TCoffee to integrate the new alignment metric for progressive multiple alignment.

Main Results:

• MTRAP demonstrated higher accuracy than ClustalW2, TCoffee, and MAFFT in benchmark tests (PREFAB 4.0, HOMSTRAD).
• Significant improvements in alignment accuracy were observed for sequences with less than 15% identity.
• The MTRAP algorithm enhanced consistency-based progressive multiple alignment when integrated with TCoffee.

Conclusions:

• The MTRAP method offers a significant increase in sequence alignment accuracy.
• Improvements are particularly pronounced in the low sequence identity range.
• The source code for MTRAP is publicly available.