COATi: Statistical Pairwise Alignment of Protein-Coding Sequences.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Sequence alignment is fundamental for bioinformatics analyses like phylogenetic inference and gene annotation.
• Sequencing artifacts and assembly errors (e.g., frameshifts, premature stop codons) can lead to erroneous conclusions in genomic studies.
• Existing aligners often incorrectly assume insertions/deletions (indels) occur only between codons, not within them, causing suboptimal alignments.

Purpose of the Study:

• Introduce COATi, a statistical, codon-aware pairwise aligner.
• Address limitations of current aligners in handling biological indels and sequencing artifacts.
• Improve accuracy and reduce data loss in sequence alignment.

Main Methods:

• Developed COATi, a statistical, codon-aware pairwise sequence aligner.
• Implemented complex insertion-deletion models capable of handling biological indels within and between codons.
• Tested COATi on orthologous protein-coding sequences from humans and gorillas, and on benchmark alignments.

Main Results:

• COATi accurately infers indels occurring both within and between codons.
• Analysis of human-gorilla sequences revealed 41% of indels occurred between codons.
• COATi demonstrated superior performance over popular alignment programs in quality and accuracy metrics on benchmark datasets.

Conclusions:

• COATi effectively handles sequencing artifacts and complex indel patterns, improving alignment accuracy.
• The aligner reduces the need to discard data, enhancing downstream comparative and functional genomic analyses.
• COATi represents a significant advancement for accurate sequence alignment in bioinformatics.