AltaiR: a C toolkit for alignment-free and temporal analysis of multi-FASTA data.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Analyzing large viral genome datasets presents computational challenges.
• Existing alignment-based methods struggle with multi-FASTA formats and massive data volumes.
• Current tools often require data partitioning, limiting comprehensive analysis.

Purpose of the Study:

• To introduce AltaiR, a novel toolkit for analyzing multiple sequences in multi-FASTA format.
• To enable efficient, alignment-free analysis of large-scale genomic data.
• To identify patterns, dynamics, and quality control in viral sequence datasets.

Main Methods:

• Utilizes exclusively alignment-free methodologies for sequence analysis.
• Implements C programming with multithreading and optimizations for computational efficiency.
• Automatically filters low-quality, biased, or deviant sequence data.

Main Results:

• Successfully analyzed over 1.5 million severe acute respiratory virus coronavirus 2 sequences.
• Identified temporal shifts in nucleotide composition and decreasing Kolmogorov sequence complexity.
• Observed the evolution of the smallest sequences not found in the human host.

Conclusions:

• AltaiR effectively identifies temporal trends in large sequence datasets, suitable for epidemic/endemic surveillance.
• The toolkit is computationally efficient, flexible, dependency-free, and accepts FASTA formats.
• AltaiR provides a powerful solution for large-scale viral genome analysis and discovery.