Evolution of biosequence search algorithms: a brief survey

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Biosequence data is central to bioinformatics.
• High-throughput technologies generate vast amounts of diverse data.
• Computational methods for biosequence analysis have undergone dramatic evolution.

Purpose of the Study:

• To provide a comprehensive overview of the evolution of algorithmic techniques for biological sequence comparison and searching.
• To highlight key algorithmic developments driven by shifts in biological analysis paradigms, new sequencing technologies, and data volume increases.

Main Methods:

• Review of algorithmic techniques for sequence comparison and searching.
• Discussion of alignment-based versus alignment-free methods.
• Emphasis on sketching methods for massive datasets.

Main Results:

• Algorithmic techniques have evolved in response to biological and technological changes.
• Alignment-free techniques are increasingly replacing alignment-based methods for large-scale analyses.
• Sketching methods are crucial for comparing massive datasets like metagenomics samples.
• The field is transitioning to population genomics, presenting new algorithmic challenges.

Conclusions:

• The evolution of bioinformatics algorithms is critical for handling the increasing scale and complexity of biological data.
• New methods like sketching are essential for modern large-scale genomic analyses.
• Future research must address algorithmic challenges in population genomics.