NASCUP: Nucleic Acid Sequence Classification by Universal Probability

TL;DR

NASCUP is a fast, accurate nucleic acid sequence classification method using universal probability and context-tree models, outperforming traditional tools in large-scale bioinformatics tasks.

Contribution

It introduces NASCUP, a novel classification approach that leverages statistical structures and universal probability for efficient large-scale nucleotide sequence analysis.

Findings

Achieved BLAST-like accuracy on large databases

Reduced runtime by orders of magnitude

Applicable to outlier detection and synthetic sequence generation

Abstract

Motivated by the need for fast and accurate classification of unlabeled nucleotide sequences on a large scale, we developed NASCUP, a new classification method that captures statistical structures of nucleotide sequences by compact context-tree models and universal probability from information theory. NASCUP achieved BLAST-like classification accuracy consistently for several large-scale databases in orders-of-magnitude reduced runtime, and was applied to other bioinformatics tasks such as outlier detection and synthetic sequence generation.