Integrating alignment-based and alignment-free sequence similarity measures for biological sequence classification

TL;DR

This paper introduces a novel classification model that combines alignment-based and alignment-free sequence similarity measures with adaptive weighting, significantly improving biological sequence classification accuracy for DNA and proteins.

Contribution

The study presents a new adaptive weighting model that integrates multiple similarity measures, enhancing classification accuracy over existing methods.

Findings

Improved accuracy in taxonomic classification of viral sequences.

Effective classification of metagenomic reads.

Enhanced protein sequence classification.

Abstract

Alignment-based sequence similarity searches, while accurate for some type of sequences, can produce incorrect results when used on more divergent but functionally related sequences that have undergone the sequence rearrangements observed in many bacterial and viral genomes. Here, we propose a classification model that exploits the complementary nature of alignment-based and alignment-free similarity measures with the aim to improve the accuracy with which DNA and protein sequences are characterized. Our model classifies sequences using a combined sequence similarity score calculated by adaptively weighting the contribution of different sequence similarity measures. Weights are determined independently for each sequence in the test set and reflect the discriminatory ability of individual similarity measures in the training set. Since the similarity between some sequences is determined more accurately with one type of measure rather than another, our classifier allows different sets of weights to be associated with different sequences. Using five different similarity measures we show that our model significantly improves the classification accuracy over the current composition and alignment based models, when predicting the taxonomic lineage for both short viral sequence fragments and complete viral sequences. We also show that our model can be used effectively for the classification of reads from a real metagenome dataset as well as protein sequences.