Genomic style: yet another deep-learning approach to characterize bacterial genome sequences

Motivation: Biological sequence classification is the most fundamental task in bioinformatics analysis. For example, in metagenome analysis, binning is a typical type of DNA sequence classification. In order to classify sequences, it is necessary to define sequence features. The k-mer frequency, base composition, and alignment-based metrics are commonly used. In contrast, in the field of image recognition using machine learning, image classification is broadly divided into those based on shape and those based on style. A style matrix was introduced as a method of expressing the style of an image (e.g., color usage and texture). Results: We propose a novel sequence feature, called genomic style, inspired by image classification approaches, for classifying and clustering DNA sequences. As with the style of images, the DNA sequence is considered to have a genomic style unique to the bacterial species, and the style matrix concept is applied to the DNA sequence. Our main aim is to introduce the genomics style as yet another basic sequence feature for metagenome binning problem in replace of the most commonly used sequence feature k-mer frequency. Performance evaluations show that our method using style matrix achieves the superior accuracy than state-of-the-art binning tools based on k-mer frequency.