WalkIm: Compact image-based encoding for high-performance classification of biological sequences using simple tuning-free CNNs

An automated ultra-fast, memory-efficient, and accurate method for viral genome classification

The classification of different organisms into subtypes is one of the most important tools of organism studies, and among them, the classification of viruses itself has been the focus of many studies due to their use in virology and epidemiology. Many methods have been proposed to classify viruses, some of which are designed for a specific family of organisms and some of which are more general. But still, especially for certain categories such as Influenza and HIV, classification is facing performance challenges as well as processing and memory bottlenecks. In this way, we designed an automated classifier, called PC-mer, that is based on k-mer and physicochemical characteristics of nucleotides, which reduces the number of features about 2 ^k times compared to the alternative methods based on k-mer, and compared to integer and one-hot encoding methods, it is possible to keep the number of features constant despite the growth of the sequence length. In this way, it also increases the training speed by an average of 17.93 times. This improvement in processing complexity is provided while PC-mer can also improve the classifying performance for a variety of virus families.