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The ICTV develops, refines and maintains a universal virus taxonomy; Order isthe highest taxon in the branching hierarchy of recognised viral taxa.Historically, ICTV (sub)committees have classified viruses on the basis ofmorphological characteristics and various other attributes. Today, virtuallyall new viral genomes are assembled from metagenomic datasets and are notlinked directly to biological agents. Thus, placing a virus into a taxonomicscheme solely from primary genome structure is an increasingly importantproblem. Various simple descriptive statistics of a viral genome sequence havebeen used successfully for virus classification. Here, we use the NCBI's viraland viroid reference sequence collection (RefSeq) and a common experimentalframework to compare the performance of different genome sequence-derivedfeatures and classifiers in the task of assigning a virus to one of seven ICTVOrders. The nucleotide-, word-, and compression-based features we considerinclude genome length, the k-mer Natural Vector (k = 1, ..., 6) and itsderivatives, return time distribution, and general-purpose and DNA-specificcompression ratios; the classifiers used are the k-NN and SVM. The combinationof genome length and k-NN has the worst, yet still respectable, performance(mean error rate of 0.137); the best performance is achieved using 4-mer countsand SVM (mean error rate of 0.006). We investigate the main causes ofmisclassification, explore which viruses are more difficult to classify, anduse the best performing combination to predict the Orders of 1,834 unclassifiedviruses. A subsequent version of RefSeq assigned Orders to 17 of thesepreviously unlabelled viruses. Since 16 of our predictions match theseassignments, our approach could aid virologists dealing with viruses that areknown only from sequence data.

Virus genome sequence classification using features based on nucleotides, words and compression