Viral Phylogenomics Using an Alignment-Free Method: A Three-Step Approach to Determine Optimal Length of k-mer

Abstract

The development of rapid, economical genome sequencing has shed new light on the classification of viruses. As of October 2016, the National Center for Biotechnology Information (NCBI) database contained >2 million viral genome sequences and a reference set of ~4000 viral genome sequences that cover a wide range of known viral families. Whole-genome sequences can be used to improve viral classification and provide insight into the viral “tree of life”. However, due to the lack of evolutionary conservation amongst diverse viruses, it is not feasible to build a viral tree of life using traditional phylogenetic methods based on conserved proteins. In this study, we used an alignment-free method that uses k-mers as genomic features for a large-scale comparison of complete viral genomes available in RefSeq. To determine the optimal feature length, k (an essential step in constructing a meaningful dendrogram), we designed a comprehensive strategy that combines three approaches: (1) cumulative relative entropy, (2) average number of common features among genomes, and (3) the Shannon diversity index. This strategy was used to determine k for all 3,905 complete viral genomes in RefSeq. The resulting dendrogram shows consistency with the viral taxonomy of the ICTV and the Baltimore classification of viruses.

Variable Number Tandem Repeats mediate the expression of proximal genes

“…The choice of k-mer length is important. Increasing the k-mer size could decrease sensitivity in our case as small variation will significantly change the k-mer composition, whereas lowering k-mer size reduces the features that are discriminative for a pattern 70 . In addition, our embedding size exponentially grows with respect to the k so there is also a practical upper bound on the k. Following Zhang 70 and Dubinkina 71 , we trained and tested in the range 4 ≤ k < 9.…”

The number of k-mer matches between two DNA sequences as a function of k and applications to estimate phylogenetic distances

“…In Fig 2, F(k) is plotted against the word length k for contiguous words. By inserting the average sequence length L into (14) and (15), we obtain k min = 19 and k max = 24. With these values, we can calculate the slope of F in the relevant range as…”

“…Therefore, considerable efforts have been made in recent years, to develop fast alignment-free approaches that can estimate phylogenetic distances without the need to calculate full alignments of the input sequences, see [3][4][5][6][7] for recent review articles. Alignment-free approaches are not only used in phylogeny reconstruction, but are also important in metagenomics [8][9][10], to find genome rearrangements [11] and in epidemiology [12] and other medical applications, for example to identify drug-resistant bacteria [13] or to classify viruses [14,15]. In all these applications, it is crucial to rapidly estimate pairwise similarity or dissimilarity values in large sets of sequence data.Some alignment-free approaches are based on word frequencies [16,17] or on the length of common substrings [18][19][20].…”

The number of spaced-word matches between twoDNAsequences as a function of the underlying pattern weight

“…Therefore, considerable efforts have been made in recent years, to develop fast alignmentfree approaches that can estimate phylogenetic distances without the need to calculate full alignments of the input sequences, see [20,50,57,4,26] for recent review articles. Alignment-free approaches are not only used in phylogeny reconstruction, but are also important in metagenomics [10,39,32] and in medical applications, for example to identify drug-resistant bacteria [5] or to classify viruses [55,2]. In all these applications, it is crucial to rapidly estimate pairwise similarity or dissimilarity values in large sets of sequence data.…”