K-mer Content, Correlation, and Position Analysis of Genome DNA Sequences for the Identification of Function and Evolutionary Features

Abstract

In genome analysis, k-mer-based comparison methods have become standard tools. However, even though they are able to deliver reliable results, other algorithms seem to work better in some cases. To improve k-mer-based DNA sequence analysis and comparison, we successfully checked whether adding positional resolution is beneficial for finding and/or comparing interesting organizational structures. A simple but efficient algorithm for extracting and saving local k-mer spectra (frequency distribution of k-mers) was developed and used. The results were analyzed by including positional information based on visualizations as genomic maps and by applying basic vector correlation methods. This analysis was concentrated on small word lengths (1 ≤ k ≤ 4) on relatively small viral genomes of Papillomaviridae and Herpesviridae, while also checking its usability for larger sequences, namely human chromosome 2 and the homologous chromosomes (2A, 2B) of a chimpanzee. Using this alignment-free analysis, several regions with specific characteristics in Papillomaviridae and Herpesviridae formerly identified by independent, mostly alignment-based methods, were confirmed. Correlations between the k-mer content and several genes in these genomes have been found, showing similarities between classified and unclassified viruses, which may be potentially useful for further taxonomic research. Furthermore, unknown k-mer correlations in the genomes of Human Herpesviruses (HHVs), which are probably of major biological function, are found and described. Using the chromosomes of a chimpanzee and human that are currently known, identities between the species on every analyzed chromosome were reproduced. This demonstrates the feasibility of our approach for large data sets of complex genomes. Based on these results, we suggest k-mer analysis with positional resolution as a method for closing a gap between the effectiveness of alignment-based methods (like NCBI BLAST) and the high pace of standard k-mer analysis.

Conservation of k-mer Composition and Correlation Contribution between Introns and Intergenic Regions of Animalia Genomes

“…Accordingly, standard tools like the NCBI Basic Local Alignment Search Tool (BLAST) [ 9 ], which deliver reliable results for the comparison of genes at the cost of relatively high consumption of computational resources, cannot be effectively used to face this task [ 10 ]. Fortunately, more recently developed alignment-free, computational, and much less complex algorithms were developed and proven to produce just as reliable results if used patiently [ 5 , 11 , 12 ]. We decided to use a quite simplistic but powerful method called k -mer-analysis [ 12 ] to pairwise-compare genome regions of a wide phylogenetic range for 39 Animalia organisms, with completely sequenced and assembled genome sequences.…”

Hypomethylated domain-enriched DNA motifs prepattern the accessible nucleosome organization in teleosts

“…Since we precisely mapped the position of nucleosome linkers in each HMD, we then asked if specific DNA sequences can be correlated with the positioning of accessible linkers. k -mer-based DNA sequence analyses have been utilized to identify specific DNA elements [ 32 ]. We applied k -mer SVM, which finds a decision boundary that distinguishes the two sets of sequence data based on the frequency of all possible k -mers [ 33 ].…”

COMBO-FISH: A Versatile Tool Beyond Standard FISH to Study Chromatin Organization by Fluorescence Light Microscopy

“…In order to improve the detection efficiency and to enter the meso-and nano-scale of chromatin architecture, SMLM can be applied [31,54,56,65]. Alignment free search routines for uniquely binding oligo-nucleotides [65,83] offer new perspectives to better understand the overall organization of chromatin and the effects of transposable and repetitive elements so far a highly challenging and controversial field of research [84][85][86][87][88][89]. The examples presented here demonstrate the capacity of SMLM in combination with COMBO-FISH to quantify the topology of chromatin organization.…”