K-mer Spectra Research Articles

Peculiar k-mer Spectra Are Correlated with 3D Contact Frequencies and Breakpoint Regions in the Human Genome.

It is widely accepted that the 3D chromatin organization in human cell nuclei is not random and recent investigations point towards an interactive relation of epigenetic functioning and chromatin (re-)organization. Although chromatin organization seems to be the result of self-organization of the entirety of all molecules available in the cell nucleus, a general question remains open as to what extent chromatin organization might additionally be predetermined by the DNA sequence and, if so, if there are characteristic differences that distinguish typical regions involved in dysfunction-related aberrations from normal ones, since typical DNA breakpoint regions involved in disease-related chromosome aberrations are not randomly distributed along the DNA sequence. Highly conserved k-mer patterns in intronic and intergenic regions have been reported in eukaryotic genomes. In this article, we search and analyze regions deviating from average spectra (ReDFAS) of k-mer word frequencies in the human genome. This includes all assembled regions, e.g., telomeric, centromeric, genic as well as intergenic regions. A positive correlation between k-mer spectra and 3D contact frequencies, obtained exemplarily from given Hi-C datasets, has been found indicating a relation of ReDFAS to chromatin organization and interactions. We also searched and found correlations of known functional annotations, e.g., genes correlating with ReDFAS. Selected regions known to contain typical breakpoints on chromosomes 9 and 5 that are involved in cancer-related chromosomal aberrations appear to be enriched in ReDFAS. Since transposable elements like ALUs are often assigned as major players in 3D genome organization, we also studied their impact on our examples but could not find a correlation between ALU regions and breakpoints comparable to ReDFAS. Our findings might show that ReDFAS are associated with instable regions of the genome and regions with many chromatin contacts which is in line with current research indicating that chromatin loop anchor points lead to genomic instability.

Giants among Cnidaria: Large Nuclear Genomes and Rearranged Mitochondrial Genomes in Siphonophores.

Siphonophores (Cnidaria: Hydrozoa) are abundant predators found throughout the ocean and are important constituents of the global zooplankton community. They range in length from a few centimeters to tens of meters. They are gelatinous, fragile, and difficult to collect, so many aspects of the biology of these roughly 200 species remain poorly understood. To survey siphonophore genome diversity, we performed Illumina sequencing of 32 species sampled broadly across the phylogeny. Sequencing depth was sufficient to estimate nuclear genome size from k-mer spectra in six specimens, ranging from 0.7 to 2.3 Gb, with heterozygosity estimates between 0.69% and 2.32%. Incremental k-mer counting indicates k-mer peaks can be absent with nearly 20× read coverage, suggesting minimum genome sizes range from 1.4 to 5.6 Gb in the 25 samples without peaks in the k-mer spectra. This work confirms most siphonophore nuclear genomes are large relative to the genomes of other cnidarians, but also identifies several with reduced size that are tractable targets for future siphonophore nuclear genome assembly projects. We also assembled complete mitochondrial genomes for 33 specimens from these new data, indicating a conserved gene order shared among nonsiphonophore hydrozoans, Cystonectae, and some Physonectae, revealing the ancestral mitochondrial gene order of siphonophores. Our results also suggest extensive rearrangement of mitochondrial genomes within other Physonectae and in Calycophorae. Though siphonophores comprise a small fraction of cnidarian species, this survey greatly expands our understanding of cnidarian genome diversity. This study further illustrates both the importance of deep phylogenetic sampling and the utility of k-mer-based genome skimming in understanding the genomic diversity of a clade.

Qmatey: an automated pipeline for fast exact matching-based alignment and strain-level taxonomic binning and profiling of metagenomes.

Metagenomics is a powerful tool for understanding organismal interactions; however, classification, profiling and detection of interactions at the strain level remain challenging. We present an automated pipeline, quantitative metagenomic alignment and taxonomic exact matching (Qmatey), that performs a fast exact matching-based alignment and integration of taxonomic binning and profiling. It interrogates large databases without using metagenome-assembled genomes, curated pan-genes or k-mer spectra that limit resolution. Qmatey minimizes misclassification and maintains strain level resolution by using only diagnostic reads as shown in the analysis of amplicon, quantitative reduced representation and shotgun sequencing datasets. Using Qmatey to analyze shotgun data from a synthetic community with 35% of the 26 strains at low abundance (0.01-0.06%), we revealed a remarkable 85-96% strain recall and 92-100% species recall while maintaining 100% precision. Benchmarking revealed that the highly ranked Kraken2 and KrakenUniq tools identified 2-4 more taxa (92-100% recall) than Qmatey but produced 315-1752 false positive taxa and high penalty on precision (1-8%). The speed, accuracy and precision of the Qmatey pipeline positions it as a valuable tool for broad-spectrum profiling and for uncovering biologically relevant interactions.

Evolutionary origin and establishment of a dioecious diploid-tetraploid complex.

Polyploids recurrently emerge in angiosperms, but most polyploids are likely to go extinct before establishment due to minority cytotype exclusion, which may be specifically a constraint for dioecious plants. Here we test the hypothesis that a stable sex-determination system and spatial/ecological isolation facilitate the establishment of dioecious polyploids. We determined the ploidy levels of 351 individuals from 28 populations of the dioecious species Salix polyclona, and resequenced 190 individuals of S.polyclona and related taxa for genomic diversity analyses. The ploidy survey revealed a frequency 52% of tetraploids in S.polyclona, and genomic k-mer spectra analyses suggested an autopolyploid origin for them. Comparisons of diploid male and female genomes identified a female heterogametic sex-determining factor on chromosome 15, which probably also acts in the dioecious tetraploids. Phylogenetic analyses revealed two diploid clades and a separate clade/grade of tetraploids with a distinct geographic distribution confined to western and central China, where complex mountain systems create higher levels of environmental heterogeneity. Fossil-calibrated phylogenies showed that the polyploids emerged during 7.6-2.3million years ago, and population demographic histories largely matched the geological and climatic history of the region. Our results suggest that inheritance of the sex-determining system from the diploid progenitor as intrinsic factor and spatial isolation as extrinsic factor may have facilitated the preservation and establishment of polyploid dioecious populations.

The genomic basis of the plant island syndrome in Darwin’s giant daisies

The repeated, rapid and often pronounced patterns of evolutionary divergence observed in insular plants, or the ‘plant island syndrome’, include changes in leaf phenotypes, growth, as well as the acquisition of a perennial lifestyle. Here, we sequence and describe the genome of the critically endangered, Galápagos-endemic species Scalesia atractyloides Arnot., obtaining a chromosome-resolved, 3.2-Gbp assembly containing 43,093 candidate gene models. Using a combination of fossil transposable elements, k-mer spectra analyses and orthologue assignment, we identify the two ancestral genomes, and date their divergence and the polyploidization event, concluding that the ancestor of all extant Scalesia species was an allotetraploid. There are a comparable number of genes and transposable elements across the two subgenomes, and while their synteny has been mostly conserved, we find multiple inversions that may have facilitated adaptation. We identify clear signatures of selection across genes associated with vascular development, growth, adaptation to salinity and flowering time, thus finding compelling evidence for a genomic basis of the island syndrome in one of Darwin’s giant daisies.

Estimating repeat spectra and genome length from low-coverage genome skims with RESPECT.

The cost of sequencing the genome is dropping at a much faster rate compared to assembling and finishing the genome. The use of lightly sampled genomes (genome-skims) could be transformative for genomic ecology, and results using k-mers have shown the advantage of this approach in identification and phylogenetic placement of eukaryotic species. Here, we revisit the basic question of estimating genomic parameters such as genome length, coverage, and repeat structure, focusing specifically on estimating the k-mer repeat spectrum. We show using a mix of theoretical and empirical analysis that there are fundamental limitations to estimating the k-mer spectra due to ill-conditioned systems, and that has implications for other genomic parameters. We get around this problem using a novel constrained optimization approach (Spline Linear Programming), where the constraints are learned empirically. On reads simulated at 1X coverage from 66 genomes, our method, REPeat SPECTra Estimation (RESPECT), had 2.2% error in length estimation compared to 27% error previously achieved. In shotgun sequenced read samples with contaminants, RESPECT length estimates had median error 4%, in contrast to other methods that had median error 80%. Together, the results suggest that low-pass genomic sequencing can yield reliable estimates of the length and repeat content of the genome. The RESPECT software will be publicly available at https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_shahab-2Dsarmashghi_RESPECT.git&d=DwIGAw&c=-35OiAkTchMrZOngvJPOeA&r=ZozViWvD1E8PorCkfwYKYQMVKFoEcqLFm4Tg49XnPcA&m=f-xS8GMHKckknkc7Xpp8FJYw_ltUwz5frOw1a5pJ81EpdTOK8xhbYmrN4ZxniM96&s=717o8hLR1JmHFpRPSWG6xdUQTikyUjicjkipjFsKG4w&e=.

Pressing NGS data through the mill of Kmer spectra and allelic coverage ratios in order to scan reproductive modes in non-model species

A recommendation of: Kamil S. Jaron, Christina N. Hodson, Jacintha Ellers, Stuart JE Baird, Laura Ross Genomic evidence of paternal genome elimination in the globular springtail Allacma fusca https://doi.org/10.1101/2021.11.12.468426

Intrinsic laws of k-mer spectra of genome sequences and evolution mechanism of genomes

BackgroundK-mer spectra of DNA sequences contain important information about sequence composition and sequence evolution. We want to reveal the evolution rules of genome sequences by studying the k-mer spectra of genome sequences.ResultsThe intrinsic laws of k-mer spectra of 920 genome sequences from primate to prokaryote were analyzed. We found that there are two types of evolution selection modes in genome sequences, named as CG Independent Selection and TA Independent Selection. There is a mutual inhibition relationship between CG and TA independent selections. We found that the intensity of CG and TA independent selections correlates closely with genome evolution and G + C content of genome sequences. The living habits of species are related closely to the independent selection modes adopted by species genomes. Consequently, we proposed an evolution mechanism of genomes in which the genome evolution is determined by the intensities of the CG and TA independent selections and the mutual inhibition relationship. Besides, by the evolution mechanism of genomes, we speculated the evolution modes of prokaryotes in mild and extreme environments in the anaerobic age and the evolving process of prokaryotes from anaerobic to aerobic environment on earth as well as the originations of different eukaryotes.ConclusionWe found that there are two independent selection modes in genome sequences. The evolution of genome sequence is determined by the two independent selection modes and the mutual inhibition relationship between them.

The Genome of Caenorhabditis bovis.

The free-living nematode Caenorhabditis elegans is akey laboratory model for metazoan biology. C.elegans has also become a model for parasitic nematodes despite being only distantly related to most parasitic species. All of the ∼65 Caenorhabditis species currently in culture are free-living, with most having been isolated from decaying plant or fungal matter. Caenorhabditis bovis is a particularly unusual species that has been isolated several times from the inflamed ears of Zebu cattle in Eastern Africa, where it is associated with thedisease bovine parasitic otitis. C.bovis is therefore of particular interest to researchers interested in the evolution of nematode parasitism. However, as C.bovis is not in laboratory culture, it remains little studied. Here, by sampling livestock markets and slaughterhouses in Western Kenya, we successfully reisolated C.bovis from the ear of adult female Zebu. We sequenced the genome of C.bovis using the Oxford Nanopore MinION platform in a nearby field laboratory and used the data to generate a chromosome-scaledraft genome sequence. We exploited this draft genome sequence to reconstruct the phylogenetic relationships of C.bovis to other Caenorhabditis species and reveal the changes in genome size and content that have occurred during its evolution. We also identified expansions in several gene families that have been implicated in parasitism in other nematode species. The high-quality draft genome and our analyses thereof represent a significant advancement in our understanding of this unusual Caenorhabditis species.

Streaming histogram sketching for rapid microbiome analytics

BackgroundThe growth in publically available microbiome data in recent years has yielded an invaluable resource for genomic research, allowing for the design of new studies, augmentation of novel datasets and reanalysis of published works. This vast amount of microbiome data, as well as the widespread proliferation of microbiome research and the looming era of clinical metagenomics, means there is an urgent need to develop analytics that can process huge amounts of data in a short amount of time.To address this need, we propose a new method for the compact representation of microbiome sequencing data using similarity-preserving sketches of streaming k-mer spectra. These sketches allow for dissimilarity estimation, rapid microbiome catalogue searching and classification of microbiome samples in near real time.ResultsWe apply streaming histogram sketching to microbiome samples as a form of dimensionality reduction, creating a compressed ‘histosketch’ that can efficiently represent microbiome k-mer spectra. Using public microbiome datasets, we show that histosketches can be clustered by sample type using the pairwise Jaccard similarity estimation, consequently allowing for rapid microbiome similarity searches via a locality sensitive hashing indexing scheme.Furthermore, we use a ‘real life’ example to show that histosketches can train machine learning classifiers to accurately label microbiome samples. Specifically, using a collection of 108 novel microbiome samples from a cohort of premature neonates, we trained and tested a random forest classifier that could accurately predict whether the neonate had received antibiotic treatment (97% accuracy, 96% precision) and could subsequently be used to classify microbiome data streams in less than 3 s.ConclusionsOur method offers a new approach to rapidly process microbiome data streams, allowing samples to be rapidly clustered, indexed and classified. We also provide our implementation, Histosketching Using Little K-mers (HULK), which can histosketch a typical 2 GB microbiome in 50 s on a standard laptop using four cores, with the sketch occupying 3000 bytes of disk space. (https://github.com/will-rowe/hulk).

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

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.

Assessment of k-mer spectrum applicability for metagenomic dissimilarity analysis.

BackgroundA rapidly increasing flow of genomic data requires the development of efficient methods for obtaining its compact representation. Feature extraction facilitates classification, clustering and model analysis for testing and refining biological hypotheses. “Shotgun” metagenome is an analytically challenging type of genomic data - containing sequences of all genes from the totality of a complex microbial community. Recently, researchers started to analyze metagenomes using reference-free methods based on the analysis of oligonucleotides (k-mers) frequency spectrum previously applied to isolated genomes. However, little is known about their correlation with the existing approaches for metagenomic feature extraction, as well as the limits of applicability. Here we evaluated a metagenomic pairwise dissimilarity measure based on short k-mer spectrum using the example of human gut microbiota, a biomedically significant object of study.ResultsWe developed a method for calculating pairwise dissimilarity (beta-diversity) of “shotgun” metagenomes based on short k-mer spectra (5≤k≤11). The method was validated on simulated metagenomes and further applied to a large collection of human gut metagenomes from the populations of the world (n=281). The k-mer spectrum-based measure was found to behave similarly to one based on mapping to a reference gene catalog, but different from one using a genome catalog. This difference turned out to be associated with a significant presence of viral reads in a number of metagenomes. Simulations showed limited impact of bacterial genetic variability as well as sequencing errors on k-mer spectra. Specific differences between the datasets from individual populations were identified.ConclusionsOur approach allows rapid estimation of pairwise dissimilarity between metagenomes. Though we applied this technique to gut microbiota, it should be useful for arbitrary metagenomes, even metagenomes with novel microbiota. Dissimilarity measure based on k-mer spectrum provides a wider perspective in comparison with the ones based on the alignment against reference sequence sets. It helps not to miss possible outstanding features of metagenomic composition, particularly related to the presence of an unknown bacteria, virus or eukaryote, as well as to technical artifacts (sample contamination, reads of non-biological origin, etc.) at the early stages of bioinformatic analysis. Our method is complementary to reference-based approaches and can be easily integrated into metagenomic analysis pipelines.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0875-7) contains supplementary material, which is available to authorized users.

Rare k-mer DNA: Identification of sequence motifs and prediction of CpG island and promoter

Empirical analysis on k-mer DNA has been proven as an effective tool in finding unique patterns in DNA sequences which can lead to the discovery of potential sequence motifs. In an extensive study of empirical k-mer DNA on hundreds of organisms, the researchers found unique multi-modal k-mer spectra occur in the genomes of organisms from the tetrapod clade only which includes all mammals. The multi-modality is caused by the formation of the two lowest modes where k-mers under them are referred as the rare k-mers. The suppression of the two lowest modes (or the rare k-mers) can be attributed to the CG dinucleotide inclusions in them. Apart from that, the rare k-mers are selectively distributed in certain genomic features of CpG Island (CGI), promoter, 5′ UTR, and exon. We correlated the rare k-mers with hundreds of annotated features using several bioinformatic tools, performed further intrinsic rare k-mer analyses within the correlated features, and modeled the elucidated rare k-mer clustering feature into a classifier to predict the correlated CGI and promoter features. Our correlation results show that rare k-mers are highly associated with several annotated features of CGI, promoter, 5′ UTR, and open chromatin regions. Our intrinsic results show that rare k-mers have several unique topological, compositional, and clustering properties in CGI and promoter features. Finally, the performances of our RWC (rare-word clustering) method in predicting the CGI and promoter features are ranked among the top three, in eight of the CGI and promoter evaluations, among eight of the benchmarked datasets.

Comparative Analysis of DNA Word Abundances in Four Yeast Genomes Using a Novel Statistical Background Model

Previous studies have shown that the identification and analysis of both abundant and rare k-mers or “DNA words of length k” in genomic sequences using suitable statistical background models can reveal biologically significant sequence elements. Other studies have investigated the uni/multimodal distribution of k-mer abundances or “k-mer spectra” in different DNA sequences. However, the existing background models are affected to varying extents by compositional bias. Moreover, the distribution of k-mer abundances in the context of related genomes has not been studied previously. Here, we present a novel statistical background model for calculating k-mer enrichment in DNA sequences based on the average of the frequencies of the two (k-1) mers for each k-mer. Comparison of our null model with the commonly used ones, including Markov models of different orders and the single mismatch model, shows that our method is more robust to compositional AT-rich bias and detects many additional, repeat-poor over-abundant k-mers that are biologically meaningful. Analysis of overrepresented genomic k-mers (4≤k≤16) from four yeast species using this model showed that the fraction of overrepresented DNA words falls linearly as k increases; however, a significant number of overabundant k-mers exists at higher values of k. Finally, comparative analysis of k-mer abundance scores across four yeast species revealed a mixture of unimodal and multimodal spectra for the various genomic sub-regions analyzed.

Global sequence characterization of rice centromeric satellite based on oligomer frequency analysis in large-scale sequencing data

Satellite DNA makes up significant portion of many eukaryotic genomes, yet it is relatively poorly characterized even in extensively sequenced species. This is, in part, due to methodological limitations of traditional methods of satellite repeat analysis, which are based on multiple alignments of monomer sequences. Therefore, we employed an alternative, alignment-free, approach utilizing k-mer frequency statistics, which is in principle more suitable for analyzing large sets of satellite repeat data, including sequence reads from next generation sequencing technologies. k-mer frequency spectra were determined for two sets of rice centromeric satellite CentO sequences, including 454 reads from ChIP-sequencing of CENH3-bound DNA (7.6 Mb) and the whole genome Sanger sequencing reads (5.8 Mb). k-mer frequencies were used to identify the most conserved sequence regions and to reconstruct consensus sequences of complete monomers. Reconstructed consensus sequences as well as the assessment of overall divergence of k-mer spectra revealed high similarity of the two datasets, suggesting that CentO sequences associated with functional centromeres (CENH3-bound) do not significantly differ from the total population of CentO, which includes both centromeric and pericentromeric repeat arrays. On the other hand, considerable differences were revealed when these methods were used for comparison of CentO populations between individual chromosomes of the rice genome assembly, demonstrating preferential sequence homogenization of the clusters within the same chromosome. k-mer frequencies were also successfully used to identify and characterize smRNAs derived from CentO repeats.

Genomic DNA k-mer spectra: models and modalities

Tetrapods, unlike other organisms, have multimodal spectra of k-mers in their genomes