K-mer Distribution Research Articles

Enhancing RNA-seq analysis by addressing all co-existing biases using a self-benchmarking approach with 2D structural insights.

We introduce a groundbreaking approach: the minimum free energy-based Gaussian Self-Benchmarking (MFE-GSB) framework, designed to combat the myriad of biases inherent in RNA-seq data. Central to our methodology is the MFE concept, facilitating the adoption of a Gaussian distribution model tailored to effectively mitigate all co-existing biases within a k-mer counting scheme. The MFE-GSB framework operates on a sophisticated dual-model system, juxtaposing modeling data of uniform k-mer distribution against the real, observed sequencing data characterized by nonuniform k-mer distributions. The framework applies a Gaussian function, guided by the predetermined parameters-mean and SD-derived from modeling data, to fit unknown sequencing data. This dual comparison allows for the accurate prediction of k-mer abundances across MFE categories, enabling simultaneous correction of biases at the single k-mer level. Through validation with both engineered RNA constructs and human tissue RNA samples, its wide-ranging efficacy and applicability are demonstrated.

Comprehensive survey of conserved RNA secondary structures in full-genome alignment of Hepatitis C virus

Hepatitis C virus (HCV) is a plus-stranded RNA virus that often chronically infects liver hepatocytes and causes liver cirrhosis and cancer. These viruses replicate their genomes employing error-prone replicases. Thereby, they routinely generate a large ‘cloud’ of RNA genomes (quasispecies) which—by trial and error—comprehensively explore the sequence space available for functional RNA genomes that maintain the ability for efficient replication and immune escape. In this context, it is important to identify which RNA secondary structures in the sequence space of the HCV genome are conserved, likely due to functional requirements. Here, we provide the first genome-wide multiple sequence alignment (MSA) with the prediction of RNA secondary structures throughout all representative full-length HCV genomes. We selected 57 representative genomes by clustering all complete HCV genomes from the BV-BRC database based on k-mer distributions and dimension reduction and adding RefSeq sequences. We include annotations of previously recognized features for easy comparison to other studies. Our results indicate that mainly the core coding region, the C-terminal NS5A region, and the NS5B region contain secondary structure elements that are conserved beyond coding sequence requirements, indicating functionality on the RNA level. In contrast, the genome regions in between contain less highly conserved structures. The results provide a complete description of all conserved RNA secondary structures and make clear that functionally important RNA secondary structures are present in certain HCV genome regions but are largely absent from other regions. Full-genome alignments of all branches of Hepacivirus C are provided in the supplement.

Effective binning of metagenomic contigs using contrastive multi-view representation learning

Contig binning plays a crucial role in metagenomic data analysis by grouping contigs from the same or closely related genomes. However, existing binning methods face challenges in practical applications due to the diversity of data types and the difficulties in efficiently integrating heterogeneous information. Here, we introduce COMEBin, a binning method based on contrastive multi-view representation learning. COMEBin utilizes data augmentation to generate multiple fragments (views) of each contig and obtains high-quality embeddings of heterogeneous features (sequence coverage and k-mer distribution) through contrastive learning. Experimental results on multiple simulated and real datasets demonstrate that COMEBin outperforms state-of-the-art binning methods, particularly in recovering near-complete genomes from real environmental samples. COMEBin outperforms other binning methods remarkably when integrated into metagenomic analysis pipelines, including the recovery of potentially pathogenic antibiotic-resistant bacteria (PARB) and moderate or higher quality bins containing potential biosynthetic gene clusters (BGCs).

Genetic basis of genome size variation of wheat.

Research on various species has revealed a connection between genome size variation and the physiological and ecological characteristics of the species, suggesting that it could be a crucial factor influencing a species' adaptability to different environments. Wheat, being one of the world's three primary grains, holds significance in this regard. Investigating the genome size of wheat and analyzing the genetic factors contributing to its variation could offer valuable insights for enhancing wheat agronomic traits. This project has developed a conservative site ratio calculation approach to determine the size of the wheat genome. Additionally, it employs flow cytometry and k-mer distribution analysis to validate this method. Furthermore, the researchers use re-sequencing data to investigate the impact of environmental selection pressure and transposon dynamics on the variation in the size of the wheat genome. The findings from this study demonstrate a strong relationship between the size of the wheat genome and several environmental factors. These results serve as a valuable reference for understanding the development of variation in the size of the hetero-hexaploid wheat genome. Moreover, they contribute to advancing fundamental research on the genetic mechanisms underlying wheat characteristics. Additionally, the study paves the way for exploring new research directions in wheat breeding, which holds promise for future advancements in this field.

New algorithms for accurate and efficient de novo genome assembly from long DNA sequencing reads.

Building de novo genome assemblies for complex genomes is possible thanks to long-read DNA sequencing technologies. However, maximizing the quality of assemblies based on long reads is a challenging task that requires the development of specialized data analysis techniques. We present new algorithms for assembling long DNA sequencing reads from haploid and diploid organisms. The assembly algorithm builds an undirected graph with two vertices for each read based on minimizers selected by a hash function derived from the k-mer distribution. Statistics collected during the graph construction are used as features to build layout paths by selecting edges, ranked by a likelihood function. For diploid samples, we integrated a reimplementation of the ReFHap algorithm to perform molecular phasing. We ran the implemented algorithms on PacBio HiFi and Nanopore sequencing data taken from haploid and diploid samples of different species. Our algorithms showed competitive accuracy and computational efficiency, compared with other currently used software. We expect that this new development will be useful for researchers building genome assemblies for different species.

SourceFinder: a Machine-Learning-Based Tool for Identification of Chromosomal, Plasmid, and Bacteriophage Sequences from Assemblies.

High-throughput genome sequencing technologies enable the investigation of complex genetic interactions, including the horizontal gene transfer of plasmids and bacteriophages. However, identifying these elements from assembled reads remains challenging due to genome sequence plasticity and the difficulty in assembling complete sequences. In this study, we developed a classifier, using random forest, to identify whether sequences originated from bacterial chromosomes, plasmids, or bacteriophages. The classifier was trained on a diverse collection of 23,211 chromosomal, plasmid, and bacteriophage sequences from hundreds of bacterial species. In order to adapt the classifier to incomplete sequences, each complete sequence was subsampled into 5,000 nucleotide fragments and further subdivided into k-mers. This three-class classifier succeeded in identifying chromosomes, plasmids, and bacteriophages using k-mer distributions of complete and partial genome sequences, including simulated metagenomic scaffolds with minimum performance of 0.939 area under the receiver operating characteristic curve (AUC). This classifier, implemented as SourceFinder, has been made available as an online web service to help the community with predicting the chromosomal, plasmid, and bacteriophage sources of assembled bacterial sequence data (https://cge.food.dtu.dk/services/SourceFinder/). IMPORTANCE Extra-chromosomal genes encoding antimicrobial resistance, metal resistance, and virulence provide selective advantages for bacterial survival under stress conditions and pose serious threats to human and animal health. These accessory genes can impact the composition of microbiomes by providing selective advantages to their hosts. Accurately identifying extra-chromosomal elements in genome sequence data are critical for understanding gene dissemination trajectories and taking preventative measures. Therefore, in this study, we developed a random forest classifier for identifying the source of bacterial chromosomal, plasmid, and bacteriophage sequences.

In-depth investigation of the point mutation pattern of HIV-1.

Mutations may produce highly transmissible and damaging HIV variants, which increase the genetic diversity, and pose a challenge to develop vaccines. Therefore, it is of great significance to understand how mutations drive the virulence of HIV. Based on the 11897 reliable genomes of HIV-1 retrieved from HIV sequence Database, we analyze the 12 types of point mutation (A>C, A>G, A>T, C>A, C>G, C>T, G>A, G>C, G>T, T>A, T>C, T>G) from multiple statistical perspectives for the first time. The global/geographical location/subtype/k-mer analysis results report that A>G, G>A, C>T and T>C account for nearly 64% among all SNPs, which suggest that APOBEC-editing and ADAR-editing may play an important role in HIV-1 infectivity. Time analysis shows that most genomes with abnormal mutation numbers comes from African countries. Finally, we use natural vector method to check the k-mer distribution changing patterns in the genome, and find that there is an important substitution pattern between nucleotides A and G, and 2-mer CG may have a significant impact on viral infectivity. This paper provides an insight into the single mutation of HIV-1 by using the latest data in the HIV sequence Database.

Genome Size Variation of Chagas Disease Vectors of the Rhodniini Tribe.

The genome size of five Rhodnius species (R. milesi, R. nasutus, R. neivai, R. prolixus, and R. robustus) and two Psammolestes species (P. coroedes and P. tertius) were estimated using flow cytometry and/or k-mer distributions in genome sequences. Phylogenetic generalized linear mixed models highlighted significant genome size variations among species and between sexes, with R. prolixus showing the largest genome. In this study we provide the first data on female genome size in Triatominae. For five species, female genome size did not differ from males, except for R. robustus, where females had smaller genomes. Genome size estimations based on the k-mer distribution method were less than those estimated from flow cytometry, but both methods exhibited the same pattern of sexual differences. Further genomic studies are needed to infer whether genome size variation could be an adaptive trait in Rhodnius.

IDDLncLoc: Subcellular Localization of LncRNAs Based on a Framework for Imbalanced Data Distributions.

Long non-coding RNAs play a crucial role in many life processes of cell, such as genetic markers, RNA splicing, signaling, and protein regulation. Considering that identifying lncRNA's localization in the cell through experimental methods is complicated, hard to reproduce, and expensive, we propose a novel method named IDDLncLoc in this paper, which adopts an ensemble model to solve the problem of the subcellular localization. In the proposal model, dinucleotide-based auto-cross covariance features, k-mer nucleotide composition features, and composition, transition, and distribution features are introduced to encode a raw RNA sequence to vector. To screen out reliable features, feature selection through binomial distribution, and recursive feature elimination is employed. Furthermore, strategies of oversampling in mini-batch, random sampling, and stacking ensemble strategies are customized to overcome the problem of data imbalance on the benchmark dataset. Finally, compared with the latest methods, IDDLncLoc achieves an accuracy of 94.96% on the benchmark dataset, which is 2.59% higher than the best method, and the results further demonstrate IDDLncLoc is excellent on the subcellular localization of lncRNA. Besides, a user-friendly web server is available at http://lncloc.club .

Nuclear DNA contents and ploidy levels of North American Vaccinium species and interspecific hybrids

Breeding strategies for improving blueberry (Vaccinium corymbosum and V. virgatum) cultivars often include introgressing regionally adapted species into the cultivated gene pools through interspecific hybridization. However, these approaches are complicated by variation in ploidy, triploid blocks and infertility, production of unreduced gametes, and aneuploidy. The objective of this study was to use flow cytometry, k-mer distribution analysis, and known pedigree information to evaluate genome sizes (2C nuclear and 1Cx monoploid), and ploidy of diverse accessions from Vaccinium sections and species. A total of 369 accessions, including a diversity panel (DP) of 251 inter- and intra-specific hybrid Vaccinium accessions, as well as 118 non-hybrid Vaccinium species across multiple sections, were sampled from the North Carolina State University blueberry breeding program and the National Clonal Germplasm Repository. The nuclear DNA content was analyzed via flow cytometry. The mean (range) DNA content of diploid, tetraploid, and hexaploid reference species were 1.20 pg (0.99 pg in V. crassifolium ‘Well's Delight’ to 1.41 pg in V. caesariense NC79–24), 2.37 pg (2.11 pg in V. corymbosum ‘Concord’ to 3.01 pg in V. corymbosum DE599), and 3.64 pg (3.24 in V. constablaei NC83–21–2 to 3.80 in V. virgatum ‘Premier’ and NC4790), respectively. Of the 369 unique accessions analyzed for ploidy, 259 were tetraploid, 46 were diploid, one was triploid, 51 were pentaploid or aneuploid with 2C values between tetraploid and hexaploid values, and 12 were hexaploid. Tetraploid hybrid pedigrees, which involved hexaploid crosses within three prior generations, had a 2C value range between 2.22 pg and 2.59 pg. Interspecific pentaploid and aneuploid progeny 2C DNA content ranged from 2.61 pg to 3.15 pg. We speculate some of these progeny to be near tetraploids with extra chromosomes from hexaploid progenitors. Further karyotyping of these individuals is necessary to ascertain aneuploidy anomalies. This research provides an expanded knowledge base of genome sizes, ploidy, and reproductive pathways for diverse species and hybrids to enhance future breeding, improvement, and the genomic study of blueberry.

A de novo reference assembly of the yellow mangrove Ceriops zippeliana genome.

Mangroves are of great ecological and economical importance, providing shelters for a wide range of species and nursery habitats for commercially important marine species. Ceriops zippeliana (yellow mangrove) belongs to Rhizophoraceae family and is commonly distributed in the tropical and subtropical coastal communities. In this study, we present a high-quality assembly of the C. zippeliana genome. We constructed an initial draft assembly of 240,139,412 bases with an N50 contig length of 564,761 bases using the 10x Genomics linked-read technology. This assembly was further scaffolded with RagTag using a chromosome-scale assembly of a closely related Ceriops species as a reference. The final assembly contained 243,228,612 bases with an N50 scaffold length of 10,559,178 Mb. The size of the final assembly was close to those estimated using DNA flow cytometry (248 Mb) and the k-mer distribution analysis (246 Mb). We predicted a total of 23,474 gene models and 21,724 protein-coding genes in the C. zippeliana genome, of which 16,002 were assigned gene ontology terms. We recovered 97.1% of the highly conserved orthologs based on the Benchmarking Universal Single-Copy Orthologs analysis. The phylogenetic analysis based on single-copy orthologous genes illustrated that C. zippeliana and Ceriops tagal diverged approximately 10.2 million years ago (MYA), and their last common ancestor and Kandelia obovata diverged approximately 29.9 MYA. The high-quality assembly of C. zippeliana presented in this work provides a useful genomic resource for studying mangroves’ unique adaptations to stressful intertidal habitats and for developing sustainable mangrove forest restoration and conservation programs.

Identification of sex-specific molecular markers and development of PCR-based sex detection techniques in tropical sea cucumber (Stichopus monotuberculatus)

Sex-specific markers are essential for understanding sex determination mechanism and developing sex detection techniques in aquatic animals. The tropical sea cucumber Stichopus monotuberculatus is a fledgling aquaculture species in southern China. In this study, a total of 399 candidate male-specific and 179 candidate female-specific sequences were obtained after genome assembly, read mapping, and sequencing depth and coverage analyses of the sequenced genomes from three males and three females. Based on the k-mer distribution and sex-specific sequence length distribution, S. monotuberculatus is speculated to belong to a male heterogametic sex determination system. Next, 10 female-specific and 10 male-specific sequences were chosen for a first round verification to identify the true sex-specific markers, and only the male-specific markers could be repeated by PCR examination. The primer pairs for male-specific sequences were subjected to a second round verification, and five of them distinguished the genetic sex of S. monotuberculatus with accuracies of near 100%. Based on one of the male-specific markers (SM-M3), we constructed a rapid and nonharmful PCR-based method for sex identification that was suitable in different geographical populations, and it was successfully applied to distinguish female and male S. monotuberculatus before they were used for artificial spawning. Overall, this study provided evidence of the male heterogametic sex determination system and established an effective way to identify sex in sea cucumbers, and these results may contribute to the aquaculture of these economic species.

K-Mer Analyses Reveal Different Evolutionary Histories of Alpha, Beta, and Gamma Papillomaviruses.

Papillomaviruses (PVs) are a heterogeneous group of DNA viruses that can infect fish, birds, reptiles, and mammals. PVs infecting humans (HPVs) phylogenetically cluster into five genera (Alpha-, Beta-, Gamma-, Mu- and Nu-PV), with differences in tissue tropism and carcinogenicity. The evolutionary features associated with the divergence of Papillomaviridae are not well understood. Using a combination of k-mer distributions, genetic metrics, and phylogenetic algorithms, we sought to evaluate the characteristics and differences of Alpha-, Beta- and Gamma-PVs constituting the majority of HPV genomes. A total of 640 PVs including 442 HPV types, 27 non-human primate PV types, and 171 non-primate animal PV types were evaluated. Our analyses revealed the highest genetic diversity amongst Gamma-PVs compared to the Alpha and Beta PVs, suggesting reduced selective pressures on Gamma-PVs. Using a sequence alignment-free trimer (k = 3) phylogeny algorithm, we reconstructed a phylogeny that grouped most HPV types into a monophyletic clade that was further split into three branches similar to alignment-based classifications. Interestingly, a subset of low-risk Alpha HPVs (the species Alpha-2, 3, 4, and 14) split from other HPVs and were clustered with non-human primate PVs. Surprisingly, the trimer-constructed phylogeny grouped the Gamma-6 species types originally isolated from the cervicovaginal region with the main Alpha-HPV clade. These data indicate that characterization of papillomavirus heterogeneity via orthogonal approaches reveals novel insights into the biological understanding of HPV genomes.

A Computational Framework for Pattern Detection on Unaligned Sequences: An Application on SARS-CoV-2 Data.

The exponential growth of genome sequences available has spurred research on pattern detection with the aim of extracting evolutionary signal. Traditional approaches, such as multiple sequence alignment, rely on positional homology in order to reconstruct the phylogenetic history of taxa. Yet, mining information from the plethora of biological data and delineating species on a genetic basis, still proves to be an extremely difficult problem to consider. Multiple algorithms and techniques have been developed in order to approach the problem multidimensionally. Here, we propose a computational framework for identifying potentially meaningful features based on k-mers retrieved from unaligned sequence data. Specifically, we have developed a process which makes use of unsupervised learning techniques in order to identify characteristic k-mers of the input dataset across a range of different k-values and within a reasonable time frame. We use these k-mers as features for clustering the input sequences and identifying differences between the distributions of k-mers across the dataset. The developed algorithm is part of an innovative and much promising approach both to the problem of grouping sequence data based on their inherent characteristic features, as well as for the study of changes in the distributions of k-mers, as the k-value is fluctuating within a range of values. Our framework is fully developed in Python language as an open source software licensed under the MIT License, and is freely available at https://github.com/BiodataAnalysisGroup/kmerAnalyzer.

Analysis method and algorithm design of biological sequence problem based on generalized k-mer vector

K-mer can be used for the description of biological sequences and k-mer distribution is a tool for solving sequences analysis problems in bioinformatics. We can use k-mer vector as a representation method of the k-mer distribution of the biological sequence. Problems, such as similarity calculations or sequence assembly, can be described in the k-mer vector space. It helps us to identify new features of an old sequence-based problem in bioinformatics and develop new algorithms using the concepts and methods from linear space theory. In this study, we defined the k-mer vector space for the generalized biological sequences. The meaning of corresponding vector operations is explained in the biological context. We presented the vector/matrix form of several widely seen sequence-based problems, including read quantification, sequence assembly, and pattern detection problem. Its advantages and disadvantages are discussed. Also, we implement a tool for the sequence assembly problem based on the concepts of k-mer vector methods. It shows the practicability and convenience of this algorithm design strategy.

Improved metagenome binning and assembly using deep variational autoencoders.

Despite recent advances in metagenomic binning, reconstruction of microbial species from metagenomics data remains challenging. Here we develop variational autoencoders for metagenomic binning (VAMB), a program that uses deep variational autoencoders to encode sequence coabundance and k-mer distribution information before clustering. We show that a variational autoencoder is able to integrate these two distinct data types without any previous knowledge of the datasets. VAMB outperforms existing state-of-the-art binners, reconstructing 29-98% and 45% more near-complete (NC) genomes on simulated and real data, respectively. Furthermore, VAMB is able to separate closely related strains up to 99.5% average nucleotide identity (ANI), and reconstructed 255 and 91 NC Bacteroides vulgatus and Bacteroides dorei sample-specific genomes as two distinct clusters from a dataset of 1,000 human gut microbiome samples. We use 2,606 NC bins from this dataset to show that species of the human gut microbiome have different geographical distribution patterns. VAMB can be run on standard hardware and is freely available at https://github.com/RasmussenLab/vamb .

Spectral Jaccard Similarity: A New Approach to Estimating Pairwise Sequence Alignments

SummaryPairwise sequence alignment is often a computational bottleneck in genomic analysis pipelines, particularly in the context of third-generation sequencing technologies. To speed up this process, the pairwise k-mer Jaccard similarity is sometimes used as a proxy for alignment size in order to filter pairs of reads, and min-hashes are employed to efficiently estimate these similarities. However, when the k-mer distribution of a dataset is significantly non-uniform (e.g., due to GC biases and repeats), Jaccard similarity is no longer a good proxy for alignment size. In this work, we introduce a min-hash-based approach for estimating alignment sizes called Spectral Jaccard Similarity, which naturally accounts for uneven k-mer distributions. The Spectral Jaccard Similarity is computed by performing a singular value decomposition on a min-hash collision matrix. We empirically show that this new metric provides significantly better estimates for alignment sizes, and we provide a computationally efficient estimator for these spectral similarity scores.

LROD: An Overlap Detection Algorithm for Long Reads Based on k-mer Distribution.

Third-generation sequencing technologies can produce large numbers of long reads, which have been widely used in many fields. When using long reads for genome assembly, overlap detection between any pair of long reads is an important step. However, the sequencing error rate of third-generation sequencing technologies is very high, and obtaining accurate overlap detection results is still a challenging task. In this study, we present a long-read overlap detection (LROD) algorithm that can improve the accuracy of overlap detection results. To detect overlaps between two long reads, LROD first retains only the solid common k-mers between them. These k-mers can simplify the process of overlap detection. Second, LROD finds a chain (i.e., candidate overlap) that includes the consistent common k-mers. In this step, LROD proposes a two-stage strategy to evaluate whether two common k-mers are consistent. Finally, LROD uses a novel strategy to determine whether the candidate overlaps are true and to revise them. To verify the performance of LROD, three simulated and three real long-read datasets are used in the experiments. Compared with two other popular methods (MHAP and Minimap2), LROD can achieve good performance in terms of the F1-score, precision and recall. LROD is available from https://github.com/luojunwei/LROD.

Weighted minimizer sampling improves long read mapping.

In this era of exponential data growth, minimizer sampling has become a standard algorithmic technique for rapid genome sequence comparison. This technique yields a sub-linear representation of sequences, enabling their comparison in reduced space and time. A key property of the minimizer technique is that if two sequences share a substring of a specified length, then they can be guaranteed to have a matching minimizer. However, because the k-mer distribution in eukaryotic genomes is highly uneven, minimizer-based tools (e.g. Minimap2, Mashmap) opt to discard the most frequently occurring minimizers from the genome to avoid excessive false positives. By doing so, the underlying guarantee is lost and accuracy is reduced in repetitive genomic regions. We introduce a novel weighted-minimizer sampling algorithm. A unique feature of the proposed algorithm is that it performs minimizer sampling while considering a weight for each k-mer; i.e. the higher the weight of a k-mer, the more likely it is to be selected. By down-weighting frequently occurring k-mers, we are able to meet both objectives: (i) avoid excessive false-positive matches and (ii) maintain the minimizer match guarantee. We tested our algorithm, Winnowmap, using both simulated and real long-read data and compared it to a state-of-the-art long read mapper, Minimap2. Our results demonstrate a reduction in the mapping error-rate from 0.14% to 0.06% in the recently finished human X chromosome (154.3 Mbp), and from 3.6% to 0% within the highly repetitive X centromere (3.1 Mbp). Winnowmap improves mapping accuracy within repeats and achieves these results with sparser sampling, leading to better index compression and competitive runtimes. Winnowmap is built on top of the Minimap2 codebase and is available at https://github.com/marbl/winnowmap.

Higher-order Markov models for metagenomic sequence classification.

Alignment-free, stochastic models derived from k-mer distributions representing reference genome sequences have a rich history in the classification of DNA sequences. In particular, the variants of Markov models have previously been used extensively. Higher-order Markov models have been used with caution, perhaps sparingly, primarily because of the lack of enough training data and computational power. Advances in sequencing technology and computation have enabled exploitation of the predictive power of higher-order models. We, therefore, revisited higher-order Markov models and assessed their performance in classifying metagenomic sequences. Comparative assessment of higher-order models (HOMs, 9th order or higher) with interpolated Markov model, interpolated context model and lower-order models (8th order or lower) was performed on metagenomic datasets constructed using sequenced prokaryotic genomes. Our results show that HOMs outperform other models in classifying metagenomic fragments as short as 100 nt at all taxonomic ranks, and at lower ranks when the fragment size was increased to 250 nt. HOMs were also found to be significantly more accurate than local alignment which is widely relied upon for taxonomic classification of metagenomic sequences. A novel software implementation written in C++ performs classification faster than the existing Markovian metagenomic classifiers and can therefore be used as a standalone classifier or in conjunction with existing taxonomic classifiers for more robust classification of metagenomic sequences. The software has been made available at https://github.com/djburks/SMM. Rajeev.Azad@unt.edu. Supplementary data are available at Bioinformatics online.