K-mer Spectrum Research Articles

Distribution rules of 8-mer spectra and characterization of evolution state in animal genome sequences

BackgroundStudying the composition rules and evolution mechanisms of genome sequences are core issues in the post-genomic era, and k-mer spectrum analysis of genome sequences is an effective means to solve this problem.ResultWe divided total 8-mers of genome sequences into 16 kinds of XY-type due to XY dinucleotides number in 8-mers. Previous works explored that the independent unimodal distributions observed only in three CG-type 8-mer spectra, while non-CG type 8-mer spectra have not the universal phenomenon from prokaryotes to eukaryotes. On this basis, we analyzed the distribution variation of non-CG type 8-mer spectra across 889 animal genome sequences. Following the evolutionary order of animals from primitive to more complex, we found that the spectrum distributions gradually transition from unimodal to tri-modal. The relative distance from the average frequency of each non-CG type 8-mers to the center frequency is different within a species and among different species. For the 8-mers contain CG dinucleotides, we further divided these into 16 subsets, where each 8-mer contains both CG and XY dinucleotides, called XY1_CG1 subsets. We found that the separability values of XY1_CG1 spectra are closely related to the evolution and specificity of animals. Considering the constraint of Chargaff’s second parity rule, we finally obtained 10 separability values as the feature set to characterize the evolution state of genome sequences. In order to verify the rationality of the feature set, we used 14 common classification algorithms to perform binary classification tests. The results showed that the accuracy (Acc) ranged between 98.70% and 83.88% among birds, other vertebrates and mammals.ConclusionWe proposed a credible feature set to characterizes the evolution state of genomes and obtained satisfied results by the feature set on large scale classification of animals.

Merqury: reference-free quality, completeness, and phasing assessment for genome assemblies

Recent long-read assemblies often exceed the quality and completeness of available reference genomes, making validation challenging. Here we present Merqury, a novel tool for reference-free assembly evaluation based on efficient k-mer set operations. By comparing k-mers in a de novo assembly to those found in unassembled high-accuracy reads, Merqury estimates base-level accuracy and completeness. For trios, Merqury can also evaluate haplotype-specific accuracy, completeness, phase block continuity, and switch errors. Multiple visualizations, such as k-mer spectrum plots, can be generated for evaluation. We demonstrate on both human and plant genomes that Merqury is a fast and robust method for assembly validation.

IRNA5hmC: The First Predictor to Identify RNA 5-Hydroxymethylcytosine Modifications Using Machine Learning.

RNA 5-hydroxymethylcytosine (5hmC) modification plays an important role in a series of biological processes. Characterization of its distributions in transcriptome is fundamentally important to reveal the biological functions of 5hmC. Sequencing-based technologies allow the high-throughput identification of 5hmC; however, they are labor-intensive, time-consuming, as well as expensive. Thus, there is an urgent need to develop more effective and efficient computational methods, at least complementary to the high-throughput technologies. In this study, we developed iRNA5hmC, a computational predictive protocol to identify RNA 5hmC sites using machine learning. In this predictor, we introduced a sequence-based feature algorithm consisting of two feature representations, (1) k-mer spectrum and (2) positional nucleotide binary vector, to capture the sequential characteristics of 5hmC sites. Afterward, we utilized a two-stage feature space optimization strategy to improve the feature representation ability, and trained a predictive model using support vector machine (SVM). Our feature analysis results showed that feature optimization can help to capture the most discriminative features. As compared to well-known existing feature descriptors, our proposed representations can more accurately separate true 5hmC from non-5hmC sites. To the best of our knowledge, iRNA5hmC is the first RNA 5hmC predictor that enables to make predictions based on RNA primary sequences only, without any need of prior experimental knowledge. Importantly, we have established an easy-to-use webserver which is currently available at http://server.malab.cn/iRNA5hmC. We expect it has potential to be a useful tool for the prediction of 5hmC sites.

Accelerating molecular discovery through data and physical sciences: Applications to peptide-membrane interactions.

Simulation and data analysis have evolved into powerful methods for discovering and understanding molecular modes of action and designing new compounds to exploit these modes. The combination provides a strong impetus to create and exploit new tools and techniques at the interfaces between physics, biology, and data science as a pathway to new scientific insight and accelerated discovery. In this context, we explore the rational design of novel antimicrobial peptides (short protein sequences exhibiting broad activity against multiple species of bacteria). We show how datasets can be harvested to reveal features which inform new design concepts. We introduce new analysis and visualization tools: a graphical representation of the k-mer spectrum as a fundamental property encoded in antimicrobial peptide databases and a data-driven representation to illustrate membrane binding and permeation of helical peptides.

A 200K SNP chip reveals a novel Pacific salmon louse genotype linked to differential efficacy of emamectin benzoate

Antiparasitic drugs such as emamectin benzoate (EMB) are relied upon to reduce the parasite load, particularly of the sea louse Lepeophtheirus salmonis, on farmed salmon. The decline in EMB treatment efficacy for this purpose is an important issue for salmon producers around the world, and particularly for those in the Atlantic Ocean where widespread EMB tolerance in sea lice is recognized as a significant problem. Salmon farms in the Northeast Pacific Ocean have not historically experienced the same issues with treatment efficacy, possibly due to the relatively large population of endemic salmonid hosts that serve to both redistribute surviving lice and dilute populations potentially under selection by introducing naïve lice to farms. Frequent migration of lice among farmed and wild hosts should limit the effect of farm-specific selection pressures on changes to the overall allele frequencies of sea lice in the Pacific Ocean. A previous study using microsatellites examined L. salmonis oncorhynchi from 10 Pacific locations from wild and farmed hosts and found no population structure. Recently however, a farm population of sea lice was detected where EMB bioassay exposure tolerance was abnormally elevated. In response, we have developed a Pacific louse draft genome that complements the previously-released Atlantic louse sequence. These genomes were combined with whole-genome re-sequencing data to design a highly sensitive 201,279 marker SNP array applicable for both subspecies (90,827 validated Pacific loci; 153,569 validated Atlantic loci). Notably, kmer spectrum analysis of the re-sequenced samples indicated that Pacific lice exhibit a large within-individual heterozygosity rate (average of 1 in every 72 bases) that is markedly higher than that of Atlantic individuals (1 in every 173 bases). The SNP chip was used to produce a high-density map for Atlantic sea louse linkage group 5 that was previously shown to be associated with EMB tolerance in Atlantic lice. Additionally, 478 Pacific louse samples from farmed and wild hosts obtained between 2005 and 2014 were also genotyped on the array. Clustering analysis allowed us to detect the apparent emergence of an otherwise rare genotype at a high frequency among the lice collected from two farms in 2013 that had reported elevated EMB tolerance. This genotype was not observed in louse samples collected from the same farm in 2010, nor in any lice sampled from other locations prior to 2013. However, this genotype was detected at low frequencies in louse samples from farms in two locations reporting elevated EMB tolerance in 2014. These results suggest that a rare genotype present in Pacific lice may be locally expanded in farms after EMB treatment. Supporting this hypothesis, 437 SNPs associated with this genotype were found to be in a region of linkage group 5 that overlaps the region associated with EMB resistance in Atlantic lice. Finally, five of the top diagnostic SNPs within this region were used to screen lice that had been subjected to an EMB survival assay, revealing a significant association between these SNPs and EMB treatment outcome. To our knowledge this work is the first report to identify a genetic link to altered EMB efficacy in L. salmonis in the Pacific Ocean.

An Empirical Evaluation of Error Correction Methods and Tools for Next Generation Sequencing Data

Next Generation Sequencing (NGS) technologies produce massive amount of low cost data that is very much useful in genomic study and research. However, data produced by NGS is affected by different errors such as substitutions, deletions or insertion. It is essential to differentiate between true biological variants and alterations occurred due to errors for accurate downstream analysis. Many types of methods and tools have been developed for NGS error correction. Some of these methods only correct substitutions errors whereas others correct multi types of data errors. In this article, a comprehensive evaluation of three types of methods (k-spectrum based, Multi- sequencing alignment and Hybrid based) is presented which are implemented and adopted by different tools. Experiments have been conducted to compare the performance based on runtime and error correction rate. Two different computing platforms have been used for the experiments to evaluate effectiveness of runtime and error correction rate. The mission and aim of this comparative evaluation is to provide recommendations for selection of suitable tools to cope with the specific needs of users and practitioners. It has been noticed that k-mer spectrum based methodology generated superior results as compared to other methods. Amongst all the tools being utilized, Racer has shown eminent performance in terms of error correction rate and execution time for both small as well as large data sets. In multisequence alignment based tools, Karect depicts excellent error correction rate whereas Coral shows better execution time for all data sets. In hybrid based tools, Jabba shows better error correction rate and execution time as compared to brownie. Computing platforms mostly affect execution time but have no general effect on error correction rate.

ChopStitch: exon annotation and splice graph construction using transcriptome assembly and whole genome sequencing data.

MotivationSequencing studies on non-model organisms often interrogate both genomes and transcriptomes with massive amounts of short sequences. Such studies require de novo analysis tools and techniques, when the species and closely related species lack high quality reference resources. For certain applications such as de novo annotation, information on putative exons and alternative splicing may be desirable.ResultsHere we present ChopStitch, a new method for finding putative exons de novo and constructing splice graphs using an assembled transcriptome and whole genome shotgun sequencing (WGSS) data. ChopStitch identifies exon-exon boundaries in de novo assembled RNA-Seq data with the help of a Bloom filter that represents the k-mer spectrum of WGSS reads. The algorithm also accounts for base substitutions in transcript sequences that may be derived from sequencing or assembly errors, haplotype variations, or putative RNA editing events. The primary output of our tool is a FASTA file containing putative exons. Further, exon edges are interrogated for alternative exon-exon boundaries to detect transcript isoforms, which are represented as splice graphs in DOT output format.Availability and implementationChopStitch is written in Python and C++ and is released under the GPL license. It is freely available at https://github.com/bcgsc/ChopStitch.Supplementary information Supplementary data are available at Bioinformatics online.

A Genetic Algorithm for Diploid Genome Reconstruction Using Paired-End Sequencing.

The genome of many species in the biosphere is a diploid consisting of paternal and maternal haplotypes. The differences between these two haplotypes range from single nucleotide polymorphisms (SNPs) to large-scale structural variations (SVs). Existing genome assemblers for next-generation sequencing platforms attempt to reconstruct one consensus sequence, which is a mosaic of two parental haplotypes. Reconstructing paternal and maternal haplotypes is an important task in linkage analysis and association studies. This study designs and implemented HapSVAssembler on the basis of Genetic Algorithm (GA) and paired-end sequencing. The proposed method builds a consensus sequence, identifies various types of heterozygous variants, and reconstructs the paternal and maternal haplotypes by solving an optimization problem with a GA algorithm. Experimental results indicate that the HapSVAssembler has high accuracy and contiguity under various sequencing coverage, error rates, and insert sizes. The program is tested on pilot sequencing of a highly heterozygous genome, and 12,781 heterozygous SNPs and 602 hemizygous SVs are identified. We observe that, although the number of SVs is much less than that of SNPs, the genomic regions occupied by SVs are much larger, implying the heterozygosity computed using SNPs or k-mer spectrum may be under-estimated.

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.

Reference-free inference of tumor phylogenies from single-cell sequencing data.

BackgroundEffective management and treatment of cancer continues to be complicated by the rapid evolution and resulting heterogeneity of tumors. Phylogenetic study of cell populations in single tumors provides a way to delineate intra-tumoral heterogeneity and identify robust features of evolutionary processes. The introduction of single-cell sequencing has shown great promise for advancing single-tumor phylogenetics; however, the volume and high noise in these data present challenges for inference, especially with regard to chromosome abnormalities that typically dominate tumor evolution. Here, we investigate a strategy to use such data to track differences in tumor cell genomic content during progression.ResultsWe propose a reference-free approach to mining single-cell genome sequence reads to allow predictive classification of tumors into heterogeneous cell types and reconstruct models of their evolution. The approach extracts k-mer counts from single-cell tumor genomic DNA sequences, and uses differences in normalized k-mer frequencies as a proxy for overall evolutionary distance between distinct cells. The approach computationally simplifies deriving phylogenetic markers, which normally relies on first aligning sequence reads to a reference genome and then processing the data to extract meaningful progression markers for constructing phylogenetic trees. The approach also provides a way to bypass some of the challenges that massive genome rearrangement typical of tumor genomes presents for reference-based methods. We illustrate the method on a publicly available breast tumor single-cell sequencing dataset.ConclusionsWe have demonstrated a computational approach for learning tumor progression from single cell sequencing data using k-mer counts. k-mer features classify tumor cells by stage of progression with high accuracy. Phylogenies built from these k-mer spectrum distance matrices yield splits that are statistically significant when tested for their ability to partition cells at different stages of cancer.

Konnector v2.0: pseudo-long reads from paired-end sequencing data.

BackgroundReading the nucleotides from two ends of a DNA fragment is called paired-end tag (PET) sequencing. When the fragment length is longer than the combined read length, there remains a gap of unsequenced nucleotides between read pairs. If the target in such experiments is sequenced at a level to provide redundant coverage, it may be possible to bridge these gaps using bioinformatics methods. Konnector is a local de novo assembly tool that addresses this problem. Here we report on version 2.0 of our tool.ResultsKonnector uses a probabilistic and memory-efficient data structure called Bloom filter to represent a k-mer spectrum - all possible sequences of length k in an input file, such as the collection of reads in a PET sequencing experiment. It performs look-ups to this data structure to construct an implicit de Bruijn graph, which describes (k-1) base pair overlaps between adjacent k-mers. It traverses this graph to bridge the gap between a given pair of flanking sequences.ConclusionsHere we report the performance of Konnector v2.0 on simulated and experimental datasets, and compare it against other tools with similar functionality. We note that, representing k-mers with 1.5 bytes of memory on average, Konnector can scale to very large genomes. With our parallel implementation, it can also process over a billion bases on commodity hardware.

Musket: a multistage k-mer spectrum-based error corrector for Illumina sequence data

The imperfect sequence data produced by next-generation sequencing technologies have motivated the development of a number of short-read error correctors in recent years. The majority of methods focus on the correction of substitution errors, which are the dominant error source in data produced by Illumina sequencing technology. Existing tools either score high in terms of recall or precision but not consistently high in terms of both measures. In this article, we present Musket, an efficient multistage k-mer-based corrector for Illumina short-read data. We use the k-mer spectrum approach and introduce three correction techniques in a multistage workflow: two-sided conservative correction, one-sided aggressive correction and voting-based refinement. Our performance evaluation results, in terms of correction quality and de novo genome assembly measures, reveal that Musket is consistently one of the top performing correctors. In addition, Musket is multi-threaded using a master-slave model and demonstrates superior parallel scalability compared with all other evaluated correctors as well as a highly competitive overall execution time. Musket is available at http://musket.sourceforge.net.

Genomic DNA k-mer spectra: models and modalities

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