PanKmer: k-mer-based and reference-free pangenome analysis

What Are We Learning from Plant Pangenomes?

A single reference genome does not fully capture species diversity. By contrast, a pangenome incorporates multiple genomes to capture the entire set of nonredundant genes in a given species, along with its genome diversity. New sequencing technologies enable researchers to produce multiple high-quality genome sequences and catalog diverse genetic variations with better precision. Pangenomic studies have detected structural variants in plant genomes, dissected the genetic architecture of agronomic traits, and helped unravel molecular underpinnings and evolutionary origins of plant phenotypes. The pangenome concept has further evolved into a so-called super-pangenome that includes wild relatives within a genus or clade and shifted to graph-based reference systems. Nevertheless, building pangenomes and representing complex structural variants remain challenging in many crops. Standardized computing pipelines and common data structures are needed to compare and interpret pangenomes. The growing body of plant pangenomics data requires new algorithms, huge data storage capacity, and training to help researchers and breeders take advantage of newly discovered genes and genetic variants.

Jan and mini-Jan, a model system for potato functional genomics

Potato (Solanum tuberosum) is the third-most important food crop in the world. Although the potato genome has been fully sequenced, functional genomics research of potato lags behind that of other major food crops, largely due to the lack of a model experimental potato line. Here, we present a diploid potato line, 'Jan,' which possesses all essential characteristics for facile functional genomics studies. Jan exhibits a high level of homozygosity after seven generations of self-pollination. Jan is vigorous, highly fertile and produces tubers with outstanding traits. Additionally, it demonstrates high regeneration rates and excellent transformation efficiencies. We generated a chromosome-scale genome assembly for Jan, annotated its genes and identified syntelogs relative to the potato reference genome assembly DMv6.1 to facilitate functional genomics. To miniaturize plant architecture, we developed two 'mini-Jan' lines with compact and dwarf plant stature through CRISPR/Cas9-mediated mutagenesis targeting the Dwarf and Erecta genes involved in growth. One mini-Jan mutant, mini-JanE, is fully fertile and will permit higher-throughput studies in limited growth chamber and greenhouse space. Thus, Jan and mini-Jan offer a robust model system that can be leveraged for gene editing and functional genomics research in potato.

K-mer-based Approaches to Bridging Pangenomics and Population Genetics

Many commonly studied species now have more than one chromosome-scale genome assembly, revealing a large amount of genetic diversity previously missed by approaches that map short reads to a single reference. However, many species still lack multiple reference genomes and correctly aligning references to build pangenomes can be challenging for many species, limiting our ability to study this missing genomic variation in population genetics. Here, we argue that k-mers are a very useful but underutilized tool for bridging the reference-focused paradigms of population genetics with the reference-free paradigms of pangenomics. We review current literature on the uses of k-mers for performing three core components of most population genetics analyses: identifying, measuring, and explaining patterns of genetic variation. We also demonstrate how different k-mer-based measures of genetic variation behave in population genetic simulations according to the choice of k, depth of sequencing coverage, and degree of data compression. Overall, we find that k-mer-based measures of genetic diversity scale consistently with pairwise nucleotide diversity (π) up to values of about π=0.025 (R2=0.97) for neutrally evolving populations. For populations with even more variation, using shorter k-mers will maintain the scalability up to at least π=0.1. Furthermore, in our simulated populations, k-mer dissimilarity values can be reliably approximated from counting bloom filters, highlighting a potential avenue to decreasing the memory burden of k-mer-based genomic dissimilarity analyses. For future studies, there is a great opportunity to further develop methods to identifying selected loci using k-mers.

Mem-based pangenome indexing for k-mer queries

Pangenomes are growing in number and size, thanks to the prevalence of high-quality long-read assemblies. However, current methods for studying sequence composition and conservation within pangenomes have limitations. Methods based on graph pangenomes require a computationally expensive multiple-alignment step, which can leave out some variation. Indexes based on k-mers and de Bruijn graphs are limited to answering questions at a specific substring length k. We present Maximal Exact Match Ordered (MEMO), a pangenome indexing method based on maximal exact matches (MEMs) between sequences. A single MEMO index can handle arbitrary-length queries over pangenomic windows. MEMO enables both queries that test k-mer presence/absence (membership queries) and that count the number of genomes containing k-mers in a window (conservation queries). MEMO's index for a pangenome of 89 human autosomal haplotypes fits in 2.04 GB, 8.8 × smaller than a comparable KMC3 index and 11.4 × smaller than a PanKmer index. MEMO indexes can be made smaller by sacrificing some counting resolution, with our decile-resolution HPRC index reaching 0.67 GB. MEMO can conduct a conservation query for 31-mers over the human leukocyte antigen locus in 13.89 s, 2.5 × faster than other approaches. MEMO's small index size, lack of k-mer length dependence, and efficient queries make it a flexible tool for studying and visualizing substring conservation in pangenomes.

Can a plant biologist fix a thermostat?

The shift to reductionist biology at the dawn of the genome era yielded a 'parts list' of plant genes and a nascent understanding of complex biological processes. Today, with the genomics era in full swing, advances in high-definition genomics enabled precise temporal and spatial analyses of biological systems down to the single-cell level. These insights, coupled with artificial intelligence-driven in silico design, are propelling the development of the first synthetic plants. By integrating reductionist and systems approaches, researchers are not only reimagining plants as sources of food, fiber, and fuel but also as 'environmental thermostats' capable of mitigating the impacts of a changing climate.

Comparison of Recombination Rate, Reference Bias, and Unique Pangenomic Haplotypes in Cannabis sativa Using Seven De Novo Genome Assemblies

Genomic characterization of Cannabis sativa has accelerated rapidly in the last decade as sequencing costs have decreased and public and private interest in the species has increased. Here, we present seven new chromosome-level haplotype-phased genomes of C. sativa. All of these genotypes were alive at the time of publication, and several have numerous years of associated phenotype data. We performed a k-mer-based pangenome analysis to contextualize these assemblies within over 200 existing assemblies. This allowed us to identify unique haplotypes and genomic diversity among Cannabis sativa genotypes. We leveraged linkage maps constructed from F2 progeny of two of the assembled genotypes to characterize the recombination rate across the genome showing strong periphery-biased recombination. Lastly, we re-aligned a bulk segregant analysis dataset for the major-effect flowering locus Early1 to several of the new assemblies to evaluate the impact of reference bias on the mapping results and narrow the locus to a smaller region of the chromosome. These new assemblies, combined with the continued propagation of the genotypes, will contribute to the growing body of genomic resources for C. sativa to accelerate future research efforts.

Jan and mini-Jan, a model system for potato functional genomics

Potato (Solanum tuberosum) is the third most important food crop in the world. Although the potato genome has been fully sequenced, functional genomics research of potato lags relative to other major food crops due primarily to the lack of a model experimental potato line. Here, we present a diploid potato line, 'Jan', which possesses all essential characteristics for facile functional genomics studies. Jan has a high level of homozygosity after seven generations of self-pollination. Jan is vigorous and highly fertile with outstanding tuber traits, high regeneration rates, and excellent transformation efficiencies. We generated a chromosome-scale genome assembly for Jan, annotated genes, and identified syntelogs relative to the potato reference genome assembly DMv6.1 to facilitate functional genomics. To miniaturize plant architecture, we developed two "mini-Jan" lines with compact and dwarf plant stature using CRISPR/Cas9-mediated mutagenesis targeting the Dwarf and Erecta genes related to growth. Mini-Jan mutants are fully fertile and will permit higher-throughput studies in limited growth chamber and greenhouse space. Thus, Jan and mini-Jan provide an outstanding model system that can be leveraged for gene editing and functional genomics research in potato.

Early Detection of Both Pyrenophora teres f. teres and f. maculata in Asymptomatic Barley Leaves Using Digital Droplet PCR (ddPCR)

Efficient early pathogen detection, before symptom apparition, is crucial for optimizing disease management. In barley, the fungal pathogen Pyrenophora teres is the causative agent of net blotch disease, which exists in two forms: P. teres f. sp. teres (Ptt), causing net-form of net blotch (NTNB), and P. teres f. sp. maculata (Ptm), responsible for spot-form of net blotch (STNB). In this study, we developed primers and a TaqMan probe to detect both Ptt and Ptm. A comprehensive k-mer based analysis was performed across a collection of P. teres genomes to identify the conserved regions that had potential as universal genetic markers. These regions were then analyzed for their prevalence and copy number across diverse Moroccan P. teres strains, using both a k-mer analysis for sequence identification and a phylogenetic assessment to establish genetic relatedness. The designed primer-probe set was successfully validated through qPCR, and early disease detection, prior to symptom development, was achieved using ddPCR. The k-mer analysis performed across the available P. teres genomes suggests the potential for these sequences to serve as universal markers for P. teres, transcending environmental variations.

Crossing the Pacific: Genomics Reveals the Presence of Japanese Sardine (Sardinops melanosticta) in the California Current Large Marine Ecosystem

Recent increases in frequency and intensity of warm water anomalies and marine heatwaves have led to shifts in species ranges and assemblages. Genomic tools can be instrumental in detecting such shifts. In the early stages of a project assessing population genetic structure in Pacific Sardine (Sardinops sagax), we detected the presence of Japanese Sardine (Sardinops melanosticta) along the west coast of North America for the first time. We assembled a high quality, chromosome-scale reference genome of the Pacific Sardine and generated low coverage, whole genome sequence (lcWGS) data for 345 sardine collected in the California Current Large Marine Ecosystem (CCLME) in 2021 and 2022. Fifty individuals sampled in 2022 were identified as Japanese Sardine based on strong differentiation observed in lcWGS SNP and full mitogenome data. Although we detected a single case of mitochondrial introgression, we did not observe evidence for recent hybridization events. These findings change our understanding of Sardinops spp. distribution and dispersal in the Pacific and highlight the importance of long-term monitoring programs.

Species annotation using a k-mer based KNN model

Bacterial identification is a critical process in microbiology, clinical diagnostics, environmental monitoring, and food safety. Machine learning holds great promise for improving bacterial identification by increasing accuracy, speed, and scalability. However, challenges such as data dependency, model interpretability, and computational demands must be addressed to fully realize it's potential. k-mer based bacterial identification algorithm is an attempt to address these issues. Sequence matching is completed using the KNN technique. This included feature extraction, dataset preparation, classifier training, and label prediction based on k-mer frequency distribution similarity. The algorithm's performance has been cross-checked through accuracy assessment metrics such as F1 score and precision with an impressive 93% accuracy rate.

Multiple Horizontal Mini-chromosome Transfers Drive Genome Evolution of Clonal Blast Fungus Lineages

Crop disease pandemics are often driven by asexually reproducing clonal lineages of plant pathogens that reproduce asexually. How these clonal pathogens continuously adapt to their hosts despite harboring limited genetic variation, and in absence of sexual recombination remains elusive. Here, we reveal multiple instances of horizontal chromosome transfer within pandemic clonal lineages of the blast fungus Magnaporthe (Syn. Pyricularia) oryzae. We identified a horizontally transferred 1.2Mb accessory mini-chromosome which is remarkably conserved between M. oryzae isolates from both the rice blast fungus lineage and the lineage infecting Indian goosegrass (Eleusine indica), a wild grass that often grows in the proximity of cultivated cereal crops. Furthermore, we show that this mini-chromosome was horizontally acquired by clonal rice blast isolates through at least nine distinct transfer events over the past three centuries. These findings establish horizontal mini-chromosome transfer as a mechanism facilitating genetic exchange among different host-associated blast fungus lineages. We propose that blast fungus populations infecting wild grasses act as genetic reservoirs that drive genome evolution of pandemic clonal lineages that afflict cereal crops.

A phage tail-like bacteriocin suppresses competitors in metapopulations of pathogenic bacteria

Bacteria can repurpose their own bacteriophage viruses (phage) to kill competing bacteria. Phage-derived elements are frequently strain specific in their killing activity, although there is limited evidence that this specificity drives bacterial population dynamics. Here, we identified intact phage and their derived elements in a metapopulation of wild plant-associated Pseudomonas genomes. We discovered that the most abundant viral cluster encodes a phage remnant resembling a phage tail called a tailocin, which bacteria have co-opted to kill bacterial competitors. Each pathogenic Pseudomonas strain carries one of a few distinct tailocin variants that target the variable polysaccharides in the outer membrane of co-occurring pathogenic Pseudomonas strains. Analysis of herbarium samples from the past 170 years revealed that the same tailocin and bacterial receptor variants have persisted in Pseudomonas populations. These results suggest that tailocin genetic diversity can be mined to develop targeted "tailocin cocktails" for microbial control.

An alignment- and reference-free strategy using k-mer present pattern for population genomic analyses

Pangenomes are replacing single reference genomes to capture all variants within a species or clade, but their analysis predominantly leverages graph-based methods that require multiple high-quality genomes and computationally intensive multiple-genome alignments. K-mer decomposition is an alternative to graph-based pangenomes. However, how to directly use k-mers for the population genetic analyses is unknown. Here, we developed a novel strategy that uses the variants of k-mer count in the genome for population analyses. To test the effectivity of this method, we compared it directly to the SNP-based method on the analysis of population structure and genetic diversity of 267 Saccharomyces cerevisiae strains within two simulated datasets and a real sequence dataset. The population structure identified with k-mers recapitulates that obtained using SNPs, indicating the effectiveness of k-mer-based approach, and higher genetic diversity within real dataset supported k-mers contained more genetic variants. Based on k-mer frequency, we found not only SNP but also some insertion/deletion and horizontal gene transfer (HGT) fragments related to the adaptive evolution of S. cerevisiae. Our study creates a framework for the alignment- and reference-free (ARF) method in population genetic analyses, which will be more pronounced in the species with no complete genome or highly diverged species.

MEM-based pangenome indexing for k-mer queries

Pangenomes are growing in number and size, thanks to the prevalence of high-quality long-read assemblies. However, current methods for studying sequence composition and conservation within pangenomes have limitations. Methods based on graph pangenomes require a computationally expensive multiple-alignment step, which can leave out some variation. Indexes based on k-mers and de Bruijn graphs are limited to answering questions at a specific substring length k. We present Maximal Exact Match Ordered (MEMO), a pangenome indexing method based on maximal exact matches (MEMs) between sequences. A single MEMO index can handle arbitrary-length queries over pangenomic windows. MEMO enables both queries that test k-mer presence/absence (membership queries) and that count the number of genomes containing k-mers in a window (conservation queries). MEMO's index for a pangenome of 89 human autosomal haplotypes fits in 2.04 GB, 8.8× smaller than a comparable KMC3 index and 11.4× smaller than a PanKmer index. MEMO indexes can be made smaller by sacrificing some counting resolution, with our decile-resolution HPRC index reaching 0.67 GB. MEMO can conduct a conservation query for 31-mers over the human leukocyte antigen locus in 13.89 seconds, 2.5x faster than other approaches. MEMO's small index size, lack of k-mer length dependence, and efficient queries make it a flexible tool for studying and visualizing substring conservation in pangenomes.

PseUpred-ELPSO Is an Ensemble Learning Predictor with Particle Swarm Optimizer for Improving the Prediction of RNA Pseudouridine Sites

RNA pseudouridine modification exists in different RNA types of many species, and it has a significant role in regulating the expression of biological processes. To understand the functional mechanisms for RNA pseudouridine sites, the accurate identification of pseudouridine sites in RNA sequences is essential. Although several fast and inexpensive computational methods have been proposed, the challenge of improving recognition accuracy and generalization still exists. This study proposed a novel ensemble predictor called PseUpred-ELPSO for improved RNA pseudouridine site prediction. After analyzing the nucleotide composition preferences between RNA pseudouridine site sequences, two feature representations were determined and fed into the stacking ensemble framework. Then, using five tree-based machine learning classifiers as base classifiers, 30-dimensional RNA profiles are constructed to represent RNA sequences, and using the PSO algorithm, the weights of the RNA profiles were searched to further enhance the representation. A logistic regression classifier was used as a meta-classifier to complete the final predictions. Compared to the most advanced predictors, the performance of PseUpred-ELPSO is superior in both cross-validation and the independent test. Based on the PseUpred-ELPSO predictor, a free and easy-to-operate web server has been established, which will be a powerful tool for pseudouridine site identification.