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PubMed2026

Benchmarking Assembly-Free K-mer Methods for Species Identification in Complex Plant Groups: A Case Study in Populus.

Species identification in taxonomically complex plant groups is frequently limited by the inadequacy of organellar markers, whose phylogenetic signal is disrupted by cytonuclear discordance and chloroplast capture. Using the taxonomically complex genus Populus as a model, we evaluated an assembly-free k-mer workflow against a curated SNP reference benchmark. Whole-genome resequencing data from 235 Populus individuals were curated to a 202-individual, 34-species reference dataset in which all retained species are strictly monophyletic in a genome-wide SNP analysis. Independent maximum likelihood analyses further confirmed that the 31 non-hybrid backbone species each maintained high-support monophyly, while taxa of documented reticulate origin showed placement patterns consistent with their reticulate histories. ABBA-BABA D-statistics detected widespread residual allele sharing within the backbone, though the strongest signals did not correspond to the species pairs responsible for the few k-mer identification failures. Against this benchmark, complete plastomes showed limited resolution, recovering only 3.0% species monophyly and 71.1% nearest-neighbor assignment. The optimized k-mer workflow, operating directly on raw reads without assembly or alignment, recovered 91.2% species monophyly, 99.0% nearest-neighbor assignment, and 98.0% group-average assignment. K-mer length was the primary accuracy-controlling parameter, with k = 31 falling within a stable accuracy plateau. Distance-based metrics reached near-saturation at 0.2× sequencing depth, indicating that low-coverage genome skimming can support scalable nuclear genome-based identification with standard computational resources. K-mer distance heatmaps also flagged unusual genomic affinities in hybrid-origin and outlier samples, providing a rapid screen for subsequent population genomic analyses. These results support assembly-free k-mer distances as an efficient tool for reference-based species identification and sample screening in complex plant groups, with residual limitations concentrated near recently diverged species boundaries. Model-based phylogenomic, coalescent, and network analyses remain necessary for resolving deeper species relationships and detailed introgression histories.

Populus↗

PubMed2026

Optimizing sparse and skew hashing: faster k-mer dictionaries.

MOTIVATION: Representing a set of k-mers-strings of length k-in small space under fast lookup queries is a fundamental requirement for several applications in Bioinformatics. A data structure based on sparse and skew hashing (SSHash) was recently proposed for this purpose (Pibiri 2022): it combines good space effectiveness with fast lookup and streaming queries. It is also order-preserving, i.e. consecutive k-mers (sharing a prefix-suffix overlap of length k-1) are assigned consecutive hash codes which helps compressing satellite data typically associated with k-mers, like abundances and color sets in colored De Bruijn graphs. RESULTS: We study the problem of accelerating queries under the sparse and skew hashing indexing paradigm, without compromising its space effectiveness. We propose a refined data structure with less complex lookups and fewer cache misses. We give a simpler and faster algorithm for streaming lookup queries. The refined architecture translates to substantial performance gains, outperforming the original version of SSHash in both index construction speed and query efficiency. Compared to indexes with similar capabilities and based on the Burrows-Wheeler transform, like SBWT and FMSI, SSHash is significantly faster to build and query. SSHash is competitive in space with the fast (and default) modality of SBWT when both k-mer strands are indexed. While larger than FMSI, it is also more than one order of magnitude faster to query. AVAILABILITY AND IMPLEMENTATION: The SSHash software is available at https://github.com/jermp/sshash, and also distributed via Bioconda. A benchmark of data structures for k-mer sets is available at https://github.com/jermp/kmer_sets_benchmark. The datasets used in this article are described and available at https://zenodo.org/records/17582116.

Algorithms↗

PubMed2026

RSHash: a fast and space-efficient hash table for k-mers.

SUMMARY: Large genomic data collections can be viewed as a continuous string of DNA characters. The essential operations for data structures indexing the k-mer content of such a string are lookup and locate. Lookup determines whether a query k-mer q exists in the string and locate returns all locations in the string where q is present. High-throughput DNA sequencing generates very many k-mer sets of size exceeding billions of characters. In such scenarios, memory consumption and query efficiency pose significant challenges to a data structure supporting the above mentioned queries. To address this problem, we describe a simple, compressed, static data structure for k-mers that answers lookup and can be extended for supporting locate. The general scheme follows the use of minimizers like the state-of-the art SSHash. However, instead of using minimum perfect hash functions our solution (RSHash for Rank-Select Hash) relies on bitvectors with rank and select support, a multiple layered minimizer scheme, and a clever buffering strategy. We can show that RSHash is on average 40% and in some cases up to two times faster than SSHash while having the same memory requirements. Indeed we can go as low as 8 bits per canonical 31-mer on a human dataset. AVAILABILITY: https://github.com/jonsmcode/rshash.

Sequence Analysis, DNA↗

PubMed2026

KCFtools: rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

MOTIVATION: In the era of multiple genome references, researchers often align sequencing reads against distinct assemblies or even multiple references simultaneously. This enables applications such as the detection of introgressed segments or highly variable genomic regions, which are especially prevalent in large-genome crop species such as lettuce or wheat. However, these applications come at the cost of increased computational burden, inconsistencies in mapping methods, and reduced reproducibility across studies. To address these limitations, we developed KCFtools, a Java-based toolkit that identifies the presence and absence of k-mers in nonoverlapping genomic or transcriptomic windows by comparing query and reference genomes. This alignment-free approach enables the efficient computation of an identity score for each window, thereby facilitating robust detection of introgressed or variable regions across genomes. RESULTS: We systematically evaluated the performance and accuracy of the k-mer-based method implemented in KCFtools, benchmarking it against conventional single nucleotide variation-based introgression detection pipelines. Our results demonstrate that KCFtools effectively captures introgressed segments and structurally diverse regions, even in species with fragmented or highly divergent reference genomes. In addition, we extended KCFtools to generate genotype matrices from k-mer variation tables. These matrices are compatible with genome-wide association studies software and allow the identification of loci associated with phenotypic traits. We showcase the utility of this approach by detecting known and novel associations for downy mildew resistance in lettuce, underscoring the pipeline's potential for high-resolution, reference-agnostic population genetic analysis. AVAILABILITY AND IMPLEMENTATION: https://github.com/sivasubramanics/kcftools.

Software↗

PubMed2026

CamK-DB: A k-mer MinHash fingerprint database for reference-free genotyping of Camellia accessions.

Tea (Camellia sinensis L.), a major global economic crop in Asia, poses challenges for genetic identification because its highly heterozygous, repetitive genome reduces the efficacy of conventional single-nucleotide polymorphism (SNP) and microsatellite markers, and interspecific hybridization further complicates the situation. To address these issues, CamK-DB was developed as a reference-free Camellia fingerprinting database built on MIKE MinHash sketches. We curated 418 candidate resequencing datasets, and built a database using standardized 5× genome-coverage fingerprints. Each accession is stored as a MIKE. jac fingerprint generated with k = 21 and recommended sketch/pre_cnt = 2000. CamK-DB provides a command-line interface for data management and a custom C++ query engine that computes top-10 matches using Jaccard similarity, complemented by a QT-based graphical interface for interactive analysis. This resource offers a robust and scalable framework for precise and routine germplasm identification, genomic phylogenetic inference, and strategic breeding program design. CamK-DB (database and code) is publicly available at https://github.com/sc-zhang/CamK-DB. CamK-DB binaries are provided for Windows 10/11 and Linux (x86_64, glibc ≥ 2.27).

Databases, Genetic↗

PubMed2026

Cleanifier: contamination removal from microbial sequences using spaced seeds of a human pangenome index.

MOTIVATION: The first step when working with DNA data of human-derived microbiomes is to remove human contamination for two reasons. First, many countries have strict privacy and data protection guidelines for human sequence data, so microbiome data containing partly human data cannot be easily further processed or published. Second, human contamination may cause problems in downstream analysis, such as metagenomic binning or genome assembly. For large-scale metagenomics projects, fast and accurate removal of human contamination is therefore critical. RESULTS: We introduce Cleanifier, a fast and memory frugal alignment-free tool for detecting and removing human contamination based on gapped k-mers, or spaced seeds. Cleanifier uses a pangenome index of known human gapped k-mers, and the creation and use of alternative references is also possible. Reads are classified and filtered according to their gapped k-mer content. Cleanifier supports two filtering modes: one that queries all gapped k-mers and one that queries only a sample of them. A comparison of Cleanifier with other state-of-the-art tools shows that the sampling mode makes Cleanifier the fastest method with comparable accuracy. When using a probabilistic Cuckoo filter to store the complete k-mer set, Cleanifier has similar memory requirements to methods that use a sampled minimizer index. At the same time, Cleanifier is more flexible, because it can use different sampling methods on the same index. AVAILABILITY AND IMPLEMENTATION: Cleanifier is available via gitlab (https://gitlab.com/rahmannlab/cleanifier), PyPi (https://pypi.org/project/cleanifier/), and Bioconda (https://anaconda.org/bioconda/cleanifier). The pre-computed human pangenome index is available at Zenodo (https://doi.org/10.5281/zenodo.15639519).

Humans↗

PubMed2026

Prediction of antimicrobial minimum inhibitory concentration from bacterial genomes using a scalable and interpretable machine learning approach.

Although machine learning models can predict antimicrobial susceptibility from bacterial whole genome sequencing (WGS), state-of-the-art approaches are computationally demanding or dependent on knowledge of genetic resistance determinants. Here, we describe an efficient data-driven approach to predicting minimum inhibitory concentration (MIC) by progressively extending and refining predictive genome segments, independent of prior knowledge of resistance determinants. Resultant models had high interpretability - known and potentially novel resistance determinants were captured. Using 762 clinical E. coli strains, 71.6% of predictions were within one dilution of the measured MIC. Models trained with this algorithm generalised better onto external data (F1 score = 0.85) compared with alternative models trained on annotated resistance determinants (F1 = 0.82) or k-mer counts (F1 = 0.74). Computational demands were low (RAM usage 23.6GB vs 38.8GB for k-mer model). These advantages represent an important advance in predicting antimicrobial susceptibility from WGS, with potential applications for clinical diagnostics, drug development, and surveillance.

Journal Article↗

PubMed2026

MegaPlantTF: a machine learning framework for comprehensive identification and classification of plant transcription factors.

MOTIVATION: Understanding the role of transcription factors (TFs) in plants is essential for the study of gene regulation and various biological processes. However, both TF detection and classification remain challenging due to the great diversity and complexity of these proteins. Conventional approaches, such as BLAST, often suffer from high computational complexity and limited performance on less common TF families. RESULTS: We introduce MegaPlantTF, the first comprehensive machine learning and deep learning framework for the prediction (TF versus non-TF) and classification (family-level) of plant TFs. Our method employs k-mer-based protein representations and a two-stage architecture combining a deep feed-forward neural network with a stacking ensemble classifier. To ensure robust performance assessment, we report micro-, macro-, and weighted-average performance metrics, providing a holistic evaluation of both frequent and underrepresented TF families. Additionally, we employ threshold-based evaluation to calibrate confidence in TF detection. The results show that MegaPlantTF achieves strong accuracy and precision, particularly with a k-mer size of 3 and a classification threshold of 0.5, and maintains stable performance even under stringent thresholds. In addition to the standard cross-validation tests, a use case study on Sorghum bicolor confirms that our method performs strongly in the genome-wide analysis, making it highly suitable for large-scale TF identification and classification tasks. MegaPlantTF represents a novel contribution by integrating k-mer encoding, binary family-specific classifiers, and a two-stage stacking ensemble into a unified, reproducible framework for large-scale plant TF identification and classification. AVAILABILITY AND IMPLEMENTATION: MegaPlantTF is freely accessible through a public web server available at https://bioinformatics.um6p.ma/MegaPlantTF. The complete source code, including pretrained models and example datasets, is available at https://github.com/Bioinformatics-UM6P/MegaPlantTF.

Transcription Factors↗

PubMed2026

MetaFX: feature extraction from whole-genome metagenomic sequencing data.

MOTIVATION: Microbial communities consist of thousands of microorganisms and viruses and have a tight connection with an environment, such as gut microbiota modulation of host body metabolism. However, the direct relationship between the presence of certain microorganism and the host state often remains unknown. Toolkits using reference-based approaches are limited to microbes present in databases. Reference-free methods often require enormous resources for metagenomic assembly or results in many poorly interpretable features based on k-mers. RESULTS: Here we present MetaFX-an open-source library for feature extraction from whole-genome metagenomic sequencing data and classification of groups of samples. Using a large volume of metagenomic samples deposited in databases, MetaFX compares samples grouped by metadata criteria (e.g. disease, treatment, etc.) and constructs genomic features distinct for certain types of communities. Features constructed based on statistical k-mer analysis and de Bruijn graphs partition. Those features are used in machine learning models for classification of novel samples. Extracted features can be visualized on de Bruijn graphs and annotated for providing biological insights. We demonstrate the utility of MetaFX by building classification models for 590 human gut samples with inflammatory bowel disease. Our results outperform the previous research disease prediction accuracy up to 17%, and improves classification results compared to taxonomic analysis by 9±10% on average. AVAILABILITY AND IMPLEMENTATION: MetaFX is a feature extraction toolkit applicable for metagenomic datasets analysis and samples classification. The source code, test data, and relevant information for MetaFX are freely accessible at https://github.com/ctlab/metafx under the MIT License. Alternatively, MetaFX can be obtained via http://doi.org/10.5281/zenodo.16949369.

Metagenomics↗

PubMed2026

FALCON2: compression-based metagenomic classification of ancient viruses.

MOTIVATION: Ancient DNA (aDNA) sequences present unique challenges for taxonomic classification due to extreme fragmentation (reads 20-100 bp), end-biased cytosine deamination, and high contamination rates. Conventional metagenomic classifiers based on exact k-mer matching or alignment lose discriminative power on such short and damaged reads, limiting the analysis of paleogenomic samples. RESULTS: We present FALCON2, a compression-based metagenomic classifier that leverages position-aware finite-context models to maintain high accuracy on degraded viral ancient viruses. FALCON2 consolidates the capabilities of its predecessor, FALCON-meta, into a unified executable with enhanced features including model persistence, direct processing of compressed inputs, multiple file handling, and optional pre-filtering methodologies for contaminated samples. Under controlled benchmarking with database, taxonomy, and thread parity on simulated viral datasets, FALCON2 achieved an Area Under the Curve of Receiver Operating Characteristic (AUC-ROC) of 0.999, an Area Under Precision-Recall Curve (AUPRC) of 0.968, and an F1-score of 0.918, substantially outperforming Centrifuge (AUPRC = 0.625), Kraken2 (AUPRC = 0.184), and CLARK-S (AUPRC = 0.013) on pooled micro-averaged metrics. FALCON2's advantage is most pronounced on ultra-short reads (20-40 bp), where exact k-mers become sparse. FALCON2 pre-filtering at threshold 0.7 improved precision by 10 percentage points with negligible recall loss. FALCON2 runs on systems with 4-8 GB RAM for typical analyses. AVAILABILITY AND IMPLEMENTATION: FALCON2 is freely available at https://github.com/cobilab/FALCON2 under GPL v3 license. Benchmarking data and scripts are archived at DOI: https://doi.org/10.5281/zenodo.17291214.

Metagenomics↗

PubMed2026

The Role of Small Segmental Duplications in Generating Identical Isoforms Through Alternative Splicing Sites.

Alternative splicing plays a crucial role in expanding proteomic diversity but can also generate identical isoforms under certain conditions. While mutually exclusive splicing of tandem exons has occasionally been reported to produce identical isoforms, the extent to which other splicing events contribute to this phenomenon remains unclear. In this study, we demonstrate that alternative 5' and 3' splice site selection can also lead to the formation of identical isoforms, providing an additional type of splicing event for functional redundancy in transcriptomes. To address this, we analyzed reference genome annotations from 15 plant species, including Arabidopsis thaliana and wheat (Triticum aestivum), obtained from the RefSeq database. Identical isoforms were computationally defined as transcripts with distinct exon-intron structures but identical coding sequences. Our analysis reveals that the majority of alternative 5' and 3' fragments originate from small segmental duplications, suggesting that sequence repetition within gene regions facilitates the emergence of such splicing patterns. We also observed differences in the annotated 5' UTRs of some identical isoforms. However, since the alternative splicing sites themselves were not located within UTRs, these differences may reflect annotation uncertainty rather than genuine AS-derived variation. Given that UTR predictions in reference databases are not always precise, such observations should be interpreted cautiously. Expression analysis using an isoform-specific k-mer approach confirmed that identical isoforms can be differentially regulated. These findings suggest that, beyond expanding protein diversity, alternative splicing can also generate redundant isoforms that are differentially expressed at the RNA level, indicating potential regulatory roles. By elucidating the structural and regulatory factors contributing to the formation and retention of identical isoforms, our study provides new insights into the evolutionary and functional significance of alternative splicing in plants.

Alternative Splicing↗

PubMed2026

Alignment-free integration of single-nucleus ATAC-seq across species with sPYce.

Changes in gene regulation largely contribute to differences in cellular identities and phenotypes between species. Single-nucleus assays for transposase-accessible chromatin with sequencing (snATAC-seq) are an efficient strategy to identify putative gene regulatory elements and provide new insight into evolutionary divergence of regulatory programmes. However, no dedicated framework exists to integrate and compare snATAC-seq data across species, while methods designed for single-cell gene expression data have serious limitations. Here we present sPYce, a cross-species snATAC-seq integration method that relies on sequence composition similarities through k-mer histograms of regulatory regions, removing the need for genome alignments to anchor data from different species. sPYce can embed datasets from multiple species into the same mathematical space and permits further downstream analysis steps. We benchmarked sPYce against existing approaches on two publicly available datasets spanning more than 160 myr of evolution, showing that it successfully uncovers conserved cellular programmes while preserving biologically relevant species-specific differences. By comparing cerebellar development in mice and opossums, sPYce identifies regulatory divergence in granule cell differentiation programmes, particularly driven by nuclear factor 1. As an easy-to-use, alignment-free cross-species snATAC-seq integration approach, sPYce opens new perspectives to compare gene regulatory evolution across species.

Animals↗

PubMed2026

mm2-ivh: simple and precise overlap detection in alpha satellite HORs with interval hashing.

SUMMARY: We propose a new algorithm, "interval hashing," which distinguishes identical k-mers arising from different repeat sequences, particularly in complex repeat arrays such as alpha satellite HORs. We implement this algorithm as a fork of minimap2, named mm2-ivh. In local assembly of alpha satellite HORs, mm2-ivh accurately reconstructs more haplotypes than assemblers using standard minimizers. AVAILABILITY AND IMPLEMENTATION: mm2-ivh is available under the MIT license at https://github.com/ocxtal/mm2-ivh and runs on common Unix-compatible systems.

Algorithms↗

PubMed2026

MegaPX: fast and space-efficient peptide assignment method using IBF-based multi-indexing.

MOTIVATION: A central problem for metaproteomic analysis is the often-unknown taxonomic composition of the analyzed microbiomes. Using a database search, the standard approach requires prior knowledge of which proteins and taxa to include in the protein reference database or to use tailored metagenome-derived databases, which are expensive and error-prone in their generation. A possible strategy to circumvent this database search issue is de novo sequencing, where peptide sequences are directly identified from mass spectra. However, these sequences must still be mapped back to potentially extensive databases. Here, alignment-based approaches enable robust and precise results, with the potential drawback of high memory usage and long run times. RESULTS: We present MegaPX, a software for rapidly classifying de novo peptide sequences against large protein databases. MegaPX implemented as a C++-based tool, uses an alignment-free, k-mer approach as a taxonomic classification method with the possibility of generating mutated reference databases for error-tolerant searching. It uses various algorithms, including interleaved Bloom filters, to efficiently compute approximate membership queries, ensuring fast processing times while querying and indexing large databases in a multi-indexing fashion. We demonstrate the potential of MegaPX by analyzing different samples, including metaproteomics, against extensive reference databases, highlighting its use as a fast screening tool.

Software↗

PubMed2026

Incorporating indel channels into average-case analysis of seed-chain-extend.

MOTIVATION: Given a sequence s1 of n letters drawn independently and identically (i.i.d.) from an alphabet of size σ and a mutated substring s2 of length m<n, we want to recover the mutation history that generated s2 from s1. Many modern sequence aligners for this task use seed-chain-extend with k-mer seeds. Previously, Shaw and Yu showed linear-gap cost chaining can produce a chain with 1-O(1m) recoverability, the proportion of the mutation history that is recovered, in O(mn2.43θ log n) expected time for seed-chain-extend (assuming pre-seeded reference), where θ<0.206 is the mutation rate under a substitution-only channel and s1 is uniformly random. A gap remains between theory and practice, as real genomes include insertions and deletions (indels). RESULTS: We introduce mathematical machinery to deal with the two new obstacles introduced by indel channels: the dependence of neighbouring anchors and the presence of anchors that are only partially correct. We prove that expected recoverability of an optimal chain is ≥1-O(1m) and expected runtime is O(mn3.15·θT log n), given the total mutation rate θT=θi+θd+θs (sum of substitution, insertion, and deletion rates) is θT≤0.159. We thus narrow (but not close) the gap between theory and practice. AVAILABILITY AND IMPLEMENTATION: https://github.com/Lazarus42/seed_chainer_indels.

INDEL Mutation↗

PubMed2026

k-mer-based Upstream Preprocessing of long reads for Isoform Discovery.

Eukaryotic genes can encode multiple protein isoforms based on alternative splicing of their transcribed regions. Most modern novel isoform discovery methods function by identifying and assembling exon splice junctions from an RNA-seq sample. However, splice junctions can only be accurately annotated with time-intensive dynamic programming alignment. This manuscript introduces KuPID, a method for preprocessing long RNA-seq reads with the goal of better identifying novel isoform transcripts. KuPID utilizes k-mer sketching as a prefilter to quickly pseudo-align reads to known reference isoforms. Full alignment need only then be applied to reads that are most relevant to isoform discovery. Not only does KuPID speed up the discovery pipeline, it also increases downstream accuracy by filtering out extraneous reads. KuPID preprocessing simultaneously increases the f1 accuracy of isoform discovery pipelines by up to 11.6 points while decreasing the runtime by a factor of 2-3×;. An optional mode permits a KuPID sample to be paired with both isoform discovery and transcript quantification.

Journal Article↗

PubMed2026

Plant species identification by genome skimming across the vascular plant tree of life.

Accurate species identification is essential for biodiversity conservation and sustainable use, yet standard plant DNA barcoding often fails to achieve species-level resolution. We present a large-scale empirical evaluation of genome skimming as a tool to improve plant species discrimination. Using standardised data from 1969 individuals representing 475 species from 32 genera across major lineages of the vascular plant tree of life, we compare conventional plastid + internal transcribed spacer (ITS) barcodes with genome skimming approaches. Standard barcoding using rbcL, matK, trnH-psbA and ITS resolved about half of species (49.3%), with six genera showing < 25% species discrimination. By contrast, genome skimming enabled the recovery of complete plastid genomes, yielding 57.6% species discrimination. It also generated sufficient nuclear genomic data for additional resolution from k-mer analysis, achieving 66.8% species discrimination - an average gain of 17.5% over standard barcodes - while eliminating cases of extreme failure (< 25% resolution). The recovery of complete plastomes and ribosomal DNAs from genome skims also ensures backward compatibility with existing barcode datasets. Our results demonstrate that genome skimming provides data that substantially improves species-level resolution across diverse plant lineages and offers a scalable, high-throughput approach for building comprehensive reference resources to support global biodiversity initiatives.

DNA Barcoding, Taxonomic↗

PubMed2026

Comparative genomics reveals lineage-associated structural variation and diversification in a barley fungal pathogen.

Leaf rust, caused by Puccinia hordei, is a major barley disease worldwide. Despite repeated shifts in virulence, contrasting reproductive histories, and emerging fungicide insensitivity, the genomic basis of its diversification and adaptation remains poorly understood. In this study, we generated haplotype-resolved, chromosome-level genome assemblies for two isolates with contrasting virulence and analyzed 41 Australian isolates collected over 54 yr (1966-2020), integrating comparative and population genomics, mating-type gene phylogenies, chromosome-specific k-mer profiling, genome-wide copy-number variation (CNV) analysis, and gene-expression analysis. We identified a structurally dynamic chromosome characterized by repeat-associated rearrangements, structural variation, and lineage-associated CNV, representing the first evidence in a rust fungus of chromosome-scale structural diversification of this extent. Population analyses distinguished clonally expanded lineages from recombination-associated lineages, with mating-type gene phylogenies providing further support for lineage differentiation. More recently collected isolates showed increased duplication-associated variation, and CNV boundaries were associated with structural-variant breakpoints. We also identified lineage-associated amplification of Cyp51, with increased copy number associated with higher transcript abundance, supporting a potential role in fungicide adaptation. Overall, our findings highlight structural variation, contrasting reproductive histories, and lineage-associated CNV as important contributors to diversification in P. hordei, providing insights for future rust pathogen surveillance and management strategies.

Cyp51 gene↗