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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

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

An alignment-free strategy for circulating tumor DNA detection and tumor fraction estimation from whole-genome sequencing data.

Circulating tumor DNA (ctDNA) is emerging as a promising biomarker for postoperative monitoring of cancer patients. Precise estimation of circulating tumor fraction is crucial for evaluating treatment effects and timely detection of disease recurrence. All current ctDNA detection methods that utilize whole-genome sequencing (WGS) data rely on the reference genome alignment of sequencing reads and often apply separate tools for detecting different variant types. However, various bioinformatic analysis confounders and the application of external variant calling tools could be avoided by analyzing k-mers from unaligned sequencing reads. While k-mer-based methods have successfully been applied for somatic variant validation and detection, the potential of k-mer-based ctDNA detection is unexplored. We have developed a tumor-informed alignment-free ctDNA detection tool called ctDNAmer that detects tumor-specific somatic variation directly from unaligned sequencing data by identifying k-mers unique to the tumor DNA. ctDNAmer detects variant information across the genome by comparing the primary tumor and germline WGS data and accounts for sample-specific germline variability and technical noise in the same framework. We tested the utility of ctDNAmer for tumor fraction estimation on postoperative plasma cfDNA WGS data (mean sequencing depth ~ 28x) from 90 stage III colorectal cancer patients with three years of follow-up. The tumor fraction (TF) estimates agreed with the available clinical information and ctDNA was detected in 77% (17/22) of recurring patients with a median lead time of 8 months compared to radiological imaging. We further validated ctDNAmer's tumor fraction estimates based on a comparison with the mean cfDNA allele frequencies of somatic clonal SNVs identified from aligned primary tumor sequencing data. The TF estimates showed a strong Pearson correlation of 0.897 with the mean allele frequencies and improved ctDNA detection results across samples with an AUC of 0.79 compared to 0.75 if the mean allele frequency of clonal mutations is used.

Circulating Tumor DNA↗

PubMed2026

AniAnn's: alignment-free annotation of tandem repeat arrays using fast average nucleotide identity estimates.

MOTIVATION: Satellite DNA has long posed challenges for genome assembly and analysis due to its low sequence complexity and poor mappability. These large heterochromatic arrays of tandem repeats are ubiquitous across eukaryotic genomes, yet remain understudied. Current methods for annotating satellite regions, and other classes of tandem repeat arrays, are limited in their ability to annotate divergent or novel sequences. RESULTS: In this work, we introduce AniAnn's, an algorithm for annotating large blocks of tandemly repeating DNAs. AniAnn's exploits the high Average Nucleotide Identity (ANI) shared between repeat units of the same array to quickly and accurately infer the boundaries of such arrays. We show that AniAnn's improves the annotation of satellites and other tandem repeats within a variety of plant and animal genomes, while requiring only a fraction of the runtime compared to previous approaches. We conclude by exploring several use cases of AniAnn's as a lightweight method for masking repeats prior to whole-genome alignment as well as the de novo annotation and classification of satellite repeats. AVAILABILITY: AniAnn's is open source software and available at github.com/marbl/anianns.

Algorithms↗

PubMed2026

CAKR: commutative algebra k-mer representations for genomics.

Despite the availability of various sequence analysis models, comparative genomic analysis remains a challenge in genomics, genetics, and phylogenetics. Commutative algebra, a fundamental tool in algebraic geometry and number theory, has rarely been used in data and biological sciences. In this study, we introduce commutative algebra k-mer representations as a nonlinear algebraic framework for analyzing genomic sequences. This representation bridges commutative algebra, algebraic topology, combinatorics, and machine learning to establish a mathematical framework for comparative genomic analysis. We evaluate its effectiveness on three tasks including genetic variant classification, phylogenetic tree reconstruction, and viral classification, typically requiring alignment-based, alignment-free, and machine-learning approaches, respectively. In this work, we show that commutative algebra k-mer representations outperform five state-of-the-art sequence analysis methods across twelve primary datasets, with two additional supplementary fragment-placement benchmarks, especially in viral classification, and maintain relatively stable predictive accuracy as dataset size increases, underscoring scalability and robustness.

Genomics↗

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

Whole-genome prediction of bacterial pathogenic capacity on novel bacteria using protein language models with PathogenFinder2.

MOTIVATION: Infectious diseases continue to be a leading cause of mortality and pose a significant global health threat. Thus, the development of tools for surveillance and early detection of emerging pathogens is needed. RESULTS: We introduce PathogenFinder2, a novel, alignment-free, taxonomy-agnostic model for predicting bacterial pathogenic capacity in humans using protein language models. It outperforms previous methods, particularly for novel taxa, and provides interpretable outputs by highlighting proteins most relevant to pathogenic potential. These insights aid the identification of virulence factors, vaccine targets, and infection-related metabolic pathways. Furthermore, we introduce the Bacterial Pathogenic Capacity Landscape, which reveals patterns linked to host condition, infection site, microbial antagonism, and environmental origin. AVAILABILITY: The model is freely available online at https://genepi.dk/pathogenfinder2, or as a standalone program (https://github.com/genomicepidemiology/PathogenFinder2).

Genome, Bacterial↗

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

CoSAG-nf: A Scalable Nextflow Pipeline for Co-assembly, Optimization, and Interactive Visualization of High-Throughput Single-Cell Genomes.

MOTIVATION: Single-cell amplified genomes (SAGs) are crucial for resolving intra-population microbial heterogeneity and accurately understanding the metabolic potential of microbial dark matter populations. However, SAGs generated through multiple displacement amplification (MDA) of genomic DNA from single cells with single-copy chromosomes are highly fragmented and prone to contamination, severely hindering high-quality genome reconstruction and functional analysis, which greatly limits their scientific utility. Co-assembly of related SAGs can substantially improve genome quality, but to our knowledge no automated pipeline exists for high-throughput processing, forcing manual implementation of complex workflows that scale poorly to modern dataset sizes. RESULTS: We present CoSAG-nf, an automated high-throughput co-assembly and optimization pipeline for SAGs, implemented following the nf-core framework standards. The pipeline performs alignment-free clustering using sourmash MinHash signatures, then employs iterative tetranucleotide frequency profiling to identify and exclude outlier SAGs from co-assembly groups. CheckM2 quality assessment guides dynamic selection of optimal SAG combinations to optimize genome completeness and minimize contamination. Fully containerized, CoSAG-nf ensures reproducibility and scalability for the high-throughput processing of large-scale SAG datasets across diverse computing environments, including HPC and cloud platforms. The pipeline generates comprehensive HTML reports with quality metrics and taxonomic annotations, providing an end-to-end solution for automated high-throughput single-cell genome reconstruction. AVAILABILITY: CoSAG-nf is freely available under the MIT License at: https://github.com/linfengxu/CoSAG-nf. Archival code repository snapshots are published at zenodo with doi: https://doi.org/10.5281/zenodo.21525244. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Journal Article↗

PubMed2026

Whole-genome characterization and phylogenetic placement of Fusarium oxysporum f. sp. vasinfectum isolates.

Fusarium wilt of cotton, caused by Fusarium oxysporum f. sp. vasinfectum (Fov), remains a persistent threat to cotton production worldwide. Among the known races, Fov race 4 and its extra-virulent variants cause particularly severe losses in Upland cotton. Although several Fov genome assemblies have been assigned to races, the genomic diversity and evolutionary relationships among pathogenic and non-pathogenic isolates associated with cotton outbreaks remain poorly understood at the whole-genome level. This study addressed these gaps by generating and comparing high-quality genome assemblies of four Fusarium isolates collected from Texas cotton fields: two pathogenic (TX17-24 and TX18-9) and two non-pathogenic (TX17-6 and TX18-6). Draft assemblies were generated using Oxford Nanopore long reads and polished with Illumina reads. Comparative genomic analyses showed that pathogenic isolates possessed larger genomes and more conserved orthologous families, whereas non-pathogenic isolates contained more unique genes. Analyses of predicted secreted effectors, transposable elements, and carbohydrate-active enzymes further distinguished pathogenic and non-pathogenic lineages, suggesting roles in virulence adaptation and genome plasticity. Phylogenomic analyses using k-mer-based, assembly- and alignment-free methods incorporated all available long-read Fov genomes and revealed substantial genetic diversity within races 1 and 4, clustering isolates into multiple sublineages. These findings show that Fov race diversification is underestimated when based on traditional classification schemes and may be shaped by host specialization, geographic separation, or horizontal gene transfer. This work advances our understanding of the genomic diversity and evolutionary dynamics of Fov and establishes a foundation for improved race identification and characterization of Fusarium wilt pathogenesis in cotton.

Fusarium oxysporum↗

PubMed2026

Parent-of-origin specific allelic expression in outbreeding Arabidopsis arenosa identifies antagonistic parental enrichment in protein degradation pathways.

In plants, the epigenetic phenomenon of parent-of-origin allele-specific expression occurs mainly in the triploid endosperm. Although well studied in inbreeding Arabidopsis thaliana, genomic imprinting has been less investigated in outcrossers. In order to investigate a wider role of parental-specific allelic expression, we have analyzed imprinting in whole seeds of the obligate outbreeder Arabidopsis arenosa. High-throughput analysis of imprinting in outbreeding species is hampered by the lack of reference genomes and available sequenced accessions. High degree of allelic variation in outbreeding species may also limit the analysis to loci with less variation. We developed a reference-independent pipeline to detect parental-specific reads. Using different accessions in reciprocal crosses, we detected more than 70 paternally biased imprinted genes and > 500 maternally biased genes. Paternally biased genes showed major enrichment for proteins with ubiquitin protein transferase and ligase activity. Maternally biased genes were enriched for protein pathways directly counteracting paternally enriched genes. Here, we demonstrate an alignment-free protocol to identify imprinted genes that may be successfully applied for imprinting studies in other highly heterozygous outcrossing species. Our results suggest a unique role of genomic imprinting affecting post-transcriptional gene regulation in outbreeding A. arenosa.

Arabidopsis arenosa↗

PubMed2026

Concordance and divergence between self-declared ancestry and genome-derived ancestry composition in 10 250 participants from the HostSeq cohort.

Accurate characterization of human genetic diversity is essential for robust genomic analyses. We compared self-declared and genome-derived ancestry composition in 10 250 participants from the pan-Canadian HostSeq cohort using whole-genome sequencing data. Global and local ancestry were inferred at the continental super-population level using the alignment-free ntRoot algorithm and evaluated through both hard-label concordance and multiclass Brier score analyses incorporating full ancestry fraction profiles. Strong agreement was observed among East Asian / Pacific Islander (mean Brier score ± SD: 0.012 ± 0.052), Black (0.013 ± 0.042), White (0.055 ± 0.022), and South Asian (0.057 ± 0.098) participants, whereas higher scores among Hispanic (0.083 ± 0.060) and Middle Eastern or Central Asian (0.122 ± 0.034) participants reflected broader and more admixed ancestry profiles. Principal component analysis of centered log-ratio-transformed ancestry fractions revealed overlapping ancestry gradients rather than discrete continental groupings. Entropy- and dominance margin-based analyses further indicated that many discordant cases reflected diffuse admixture rather than categorical mismatch. Together, these findings support representing ancestry as a continuous compositional spectrum rather than discrete categories. Genome-derived ancestry estimates describe patterns of genomic variation and should not be interpreted as proxies for race.

Humans↗

PubMed2026

Pilea: profiling bacterial growth dynamics from metagenomes with sketching.

BACKGROUND: Quantifying bacteria's growth rates is essential for understanding their ecological roles and for building predictive models in environmental and clinical settings. Peak-to-trough ratios (PTRs) derived from shotgun metagenomes offer a culture-independent proxy for in situ growth rates of bacterial species, yet their reliable computation remains challenging. RESULTS: We introduce Pilea ( https://github.com/xinehc/pilea ), an alignment-free, sketching-based method that incorporates statistical models for robust PTR estimation. Pilea achieves speed improvements over existing methods while also enhancing accuracy, as demonstrated on both simulated and real datasets. CONCLUSIONS: By scaling efficiently to comprehensive reference collections such as the Genome Taxonomy Database (GTDB), Pilea enables large-scale analyses of bacterial growth dynamics across biomes, unlocking new insights for ecological research. Video Abstract.

Bacteria↗

PubMed2026

PanDelos-plus: A parallel algorithm for computing sequence homology in pangenomic analysis.

The identification of homologous gene families across multiple genomes is a central task in bacterial pangenomics traditionally requiring computationally demanding all-against-all comparisons. PanDelos addresses this challenge with an alignment-free and parameter-free approach based on k-mer profiles, combining high speed, ease of use, and competitive accuracy with state-of-the-art methods. However, the increasing availability of genomic data requires tools that can scale efficiently to larger datasets. To address this need, we present PanDelos-plus, a fully parallel, gene-centric redesign of PanDelos. The algorithm parallelizes the most computationally intensive phases (Best Hit detection and Bidirectional Best Hit extraction) through data decomposition and a thread pool strategy, while employing lightweight data structures to reduce memory usage. Benchmarks on synthetic datasets show that PanDelos-plus achieves up to 14x faster execution and reduces memory usage by up to 96%, while maintaining consistency with the original algorithm. These improvements allow the PanDelos methodology to be applied to population-scale comparative genomics, thus enabling more precise characterisation of pangenome structure and dynamics. PanDelos-plus is available at github.com/synbionics/PanDelos-plus.

Journal Article↗

PubMed2025

CAKL: Commutative algebra k-mer learning of genomics.

Despite the availability of various sequence analysis models, comparative genomic analysis remains a challenge in genomics, genetics, and phylogenetics. Commutative algebra, a fundamental tool in algebraic geometry and number theory, has rarely been used in data and biological sciences. In this study, we introduce commutative algebra k-mer learning (CAKL) as the first-ever nonlinear algebraic framework for analyzing genomic sequences. CAKL bridges between commutative algebra, algebraic topology, combinatorics, and machine learning to establish a new mathematical paradigm for comparative genomic analysis. We evaluate its effectiveness on three tasks-genetic variant identification, phylogenetic tree analysis, and viral genome classification-typically requiring alignment-based, alignment-free, and machine-learning approaches, respectively. Across eleven datasets, CAKL outperforms five state-of-the-art sequence analysis methods, particularly in viral classification, and maintains stable predictive accuracy as dataset size increases, underscoring its scalability and robustness. This work ushers in a new era in commutative algebraic data analysis and learning.

Journal Article↗

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↗

PubMed2025

Reference-Free Variant Calling with Local Graph Construction with ska lo (SKA).

The study of genomic variants is increasingly important for public health surveillance of pathogens. Traditional variant-calling methods from whole-genome sequencing data rely on reference-based alignment, which can introduce biases and require significant computational resources. Alignment- and reference-free approaches offer an alternative by leveraging k-mer-based methods, but existing implementations often suffer from sensitivity limitations, particularly in high mutation density genomic regions. Here, we present ska lo, a graph-based algorithm that aims to identify within-strain variants in pathogen whole-genome sequencing data by traversing a colored De Bruijn graph and building variant groups (i.e. sets of variant combinations). Through in silico benchmarking and real-world dataset analyses, we demonstrate that ska lo achieves high sensitivity in single-nucleotide polymorphism (SNP) calls while also enabling the detection of insertions and deletions, as well as SNP positioning on a reference genome for recombination analyses. These findings highlight ska lo as a simple, fast, and effective tool for pathogen genomic epidemiology, extending the range of reference-free variant-calling approaches. ska lo is freely available as part of the SKA program (https://github.com/bacpop/ska.rust).

Polymorphism, Single Nucleotide↗