Lauren Coombe

With the explosion of chromosome-scale genome assemblies being generated in recent years, there is vast potential for comparative genomics analyses through detecting multi-genome synteny. While existing tools can detect synteny blocks between multiple genomes, their text-based outputs make it challenging to intuitively explore large-scale synteny patterns. Interpretable, information-rich and easy-to-use synteny visualization tools are imperative to enable important biological insights from the synteny block data output by the aforementioned utilities. Here, we present ntSynt-viz, a command-line tool for automated sorting, normalization, and plotting of multi-genome synteny blocks. We show how ntSynt-viz provides clearer and more easily interpretable chromosome painting ribbon plots compared to the state-of-the-art tools NGenomeSyn and plotsr when evaluating synteny between 14 human genomes, and compared to NGenomeSyn when comparing 9 hoverfly genomes. As plotsr is limited to comparing genomes with equal chromosome numbers, it was not applicable to the hoverfly dataset. Furthermore, we demonstrate how ntSynt-viz can also be applied to visualize syntenic patterns encoded in pangenome graphs, using a Minigraph-Cactus graph built from 16 Drosophila genomes. We expect that ntSynt-viz will provide crucial insights into large-scale synteny patterns between divergent genomes, thereby advancing research into key evolutionary questions.

In de novo sequencing projects, genome assembly optimization requires evaluating a number of candidate assemblies to identify optimal tool parameters. Yet, current completeness assessment tools like BUSCO and compleasm require 10-80 minutes per evaluation for gigabase-scale genomes, transforming what should be rapid iteration into time-intensive processes. These tools rely on alignment-based approaches and fixed ortholog databases, limiting their scalability across the tree of life. We present aaKomp, a scalable alignment-free tool that leverages amino acid k -mer matching and multi-index Bloom filters for rapid genome completeness assessment. Unlike current utilities, aaKomp supports user-defined reference databases, enabling customized assessments for any organism. In benchmarking against state-of-the-art tools using simulated T2T-CHM13 datasets, aaKomp achieved 68-fold faster execution and 15-fold lower memory consumption while maintaining accuracy. Testing on 94 Human Pangenome Reference Consortium assemblies and a European Eel assembly, aaKomp maintained one-minute runtimes (1.2 ± 0.35 min) and low memory usage (<13.64 GB). aaKomp’s scoring system provides nuanced estimates rather than threshold-based classifications, offering increased resolution for tracking incremental improvements during iterative workflows. aaKomp’s speed, memory efficiency, and flexible database generation makes it well-suited for modern and biodiverse projects requiring the evaluation of hundreds of assemblies.

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.

Background: With the growing availability of reference-grade genome assemblies across diverse taxa, there is an increasing need for efficient and scalable tools for multi-species comparative genomics, including synteny detection. Here, we introduce ntSynt, a scalable utility for computing large-scale multi-genome synteny using an alignment-free, minimizer graph-based approach. Results: Through benchmarking on vertebrate genomes (~ 3 Gbp) and 11 bee genomes, we demonstrate that ntSynt produces accurate synteny maps with high genome coverage (79–100%) while using modest computational resources (~ 2 h, 34 GB memory). Conclusions: ntSynt’s efficiency and scalability enable large-scale comparative analyses across the tree of life, providing a robust foundation for downstream comparative and functional genomic studies.

With the explosion of chromosome-scale genome assemblies being generated in recent years, there is vast potential for comparative genomics analyses through detecting multi-genome synteny. While existing tools can detect synteny blocks between multiple genomes, their text-based outputs make it challenging to intuitively explore large-scale synteny patterns. Interpretable, information-rich and easy-to-use synteny visualization tools are imperative to enable important biological insights from the synteny block data output by the aforementioned utilities. Here, we present ntSynt-viz, a command-line tool for automated sorting, normalization and plotting of multi-genome synteny blocks. We show how ntSynt-viz provides clearer and more easily interpretable chromosome-painting ribbon plots compared to the state-of-the-art tool NGenomeSyn when evaluating synteny between 14 human genomes and 9 hoverfly genomes. We expect that ntSynt-viz will provide crucial insights into large-scale synteny patterns between divergent genomes, thereby advancing research into key evolutionary questions. ntSynt-viz is freely available on GitHub ( https://github.com/bcgsc/ntsynt-viz ).

Motivation: Ancestry information is essential to large cohort studies but is often unavailable or inconsistently measured. For studies involving genome sequencing, existing ancestry prediction methods are constrained by computational demands and complex input requirements. Efficient, scalable approaches are needed to infer ancestry directly from sequencing data while maintaining accuracy and reproducibility. Results: We present ntRoot, a computationally lightweight method for inferring human super-population-level ancestry from whole genome assemblies or short or long sequencing data. Utilizing a reference-guided, alignment-free single nucleotide variant detection framework, ntRoot employs a succinct Bloom filter to efficiently query diverse genomic inputs against a variant reference panel with known genotypes and ancestry. Demonstrated on over 600 human genome samples, including complete genomes, draft assemblies, and 280 independently generated samples, ntRoot accurately predicts geographic labels and shows high concordance with traditional methods such as ADMIXTURE (R2 = 0.9567) when estimating ancestry fractions. Analyses complete within 30 minutes for assemblies and 75 min for 30-fold sequencing data using 13–68 GB of memory. ntRoot provides global and local ancestry inference, delivering high-resolution predictions across genomic loci. This paradigm fills a critical gap in cohort studies by enabling rapid, resource-efficient, and accurate ancestry inference at scale, advancing ancestry characterization in genomic research. Availability: ntRoot is freely available on GitHub (https://github.com/bcgsc/ntroot).

Ancestry information is essential to large cohort studies, yet it is often unavailable or inconsistently measured. For studies with a genome sequencing component, current ancestry prediction approaches are hindered by high computational demands and complex input requirements. We present ntRoot, a computationally-lightweight method for inferring human super-population-level ancestry from whole genome assemblies or raw short or long sequencing data. Utilizing an alignment-free variant detection framework, ntRoot employs a succinct Bloom filter data structure to efficiently query diverse genomic data inputs. Demonstrated on over 600 human genome sequencing datasets—including complete genomes, draft assemblies, and over 280 independently-generated datasets—ntRoot accurately predicts geographic labels, a descriptor of human populations, and shows high concordance with traditional methods such as ADMIXTURE ( R ² = 0.9567) when predicting ancestry fractions. It achieves these predictions within 30 minutes for complete and draft genomes and within 1 hour and 15 minutes for 30X sequencing data, using a maximum of 13GB and 68GB of RAM, respectively. ntRoot offers both global and local ancestry inference, delivering high-resolution predictions across genomic loci. This paradigm fills a critical gap in cohort studies by enabling rapid, resource-efficient, and accurate ancestry inference at scale, advancing the characterization of continental-level ancestry in the genomic era. Author Summary: Study concept: RLW. Software implementation: RLW, LC, JW, PK. Data analysis: RLW, LC. Manuscript development: RLW, LC. Manuscript editing: RLW, LC, JW, PK, IB. Funding acquisition: IB.

In recent years, the landscape of reference-grade genome assemblies has seen substantial diversification. With such rich data, there is pressing demand for robust tools for scalable, multi-species comparative genomics analyses, including detecting genome synteny, which informs on the sequence conservation between genomes and contributes crucial insights into species evolution. Here, we introduce ntSynt, a scalable utility for computing large-scale multi-genome synteny blocks using a minimizer graph-based approach. Through extensive testing utilizing multiple ∼3 Gbp genomes, we demonstrate how ntSynt produces synteny blocks with coverages between 79–100% in at most 2h using 34 GB of memory, even for genomes with appreciable (>15%) sequence divergence. Compared to existing state-of-the-art methodologies, ntSynt offers enhanced flexibility to diverse input genome sequences and synteny block granularity. We expect the macrosyntenic genome analyses facilitated by ntSynt will have broad utility in generating critical evolutionary insights within and between species across the tree of life.

Current state-of-the-art de novo long read genome assemblers follow the Overlap-Layout-Consensus paradigm. While read-to-read overlap – its most costly step – was improved in modern long read genome assemblers, these tools still often require excessive RAM when assembling a typical human dataset. Our work departs from this paradigm, foregoing all-vs-all sequence alignments in favor of a dynamic data structure implemented in GoldRush, a de novo long read genome assembly algorithm with linear time complexity. We tested GoldRush on Oxford Nanopore Technologies long sequencing read datasets with different base error profiles sourced from three human cell lines, rice, and tomato. Here, we show that GoldRush achieves assembly scaffold NGA50 lengths of 18.3-22.2, 0.3 and 2.6 Mbp, for the genomes of human, rice, and tomato, respectively, and assembles each genome within a day, using at most 54.5 GB of random-access memory, demonstrating the scalability of our genome assembly paradigm and its implementation.