Biology subjects

Birol, I.

Publications and source records attributed to Birol, I..

At least 19 recordsLinked to original sources

bioRxiv2026

biHash: bisulfite-aware recursive nucleotide hashing

Bisulfite sequencing (BS-seq) measures cytosine methylation on the DNA at single-base resolution, but converts unmethylated cytosines to thymines, disrupting conventional strand complementarity and reducing sequence complexity. These pose a fundamental challenge to efficient sequence comparison and indexing tasks. Although k-mer hashing is foundational to bioinformatics for enabling such high-throughput operations, generic algorithms for string hashing are poorly equipped to handle the reduced-complexity alphabet and sequence ambiguity created by this conversion. We therefore require fast k-mer hashing that directly recognizes bisulfite-compatible sequence states. Here, we present biHash, a rolling hash algorithm tailored for bisulfite-sequenced data. Using context-specific dinucleotide encodings, biHash maps designated bisulfite-compatible sequence variants to the same hash value while retaining context-specific specificity. When hashing adjacent k-mers, biHash performs ~13 times faster than generic string hashing algorithms.

bioinformatics↗

bioRxiv2026

Pathogen-specific antimicrobial activity prediction with biological large language model-based methods

Driven by the rise of antimicrobial resistance, antimicrobial peptides (AMPs) have emerged as promising therapeutics capable of targeting multidrug-resistant pathogens. Because identifying AMPs and their specific targets requires costly and labor-intensive wet-lab experiments, in silico methods to prioritize candidates are highly valuable. However, current computational methods often lack pathogen specificity or fail to incorporate crucial targeted proteomic and genomic contexts. To bridge this gap, we developed triAMPh, a robust, zero-shot framework for pathogen-specific peptide bioactivity prediction. triAMPh integrates a heterogeneous graph attention network-based link predictor (HLP), Extreme Gradient Boosting, and a multilayer perceptron trained on features from biological large language models (bLLMs). Our novel HLP constructs a knowledge graph that maps peptides and pathogens as distinct nodes, connected by similarity and bioactivity edges. The model extracts information through semantic traversals, prioritizing neighboring nodes and their biological contexts. Benchmarking shows that triAMPh provides unbiased, peptide- and pathogen-centered zero-shot predictions, matching or outperforming state-of-the-art methods across all metrics except precision. Ultimately, triAMPh offers a powerful computational tool to accelerate wet-lab AMP discovery while demonstrating the capability of bLLMs to capture complex, pathogen-specific bioactivity patterns.

bioinformatics↗

bioRxiv2026

aaKomp: Alignment-free amino acid k-mer matching for genome completeness assessment at scale

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). aaKomps scoring system provides nuanced estimates rather than threshold-based classifications, offering increased resolution for tracking incremental improvements during iterative workflows. aaKomps speed, memory efficiency, and flexible database generation makes it well-suited for modern and biodiverse projects requiring the evaluation of hundreds of assemblies.

bioinformatics↗

bioRxiv2025

Genome misassembly detection using Stash: A data structure based on stochastic tile hashing

MotivationAnalyzing large data from high-throughput sequencing technologies presents significant challenges in terms of memory and computational requirements. It is crucial to develop efficient data structures and computational methods that handle sequencing information. These challenges impact bioinformatics studies, including de novo genome assembly which serves as the foundation of genomics. Issues like read errors or limitations of heuristic decisions in assembly algorithms lead to genome misassemblies and inaccurate genomic representations, compromising the quality of downstream analyses. Hence, de novo assemblies can benefit from misassembly detection and correction, to produce a more optimal assembly. ResultsWe present Stash, a novel hash-based data structure designed for storing and querying large sequencing data. For an input sequence, Stash uses sliding windows of spaced seed patterns to extract and hash k-mers. The hash values combined with the sequence ID determine the value stored in Stash. A filled Stash can be used to query whether two genomic regions are covered by the same set of reads. This can be used in genome misassembly detection. We demonstrate the effectiveness of Stash in detecting misassemblies in human genome assemblies generated by Flye and Shasta, using Pacbio HiFi reads from the human cell line NA24385. We observed that scaffolding Stash-cut assemblies reduces 7.6% and 3.4% of misassemblies in the Flye and Shasta assemblies, respectively. This is accomplished in 310 minutes utilizing 8 GB of memory. Stash is comparable to alternative long read misassembly correction methods and can result in superior assemblies compared to the baseline. AvailabilityStash is implemented in C++ as a static library and is available here: https://github.com/bcgsc/Stash. Contactasarvar@bccrc.ca or inanc.birol@ubc.ca

bioinformatics↗

bioRxiv2025

Efficacy and safety evaluation of artificial intelligence-identified antimicrobial peptides for use against avian pathogenic Escherichia coli in the poultry industry

The overuse of antibiotics in both veterinary and human medicine has resulted in the emergence of antibiotic-resistant bacteria, prompting a search for effective alternatives. Antimicrobial peptides (AMP) are short, often cationic, peptide-based molecules with antimicrobial and immunomodulatory activity, which makes them promising alternatives to conventional antibiotics in poultry production. From a prior machine-learning-guided screen of 875 candidate AMPs, 62 exhibited activity against avian pathogenic Escherichia coli (APEC) and low in vitro hemolytic and cytotoxic activity. We selected three lead AMPs from this list (named TeRu4, TeBi1, and PeNi4), and evaluated their in vitro and in vivo efficacy, safety, and immunomodulatory potential for use in poultry farming. In animal experiments, AMPs were administered via in ovo injection on day 18 of embryonic development. In APEC challenge trials, yolk sacs were inoculated with APEC post-hatch to assess early chick mortality, while in pen trials, birds were raised in a commercial production setting for 35 days. For challenged birds, TeBi1 (10 g/egg) significantly reduced bacterial detection in the air sac and pericardium, increased body weight by 50% and reduced cytokine transcript levels by 10-30% on day 7 post hatch. In HD11 chicken macrophage-like cultured cells, TeRu4 (16 g/mL) suppressed lipopolysaccharide (LPS)-induced pro-inflammatory cytokine transcript levels. In pen trials, TeRu4 (20 g/egg) increased the survival probability of female birds by 4.9%, while TeBi1 (20 g/egg) increased the survival probability of all birds by 4.4%, by day 35. Gene expression analysis revealed AMP- and sex-specific cytokine responses. In pen trials, no significant differences were observed in mean weights, feed conversion ratio (FCR), and flock uniformity on day 35. These findings demonstrate that the three selected AMPs are safe antibiotic alternatives that improve survival, modulate immune responses, and maintain normal growth performance in broiler chickens.

microbiology↗

bioRxiv2025

Concordance and dissonance: A genome-wide analysis of self-declared versus inferred ancestry in 10,250 participants from the HostSeq cohort

Accurate characterization of human diversity is foundational to equitable genomics. In this study, we analyzed self-declared and genome-derived ancestry in 10,250 participants from the pan-Canadian HostSeq cohort. Using the alignment-free ntRoot algorithm on whole genome sequencing data, we inferred global and local ancestry at the continental super-population level and compared these with self-reported sociocultural identity categories. We observed high concordance among individuals self-identifying as White (98.8%), Black (97.2%), East Asian (96.1%), and South Asian (89.9%). Concordance was lower among those self-identifying as Hispanic (74.6%), Middle Eastern / Central Asian (67.9%), or Indigenous (40.7%), reflecting greater admixture complexity. Agreement between expected and inferred ancestry labels was modest (Cohens kappa {kappa} = -0.01 unweighted; 0.35 weighted), and ancestry discordance was strongly associated with higher Shannon entropy of ancestry fractions. Principal component analysis of ntRoot-derived ancestry composition revealed tightly clustered profiles in some groups and broader, overlapping distributions in others, illustrating how sociocultural identities and genomic data capture distinct but intersecting dimensions of human diversity. These findings support the complementary use of genome-derived continental ancestry fractions alongside self-identification, particularly in settings where sociocultural labels may be incomplete, heterogenous, or poorly aligned with genetic background. This approach can improve scientific rigor and enhance inclusion in population-scale genomics while respecting the social meaning of identity. We emphasize that genetic ancestry estimates are not proxies for race, which is a social construct with no biological basis.

genomics↗

bioRxiv2025

ntSynt-viz: Visualizing synteny patterns across multiple genomes

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).

genomics↗

bioRxiv2024

GoldPolish-Target: Targeted long-read genome assembly polishing

BackgroundAdvanced long-read sequencing technologies, such as those from Oxford Nanopore Technologies and Pacific Biosciences, are finding a wide use in de novo genome sequencing projects. However, long reads typically have higher error rates relative to short reads. If left unaddressed, subsequent genome assemblies may exhibit high base error rates that compromise the reliability of downstream analysis. Several specialized error correction tools for genome assemblies have since emerged, employing a range of algorithms and strategies to improve base quality. However, despite these efforts, many genome assembly workflows still produce regions with elevated error rates, such as gaps filled with unpolished or ambiguous bases. To address this, we introduce GoldPolish-Target, a modular targeted sequence polishing pipeline. Coupled with GoldPolish, a linear-time genome assembly algorithm, GoldPolish-Target isolates and polishes user-specified assembly loci, offering a resource-efficient means for polishing targeted regions of draft genomes. ResultsExperiments using Drosophila melanogaster and Homo sapiens datasets demonstrate that GoldPolish-Target can reduce insertion/deletion (indel) and mismatch errors by up to 49.2% and 53.4% respectively, achieving base accuracy values upwards of 99.9% (Phred score Q>30). This polishing accuracy is comparable to the current state-of-the-art, Medaka, while exhibiting up to 36-fold shorter run times and consuming 94% less memory, on average. ConclusionGoldPolish-Target, in contrast to most other polishing tools, offers the ability to target specific regions of a genome assembly for polishing, providing a computationally light-weight and highly scalable solution for base error correction. Availabilityhttps://github.com/bcgsc/goldpolish

genomics↗

bioRxiv2024

Mining the UniProtKB/Swiss-Prot database forantimicrobial peptides

The ever-growing global health threat of antibiotic resistance is compelling researchers to explore alternatives to conventional antibiotics. Antimicrobial peptides (AMPs) are emerging as a promising solution to fill this need. Naturally occurring AMPs are produced by all forms of life as part of the innate immune system. High-throughput bioinformatics tools have enabled fast and large-scale discovery of AMPs from genomic, transcriptomic, and proteomic resources of selected organisms. Public protein sequence databases, comprising over 200 million records and growing, serve as comprehensive compendia of sequences from a broad range of source organisms. Yet, large-scale in silico probing of those databases for novel AMPs has never been reported. In the present study, we propose an AMP mining workflow to predict novel AMPs from the UniProtKB/Swiss-Prot database using the AMP prediction tool, AMPlify, as its discovery engine. Using this workflow, we identified 8,008 novel putative AMPs from all eukaryotic sequences in the database. Focusing on the practical use of AMPs as suitable antimicrobial agents with applications in the poultry industry, we prioritized 40 of those AMPs based on their similarities to known chicken AMPs in predicted structures. In our tests, 13 out of the 38 successfully synthesized peptides showed antimicrobial activity against Escherichia coli and/or Staphylococcus aureus.

bioinformatics↗

bioRxiv2024

ntEmbd: Deep learning embedding for nucleotide sequences

Enabled by the explosion of data and substantial increase in computational power, deep learning has transformed fields such as computer vision and natural language processing (NLP) and it has become a successful method to be applied to many transcriptomic analysis tasks. A core advantage of deep learning is its inherent capability to incorporate feature computation within the machine learning models. This results in a comprehensive and machine-readable representation of sequences, facilitating the downstream classification and clustering tasks. Compared to machine translation problems in NLP, feature embedding is particularly challenging for transcriptomic studies as the sequences are string of thousands of nucleotides in length, which make the long-term dependencies between features from different parts of the sequence even more difficult to capture. This highlights the need for nucleotide sequence embedding methods that are capable of learning input sequence features implicitly. Here we introduce ntEmbd, a deep learning embedding tool that captures dependencies between different features of the sequences and learns a latent representation for given nucleotide sequences. We further provide two sample use cases, describing how learned RNA features can be used in downstream analysis. The first use case demonstrates ntEmbds utility in classifying coding and noncoding RNA benchmarked against existing tools, and the second one explores the utility of learned representations in identifying adapter sequences in nanopore RNA-seq reads. The tool as well as the trained models are freely available on GitHub at https://github.com/bcgsc/ntEmbd

bioinformatics↗

bioRxiv2024

Human ancestry inference at scale, from genomic data

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 (R2 = 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 SummaryStudy 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.

genetics↗

bioRxiv2024

Multi-genome synteny detection using minimizer graph mappings

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.

bioinformatics↗

bioRxiv2023

Genomic structures and regulation patterns at HPV integration sites in cervical cancer

Human papillomavirus (HPV) integration has been implicated in transforming HPV infection into cancer, but its genomic consequences have been difficult to study using short-read technologies. To resolve the dysregulation associated with HPV integration, we performed long-read sequencing on 63 cervical cancer genomes. We identified six categories of integration events based on HPV-human genomic structures. Of all HPV integrants, defined as two HPV-human breakpoints bridged by an HPV sequence, 24% contained variable copies of HPV between the breakpoints, a phenomenon we termed heterologous integration. Analysis of DNA methylation within and in proximity to the HPV genome at individual integration events revealed relationships between methylation status of the integrant and its orientation and structure. Dysregulation of the human epigenome and neighboring gene expression in cis with the HPV-integrated allele was observed over megabase-ranges of the genome. By elucidating the structural, epigenetic, and allele-specific impacts of HPV integration, we provide insight into the role of integrated HPV in cervical cancer.

genomics↗

bioRxiv2023

Conifers concentrate large numbers of NLR immune receptor genes on one chromosome

Nucleotide-binding domain and Leucine-rich Repeat (NLR) immune receptor genes form a major line of defence in plants, acting in both pathogen recognition and resistance machinery activation. NLRs are reported to form large gene clusters in limber pine (Pinus flexilis) but it is unknown how widespread this genomic architecture may be among the extant species of conifers (Pinophyta). We used comparative genomic analyses to assess patterns in the abundance, diversity and genomic distribution of NLR genes. Chromosome-level whole genome assemblies and high-density linkage maps in the Pinaceae, Cupressaceae, Taxaceae and other gymnosperms were scanned for NLR genes using existing and customised pipelines. Discovered genes were mapped across chromosomes and linkage groups, and analysed phylogenetically for evolutionary history. Conifer genomes are characterised by dense clusters of NLR genes, highly localised on one chromosome. These clusters are rich in TNL-encoding genes, which seem to have formed through multiple tandem duplication events. In contrast to angiosperms and non-coniferous gymnosperms, genomic clustering of NLR genes is ubiquitous in conifers. NLR-dense genomic regions are likely to influence a large part of the plants resistance, informing our understanding of adaptation to biotic stress and the development of genetic resources through breeding. Plain language summaryNLR immune receptor genes are important in pest, disease and drought resistance of plants. In the giga-genomes of conifers, they concentrate on very small chromosomal regions. These regions act as important reservoirs for NLR diversity and can be used in breeding to improve the resilience of conifer trees.

genomics↗

bioRxiv2023

Long-insert sequence capture detects high copy numbers in a defence-related beta-glucosidase gene Betaglu-1 with large variations in white spruce but not Norway spruce

Conifers are long-lived and slow-evolving, thus requiring effective defences against their fast-evolving insect natural enemies. The copy number variation (CNV) of two key acetophenone biosynthesis genes Ugt5/Ugt5b and {beta}glu-1 may provide a plausible mechanism underlying the constitutively variable defence in white spruce (Picea glauca) against its primary defoliator, spruce budworm. This study develops a long-insert sequence capture probe set (Picea_hung_p1.0) for quantifying copy number of {beta}glu-1-like, Ugt5-like genes and single-copy genes on 38 Norway spruce (Picea abies) and 40 P. glauca individuals from eight and nine provenances across Europe and North America respectively. We developed local assemblies (Piabi_c1.0 and Pigla_c.1.0), full-length transcriptomes (PIAB_v1 and PIGL_v1), and gene models to characterise the diversity of {beta}glu-1 and Ugt5 genes. We observed very large copy numbers of {beta}glu-1, with up to 381 copies in a single P. glauca individual. We observed among-provenance CNV of {beta}glu-1 in P. glauca but not P. abies. Ugt5b was predominantly single-copy in both species. This study generates critical hypotheses for testing the emergence and mechanism of extreme CNV, the dosage effect on phenotype, and the varying copy number of genes with the same pathway. We demonstrate new approaches to overcome experimental challenges in genomic research in conifer defences.

evolutionary biology↗

bioRxiv2023

Systematic assessment of long-read RNA-seq methods for transcript identification and quantification

AbstractThe Long-read RNA-Seq Genome Annotation Assessment Project (LRGASP) Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. The consortium generated over 427 million long-read sequences from cDNA and direct RNA datasets, encompassing human, mouse, and manatee species, using different protocols and sequencing platforms. These data were utilized by developers to address challenges in transcript isoform detection and quantification, as well as de novo transcript isoform identification. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. When aiming to detect rare and novel transcripts or when using reference-free approaches, incorporating additional orthogonal data and replicate samples are advised. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.

genomics↗

bioRxiv2023

aaHash: recursive amino acid sequence hashing

MotivationK-mer hashing is a common operation in many foundational bioinformatics problems. However, generic string hashing algorithms are not optimized for this application. Strings in bioinformatics use specific alphabets, a trait leveraged for nucleic acid sequences in earlier work. We note that amino acid sequences, with complexities and context that cannot be captured by generic hashing algorithms, can also benefit from a domain-specific hashing algorithm. Such a hashing algorithm can accelerate and improve the sensitivity of bioinformatics applications developed for protein sequences. ResultsHere, we present aaHash, a recursive hashing algorithm tailored for amino acid sequences. This algorithm utilizes multiple hash levels to represent biochemical similarities between amino acids. aaHash performs [~]10X faster than generic string hashing algorithms in hashing adjacent k-mers. Availability and implementationaaHash is available online at https://github.com/bcgsc/btllib and is free for academic use.

bioinformatics↗

bioRxiv2022

GoldRush: A de novo long read genome assembler with linear time complexity

MotivationCurrent state-of-the-art long read de novo genome assemblers follow the Overlap Layout Consensus (OLC) paradigm, an O(n2) algorithm in its naive implementation. While the most time- and memory-intensive step of OLC --the all-vs-all sequencing read alignment process-- was improved and reimplemented in modern long read assemblers, these tools still often require excessive computational memory when assembling a typical 50X human genome dataset. ResultsHere we present GoldRush, a de novo genome assembly algorithm with linear time complexity in the number of input long sequencing reads. We tested GoldRush on Oxford Nanopore Technologies datasets with different base error profiles describing the genomes of three human cell lines (NA24385, HG01243 and HG02055), Oryza sativa (rice), and Solanum lycopersicum (tomato). GoldRush achieved NGA50 lengths of 18.3-22.2 Mbp for the three human datasets, with two of the three assemblies having the fewest extensive misassemblies, and NGA50 lengths of 0.3 and 2.6 Mbp for the 373 Mbp and 824 Mbp genomes of rice and tomato, respectively. Further, GoldRush assembled all genomes within a day, using at most 54.5 GB of RAM. These results demonstrate that our algorithm and new assembly paradigm can be used to assemble large genomes de novo efficiently in compute memory space, with resulting assembly contiguity comparable to that of state-of-the-art OLC genome assemblers. Availabilityhttps://github.com/bcgsc/goldrush

genomics↗