Biology subjects

Hill, K. A.

Publications and source records attributed to Hill, K. A..

9 recordsLinked to original sources

bioRxiv2025

Genome-wide Pervasiveness and Localized Variation of k-mer-based Genomic Signatures in Eukaryotes

Genomic signatures are taxon-specific patterns in nucleotide sequence composition observed across different regions of a genome, used in the taxonomic classification of organisms and in inferring their evolutionary relationships. However, the nature and extent of the pervasiveness of a genomic signature across the expanse of a Telomere-to-Telomere (T2T) assembly, especially across the functionally diverse sequence elements and highly repetitive regions, remain counterintuitive and underexplored. This study aims to bridge this knowledge gap by systematically investigating the pervasiveness and variation of the genomic signature across the human genome and the genome of each of three other eukaryotic species from different kingdoms. Using the alignment-free k-mer-based Frequency Chaos Game Representation (FCGR) of DNA sequences, this study qualitatively and quantitatively analyzes the variations of the genomic signature along an entire genome. Qualitative analysis is first performed through visual inspection of FCGR patterns across different chromosomes of a species. In parallel, a quantitative analysis evaluates the variation of the genomic signature within a genome by comparing eight distance measures to identify the optimal one for the datasets in this study. By taking an intragenomic perspective with detailed analysis of chromosome landscapes these analyses reveal that, while the genomic signature is preserved in most genomic regions, exceptions exist in localized regions, such as tandem repetitions of short and long repeat units. Upon determining this pervasiveness, we assemble novel pipelines aimed at selecting a short contiguous representative genomic segment that encapsulates the sequence composition patterns characteristic of the entire genome. These representative segments are then used to assess intragenomic variation of the genomic signature, demonstrating that only a small proportion of segments (namely those characterized by regional density of short and long tandem repeats) show high distance values from the representative. No-tably, in the human genome, 80% of the segments have a distance of less than 0.24 (on a [0,1] DSSIM scale) from the representative. Moreover, we demonstrate that using these representative segments improves down-stream tasks, e.g., increasing one-nearest-neighbor (1-NN) taxonomic classification accuracy by 7% compared to selecting a random genomic segment to serve as a proxy of the genome. Lastly, this study presents a special-purpose graphical user interface (GUI) software tool, CGR-Diff, designed to provide both visual and quantitative comparisons of FCGRs of sample or user-provided DNA sequences, thereby facilitating intragenomic variation analysis of genomic signature within and across species.

bioinformatics↗

bioRxiv2025

Life at the extremes: Maximally divergent microbes with similar genomic signatures linked to extreme environments

Extreme environments impose strong mutation and selection pressures that drive distinctive, yet understudied, genomic adaptations in extremophiles. In this study, we identify 15 bacterium-archaeon pairs that exhibit highly similar k-mer-based genomic signatures despite maximal taxonomic divergence, suggesting that shared environmental conditions can produce convergent, genome-wide patterns that transcend evolutionary distance. To uncover these patterns, we developed a computational pipeline to select a composite genome proxy assembled from non-contiguous subsequences of the genome. Using supervised machine learning on a curated dataset of 693 extremophile microbial genomes, we found that 6-mers and 100 kbp genome proxy lengths provide the best balance between classification accuracy and computational efficiency. Our results provide conclusive evidence of the pervasive nature of k-mer-based patterns across the genome, and uncover the presence of taxonomic and environmental components that persist across all regions of the genome. The 15 bacterium-archaeon pairs identified by our method as having similar genomic signatures were validated through multiple independent analyses, including 3-mer frequency profile comparisons, phenotypic trait similarity, and geographic co-occurrence data. These complementary validations confirmed that extreme environmental pressures can override traditionally recognized taxonomic components at the whole-genome level. Together, these findings reveal that adaptation to extreme conditions can carry robust, taxonomic domain-spanning imprints on microbial genomes, offering new insight into the relationship between environmental mutagenesis and selection and genome-wide evolutionary convergence.

bioinformatics↗

bioRxiv2023

Functional Screening in human HSPCs identifies optimized protein-based enhancers of Homology Directed Repair

Homology Directed Repair (HDR) enables precise genome editing and holds great promise in the gene therapy field. However, the implementation of HDR-based therapies is hindered by limited efficiency in comparison to methods that exploit alternative DNA repair routes, such as Non-Homologous End Joining (NHEJ). In this study, we demonstrate the development of a functional, pooled screening platform utilizing an HDR-based readout to identify protein-based reagents that improve HDR outcomes in human hematopoietic stem and progenitor cells (HSPCs), a clinically relevant cell type for gene therapy. We leveraged this screening platform to explore sequence diversity at the binding interface of the NHEJ inhibitor i53 and its target, 53BP1, and we identified optimized i53 variants that enable new intermolecular bonds and robustly increase HDR. These variants specifically reduce insertion-deletion outcomes and also synergize with a DNAPK inhibitor to increase HDR rates. When applied at manufacturing scale, the incorporation of improved variants results in a significant increase in cells with at least one repaired allele and improved HDR in long-term HSPCs subpopulations, while not increasing off-target editing or gross chromosomal rearrangements. We anticipate the pooled screening platform will enable discovery of future gene editing reagents that improve HDR outcomes, such as the i53 variants reported here.

molecular biology↗

bioRxiv2023

Environment and taxonomy shape the genomic signature of prokaryotic extremophiles

This study provides comprehensive quantitative evidence suggesting that adaptations to extreme temperatures and pH imprint a discernible environmental component in the genomic signature of microbial extremophiles. Both supervised and unsupervised machine learning algorithms were used to analyze genomic signatures, each computed as the k-mer frequency vector of a 500 kbp DNA fragment arbitrarily selected to represent a genome. Computational experiments classified/clustered genomic signatures extracted from a curated dataset of [~]700 extremophile (temperature, pH) bacteria and archaea genomes, at multiple scales of analysis, 1 [≤]k [≤]6. The supervised learning resulted in high accuracies for taxonomic classifications at 2 [≤]k [≤]6, and medium to medium-high accuracies for environment category classifications of the same datasets at 3 [≤]k [≤]6. For k = 3, our findings were largely consistent with amino acid compositional biases and codon usage patterns in coding regions, previously attributed to extreme environment adaptations. The unsupervised learning of unlabelled sequences identified several exemplars of hyperthermophilic organisms with large similarities in their genomic signatures, in spite of belonging to different domains in the Tree of Life.

bioinformatics↗

bioRxiv2023

iDeLUCS: A deep learning interactive tool for alignment-free clustering of DNA sequences

SummaryWe present an interactive Deep Learning-based software tool for Unsupervised Clustering of DNA Sequences (iDeLUCS), that detects genomic signatures and uses them to cluster DNA sequences, without the need for sequence alignment or taxonomic identifiers. iDeLUCS is scalable and user-friendly: Its graphical user interface, with support for hardware acceleration, allows the practitioner to fine-tune the different hyper-parameters involved in the training process without requiring extensive knowledge of deep learning. The performance of iDeLUCS was evaluated on a diverse set of datasets: several real genomic datasets from organisms in kingdoms Animalia, Protista, Fungi, Bacteria, and Archaea, three datasets of viral genomes, a dataset of simulated metagenomic reads from microbial genomes, and multiple datasets of synthetic DNA sequences. The performance of iDeLUCS was compared to that of two classical clustering algorithms (k-means++ and GMM) and two clustering algorithms specialized in DNA sequences (MeShClust v3.0 and DeLUCS), using both intrinsic cluster evaluation metrics and external evaluation metrics. In terms of unsupervised clustering accuracy, iDeLUCS outperforms the two classical algorithms by an average of [~] 20%, and the two specialized algorithms by an average of [~] 12%, on the datasets of real DNA sequences analyzed. Overall, our results indicate that iDeLUCS is a robust clustering method suitable for the clustering of large and diverse datasets of unlabelled DNA sequences. Availability and implementationiDeLUCS is available at our github repository under the terms of the MIT licence. Contactpmillana@uwaterloo.ca Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

Utility of a high-resolution mouse single nucleotide polymorphism microarray assessed for rodent comparative genomics

In the study of genetic diversity in non-model species there is a notable lack of the low-cost, high resolution tools that are readily available for model organisms. Genotyping microarray technology for model organisms is well-developed, affordable, and potentially adaptable for cross-species hybridization. The Mouse Diversity Genotyping Array (MDGA), a single nucleotide polymorphism (SNP) genotyping tool designed for Mus musculus, was tested as a tool to survey genomic diversity of wild species for inter-order, inter-genus, and intra-genus comparisons. Application of the MDGA cross-species provides genetic distance information that reflects known taxonomic relationships reported previously between non-model species, but there is an underestimation of genetic diversity for non-Mus samples, indicated by a plateau in loci genotyped beginning 10-15 millions of years divergence from the house mouse. The number and types of samples included in datasets genotyped together must be considered in cross-species hybridization studies. The number of loci with heterozygous genotypes mapped to published genome sequences indicates potential for cross-species MDGA utility. A case study of seven deer mice yielded 159,797 loci (32% of loci queried by the MDGA) that were genotyped in these rodents. For one species, Peromyscus maniculatus, 6,075 potential polymorphic loci were identified. Cross-species utility of the MDGA provides needed genetic information for non-model species that are lacking genomic resources. Genotyping arrays are widely available, developed tools that are capable of capturing large amounts of genetic information in a single application, and represent a unique opportunity to identify genomic variation in closely related species that currently have a paucity of genomic information available. A candidate list of MDGA loci that can be utilized in cross-species hybridization studies was identified and may prove to be informative for rodent species that are known as environmental sentinels. Future studies may evaluate the utility of candidate SNP loci in populations of non-model rodents. Author SummaryThere is a need for a tool that can assay DNA sequence differences in species for which there is little or no DNA information available. One method of analyzing differences in DNA sequences in species with well-understood genomes is through a genotyping microarray, which has demonstrated utility cross-species. The Mouse Diversity Genotyping Array (MDGA) is a tool designed to examine known differences across the genome of the house mouse, Mus musculus. Given that related organisms share genetic similarity, the MDGA was tested for utility in identifying genome variation in other wild mice and rodents. Variation identified from distantly related species that were not of the same genus as the house mouse was an underestimate of the true amount of variation present in the genomes of wild species. Utility of the MDGA for wild species is best suited to mice from the same genus as the house mouse, and candidate variation identified can be tested in rodent populations in future studies. Identifying changes in genetic variation within populations of wild rodents can help researchers understand the links between specific genome changes and the ability to adapt to pressures in the environment, as well as better understand the evolution of rodents.

genomics↗

bioRxiv2020

Machine learning-based analysis of genomes suggests associations between Wuhan 2019-nCoV and bat Betacoronaviruses

As of February 20, 2020, the 2019 novel coronavirus (renamed to COVID-19) spread to 30 countries with 2130 deaths and more than 75500 confirmed cases. COVID-19 is being compared to the infamous SARS coronavirus, which resulted, between November 2002 and July 2003, in 8098 confirmed cases worldwide with a 9.6% death rate and 774 deaths. Though COVID-19 has a death rate of 2.8% as of 20 February, the 75752 confirmed cases in a few weeks (December 8, 2019 to February 20, 2020) are alarming, with cases likely being under-reported given the comparatively longer incubation period. Such outbreaks demand elucidation of taxonomic classification and origin of the virus genomic sequence, for strategic planning, containment, and treatment. This paper identifies an intrinsic COVID-19 genomic signature and uses it together with a machine learning-based alignment-free approach for an ultra-fast, scalable, and highly accurate classification of whole COVID-19 genomes. The proposed method combines supervised machine learning with digital signal processing for genome analyses, augmented by a decision tree approach to the machine learning component, and a Spearmans rank correlation coefficient analysis for result validation. These tools are used to analyze a large dataset of over 5000 unique viral genomic sequences, totalling 61.8 million bp. Our results support a hypothesis of a bat origin and classify COVID-19 as Sarbecovirus, within Betacoronavirus. Our method achieves high levels of classification accuracy and discovers the most relevant relationships among over 5,000 viral genomes within a few minutes, ab initio, using raw DNA sequence data alone, and without any specialized biological knowledge, training, gene or genome annotations. This suggests that, for novel viral and pathogen genome sequences, this alignment-free whole-genome machine-learning approach can provide a reliable real-time option for taxonomic classification.

bioinformatics↗

bioRxiv2019

MLDSP-GUI: An alignment-free standalone tool with an interactive graphical user interface for DNA sequence comparison and analysis

SummaryMLDSP-GUI (Machine Learning with Digital Signal Processing) is an open-source, alignment-free, ultrafast, computationally lightweight, standalone software tool with an interactive Graphical User Interface (GUI) for comparison and analysis of DNA sequences. MLDSP-GUI is a general-purpose tool that can be used for a variety of applications such as taxonomic classification, disease classification, virus subtype classification, evolutionary analyses, among others.\n\nAvailabilityMLDSP-GUI is open-source, cross-platform compatible, and is available under the terms of the Creative Commons Attribution 4.0 International license (http://creativecommons.org/licenses/by/4.0/). The executable and dataset files are available at https://sourceforge.net/projects/mldsp-gui/.\n\nContactgrandha8@uwo.ca\n\nSupplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2018

ML-DSP: Machine Learning with Digital Signal Processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels

BackgroundAlthough methods and software tools abound for the comparison, analysis, identification, and taxonomic classification of the enormous amount of genomic sequences that are continuously being produced, taxonomic classification remains challenging. The difficulty lies within both the magnitude of the dataset and the intrinsic problems associated with classification. The need exists for an approach and software tool that addresses the limitations of existing alignment-based methods, as well as the challenges of recently proposed alignment-free methods.\n\nResultsWe combine supervised Machine Learning with Digital Signal Processing to design ML-DSP, an alignment-free software tool for ultrafast, accurate, and scalable genome classification at all taxonomic levels.\n\nWe test ML-DSP by classifying 7,396 full mitochondrial genomes from the kingdom to genus levels, with 98% classification accuracy. Compared with the alignment-based classification tool MEGA7 (with sequences aligned with either MUSCLE, or CLUSTALW), ML-DSP has similar accuracy scores while being significantly faster on two small benchmark datasets (2,250 to 67,600 times faster for 41 mammalian mitochondrial genomes). ML-DSP also successfully scales to accurately classify a large dataset of 4,322 complete vertebrate mtDNA genomes, a task which MEGA7 with MUSCLE or CLUSTALW did not complete after several hours, and had to be terminated. ML-DSP also outperforms the alignment-free tool FFP (Feature Frequency Profiles) in terms of both accuracy and time, being three times faster for the vertebrate mtDNA genomes dataset.\n\nConclusionsWe provide empirical evidence that ML-DSP distinguishes complete genome sequences at all taxonomic levels. Ultrafast and accurate taxonomic classification of genomic sequences is predicted to be highly relevant in the classification of newly discovered organisms, in distinguishing genomic signatures, in identifying mechanistic determinants of genomic signatures, and in evaluating genome integrity.

genomics↗