Biology subjects

Coin, L. J. M.

Publications and source records attributed to Coin, L. J. M..

At least 19 recordsLinked to original sources

bioRxiv2026

Structural variation shapes regulatory and evolutionary diversity at the HLA locus

The human leukocyte antigen (HLA) region is among the most polymorphic loci in the human genome and plays a central role in immune function, yet the contribution of structural variation to its genetic and regulatory diversity remains poorly characterised. Using 460 phased, near-complete human genome assemblies from globally diverse populations, we systematically mapped structural variation and gene content across the HLA locus. We show that the HLA region contains substantially more structural variation than any other region of chromosome 6. At the HLA class II locus, all individuals could be assigned to one of 13 distinct HLA-DR-DQ structural haplotypes, whereas the HLA-A region comprised four major haplotypes, which we found to be interspersed among non-human primate lineages. These structural haplotypes exhibit marked differences in population frequency and show increasing allelic diversity over European prehistory. Integration of Iso-Seq and RNA-Seq data revealed that structural haplotypes are associated with differences in HLA gene expression, suggesting that structural variation directly influences immune gene regulation. Together, our results identify structural variation as a key and previously underappreciated contributor to HLA regulatory diversity, with broad functional and evolutionary implications for human immunity. Manuscript summaryStructural variation drives HLA haplotype diversity and gene expression differences across global human populations.

bioinformatics↗

bioRxiv2026

Interrogating the Escherichia coli epitranscriptome via CRISPR interference and Nanopore native RNA sequencing

Epitranscriptomics has recently gained significant momentum due to technological advances and translational applications, however, studies on bacterial RNA modifications remain limited. Bacterial RNA remains notoriously prone to degradation and methodologies to investigate the epitranscriptome are challenging. Prior research has shown RNA modifications modulate antimicrobial resistance, virulence and pathogenicity. This research employed CRISPR interference to knock down five known Escherichia coli rRNA modification genes (rlmF, rlmJ, rluD, rsmF and rsmG) in three E. coli strains. These isolates underwent growth curves, proteome analysis and native RNA sequencing CRISPRi adequately silenced the majority of RNA modification genes in E. coli (>80% reduction). Significant growth delays were associated with rlmF, rsmF and rsmG repression. Unique protein pathways corresponding with RNA modification loss were found for rlmJ (TreB, XylF), rluD (CysH, HycB, PutP, TrpB), rsmF (EvgA) and rsmG (OppC). Known rRNA modification sites for rluD ({Psi}) and rsmG (m7G) were detected from analysis of nanopore electrical signal, however, only a weak signal was apparent for m6A (rlmF, rlmJ) and m5C (rsmF) modifications. The inhibition of rRNA modifications resulted in mRNA modification changes including for genes ompC, cspC, dbhA, dbhB and secY. Our work provides an approach for unravelling the epitranscriptome of E. coli and gain insight into its functional role.

microbiology↗

bioRxiv2025

Characterizing the transcriptomic response to interferon and infection in European Domestic Ferret respiratory tissues using long-read RNA sequencing

The European domestic ferret (Mustela putorius furo) is considered the gold-standard small animal model for studying human and avian influenza virus infections. However, experimental characterization of the transcriptomic response to interferon (IFN) stimulation and/or influenza virus infection has been limited, particularly in defining the induction of interferon-stimulated genes (ISGs), with most being computationally predicted. In this study, we present a comprehensive transcriptome-wide assessment of the ferret transcriptome following IFN- treatment of a ferret lung (FRL) cell line, as well as in nasal turbinates from influenza A virus (IAV)-infected ferrets using long-read RNA sequencing. We have identified a panel of ferret genes orthologous to human ISGs that are upregulated both in response to IFN- stimulation in vitro and IAV infection in vivo. We have also identified novel interferon stimulated genes and transcripts. Furthermore, we observed elongation of the poly(A) tails of genes in the ribosome and Coronavirus Disease-19 pathways in response to IFN- treatment in vitro, suggesting a relationship between poly(A) elongation and the antiviral responses of the host. These results illuminate the dynamics of the transcriptional innate immune response of the domestic ferret and provide an important resource for better utilizing ferrets as a small animal model to study influenza virus infections.

immunology↗

bioRxiv2025

Customizable host and viral transcript enrichment using CRISPR-Cas9 long-read sequencing for isoform discovery and validation

Long-read RNA sequencing has been broadly utilized to examine the diversity of transcriptomes, understand differential expression and discover novel transcript isoforms. One of the major limitations of whole transcriptome sequencing is the difficulty in obtaining sufficient depth for low abundant transcripts. Methods which address this are either difficult to scale or customize: long- range PCR is customizable but difficult to scale beyond a few targets; probe hybridization panels are suited for scaling but require substantial investment to customize. In this study, we adopted RNA-guided CRISPR-Cas9 nuclease-based enrichment to target specific human and SARS-CoV-2 transcripts followed by long-read sequencing, utilizing minimal number of guide RNAs per target isoform. Our findings demonstrate that the CRISPR-Cas system is a highly effective method for customizable long-read sequencing of target transcripts while maintaining the accuracy of relative gene expression levels. The results highlight a valuable method for future research on transcript enrichment for isoform identification and low abundance transcript detection in infectious disease diagnosis.

genomics↗

bioRxiv2024

Characterisation of in vitro resistance selection against second-/last-line antibiotics in methicillin-resistant Staphylococcus aureus

SYNOPSISO_ST_ABSBackgroundC_ST_ABSThe increasing occurrence of MRSA clinical isolates harbouring reduced susceptibility to mainstay antibiotics has escalated the use of second and last line antibiotics. Hence, it is critical to evaluate the likelihood of MRSA developing clinical resistance to these antibiotics. ObjectivesOur study sought to identify the rate in which MRSA develop resistance to vancomycin, daptomycin and linezolid in vitro and further determine the mechanisms underpinning resistance. MethodsMRSA was exposed to increasing concentrations of vancomycin, daptomycin, and linezolid for 20 days, with eight replicates for each antibiotic conducted in parallel. The resulting day 20 (D20) isolates were subjected to antimicrobial susceptibility testing, whole genome sequencing, autolysis assays, and growth curves to determine bacterial fitness. ResultsExposure to vancomycin or linezolid for 20 days resulted in a subtle two-fold increase in the MIC, whereas daptomycin exposure yielded daptomycin-nonsusceptible isolates with up to 16-fold MIC increase. The MIC increase was accompanied by variable changes in relative fitness and reduced resistance to autolysis in some isolates. D20 isolates harboured mutations in genes commonly associated with resistance to the respective antibiotics (e.g. walK for vancomycin, mprF and rpoB for daptomycin, rplC for linezolid), along with several previously unreported variants. Introduction of key mutations to these identified genes in the parental strain via allelic exchange confirmed their role in the development of resistance. ConclusionsIn vitro selection against vancomycin, daptomycin, or linezolid resulted in the acquisition of mutations similar to those correlated with clinical resistance, including the associated phenotypic alterations.

microbiology↗

bioRxiv2024

Genome size estimation from long read overlaps

SummaryAccurate genome size estimation is an important component of genomic analyses, though existing tools are primarily optimised for short-read data. We present LRGE, a novel tool that uses read-to-read overlap information to estimate genome size in a reference-free manner. LRGE calculates per-read genome size estimates by analysing the expected number of overlaps for each read, considering read lengths and a minimum overlap threshold. The final size is taken as the median of these estimates, ensuring robustness to outliers such as reads with no overlaps. Additionally, LRGE provides an expected confidence range for the estimate. LRGE outperforms k-mer-based methods in both accuracy and computational efficiency and produces genome size estimates comparable to those from assembly-based approaches, like Raven, while using significantly less computational resources. We validate LRGE on a large, diverse bacterial dataset and confirm it generalises to eukaryotic datasets. Availability and implementationOur method, LRGE (Long Read-based Genome size Estimation from overlaps), is implemented in Rust and is available as a precompiled binary for most architectures, a Bioconda package, a prebuilt container image, and a crates.io package as a binary (lrge) or library (liblrge). The source code is available at https://github.com/mbhall88/lrge under an MIT license.

bioinformatics↗

bioRxiv2024

Using synthetic RNA to benchmark poly(A) length inference from direct RNA sequencing.

Polyadenylation is a dynamic process which is important in cellular physiology. Oxford Nanopore Technologies direct RNA-sequencing provides a strategy for sequencing the full-length RNA molecule and analysis of the transcriptome and epi-transcriptome. There are currently several tools available for poly(A) tail-length estimation, including well-established tools such as tailfindr and nanopolish, as well as two more recent deep learning models: Dorado and BoostNano. However, there has been limited benchmarking of the accuracy of these tools against gold-standard datasets. In this paper we evaluate four poly(A) estimation tools using synthetic RNA standards (Sequins), which have known poly(A) tail-lengths and provide a valuable approach to measuring the accuracy of poly(A) tail-length estimation. All four tools generate mean tail-length estimates which lie within 12% of the correct value. Overall, Dorado is recommended as the preferred approach due to its relatively fast run times, low coefficient of variation and ease of use with integration with base-calling.

bioinformatics↗

bioRxiv2024

A flexible framework for minimal biomarker signature discovery from clinical omics studies without library size normalisation

Application of transcriptomics, proteomics and metabolomics technologies to clinical cohorts has uncovered a variety of signatures for predicting disease. Many of these signatures require the full omics data for evaluation on unseen samples, either explicitly or implicitly through library size normalisation. Translation to low-cost point-of-care tests requires development of signatures which measure as few analytes as possible without relying on direct measurement of library size. To achieve this, we have developed a feature selection method (Forward Selection-Partial Least Squares) which generates minimal disease signatures from high-dimensional omics datasets with applicability to continuous, binary or multi-class outcomes. Through extensive benchmarking, we show that FS-PLS has comparable performance to commonly used signature discovery methods while delivering signatures which are an order of magnitude smaller. We show that FS-PLS can be used to select features predictive of library size, and that these features can be used to normalize unseen samples, meaning that the features in the complete model can be measured in isolation for making new predictions. By enabling discovery of small, high-performance signatures, FS-PLS addresses an important impediment for the further development of precision medical care.

bioinformatics↗

bioRxiv2024

Benchmarking reveals superiority of deep learning variant callers on bacterial nanopore sequence data

Variant calling is fundamental in bacterial genomics, underpinning the identification of disease transmission clusters, the construction of phylogenetic trees, and antimicrobial resistance prediction. This study presents a comprehensive benchmarking of SNP and indel variant calling accuracy across 14 diverse bacterial species using Oxford Nanopore Technologies (ONT) and Illumina sequencing. We generate gold standard reference genomes and project variations from closely-related strains onto them, creating biologically realistic distributions of SNPs and indels. Our results demonstrate that ONT variant calls from deep learning-based tools delivered higher SNP and indel accuracy than traditional methods and Illumina, with Clair3 providing the most accurate results overall. We investigate the causes of missed and false calls, highlighting the limitations inherent in short reads and discover that ONTs traditional limitations with homopolymer-induced indel errors are absent with high-accuracy basecalling models and deep learning-based variant calls. Furthermore, our findings on the impact of read depth on variant calling offer valuable insights for sequencing projects with limited resources, showing that 10x depth is sufficient to achieve variant calls that match or exceed Illumina. In conclusion, our research highlights the superior accuracy of deep learning tools in SNP and indel detection with ONT sequencing, challenging the primacy of short-read sequencing. The reduction of systematic errors and the ability to attain high accuracy at lower read depths enhance the viability of ONT for widespread use in clinical and public health bacterial genomics.

bioinformatics↗

bioRxiv2023

Influenza infected macrophages release viral ribonucleoproteins that shape the local host response

Airway epithelial cells and macrophages (M{Phi}) represent cellular targets of infection by influenza A virus (IAV). Epithelial cells support IAV infection and replication by enabling the generation and release of new viral particles (productive replication). In contrast, M{Phi} are susceptible to initial IAV infection but the release of infectious viral particles is inhibited through abortive replication. Despite the lack of infectious virions released from infected M{Phi}, we detected newly synthesised viral RNA and nucleoprotein (NP) in M{Phi} supernatants. We show that viral RNA is released from infected M{Phi} as viral ribonucleoprotein (vRNP) complexes which elicits potent inflammatory responses when exposed to uninfected cells. These vRNPs specifically induced IL-1{beta}, CXCL13, IL-32, CCL4 and CXCL10 in uninfected cells and were at least partially sensed through the RIG-I/MDA5 pathway. While M{Phi} represent a dead-end for IAV infection through abortive replication, the release of vRNPs shapes immune repsonses of uninfected cells in the local microenvironment.

microbiology↗

bioRxiv2023

Comparison of computational methods for host removal and classification of mycobacteria from clinical metagenomic data

BackgroundCulture-free real-time sequencing of clinical metagenomic samples promises both rapid pathogen detection and antimicrobial resistance profiling. However, this approach introduces the risk of patient DNA leakage. To mitigate this risk, we need near-comprehensive removal of human DNA sequence at the point of sequencing, typically involving use of resource-constrained devices. Existing benchmarks have largely focused on use of standardised databases and largely ignored the computational requirements of depletion pipelines as well as the impact of human genome diversity. ResultsWe benchmarked host removal pipelines on simulated Illumina and Nanopore metagenomic samples. We found that construction of a custom kraken database containing diverse human genomes results in the best balance of accuracy and computational resource usage. In addition, we benchmarked pipelines using kraken and minimap2 for taxonomic classification of Mycobacterium reads using standard and custom databases. With a database representative of the Mycobacterium genus, both tools obtained near-perfect precision and recall for classification of Mycobacterium tuberculosis. Computational efficiency of these custom databases was again superior to most standard approaches, allowing them to be executed on a laptop device. ConclusionsNanopore sequencing and a custom kraken human database with a diversity of genomes leads to superior host read removal from simulated metagenomic samples while being executable on a laptop. In addition, constructing a taxon-specific database provides excellent taxonomic read assignment while keeping runtime and memory low. We make all customised databases and pipelines freely available.

bioinformatics↗

bioRxiv2023

Drug resistance prediction for Mycobacterium tuberculosis with reference graphs

2.The dominant paradigm for analysing genetic variation relies on a central idea: all genomes in a species can be described as minor differences from a single reference genome. However, this approach can be problematic or inadequate for bacteria, where there can be significant sequence divergence within a species. Reference graphs are an emerging solution to the reference bias issues implicit in the "single-reference" model. Such a graph represents variation at multiple scales within a population - e.g., nucleotide- and locus-level. The genetic causes of drug resistance in bacteria have proven comparatively easy to decode compared with studies of human diseases. For example, it is possible to predict resistance to numerous anti-tuberculosis drugs by simply testing for the presence of a list of single nucleotide polymorphisms and insertion/deletions, commonly referred to as a catalogue. We developed DrPRG (Drug resistance Prediction with Reference Graphs) using the bacterial reference graph method Pandora. First, we outline the construction of a Mycobacterium tuberculosis drug resistance reference graph, a process that can be replicated for other species. The graph is built from a global dataset of isolates with varying drug susceptibility profiles, thus capturing common and rare resistance- and susceptible-associated haplotypes. We benchmark DrPRG against the existing graph-based tool Mykrobe and the haplotype-based approach of TBProfiler using 44,709 and 138 publicly available Illumina and Nanopore samples with associated phenotypes. We find DrPRG has significantly improved sensitivity and specificity for some drugs compared to these tools, with no significant decreases. It uses significantly less computational memory than both tools, and provides significantly faster runtimes, except when runtime is compared to Mykrobe on Nanopore data. We discover and discuss novel insights into resistance-conferring variation for M. tuberculosis - including deletion of genes katG and pncA - and suggest mutations that may warrant reclassification as associated with resistance. 3. Impact statementMycobacterium tuberculosis is the bacterium responsible for tuberculosis (TB). TB is one of the leading causes of death worldwide; before the coronavirus pandemic it was the leading cause of death from a single pathogen. Drug-resistant TB incidence has recently increased, making the detection of resistance even more vital. In this study, we develop a new software tool to predict drug resistance from whole-genome sequence data of the pathogen using new reference graph models to represent a reference genome. We evaluate it on M. tuberculosis against existing tools for resistance prediction and show improved performance. Using our method, we discover new resistance-associated variations and discuss reclassification of a selection of existing mutations. As such, this work contributes to TB drug resistance diagnostic efforts. In addition, the method could be applied to any bacterial species, so is of interest to anyone working on antimicrobial resistance. 4. Data summaryThe authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. The software method presented in this work, DrPRG, is freely available from GitHub under an MIT license at https://github.com/mbhall88/drprg. We used commit 9492f25 for all results via a Singularity[1] container from the URI docker://quay.io/mbhall88/drprg:9492f25. All code used to generate results for this study are available on GitHub at https://github.com/mbhall88/drprg-paper. All data used in this work are freely available from the SRA/ENA/DRA and a copy of the datasheet with all associated phenotype information can be downloaded from the archived repository at https://doi.org/10.5281/zenodo.7819984 or found in the previously mentioned GitHub repository. The Mycobacterium tuberculosis index used in this work is available to download through DrPRG via the command drprg index --download mtb@20230308 or from GitHub at https://github.com/mbhall88/drprg-index.

bioinformatics↗

bioRxiv2023

Uncovering strain- and age- dependent differences in innate immune response to SARS-CoV-2 infection in nasal epithelia using combined short and long-read scRNA-seq

Assessing the impact of SARS-CoV-2 variants on the host is crucial with continuous emergence of new variants. We employed single-cell sequencing to investigate host transcriptomic response to ancestral and Alpha-strain SARS-CoV-2 infections within air-liquid-interface human nasal epithelial cells from adults and adolescents. Strong innate immune responses were observed across lowly-infected and bystander cell-types, and heightened in Alpha-infection. Contrastingly, the innate immune response of highly-infected cells was like mock-control cells. Alpha highly-infected cells showed increased expression of protein refolding genes compared with ancestral-strain-infected adolescent cells. Oxidative phosphorylation- and translation-related genes were down-regulated in bystander cells versus infected and mock-control cells, suggesting that the down-regulation is protective and up-regulation supports viral activity. Infected adult cells revealed up-regulation of these pathways compared with infected adolescents, implying enhanced pro-viral states in infected adults. Overall, this highlights the complexity of cell-type-, age- and viral-strain-dependent host epithelial responses to SARS-CoV-2 and the value of air-liquid-interface cultures.

immunology↗

bioRxiv2021

Long-read RNA sequencing identifies polyadenylation elongation and differential transcript usage of host transcripts during SARS-CoV-2 in vitro infection

Better methods to interrogate host-pathogen interactions during Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infections are imperative to help understand and prevent this disease. Here we implemented RNA-sequencing (RNA-seq) combined with the Oxford Nanopore Technologies (ONT) long-reads to measure differential host gene expression, transcript polyadenylation and isoform usage within various epithelial cell lines permissive and non-permissive for SARS-CoV-2 infection. SARS-CoV-2-infected and mock-infected Vero (African green monkey kidney epithelial cells), Calu-3 (human lung adenocarcinoma epithelial cells), Caco-2 (human colorectal adenocarcinoma epithelial cells) and A549 (human lung carcinoma epithelial cells) were analysed over time (0, 2, 24, 48 hours). Differential polyadenylation was found to occur in both infected Calu-3 and Vero cells during a late time point (48 hpi), with Gene Ontology (GO) terms such as viral transcription and translation shown to be significantly enriched in Calu-3 data. Poly(A) tails showed increased lengths in the majority of the differentially polyadenylated transcripts in Calu-3 and Vero cell lines (up to ~136 nt in mean poly(A) length, padj = 0.029). Of these genes, ribosomal protein genes such as RPS4X and RPS6 also showed downregulation in expression levels, suggesting the importance of ribosomal protein genes during infection. Furthermore, differential transcript usage was identified in Caco-2, Calu-3 and Vero cells, including transcripts of genes such as GSDMB and KPNA2, which have previously been implicated in SARS-CoV-2 infections. Overall, these results highlight the potential role of differential polyadenylation and transcript usage in host immune response or viral manipulation of host mechanisms during infection, and therefore, showcase the value of long-read sequencing in identifying less-explored host responses to disease.

immunology↗

bioRxiv2020

Transcriptional and epi-transcriptional dynamics of SARS-CoV-2 during cellular infection

SARS-CoV-2 uses subgenomic (sg)RNA to produce viral proteins for replication and immune evasion. We applied long-read RNA and cDNA sequencing to in vitro human and primate infection models to study transcriptional dynamics. Transcription-regulating sequence (TRS)-dependent sgRNA was upregulated earlier in infection than TRS-independent sgRNA. An abundant class of TRS-independent sgRNA consisting of a portion of ORF1ab containing nsp1 joined to ORF10 and 3UTR was upregulated at 48 hours post infection in human cell lines. We identified double-junction sgRNA containing both TRS-dependent and independent junctions. We found multiple sites at which the SARS-CoV-2 genome is consistently more modified than sgRNA, and that sgRNA modifications are stable across transcript clusters, host cells and time since infection. Our work highlights the dynamic nature of the SARS-CoV-2 transcriptome during its replication cycle. Our results are available via an interactive web-app at http://coinlab.mdhs.unimelb.edu.au/.

microbiology↗

bioRxiv2020

Comparison of long read methods for sequencing and assembly of a plant genome

Sequencing technologies have advanced to the point where it is possible to generate high accuracy, haplotype resolved, chromosome scale assemblies. Several long read sequencing technologies are available on the market and a growing number of algorithms have been developed over the last years to assemble the reads generated by those technologies. When starting a new genome project, it is therefore challenging to select the most cost-effective sequencing technology as well as the most appropriate software for assembly and polishing. For this reason, it is important to benchmark different approaches applied to the same sample. Here, we report a comparison of three long read sequencing technologies applied to the de novo assembly of a plant genome, Macadamia jansenii. We have generated sequencing data using Pacific Biosciences (Sequel I), Oxford Nanopore Technologies (PromethION) and BGI (single-tube Long Fragment Read) technologies for the same sample. Several assemblers were benchmarked in the assembly of PacBio and Nanopore reads. Results obtained from combining long read technologies or short read and long read technologies are also presented. The assemblies were compared for contiguity, accuracy and completeness as well as sequencing costs and DNA material requirements. Overall, the three long read technologies produced highly contiguous and complete genome assemblies of Macadamia jansenii. At the time of sequencing, the cost associated with each method was significantly different but continuous improvements in technologies have resulted in greater accuracy, increased throughput and reduced costs. We propose updating this comparison regularly with reports on significant iterations of the sequencing technologies.

genomics↗

bioRxiv2020

Real-time resolution of short-read assembly graph using ONT long reads

A streaming assembly pipeline utilising real-time Oxford Nanopore Technology (ONT) sequencing data is important for saving sequencing resources and reducing time-to-result. A previous approach implemented in npScarf provided an efficient streaming algorithm for hybrid assembly but was relatively prone to mis-assemblies compared to other graph-based methods. Here we present npGraph, a streaming hybrid assembly tool using the assembly graph instead of the separated pre-assembly contigs. It is able to produce more complete genome assembly by resolving the path finding problem on the assembly graph using long reads as the traversing guide. Application to synthetic and real data from bacterial isolate genomes show improved accuracy while still maintaining a low computational cost. npGraph also provides a graphical user interface (GUI) which provides a real-time visualisation of the progress of assembly. The tool and source code is available at https://github.com/hsnguyen/assembly.

bioinformatics↗

bioRxiv2019

Analysis of polyclonal vector integration sites using Nanopore sequencing as a scalable, cost-effective platform

Vector integration site analysis can be important in the follow-up of patients who received gene-modified cells, but current platforms based on next-generation sequencing are expensive and relatively inaccessible. We analyzed polyclonal T cells transduced by a gammaretroviral vector, SFG.iCasp9.2A.{Delta}CD19, from a clinical trial. Following restriction enzyme digestion, the unknown flanking genomic sequences were amplified by inverse polymerase chain reaction (PCR) or cassette ligation PCR. Nanopore sequencing could identify thousands of unique integration sites within polyclonal samples, with cassette ligation PCR showing less bias. The assay is scalable and requires minimum capital, which together enable cost-effective and timely analysis.

genomics↗