Biology subjects

Brown, C.

Publications and source records attributed to Brown, C..

At least 73 records · Page 4Linked to original sources

bioRxiv2024

Resistance genes are distinct in protein-protein interaction networks according to drug class and gene mobility

With growing calls for increased surveillance of antibiotic resistance as an escalating global health threat, improved bioinformatic tools are needed for tracking antibiotic resistance genes (ARGs) across One Health domains. Most studies to date profile ARGs using sequence homology, but such approaches provide limited information about the broader context or function of the ARG in bacterial genomes. Here we introduce a new pipeline for identifying ARGs in genomic data that employs machine learning analysis of Protein-Protein Interaction Networks (PPINs) as a means to improve predictions of ARGs while also providing vital information about the context, such as gene mobility. A random forest model was trained to effectively differentiate between ARGs and nonARGs and was validated using the PPINs of ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter cloacae), which represent urgent threats to human health because they tend to be multi-antibiotic resistant. The pipeline exhibited robustness in discriminating ARGs from nonARGs, achieving an average area under the precision-recall curve of 88%. We further identified that the neighbors of ARGs, i.e., genes connected to ARGs by only one edge, were disproportionately associated with mobile genetic elements, which is consistent with the understanding that ARGs tend to be mobile compared to randomly sampled genes in the PPINs. This pipeline showcases the utility of PPINs in discerning distinctive characteristics of ARGs within a broader genomic context and in differentiating ARGs from nonARGs through network-based attributes and interaction patterns. The code for running the pipeline is publicly available at https://github.com/NazifaMoumi/PPI-ARG-ESKAPE

bioinformatics↗

bioRxiv2024

OGT prevents DNA demethylation and suppresses the expression of transposable elements in heterochromatin by restraining TET activity genome-wide

The O-GlcNAc transferase OGT interacts robustly with all three mammalian TET methylcytosine dioxygenases. We show here that deletion of the Ogt gene in mouse embryonic stem cells (mESC) results in a widespread increase in the TET product 5-hydroxymethylcytosine (5hmC) in both euchromatic and heterochromatic compartments, with concomitant reduction of the TET substrate 5-methylcytosine (5mC) at the same genomic regions. mESC engineered to abolish the TET1-OGT interaction likewise displayed a genome-wide decrease of 5mC. DNA hypomethylation in OGT-deficient cells was accompanied by de-repression of transposable elements (TEs) predominantly located in heterochromatin, and this increase in TE expression was sometimes accompanied by increased cis-expression of genes and exons located 3 of the expressed TE. Thus, the TET-OGT interaction prevents DNA demethylation and TE expression in heterochromatin by restraining TET activity genome-wide. We suggest that OGT protects the genome against DNA hypomethylation and impaired heterochromatin integrity, preventing the aberrant increase in TE expression observed in cancer, autoimmune-inflammatory diseases, cellular senescence and ageing.

molecular biology↗

bioRxiv2024

Genomic epidemiology of Candida auris introduction and outbreaks in the United Kingdom

BackgroundCandida auris is a globally emerging fungal pathogen that causes nosocomial invasive infections, particularly in intensive care units (ICU). Four prominent genetic clades originating from distinct geographic regions have been identified: South Asia (Clade I), East Asia (Clade II), Africa (Clade III) and South America (Clade IV) with each clade presenting differing antifungal resistance profiles. We aimed to elucidate the epidemiology of this infection in the United Kingdom (UK) 2014 - 2019 using genomic approaches. MethodsGenome sequences from 24 isolates from six patients in four London hospitals were combined with genome sequences from 183 additional outbreak isolates from nine UK hospitals. These data were used to assess the numbers and timings of C. auris lineage introductions into the UK and to infer subsequent patterns of inter- and intra-hospital transmission. FindingsWe found evidence for at least three introductions of C. auris into the UK, one from Clade I and one from Clade III. The evolutionary rate of C. auris Clade I and Clade III were estimated at 2.764 x 10-4 and 3.186 x 10-4 substitutions per polymorphic site per year, respectively, with entry into the UK as 2013 and 2016 for Clades I and III respectively. We observed temporal and geographical evidence for multiple transmission events between hospitals and identified local within-hospital patient-to-patient transmission events. InterpretationThese data confirm that C. auris is a newly emerged infection in the UK with at least three original introductions into this country. Our analysis shows that hospital outbreaks are linked and confirms that transmission amongst patients has occurred, explaining local hospital outbreaks. Our findings argue that enhanced surveillance of C. auris infection within the UK is necessary in order to protect healthcare and to curtail expansion of this emerging infection. FundingThis work was supported by a Wellcome Trust Institutional Strategic Support Fund Springboard Fellowship, and by the Medical Research Council (MR/R015600/1), the Natural Environment Research Council (NE/P001165/1) and the Wellcome Trust (219551/Z/19/Z). Research in contextSince its first description in 2009, Candida auris has spread across the globe. For this study, authors considered all publications describing whole genome sequences of isolates within the UK up until 2020 to assess the genomic epidemiology of this fungal pathogen. This study adds value to the current understanding of C. auris epidemiology by undertaking the first analysis to assess transmission between and within hospitals using genomic data. We also provide the first in-depth temporal analysis to estimate likely times of introduction into the UK. These results have clinical implications, encouraging hospitals to identify yeast upon admission and to assess multiple isolates from single patients, which may harbour much diversity in terms of genetics and drug resistance.

genomics↗

bioRxiv2023

Exogenous mitochondrial transfer increases energy expenditure and attenuates adiposity gains in mice with diet-induced obesity

Obesity is associated with chronic multi-system bioenergetic stress that may be improved by increasing the number of healthy mitochondria available across organ systems. However, treatments capable of increasing mitochondrial content are generally limited to endurance exercise training paradigms, which are not always sustainable long-term, let alone feasible for many patients with obesity. Recent studies have shown that local transfer of exogenous mitochondria from healthy donor tissues can improve bioenergetic outcomes and alleviate the effects of tissue injury in recipients with organ specific disease. Thus, the aim of this project was to determine the feasibility of systemic mitochondrial transfer for improving energy balance regulation in the setting of diet-induced obesity. We found that transplantation of mitochondria from lean mice into mice with diet-induced obesity attenuated adiposity gains by increasing energy expenditure and promoting the mobilization and oxidation of lipids. Additionally, mice that received exogenous mitochondria demonstrated improved glucose uptake, greater insulin responsiveness, and complete reversal of hepatic steatosis. These changes were, in part, driven by adaptations occurring in white adipose tissue. Together, these findings are proof-of-principle that mitochondrial transplantation is an effective therapeutic strategy for limiting the deleterious metabolic effects of diet-induced obesity in mice.

physiology↗

bioRxiv2023

Revisiting Reddy: A DLBCL Do-over

The 2017 study by Reddy et al described the comprehensive characterization of somatic drivers of diffuse large B-cell lymphoma using whole exome sequencing.1 After additional large studies relying on exome or whole genome sequencing were published, several oddities unique to the Reddy results have emerged. Seeking to explain the discrepancies, we reanalyzed their data using established open-source pipelines. This revealed thousands of mutations that could not be independently reproduced by these pipelines and a larger set of high-quality mutations that were not reported by Reddy. This caused an artificial under-representation of the mutation prevalence in many genes including clinically relevant hot spots affecting EZH2 and CD79B. More generally, the study had an under-representation of mutations in DLBCL genes that disproportionately affected genes known to have the highest mutation rates. The missing variants and the spurious variants can be attributed to distinct problems with the analytical approaches employed in that study. Our analysis also identified strong associations between mutations and patient outcome including TP53, KMT2D and PIM1, which were not found in the Reddy study. Overall, we demonstrate that this combination of errors influenced many of the central novel findings from their study rendering their results largely non-replicable. The full results of our analyses are included as supplemental items as a resource for other researchers with an interest in the genetics of B-cell lymphomas.

genomics↗

bioRxiv2023

Acetylcholine esterase of Drosophila melanogaster: a laboratory model to explore applications of insecticide susceptibility gene drives

BACKGROUNDOne of the proposed applications of gene drives has been to revert pesticide resistant mutations back to the ancestral susceptible state. Insecticides that have become ineffective because of the rise of resistance could have reinvigorated utility and be used to suppress pest populations again, perhaps at lower application doses. RESULTSWe have created a laboratory model for susceptibility gene drives that replaces field-selected resistant variants of the acetylcholine esterase (Ace) locus of Drosophila melanogaster with ancestral susceptible variants. We constructed a CRISPR/Cas9 homing drive and found that homing occurred in many genetic backgrounds with varying efficiencies. While the drive itself could not be homozygosed, it converted resistant alleles into susceptible ones and produced recessive lethal alleles that could suppress populations. Our studies provided evidence for two distinct classes of Gene Drive Resistance (GDR): rather than being mediated by the conventional Non-Homologous End-joining (NHEJ) pathway, one seemed to involve short homologous repair and the other was defined by genetic background. Additionally, we used simulations to explore a distinct application of susceptibility drives; the use of chemicals to prevent the spread of synthetic gene drives into protected areas. CONCLUSIONSInsecticide susceptibility gene drives could be useful tools to control pest insects however problems associated with particularities of the target loci and GDR will need to be overcome for them to be effective. Furthermore, realistic patterns of pest dispersal and high insecticide exposure rates would be required if susceptibility were to be useful as a safety-switch to prevent the unwanted spread of gene drives.

genetics↗

bioRxiv2023

External Validation of Machine Learning and EEG for Continuous Pain Intensity Prediction in Healthy Individuals

Previous research has predicted subjective pain intensity from electroencephalographic (EEG) data using machine learning (ML) models. However, there is a paucity of externally validated ML models for pain assessment, particularly for continuous pain prediction (e.g., decoding pain ratings on a 101-point scale). We aimed to conduct the first external validation paradigm for ML regression models for pain intensity prediction from EEG data. Ninety-one subjects were recruited across three samples. Sample one (n = 40) was used for model development, sample two (n = 51) was used as a cross-subject external validation set, whilst sample three (n = 25) was used as a within-subjects temporal external validation set. Pneumatic pressure stimuli were delivered to the left-hand index fingernail bed at 10 graded intensity levels. Single-trial time-frequency features of peri-stimulus EEG were used to train a Random Forest (RF) model and long short-term memory (LSTM) network to predict pain intensity responses. Results demonstrated that both the RF model and LSTM network predicted pain intensity significantly more accurately than a random prediction model, with the mean absolute error (MAE) of the RF (best performing model) at 19.59, 21.29, and 18.90 for internal validation, cross-subject external validation, and within-subject external validation, respectively. However, neither model was able to predict pain intensity better than a baseline dummy model, which predicted the mean behavioural rating of the training set and did not have access to neural data. Moreover, in a replication of our recent work, we developed a RF model for the classification of low and high-pain trials, which demonstrated internal and external validation accuracies up to 64% and 58%, respectively. Taken together, our results suggest that using ML and EEG to predict continuous pain ratings is not currently feasible. However, classification models demonstrate some potential, consistently outperforming chance across validation samples. Further improvements such as composite measures are required to elevate ML performance to a clinically meaningful level.

neuroscience↗

bioRxiv2023

Utilizing gene co-expression networks with the rat kidney TXG-MAPr tool to enhance safety assessment, biomarker identification and human translation

Toxicogenomic data represent a valuable source of biological information at molecular and cellular level to understand unanticipated organ toxicities. Weighted gene co-expression networks analysis can reduce the complexity of gene-level transcriptomic data to a set of biological response-networks useful for providing insights into mechanisms of drug-induced adverse outcomes. In this study, we have built co-regulated gene networks (modules) from the TG-GATEs and DrugMatrix rat kidney datasets consisting of time- and dose-response data for 180 compounds, including nephrotoxicants. Data from the 347 modules were incorporated into the rat kidney TXG-MAPr web tool, a user-friendly interface that enables visualization and analysis of module perturbations, quantified by a module eigengene score (EGS) for each treatment condition. Several modules annotated for cellular stress, renal injury and inflammation were statistically associated with concurrent renal pathologies, including modules that contain both well-known and novel renal biomarker genes. In addition, many rat kidney modules contain well annotated, robust gene networks that are preserved across transcriptome datasets, suggesting that these biological networks translate to other (drug-induced) kidney injury cases. Moreover, preservation analysis of human kidney transcriptomic data provided a quantitative metric to assess the likelihood that rat kidney modules, and the associated biological interpretation, translate from non-clinical species to human. In conclusion, the rat kidney TXG-MAPr enables uploading and analysis of kidney gene expression data in the context of rat kidney co-expression networks, which could identify possible safety liabilities and/or mechanisms that can lead to adversity for chemical or drug candidates. Translational StatementGene co-expression networks (modules) were generated using rat kidney toxicogenomics data, which reduced data complexity and retained quantitative mechanisms to enhance safety assessment. Several stress, injury and inflammation modules were statistically associated with renal pathologies, useful for biomarker identification. Moreover, many rat kidney modules contained well-annotated, robust gene-networks that were preserved in human patients transcriptome data after renal transplantation, suggesting that these biological networks translate to human relevant kidney-injury. So, the rat kidney TXG-MAPr tool enables transcriptome analysis in the context of kidney co-expression networks, which could identify chemical-induced safety liabilities and/or mechanisms leading to adversity, relevant for human risk-assessment.

pharmacology and toxicology↗

bioRxiv2023

Reconciling the spectrum of RORγt+ antigen-presenting cells

Three recent contemporaneously published papers1-3 showed that antigen-presenting cells (APC) expressing the nuclear receptor ROR{gamma}t instruct peripheral regulatory T (pTreg) cell differentiation and establish tolerance to the gut microbiota. These studies identified a spectrum of ROR{gamma}t+ APCs, distinct from dendritic cells, that included type 3 innate lymphoid cells (ILC3s) as well as novel APC types. Given the discordant conclusions as to the nature of the pTreg-inducing APC and the divergent nomenclature used in these three studies, there is a clear need to reconcile these analyses and the identity of the cell types described. Our reanalysis of the single cell RNA-seq data from these studies revealed the presence of four distinct subsets of non-ILC3 ROR{gamma}t+ APCs, present in all three datasets reported, and confirmed expression of Itgb8, the critical factor for ROR{gamma}t+ APC mediated pTreg induction, in cells synonymous with the non-Aire expressing Thetis cell subset, TC IV.

immunology↗

bioRxiv2023

A novel stabilization mechanism accommodating genome length variation in evolutionarily related viral capsids

Tailed bacteriophages are one of the most numerous and diverse group of viruses. They store their genome at quasi-crystalline densities in capsids built from multiple copies of proteins adopting the HK97-fold. The high density of the genome exerts an internal pressure, requiring a maturation process that reinforces their capsids. However, it is unclear how capsid stabilization strategies have adapted to accommodate the evolution of larger genomes in this virus group. Here we characterized a novel capsid reinforcement mechanism in two evolutionary-related actinobacteriophages that modifies the length of a stabilization protein to accommodate a larger genome while maintaining the same capsid size. We used cryo-EM to reveal that capsids contained split hexamers of HK97-fold proteins with a stabilization protein in the chasm. The observation of split hexamers in mature capsids was unprecedented, so we rationalized this result mathematically, discovering that icosahedral capsids can be formed by all split or skewed hexamers as long as their T-number is not a multiple of three. Our results suggest that analogous stabilization mechanisms can be present in other icosahedral capsids, and they provide a strategy for engineering capsids accommodating larger DNA cargoes as gene delivery systems. Significance StatementHow capsids are stabilized and change size is an important part of understanding how to design protein containers and understand viral evolution. We describe a novel capsid stability mechanism that allows the capsid to package a larger genome without changing the capsid architecture and have predicted other capsids using this mechanism. Beyond the evolutionary implications, our findings provide a mechanism to increase the amount of DNA packaged in a capsid, offering a solution to engineer gene delivery systems with larger DNA content, a pressing challenge in gene therapy.

molecular biology↗

bioRxiv2023

Heterozygous Kmt2d loss diminishes enhancers to render medulloblastoma cells vulnerable to combinatory inhibition of lysine demethylation and oxidative phosphorylation

The histone H3 lysine 4 (H3K4) methyltransferase KMT2D (also called MLL4) is one of the most frequently mutated epigenetic modifiers in medulloblastoma (MB) and many other types of cancer. Notably, heterozygous loss of KMT2D is prevalent in MB and other cancer types. However, what role heterozygous KMT2D loss plays in tumorigenesis has not been well characterized. Here, we show that heterozygous Kmt2d loss highly promotes MB driven by heterozygous loss of the MB suppressor gene Ptch in mice. Heterozygous Kmt2d loss upregulated tumor-promoting programs, including oxidative phosphorylation and G-protein-coupled receptor signaling, in Ptch+/--driven MB genesis. Mechanistically, both downregulation of the transcription-repressive tumor suppressor gene NCOR2 by heterozygous Kmt2d loss and upregulation of the oncogene MycN by heterozygous Ptch loss increased the expression of tumor-promoting genes. Moreover, heterozygous Kmt2d loss extensively diminished enhancer signals (e.g., H3K27ac) and H3K4me3 signature, including those for tumor suppressor genes (e.g., Ncor2). Combinatory pharmacological inhibition of oxidative phosphorylation and the enhancer-decommissioning H3K4 demethylase LSD1 drastically reduced tumorigenicity of MB cells bearing heterozygous Kmt2d loss. These findings reveal the mechanistic basis underlying the MB-promoting effect of heterozygous KMT2D loss, provide a rationale for a therapeutic strategy for treatment of KMT2D-deficient MB, and have mechanistic implications for the molecular pathogenesis of other types of cancer bearing heterozygous KMT2D loss.

cancer biology↗

bioRxiv2023

Non-invasive measurement of intra-tumoral fluid dynamics with localized convolutional function regression

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a routine method to non-invasively quantify perfusion dynamics in tissues. The standard practice for analyzing DCE-MRI data is to fit an ordinary differential equation to each voxel. Recent advances in data science provide an opportunity to move beyond existing methods to obtain more accurate measurements of fluid properties. Here, we developed a localized convolutional function regression that enables simultaneous measurement of interstitial fluid velocity, diffusion, and perfusion in 3D. We validated the method computationally and experimentally, demonstrating accurate measurement of fluid dynamics in situ and in vivo. Applying the method to human MRIs, we observed tissue-specific differences in fluid dynamics, with an increased fluid velocity in breast cancer as compared to brain cancer. Overall, our method represents an improved strategy for studying interstitial flows and interstitial transport in tumors and patients. We expect that it will contribute to the better understanding of cancer progression and therapeutic response. One-Sentence SummaryA physics-informed computational method enables accurate and efficient measurement of fluid dynamics in individual patient tumors and demonstrates differences between tissues.

bioengineering↗

bioRxiv2023

Meta-analysis of macrophage nanoparticle targeting across blood and solid tumors using an eLDA Topic modeling Machine Learning approach

The role of macrophages in regulating the tumor microenvironment has spurned the exponential generation of nanoparticle targeting technologies. With the large amount of literature and the speed at which it is generated it is difficult to remain current with the most up-to-date literature. In this study we performed a topic modeling analysis of the most common usages of nanoparticle targeting of macrophages in solid tumors. The data spans 20 years of literature, providing an extensive meta-analysis of the nanoparticle strategies. Our topic model found 6 distinct topics: Immune and TAMs, Nanoparticles, Imaging, Gene Delivery and Exosomes, Vaccines, and Multi-modal Therapies. We also found distinct nanoparticle usage, tumor types, and therapeutic trends across these topics. Moreover, we established that the topic model could be used to assign new papers into the existing topics, thereby creating a Living Review. This type of meta-analysis provides a useful assessment tool for aggregating data about a large field.

bioengineering↗

bioRxiv2023

Direct determination of oligomeric organization of integral membrane proteins and lipids from intact customizable bilayer

Hierarchical organization of integral membrane proteins (IMP) and lipids at the membrane is essential for regulating myriad downstream signaling. A quantitative understanding of these processes requires both detections of oligomeric organization of IMPs and lipids directly from intact membranes and determination of key membrane components/properties that regulate them. Addressing this, we have developed a platform that enables native mass spectrometry (nMS) analysis of IMP-lipid complexes directly from intact and customizable lipid membranes. Both the lipid composition and membrane properties (such as curvature, tension, fluidity) of these bilayers can be precisely customized to a target membrane. Subsequent direct nMS analysis of these intact proteo-lipid vesicles can yield the oligomeric states of the embedded IMPs, identify bound lipids, and determine the membrane properties that can regulate the observed IMP-lipid organization. Applying this, we show how lipid binding regulates neurotransmitter release and how membrane composition regulates the functional oligomeric state of a transporter.

biophysics↗

bioRxiv2023

pyCapsid: Identifying dominant dynamics and quasi-rigid mechanical units in protein shells

Structured AbstractO_ST_ABSSummaryC_ST_ABSpyCapsid is a Python package developed to facilitate the characterization of the dynamics and mechanical units of protein shells and other protein complexes. The package was developed in response to the rapid increase of high-resolution structures, particularly capsids of viruses, requiring multiscale biophysical analyses. Given a protein shell, pyCapsid generates the collective vibrations of its amino-acid residues, identifies quasi-rigid mechanical regions, and maps the results back to the input proteins for interpretation. pyCapsid summarizes the main results in a report that includes publication-quality figures. Availability and ImplementationpyCapsids source code is available under MIT License on GitHub (https://github.com/luquelab/pycapsid). It is compatible with Python 3.8-3.10 and has been deployed in two leading Python package-management systems, PIP (https://pypi.org/project/pyCapsid/) and Conda (https://anaconda.org/luque_lab/pycapsid). Installation instructions and tutorials are available in the GitHub Page-style online documentation (https://luquelab.github.io/pyCapsid). Additionally, a cloud-based implementation of pyCapsid is available as a Google Colab notebook (https://colab.research.google.com/github/luquelab/pyCapsid/blob/main/notebooks/pyCapsid_colab_notebook.ipynb). pyCapsid Colab does not require installation and generates the same report and outputs as the installable version. Users can post issues regarding pyCapsid in the GitHub repository (https://github.com/luquelab/pyCapsid/issues).

bioinformatics↗

bioRxiv2023

Determination of oligomeric organization of membrane proteins from native membranes at nanoscale-spatial and single-molecule resolution

The oligomeric organization of membrane proteins in native cell membranes is a critical regulator of their function. High-resolution quantitative measurements of oligomeric assemblies and how they change under different conditions are indispensable to the understanding of membrane protein biology. We report a single-molecule imaging technique (Native-nanoBleach) to determine the oligomeric distribution of membrane proteins directly from native membranes at an effective spatial resolution of [~]10 nm. We achieved this by capturing target membrane proteins in "native nanodiscs" with their proximal native membrane environment using amphipathic copolymers. We established this method using structurally and functionally diverse membrane proteins with well-established stoichiometries. We then applied Native-nanoBleach to quantify the oligomerization status of a receptor tyrosine kinase (TrkA) and a small GTPase (KRas) under conditions of growth-factor binding or oncogenic mutations, respectively. Native-nanoBleach provides a sensitive, single-molecule platform to quantify membrane protein oligomeric distributions in native membranes at an unprecedented spatial resolution.

biophysics↗

bioRxiv2023

Different Coexisting Mpox Lineages Were Continuously Circulating in Humans Prior to 2022

The origin and hazardous potential of human mpox is obscured by a lack of genomic data between the 2018, when exportations from Nigeria were recorded, and 2022 when the global outbreak started. Here, 18 genomes from patients across southern Nigeria in 2019/20 reveal multiple lineages of Monkeypox virus have achieved sustained human-to-human transmission, co-existing in humans for several years and accumulating mutations consistent with APOBEC3 activity suggesting the virus in humans is now segregated from its natural reservoir. Remarkably, three genomes have disruptions in the A46R gene, which contributes to innate immune modulation. The data demonstrates that the A.2 lineage, multiply exported to North America since 2021 independently of the global outbreak, has persisted in Nigeria for more than two years prior to its latest exportation. One-Sentence SummaryMpox is now a human diseae evolving in humans with multiple variants taking separate paths towards adaptation, some analogous to those of Variola

microbiology↗

bioRxiv2022

Sequencing by avidity enables high accuracy with low reagent consumption

We present avidity sequencing - a novel sequencing chemistry that separately optimizes the process of stepping along a DNA template and the process of identifying each nucleotide within the template. Nucleotide identification uses multivalent nucleotide ligands on dye-labeled cores to form polymerase-polymer nucleotide complexes bound to clonal copies of DNA targets. These polymer-nucleotide substrates, termed avidites, decrease the required concentration of reporting nucleotides from micromolar to nanomolar, and yield negligible dissociation rates. We demonstrate the use of avidites as a key component of a sequencing technology that surpasses Q40 accuracy and enables a diversity of applications that include single cell RNA-seq and whole human genome sequencing. We also show the advantages of this technology in sequencing through long homopolymers.

genomics↗