Biology subjects

Brown, J. B.

Publications and source records attributed to Brown, J. B..

18 recordsLinked to original sources

bioRxiv2026

Activity-based anorexia enhances glutamatergic synaptic transmission and neuronal excitability within the nucleus accumbens of female mice

Anorexia nervosa is a severe psychiatric disorder characterized by persistent food restriction and often excessive physical activity, implicating dysfunction in neural circuits governing motivation, reward, and behavioral persistence. The nucleus accumbens (NAc) is a central component of these circuits, yet synaptic and cellular adaptations within this region during anorexia-like states remain poorly defined. Using the activity-based anorexia (ABA) paradigm in adult female mice, we examined glutamatergic signaling and intrinsic neuronal properties in the NAc shell. ABA exposure produced rapid weight loss, reduced food intake, and progressively increased running-wheel activity. Biochemical analyses of NAc shell tissue revealed elevated membrane-associated GluA2 AMPA receptor protein. Consistent with this finding, whole-cell patch-clamp recordings from medium spiny neurons showed increased amplitude of spontaneous excitatory postsynaptic currents. ABA also enhanced intrinsic neuronal excitability, reflected by greater firing in response to depolarizing current injections. Together, these convergent biochemical and electrophysiological results demonstrate that ABA induces coordinated postsynaptic strengthening and increased intrinsic excitability in NAc shell medium spiny neurons. These adaptations suggest a sustained increase in accumbal output that may bias motivational circuit function and contribute to excessive activity and suppressed feeding during anorexia-like conditions, paralleling glutamatergic plasticity observed in other compulsive disorders, including substance use disorder.

neuroscience↗

bioRxiv2026

CAZyme domain architectures suggest fine-scale functional differentiation among anaerobic fungi and bacteria during lignocellulose conversion to volatile fatty acids

Anaerobic fermentation with microbial communities (microbiomes) is an emerging platform for conversion of lignocellulosic biomass to biofuels and bioproducts. The process relies on diverse anaerobic microbes that interact to deconstruct and convert lignocellulosic biomass into a range of products, such as volatile fatty acids (VFAs), which can be achieved by arresting methanogenesis during fermentation. However, defining the distinct functional roles played by various fungi and bacteria during anaerobic biodegradation remains poorly understood. Here, we performed parallel enrichment experiments from cow faeces, goat faeces, and anaerobic digester sludge, selecting for fungal or bacterial dominated communities that convert sorghum biomass into VFAs. Subsequently we reconstructed metabolic networks across these enrichments based on recovered bacterial metagenome-assembled genomes (MAGs) and fungal isolate genomes and profiled their metabolic activity using metatranscriptomics to identify potential functional niches. Our findings implicate diverse bacteria affiliated with the Bacteroidales and Lachnospiraceae in the direct conversion of lignocellulosic biomass to propionate and butyrate, respectively, whereas Neocallimastix-dominated fungal enrichments converted lignocellulose to lactate, acetate and formate. Analysis of carbohydrate-active enzymes (CAZymes) revealed fine-scale differences between microbes that expressed unique multi-functional enzymes linking two or more CAZymes together with distinct carbohydrate binding motifs, implicating lignocellulose structure as a key driver of selection and niche differentiation. Most of these multi-functional enzymes localized complementary degradation functions together, likely conferring synergistic degradation effects within and between microbiome members. We anticipate that these findings will help inform efforts to develop synthetic microbiomes with tailored functionality for low-cost conversion of lignocellulosic biomass to fuels and bio-based chemicals.

microbiology↗

bioRxiv2025

The Response Regulator BqsR/CarR Controls Ferrous Iron (Fe2+) Acquisition in Pseudomonas aeruginosa

Pseudomonas aeruginosa is a ubiquitous, Gram-negative bacterium that forms biofilms and is responsible for antibiotic-resistant hospital-acquired infections in humans. The P. aeruginosa BqsRS two-component system regulates biofilm formation and dispersal by sensing extracytoplasmic Fe2+, but the mechanistic details of this process are poorly understood. In this work, we report the crystal and solution structures of the PaBqsR response regulator receiver domain, comprising a ({beta})5 response regulator assembly, and the DNA-binding domain, comprising a helix-turn-helix motif. Consistent with its cognate stimulus being Fe2+, we show that PaBqsR binds directly to the promoter region of the feo operon that encodes the bacterial Fe2+ transport system FeoABC. Corroborating these in vitro results, transcriptional studies show that PaBqsR is a global regulator controlling many important genes in PAO1, including the feo operon. Intriguingly, promoter-based assays reveal that PaBqsR is a dynamic regulator that responds to bioavailable Fe2+, likely through the ability of PaBqsR to bind Fe2+ directly via a His-rich motif, independent of the PaBqsS membrane His kinase. This mode of regulation is unprecedented among OmpR-like response regulators but represents an important level of control over Fe2+ acquisition in P. aeruginosa that could be an attractive therapeutic target to treat hospital-acquired infections.

biochemistry↗

bioRxiv2025

A scalable framework for current steering at single-neuron resolution

Electrical stimulation at cellular resolution to restore the function of neural circuits is limited by the density of available electrode arrays. Although current steering with multi-electrode stimulation can be used to target cells between electrodes, it has not been proven for systematically targeting individual cells. We develop a framework for cellular-resolution current steering, leveraging the biophysics of electrically-evoked spike generation, and test its efficacy in isolated macaque and human retina. Currents were passed through three electrodes simultaneously using large-scale high-density microelectrode arrays, directly evoking single spikes in retinal ganglion cells. The currents combined either linearly or nonlinearly to drive spiking, depending on the geometry of the electrodes relative to the cell. These findings were captured by a biophysical model and by a simpler parametric model in which spikes can initiate at several sites on the cell membrane, and were leveraged to efficiently identify multi-electrode stimulation patterns that optimized cellular selectivity.

bioengineering↗

bioRxiv2025

Adaptive Example Selection: Prototype-Based Explainability for Interpretable Mitosis Detection

Understanding the decision-making process of black-box neural network classifiers is crucial for their adoption in medical applications, including histopathology and cancer diagnostics. An approach of increasing interest is to clarify how the decisions of neural networks compare to, and perform parallel to, those of highly-trained and knowledgeable clinicians and other medical professionals within their prototypical classes of interest. Motivated by this, we introduce Adaptive Example Selection (AES), a prototype-based explainable AI (XAI) framework that facilitates the interpretability of deep learning models for mitosis detection. AES works by selecting and presenting a small set of real-world mitotic images most informative to a given classification, allowing pathologists to visually assess and understand the neural networks decision by comparing test cases with similar previously annotated examples. AES achieves this by expanding the neural networks confidence/belief function and fitting it to a radial basis function (RBF) approximator, an approach we term Decision Boundary-based Analysis (DBA). This method makes the decision boundary more transparent, offering robust visual insights into the models decisions, and thereby equipping clinicians with the information needed to effectively utilize AI-driven diagnostics. Additionally, AES includes customizable user controls, allowing clinicians to tailor decision thresholds and select prototype examples to better align with their specific diagnostic needs. This flexibility empowers users to engage with the AI model more directly and meaningfully, increasing its practical relevance in clinical settings.

pathology↗

bioRxiv2022

Molecular and functional characterization of the Drosophila melanogaster conserved smORFome

Short polypeptides encoded by small open reading frames (smORFs) are ubiquitously found in eukaryotic genomes and are important regulators of physiology, development, and mitochondrial processes. Here, we focus on a subset of 298 smORFs that are evolutionarily conserved between Drosophila melanogaster and humans. Many of these smORFs are conserved broadly in the bilaterian lineage, with [~]182 conserved in plants. Within these conserved smORFs, we observed remarkably heterogenous spatial and temporal expression patterns - indicating wide-spread tissue-specific and stage-specific mitochondrial architectures. In addition, an analysis of annotated functional domains revealed a predicted enrichment of smORF polypeptides localizing to mitochondria. We conducted an embryonic ribosome profiling experiment finding support for translation of 137 of these smORFs during embryogenesis. We further embarked on functional characterization using CRISPR knockout/activation, RNAi knockdown, and cDNA overexpression, revealing diverse phenotypes. This study underscores the importance of identifying smORF function in disease and phenotypic diversity.

genomics↗

bioRxiv2022

The structure of Vibrio cholerae FeoC reveals conservation of the helix-turn-helix motif but not the cluster-binding domain

Most pathogenic bacteria require ferrous iron (Fe2+) in order to sustain infection within hosts. The ferrous iron transport (Feo) system is the most highly-conserved prokaryotic transporter of Fe2+, but its mechanism remains to be fully characterized. Most Feo systems are composed of two proteins: FeoA, a soluble SH3-like accessory protein and FeoB, a membrane protein that translocates Fe2+ across a lipid bilayer. Some bacterial feo operons encode FeoC, a third soluble, winged-helix protein that remains enigmatic in function. We previously demonstrated that select FeoC proteins bind O2-sensitive [4Fe-4S] clusters via Cys residues, leading to the proposal that some FeoCs could sense O2 to regulate Fe2+ transport. However, not all FeoCs conserve these Cys residues, and FeoC from the causative agent of cholera (Vibrio cholerae) notably lacks any Cys residues, precluding cluster binding. In this work, we determined the NMR structure of VcFeoC, which is monomeric and conserves the helix-turn-helix domain seen in other FeoCs. In contrast, however, the structure of VcFeoC reveals a truncated winged {beta}-sheet in which the cluster-binding domain is notably absent. To test the interactions of VcFeoC with VcFeoB, we used NMR to demonstrate that these proteins interact in a 1:1 stoichiometry with a Kd of ca. 25 M. Finally, using homology modeling, we predicted the structure of VcNFeoB and used docking to identify an interaction site with VcFeoC, which is confirmed by NMR spectroscopy. These findings provide the first atomic-level structure of VcFeoC and contribute to a better understanding of its role vis-a-vis FeoB.

biochemistry↗

bioRxiv2021

Biochemical and structural characterization of the fused Bacteroides fragilis NFeoAB domain reveals a role for FeoA

Iron is an essential element for nearly all organisms, and under anoxic and/or reducing conditions, Fe2+ is the dominant form of iron available to bacteria. The ferrous iron transport (Feo) system has been identified as the primary prokaryotic Fe2+ import machinery, and two proteins (FeoA and FeoB) are conserved across most bacterial species. However, how FeoA and FeoB function relative to one another remained enigmatic. In this work we explored the distribution of feoAB operons predicted to encode for a fusion of FeoA tethered to the soluble N-terminal, G-protein domain of FeoB via a connecting linker region. We hypothesized that this fusion might poise FeoA to interact with FeoB in order to affect function. To test this hypothesis, we cloned, expressed, purified, and biochemically characterized the soluble NFeoAB fusion protein from Bacteroides fragilis, a commensal organism implicated in drug-resistant peritoneal infections. Using X-ray crystallography, we determined to 1.50 [A] resolution the structure of BfFeoA, which adopts an SH3-like fold implicated in protein-protein interactions. In combination with structural modeling, small-angle X-ray scattering, and hydrogen-deuterium exchange mass spectrometry, we show that FeoA and NFeoB indeed interact in a nucleotide-dependent manner, and we have mapped the protein-protein interaction interface. Finally, using GTP hydrolysis assays, we demonstrate that BfNFeoAB exhibits one of the slowest known rates of Feo-mediated GTP hydrolysis and is not potassium-stimulated, indicating that FeoA-NFeoB interactions may function to stabilize the GTP-bound form of FeoB. Our work thus reveals a role for FeoA function in the fused FeoAB systems and suggests a broader role for FeoA function amongst prokaryotes.

biochemistry↗

bioRxiv2021

Iron-sulfur clusters are involved in post-translational arginylation

Eukaryotic arginylation is an essential post-translational modification that both modulates protein stability and regulates protein half-life through the N-degron pathway. Arginylation is catalyzed by a family of enzymes known as the arginyl-tRNA transferases (ATE1s), which are conserved across the eukaryotic domain. Despite its conservation and importance, little is known regarding the structure, mechanism, and regulation of ATE1s. In this work, we have discovered that ATE1s bind a previously unknown iron-sulfur cluster that is conserved across evolution. We have extensively characterized the nature of this iron-sulfur cluster, and we show that the presence of the iron-sulfur cluster is linked to alterations in arginylation efficacy. Finally, we demonstrate that the ATE1 iron-sulfur cluster is oxygen sensitive, which could be a molecular mechanism of the N-degron pathway to sense oxidative stress. Thus, our data provide the framework of a cluster-based paradigm of ATE1 regulatory control.

biochemistry↗

bioRxiv2020

Learning epistatic polygenic phenotypes with Boolean interactions

Detecting epistatic drivers of human phenotypes is a considerable challenge. Traditional approaches use regression to sequentially test multiplicative interaction terms involving pairs of genetic variants. For higher-order interactions and genome-wide large-scale data, this strategy is computationally intractable. Moreover, multiplicative terms used in regression modeling may not capture the form of biological interactions. Building on the Predictability, Computability, Stability (PCS) framework, we introduce the epiTree pipeline to extract higher-order interactions from genomic data using tree-based models. The epiTree pipeline first selects a set of variants derived from tissue-specific estimates of gene expression. Next, it uses iterative random forests (iRF) to search training data for candidate Boolean interactions (pairwise and higher-order). We derive significance tests for interactions, based on a stabilized likelihood ratio test, by simulating Boolean tree-structured null (no epistasis) and alternative (epistasis) distributions on hold-out test data. Finally, our pipeline computes PCS epistasis p-values that probabilisticly quantify improvement in prediction accuracy via bootstrap sampling on the test set. We validate the epiTree pipeline in two case studies using data from the UK Biobank: predicting red hair and multiple sclerosis (MS). In the case of predicting red hair, epiTree recovers known epistatic interactions surrounding MC1R and novel interactions, representing non-linearities not captured by logistic regression models. In the case of predicting MS, a more complex phenotype than red hair, epiTree rankings prioritize novel interactions surrounding HLA-DRB1, a variant previously associated with MS in several populations. Taken together, these results highlight the potential for epiTree rankings to help reduce the design space for follow up experiments.

bioinformatics↗

bioRxiv2020

Iron Supplementation Eliminates Antagonistic Interactions Between Root Associated Bacteria

The rhizosphere microbiome (rhizobiome) plays a critical role in plant health and development. However the processes by which the constituent microbes interact to form and maintain a community are not well understood. To investigate these molecular processes, we examined pairwise interactions between 11 different microbial isolates under selected nutrient-rich and nutrient-limited conditions. We observed that when grown with media supplemented with 56 mM glucose, 2 microbial isolates were able to inhibit the growth of 6 out of 11 other microbes tested. The interaction between microbes persisted even after the antagonistic microbe was removed, upon exposure to spent media. To probe the genetic basis for these antagonistic interactions, we used a barcoded transposon library in a proxy bacterium, Pseudomonas putida, to identify genes which showed enhanced sensitivity to the antagonistic factor(s) secreted by Acinetobacter sp. 02. Iron metabolism-related gene clusters in P. putida were implicated by this systems-level analysis. The supplementation of iron prevented the antagonistic interaction in the original microbial pair supporting the hypothesis that iron limitation drives antagonistic microbial interactions between rhizobionts. We conclude that rhizobiome community composition is influenced by competition for limiting nutrients with implications for growth and development of the plant.

microbiology↗

bioRxiv2020

A long noncoding RNA, LOC157273, is the effector transcript at the chromosome 8p23.1-PPP1R3B metabolic traits and type 2 diabetes risk locus

AimsCausal transcripts at genomic loci associated with type 2 diabetes are mostly unknown. The chr8p23.1 variant rs4841132, associated with an insulin resistant diabetes risk phenotype, lies in the second exon of a long non-coding RNA (lncRNA) gene, LOC157273, located 175 kilobases from PPP1R3B, which encodes a key protein regulating insulin-mediated hepatic glycogen storage in humans. We hypothesized that LOC157273 regulates expression of PPP1R3B in human hepatocytes. MethodsWe tested our hypothesis using Stellaris fluorescent in-situ hybridization to assess subcellular localization of LOC157273; siRNA knockdown of LOC157273, followed by RT-PCR to quantify LOC157273 and PPP1R3B expression; RNA-seq to quantify the whole-transcriptome gene expression response to LOC157273 knockdown and an insulin-stimulated assay to measure hepatocyte glycogen deposition before and after knockdown. ResultsWe found that siRNA knockdown decreased LOC157273 transcript levels by approximately 80%, increased PPP1R3B mRNA levels by 1.7-fold and increased glycogen deposition by >50% in primary human hepatocytes. An A/G heterozygous carrier (vs. three G/G carriers) had reduced LOC157273 abundance due to reduced transcription of the A allele and increased PPP1R3B expression and glycogen deposition. ConclusionWe show that the lncRNA LOC157273 is a negative regulator of PPP1R3B expression and glycogen deposition in human hepatocytes and the causal transcript at an insulin resistant type 2 diabetes risk locus.

genomics↗

bioRxiv2018

Refining interaction search through signed iterative Random Forests

Advances in supervised learning have enabled accurate prediction in biological systems governed by complex interactions among biomolecules. However, state-of-the-art predictive algorithms are typically \"black-boxes,\" learning statistical interactions that are difficult to translate into testable hypotheses. The iterative Random Forest (iRF) algorithm took a step towards bridging this gap by providing a computationally tractable procedure to identify the stable, high-order feature interactions that drive the predictive accuracy of Random Forests (RF). Here we refine the interactions identified by iRF to explicitly map responses as a function of interacting features. Our method, signed iRF (s-iRF), describes \"subsets\" of rules that frequently occur on RF decision paths. We refer to these \"rule subsets\" as signed interactions. Signed interactions share not only the same set of interacting features but also exhibit similar thresholding behavior, and thus describe a consistent functional relationship between interacting features and responses. We describe stable and predictive importance metrics (SPIMs) to rank signed interactions in terms of their stability, predictive accuracy, and strength of interaction. For each SPIM, we define null importance metrics that characterize its expected behavior under known structure. We evaluate our proposed approach in biologically inspired simulations and two case studies: predicting enhancer activity and spatial gene expression patterns. In the case of enhancer activity, s-iRF recovers one of the few experimentally validated high-order interactions and suggests novel enhancer elements where this interaction may be active. In the case of spatial gene expression patterns, s-iRF recovers all 11 reported links in the gap gene network. By refining the process of interaction recovery, our approach has the potential to guide mechanistic inquiry into systems whose scale and complexity is beyond human comprehension.

genomics↗

bioRxiv2018

An important class of intron retention events in human erythroblasts is regulated by cryptic exons proposed to function as splicing decoys

During terminal erythropoiesis, the splicing machinery in differentiating erythroblasts executes a robust intron retention (IR) program that impacts expression of hundreds of genes. We studied IR mechanisms in the SF3B1 splicing factor gene, which expresses ~50% of its transcripts in late erythroblasts as a nuclear isoform that retains intron 4. RNA-seq analysis of nonsense-mediated decay (NMD)-inhibited cells revealed previously undescribed splice junctions, rare or not detected in normal cells, that connect constitutive exons 4 and 5 to highly conserved cryptic cassette exons within the intron. Minigene splicing reporter assays showed that these cassettes promote IR. Genome-wide analysis of splice junction reads demonstrated that cryptic noncoding cassettes are much more common in large (>1kb) retained introns than they are in small retained introns or in non-retained introns. Functional assays showed that heterologous cassettes can promote retention of intron 4 in the SF3B1 splicing reporter. Although many of these cryptic exons were spliced inefficiently, they exhibited substantial binding of U2AF1 and U2AF2 adjacent to their splice acceptor sites. We propose that these exons function as decoys that engage the intron-terminal splice sites, blocking cross-intron interactions required for excision. Developmental regulation of decoy function underlies a major component of the erythroblast IR program.

molecular biology↗

bioRxiv2018

Exploiting regulatory heterogeneity to systematically identify enhancers with high accuracy

Identifying functional enhancers elements in metazoan systems is a major challenge. For example, large-scale validation of enhancers predicted by ENCODE reveal false positive rates of at least 70%. Here we use the pregrastrula patterning network of Drosophila melanogaster to demonstrate that loss in accuracy in held out data results from heterogeneity of functional signatures in enhancer elements. We show that two classes of enhancer are active during early Drosophila embryogenesis and that by focusing on a single, relatively homogeneous class of elements, over 98% prediction accuracy can be achieved in a balanced, completely held-out test set. The class of well predicted elements is composed predominantly of enhancers driving multi-stage, segmentation patterns, which we designate segmentation driving enhancers (SDE). Prediction is driven by the DNA occupancy of early developmental transcription factors, with almost no additional power derived from histone modifications. We further show that improved accuracy is not a property of a particular prediction method: after conditioning on the SDE set, naive Bayes and logistic regression perform as well as more sophisticated tools. Applying this method to a genome-wide scan, we predict 1,640 SDEs that cover 1.6% of the genome, 916 of which are novel. An analysis of 32 novel SDEs using wholemount embryonic imaging of stably integrated reporter constructs chosen throughout our prediction rank-list showed >90% drove expression patterns. We achieved 86.7% precision on a genome-wide scan, with an estimated recall of at least 98%, indicating high accuracy and completeness in annotating this class of functional elements.\n\nSignificance StatementWe demonstrate a high accuracy method for predicting enhancers genome wide with > 85% precision as validated by transgenic reporter assays in Drosophila embryos. This is the first time such accuracy has been achieved in a metazoan system, allowing us to predict with high-confidence 1640 enhancers, 916 of which are novel. The predicted enhancers are demarcated by heterogeneous collections of epigenetic marks; many strong enhancers are free from classical indicators of activity, including H3K27ac, but are bound by key transcription factors. H3K27ac, often used as a one-dimensional predictor of enhancer activity, is an uninformative parameter in our data.

genomics↗

bioRxiv2017

iterative Random Forests to discover predictive and stable high order interactions

Genomics has revolutionized biology, enabling the interrogation of whole transcriptomes, genome-wide binding sites for proteins, and many other molecular processes. However, individual genomic assays measure elements that interact in vivo as components of larger molecular machines. Understanding how these high-order interactions drive gene expression presents a substantial statistical challenge. Building on Random Forests (RF), Random Intersection Trees (RITs), and through extensive, biologically inspired simulations, we developed the iterative Random Forest algorithm (iRF). iRF trains a feature-weighted ensemble of decision trees to detect stable, high-order interactions with same order of computational cost as RF. We demonstrate the utility of iRF for high-order interaction discovery in two prediction problems: enhancer activity in the early Drosophila embryo and alternative splicing of primary transcripts in human derived cell lines. In Drosophila, among the 20 pairwise transcription factor interactions iRF identifies as stable (returned in more than half of bootstrap replicates), 80% have been previously reported as physical interactions. Moreover, novel third-order interactions, e.g. between Zelda (Zld), Giant (Gt), and Twist (Twi), suggest high-order relationships that are candidates for follow-up experiments. In human-derived cells, iRF re-discovered a central role of H3K36me3 in chromatin-mediated splicing regulation, and identified novel 5th and 6th order interactions, indicative of multi-valent nucleosomes with specific roles in splicing regulation. By decoupling the order of interactions from the computational cost of identification, iRF opens new avenues of inquiry into the molecular mechanisms underlying genome biology.

genomics↗

bioRxiv2016

Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease.

Genetic variants underlying complex traits, including disease susceptibility, are enriched within the transcriptional regulatory elements, promoters and enhancers. There is emerging evidence that regulatory elements associated with particular traits or diseases share patterns of transcriptional regulation. Accordingly, shared transcriptional regulation (coexpression) may help prioritise loci associated with a given trait, and help to identify the biological processes underlying it. Using cap analysis of gene expression (CAGE) profiles of promoter and enhancer-derived RNAs across 1824 human samples, we have quantified coexpression of RNAs originating from trait-associated regulatory regions using a novel analytical method (network density analysis; NDA). For most traits studied, sequence variants in regulatory regions were linked to tightly coexpressed networks that are likely to share important functional characteristics. These networks implicate particular cell types and tissues in disease pathogenesis; for example, variants associated with ulcerative colitis are linked to expression in gut tissue, whereas Crohns disease variants are restricted to immune cells. We show that this coexpression signal provides additional independent information for fine mapping likely causative variants. This approach identifies additional genetic variants associated with specific traits, including an association between the regulation of the OCT1 cation transporter and genetic variants underlying circulating cholesterol levels. This approach enables a deeper biological understanding of the causal basis of complex traits.\n\nONE SENTENCE SUMMARYWe discover that variants associated with a specific disease share expression profiles across tissues and cell types, enabling fine mapping and identification of new disease-associated variants, illuminating key cell types involved in disease pathogenesis.

genomics↗

bioRxiv2016

De novo Identification of DNA Modifications Enabled by Genome-Guided Nanopore Signal Processing

Advances in nanopore sequencing technology have enabled investigation of the full catalogue of covalent DNA modifications. We present the first algorithm for the identification of modified nucleotides without the need for prior training data along with the open source software implementation, nanoraw. Nanoraw accurately assigns contiguous raw nanopore signal to genomic positions, enabling novel data visualization, and increasing power and accuracy for the discovery of covalently modified bases in native DNA. Ground truth case studies utilizing synthetically methylated DNA show the capacity to identify three distinct methylation marks, 4mC, 5mC, and 6mA, in seven distinct sequence contexts without any changes to the algorithm. We demonstrate quantitative reproducibility simultaneously identifying 5mC and 6mA in native E. coli across biological replicates processed in different labs. Finally we propose a pipeline for the comprehensive discovery of DNA modifications in any genome without a priori knowledge of their chemical identities.

bioinformatics↗