Biology subjects

Ford, C. T.

Publications and source records attributed to Ford, C. T..

At least 19 recordsLinked to original sources

bioRxiv2026

Hoike: A Joint-Embedding Predictive Architecture for Transcriptome Data Generation with Diffusion Models

In biomarker discovery, access to sufficient quantities of condition-specific transcriptomic data is often limited by cohort size, privacy concerns, and domain shift between normal and condition populations. Generative modeling can augment scarce cohorts and probe distributional transitions. Furthermore, synthetic transcriptome generation can support differential expression analyses, machine learning, privacy-preserving data sharing, benchmarking, and hypothesis generation in translational bioinformatics workloads in fields such as oncology. Here, we present Hoike, a framework that combines a crossdomain Joint-Embedding Predictive Architecture (JEPA) with a latent diffusion model to generate condition-specific bulk transcriptomes from a normal reference context. In Hoike, normal tissue profiles provide continuous conditioning signals, while the model learns disease-linked shifts in latent space and reconstructs gene-level expression in log2(TPM+1) space. The implementation supports paired normal-condition training, tissuealigned conditioning, and constrained non-negative decoding for biologically valid outputs. We describe the architecture, objective design, and evaluation protocol used in this work across GTEx-derived normal references and multiple TCGA condition cohorts as a case study. This serves as the technical specification of the Hoike framework and its reproducible analysis workflow.

bioinformatics↗

bioRxiv2026

Kukulu: Diffusion-Based Reconstruction of Antibody CDR Loops using a Structure-Aware Joint Embedding Predictive Architecture

Antibody complementarity-determining regions (CDRs), especially CDR-H3, are a dominant source of binding specificity but remain difficult to design due to coupled sequence-structure constraints and local geometric variability. Here we present Kukulu, a structure-aware Joint Embedding Predictive Architecture (JEPA) combined with conditional diffusion for CDR loop reconstruction in antibody-antigen complexes. Our pipeline prepares structures by chain-aware cleanup, Fv trimming, Chothia-indexed CDR identification, and in silico CDR masking, then trains on paired prepared/masked structures represented in an atom37 format. The model uses a context encoder over masked structures, a transformer predictor for latent CDR representations, and a diffusion head that reconstructs loop coordinates, atom presence, and residue identities under geometry-aware losses. During generation, Kukulu denoises only masked CDR residues while preserving frame-work context, then optionally rebuilds sidechains with local frame templates and performs post-generation structural relaxation. This manuscript provides a methods-focused overview of the models implementation details and an evaluation protocol based on structure quality and docking-oriented scoring for integration into existing antibody design workflows.

synthetic biology↗

bioRxiv2026

Stage-aware transcriptomics reveals selective haplotype persistence in short-term ex vivo cultured Plasmodium vivax

Plasmodium vivax (Pv) infections are developmentally asynchronous and often polyclonal, complicating interpretation of bulk parasite transcriptomes. Here, we analyzed paired in vivo and short-term ex vivo transcriptomes from Ethiopian clinical isolates using stage deconvolution and PvMSP1 haplotyping. Ex vivo maturation modestly increased inferred schizont representation while largely preserving the proportion of trophozoites and gametocytes. After adjustment for parasite stage composition, in vivo and ex vivo transcriptomes remained globally similar, with no genes significantly differentially expressed, indicating the absence of major culture-induced transcriptional response. In contrast, short-term culture reduced multiplicity of infection, contracted within-host haplotype diversity, and non-randomly depleted specific haplotypes, consistent with a clonal bottleneck. In a subset of low-complexity infections, residual expression patterns were clustered by dominant haplotype, suggesting genotype-associated transcriptional heterogeneity independent of developmental stage. Together, these findings indicate that short-term ex vivo culture enriches late asexual stages and selectively filters clones rather than inducing a common transcriptional program. These results shows that ex vivo cultures are reliable way to study gene expression, especially for late stages. However, these needs explicitly model developmental composition and infection complexity when interpreting Pv transcriptomes from natural infections Author summaryMalaria caused by Plasmodium vivax is difficult to study because this parasite cannot yet be grown continuously in the laboratory and infections in patients often contain parasites at different developmental stages and multiple parasite lineages at the same time. In this study, we wanted to understand how much of the parasite gene-expression signal reflects true biological differences, and how much is explained by parasite development or changes that occur during short-term laboratory maturation. We compared parasites collected directly from patients in Ethiopia with matched parasite matured briefly outside the body. We found that short-term culture mainly increased the proportion of later-stage parasites, but after accounting for developmental stage, the overall gene-expression patterns remained very similar. However, culture reduced the diversity of parasite lineages within infections, suggesting that some parasite lineages survive better than others under laboratory conditions. Our findings highlight that natural Pv infections are complex mixtures of parasite stages and lineages. Accounting for this complexity will improve how researchers interpret parasite gene-expression studies and design future studies of parasite invasion, transmission, and survival.

cell biology↗

bioRxiv2025

peleke-1: A Suite of Protein Language Models Fine-Tuned for Targeted Antibody Sequence Generation

The discovery of therapeutic antibodies is a traditionally arduous process. Today, the lab-based process of antibody discovery consists of several time-consuming steps that involve live animal immunization, B-cell harvesting, hybridoma creation, and then downstream engineering and evaluation. However, the use of artificial intelligence in drug design has previously been shown effective in the rapid generation of proteinspecific binders, small molecules, and even antibody therapeutics, thereby replacing some of the primary steps of the drug discovery process. Here we present peleke-1, a suite of protein language models fine-tuned from state-of-the-art large language models using curated antibody-antigen complex data. These models generate targeted antibody Fv sequences for a given antigen sequence input at-scale. This suite of models provides a reliable, artificial intelligence-driven approach for in silico therapeutic antibody discovery along with an open-source framework for future antibody language model tuning.

immunology↗

bioRxiv2025

AI-Based Antibody Design Targeting Recent H5N1 Avian Influenza Strains

In 2025 alone, H5N1 avian influenza is responsible for thousands of infections across various animal species, including avian and mammalian livestock such as chickens and cows, and poses a threat to human health due to avian-to-mammalian transmission. There have been 70 human cases of H5N1 influenza in the United States since April 2024 and, as shown in recent studies, our current antibody defenses are waning. Thus, it is imperative to discover new therapeutics in the fight against more recent strains of the virus. In this study, we present the Frankies framework for automated antibody diffusion and assessment. This pipeline was used to automate the generation of 30 novel anti-HA1 Fv anti-body fragment sequences, fold them into 3-dimensional structures, and then dock against a recent H5N1 HA1 antigen structure for binding evaluation. Here we show the utility of artificial intelligence in the discovery of novel antibodies against specific H5N1 strains of interest, which bind similarly to known therapeutic and elicited antibodies.

synthetic biology↗

bioRxiv2025

Analyses of phylogenetics, natural selection, and protein structure of clade 2.3.4.4b H5N1 Influenza A reveal that recent viral lineages have evolved promiscuity in host range and improved replication in mammals in North America

Influenza A virus has been circulating in birds from Eurasia for more than 146 years, but human infection has been sporadic. H5N1 (clade 2.3.4.4b) has recently infected hundreds of species of wild and domestic birds and mammals in North America. Infections include 70 people with two fatalities. We have developed an analytical bioinformatics, genomics, and structural workflow to understand better how H5N1 is circulating in North America and adapting to new host species.Our time-series analysis reveals that the circulation of H5N1 (clade 2.3.4.4b) in North America follows a distinct annual pattern, with cases in the United States consistently peaking each December. Separate from this seasonal cycle, our analysis also documents an increase in the total number of cases reported since 2021. We also show that H5N1 (clade 2.3.4.4b) spreads in North America as two distinct subclades of interest for human and animal health. These viral lineages have achieved a vast host range by efficiently binding the viral surface protein Hemagglutinin to both mammalian and avian cell surface receptors. This novel promiscuity of host range is concomitant with the additional strengthening of the Polymerase basic 2 viral proteins binding for mammalian and avian immune proteins. Once bound, the immune proteins will have diminished ability to fight the virus, thus allowing for more efficient replication of H5N1 in mammalian and avian cells than seen in the recent past. Finally, structural docking analyses predict that while most current antivirals remain effective, a fatal human isolate showed significantly reduced binding to multiple drugs from different classes. In conclusion, the H5N1 virus is causing an animal pandemic through promiscuity of host rage and strengthening ability to evade the innate immune systems of both mammalian and avian cells.

genomics↗

bioRxiv2024

Comprehensive Guide of Epigenetics and Transcriptomics Data Quality Control

Host response to environmental exposures such as pathogens and chemicals can cause modifications to the epigenome and transcriptome. Analysis of these modifications can reveal signatures with regards to the agent and timing of exposure. Exhaustive interrogation of the cascade of the epigenome and transcriptome requires analysis of disparate datasets from multiple assay types, often at single cell resolution, from the same biospecimen. Improved signature discovery has been enabled by advancements in assaying techniques to detect RNA expression, DNA base modifications, histone modifications, and chromatin accessibility. However, there remains a paucity of rigorous quality control standards of those datasets that reflect quality assurance of the underlying assay. This guide outlines a comprehensive suite of metrics that can be used to ensure quality from 11 different epigenetics and transcriptomics assays. Recommendations on mitigation approaches to address failed metrics and poor quality data are provided. The workflow consists of assessing dataset quality and reiterating benchwork protocols for improved results to generate accurate exposure signatures.

bioinformatics↗

bioRxiv2024

Large-Scale Computational Modeling of H5 Influenza Variants Against HA1-Neutralizing Antibodies

The United States Department of Agriculture has recently released reports that show samples from 2022-2024 of highly pathogenic avian influenza (H5N1) have been detected in mammals and birds (1). To date, the United States Centers for Disease Control reports that there have been 27 humans infected with H5N1 in 2024 (2). The broader potential impact on human health remains unclear. In this study, we computationally model 1,804 protein complexes consisting of various H5 isolates from 1959 to 2024 against 11 hemagglutinin domain 1 (HA1)-neutralizing antibodies. This study shows a trend of weakening binding affinity of existing antibodies against H5 isolates over time, indicating that the H5N1 virus is evolving immune escape of our medical defenses. We also found that based on the wide variety of host species and geographic locations in which H5N1 was observed to have been transmitted from birds to mammals, there is not a single central reservoir host species or location associated with H5N1s spread. These results indicate that the virus has potential to move from epidemic to pandemic status in the near future. This study illustrates the value of high-performance computing to rapidly model protein-protein interactions and viral genomic sequence data at-scale for functional insights into medical preparedness. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSPrevious studies have shown cases of avian influenza transmissions to mammals that are increasing in frequency, which is of concern to human health. Since 1997, nearly a thousand H5N1 cases have been reported in humans with a 52% fatality rate. Previous analyses have indicated specific mutations on the hemagglutinin protein that allow for this "host jumping" between birds and mammals (3). There are also existing evidence of recent viral strains with reduced neutralization to sera (4). Added value of this studyThis study provides a comprehensive look at the mutational space of hemagglutinin of H5N1 influenza and presents computational predictions of the binding between various HA1-neutralizing antibodies derived from infected vaccinated patients and humanized mice and 1,804 representative H5 HA1 proteins. These analyses show a weakening trend of existing antibodies. We also confirm that the mutations found in other studies that enable zoonosis also affect binding affinities of the antibodies tested. Furthermore, through phylogenetic analyses, we quantify the avian-to-mammalian transmissions from 1959 to 2024 and show a persistent circulation of isolates between North America and Europe. Taken together, the continuous transmission of H5N1 from birds to mammals and the increase in immuno-evasive HA strains in mammals sampled over time suggest that antigenic drift is a source of spillover risk. Implications of all the available evidenceOur findings indicate that the worsening in antibody binding, along with the increase in of avian-to-mammalian H5N1 influenza transmissions are risks to public health. Through the findings of previous studies along with the predictions reported in this study, we can now monitor specific mutations of interest, quantified by their potential impact on antibody evasion, and inform public health monitoring of circulating isolates in 2024 and beyond. In addition, these findings may help to guide future vaccine and therapeutic development in the fight against H5N1 influenza infections in humans.

bioinformatics↗

bioRxiv2024

PD-1 Targeted Antibody Discovery Using AI Protein Diffusion

The programmed cell death protein 1 (PD-1, CD279) is an important therapeutic target in many oncological diseases. This checkpoint protein inhibits T lymphocytes from attacking other cells in the body and thus blocking it improves the clearance of tumor cells by the immune system. While there are already multiple FDA-approved anti-PD-1 antibodies, including nivolumab (Opdivo(R) from Bristol-Myers Squibb) and pembrolizumab (Keytruda(R) from Merck), there are ongoing efforts to discover new and improved checkpoint inhibitor therapeutics. In this study, we present multiple anti-PD-1 antibody fragments that were derived computationally using protein diffusion and evaluated through our scalable, in silico pipeline. Here we present nine synthetic Fv structures that are suitable for further empirical testing of their anti-PD-1 activity due to desirable predicted binding performance.

cancer biology↗

bioRxiv2023

Human Cytokine and Coronavirus Nucleocapsid Protein Interactivity Using Large-Scale Virtual Screens

Understanding the interactions between SARS-CoV-2 and the human immune system is paramount to the characterization of novel variants as the virus co-evolves with the human host. In this study, we employed state-of-the-art molecular docking tools to conduct large-scale virtual screens, predicting the binding affinities between 64 human cytokines against 17 nucleocapsid proteins from six betacoronaviruses. Our comprehensive in silico analyses reveal specific changes in cytokine-nucleocapsid protein interactions, shedding light on potential modulators of the host immune response during infection. These findings offer valuable insights into the molecular mechanisms underlying viral pathogenesis and may guide the future development of targeted interventions. This manuscript serves as insight into the comparison of deep learning based AlphaFold2-Multimer and the semi-physicochemical based HADDOCK for protein-protein docking. We show the two methods are complementary in their predictive capabilities. We also introduce a novel algorithm for rapidly assessing the binding interface of protein-protein docks using graph edit distance: graph-based interface residue assessment function (GIRAF). The high-performance computational framework presented here will not only aid in accelerating the discovery of effective interventions against emerging viral threats, but extend to other applications of high throughput protein-protein screens.

bioinformatics↗

bioRxiv2023

Predicting Antibody and ACE2 Affinity forSARS-CoV-2 BA.2.86 with In Silico ProteinModeling and Docking

The emergence of SARS-CoV-2 lineages derived from Omicron, including BA.2.86 (nicknamed "Pirola") and its relative, JN.1, has raised concerns about their potential impact on public and personal health due to numerous novel mutations. Despite this, predicting their implications based solely on mutation counts proves challenging. Empirical evidence of JN.1s increased immune evasion capacity in relation to previous variants is mixed. To improve predictions beyond what is possible based solely on mutation counts, we conducted extensive in silico analyses on the binding affinity between the RBD of different SARS-CoV-2 variants (Wuhan-Hu-1, BA.1/B.1.1.529, BA.2, XBB.1.5, BA.2.86, and JN.1) and neutralizing antibodies from vaccinated or infected individuals, as well as the human angiotensin-converting enzyme 2 (ACE2) receptor. We observed no statistically significant difference in binding affinity between BA.2.86 or JN.1 and other variants. Therefore, we conclude that the new SARS-CoV-2 variants have no pronounced immune escape or infection capacity compared to previous variants. However, minor reductions in binding affinity for both the antibodies and ACE2 were noted for JN.1. We discuss the implications of the in silico findings and highlight the need for modeling and docking studies to go above and beyond mutation and basic serological neutralization analysis. Future research in this area will benefit from increased structural analyses of memory B-cell derived antibodies and should emphasize the importance of choosing appropriate samples for in silico studies to assess protection provided by vaccination and infection. More-over, the fitness benefits of genomic variation outside of the RBD of BA.2.86 and JN.1 need to be investigated. This research contributes to understanding the BA.2.86 and JN.1 variants potential impact on public health. Taken together, this work introduces a paradigm for functional genomic epidemiology in ongoing efforts to combat the evolving SARS-CoV-2 pandemic and prepare for other hazards.

bioinformatics↗

bioRxiv2023

Behavioral and transcriptomic analyses of mecp2 function in zebrafish

Rett Syndrome (RTT), a human neurodevelopmental disorder characterized by severe cognitive and motor impairments, is caused by dysfunction of the conserved transcriptional regulator Methyl-CpG-binding protein 2 (MECP2). Genetic analyses in mouse Mecp2 mutants, which exhibit key features of human RTT, have been essential for deciphering the mechanisms of MeCP2 function; nonetheless, our understanding of these complex mechanisms is incomplete. Zebrafish mecp2 mutants exhibit mild behavioral deficits but have not been analyzed in depth. Here we combine transcriptomic and behavioral assays to assess baseline and stimulus-evoked motor responses and sensory filtering in zebrafish mecp2 mutants from 5-7 days post-fertilization (dpf). We show that zebrafish mecp2 function is dispensable for gross movement, acoustic startle response, and sensory filtering (habituation and sensorimotor gating), and reveal a previously unknown role for mecp2 in behavioral responses to visual stimuli. RNA-seq analysis identified a large gene set that requires mecp2 function for correct transcription at 4 dpf, and pathway analysis revealed several pathways that require MeCP2 function in both zebrafish and mammals. These findings show that MeCP2s function as a transcriptional regulator is conserved across vertebrates and supports using zebrafish to complement mouse modeling in elucidating these conserved mechanisms.

genetics↗

bioRxiv2023

Comparative transcriptomics reveal differential gene expression in Plasmodium vivax geographical isolates and implications on erythrocyte invasion mechanisms

Plasmodium vivax uses Duffy binding protein (PvDBP1) to bind to the Duffy Antigen-Chemokine Receptor (DARC) to invade human erythrocytes. Individuals who lack DARC expression (Duffy-negative) are thought to be resistance to P. vivax. In recent years, P. vivax malaria is becoming more prevalent in Africa with a portion of these cases detected in Duffy-negatives. Apart from DBP1, members of the reticulocyte binding protein (RBP) and tryptophan-rich antigen (TRAg) families may also play a role in erythrocyte invasion. While the transcriptomes of the Southeast Asian and South American P. vivax are well documented, the gene expression profile of P. vivax in Africa and more specifically the expression level of several erythrocyte binding gene candidates as compared to DBP1 are largely unknown. This paper characterized the first P. vivax transcriptome in Africa and compared with those from the Southeast Asian and South American isolates. The expression of 4,404 gene transcripts belong to 12 functional groups including 43 specific erythrocyte binding gene candidates were examined. Overall, there were 10-26% differences in the gene expression profile amongst the geographical isolates, with the Ethiopian and Cambodian P. vivax being most similar. Majority of the gene transcripts involved in protein transportation, housekeeping, and host interaction were highly transcribed in the Ethiopian P. vivax. Erythrocyte binding genes including PvRBP2a and PvRBP3 expressed six-fold higher than PvDBP1and 60-fold higher than PvEBP/DBP2. Other genes including PvRBP1a, PvMSP3.8, PvMSP3.9, PvTRAG2, PvTRAG14, and PvTRAG22 also showed relatively high expression. Differential expression was observed among geographical isolates, e.g., PvDBP1 and PvEBP/DBP2 were highly expressed in the Cambodian but not the Brazilian and Ethiopian isolates, whereas PvRBP2a and PvRBP2b showed higher expression in the Ethiopian and Cambodian than the Brazilian isolates. Compared to Pvs25, the standard biomarker for detecting female gametocytes, PvAP2-G (PVP01_1440800), GAP (PVP01_1403000), and Pvs47 (PVP01_1208000) were highly expressed across geographical samples. These findings provide an important baseline for future comparisons of P. vivax transcriptomes from Duffy-negative infections and highlight potential biomarkers for improved gametocyte detection.

cell biology↗

bioRxiv2023

Positing changes in neutralizing antibody activity for SARS-CoV-2 XBB.1.5 using in silico protein modeling

The SARS-CoV-2 variant XBB.1.5 is of concern as it has high transmissibility. XBB.1.5 currently accounts for upwards of 30% of new infections in the United States. One year after our group published the predicted structure of the Omicron (B.1.1.529) variants receptor binding domain (RBD) and antibody binding affinity, we return to investigate the new mutations seen in XBB.1.5 which is a descendant of Omicron. Using in silico ico modeling approaches against newer neutralizing antibodies that are shown effective against B.1.1.529, we predict the immune consequences of XBB.1.5s mutations and show that there is no statistically significant difference in overall antibody evasion when comparing to the B.1.1.529 and other related variants (e.g., BJ.1 and BM.1.1.1). However, noticeable changes in antibody binding affinity were seen due to specific amino acid changes of interest in the newer variants.

biochemistry↗

bioRxiv2021

Protein interaction analysis of Plasmodium falciparum circumsporozoite protein variants with human immunoproteins explains RTS,S vaccine efficacy in Ghana

BackgroundThe worlds first malaria vaccine RTS,S provides only partial protection against Plasmodium falciparum infections. The explanation for such low efficacy is unclear. This study examined the associations of parasite genetic variations with binding affinity to human immunological proteins including human leukocyte antigen (HLA) and T cell receptors (TCR) involved in RTS,S-induced immune responses. MethodsMultiplicity of infections was determined by amplicon deep sequencing of merozoite surface protein 1 (PfMSP1). Genetic variations in the C-terminal of circumsporozoite protein (PfMSP1) gene were examined across 88 samples of P. falciparum collected from high and low transmission settings of Ghana. Binding interactions of PfMSP1 variants and HLA/TCR were analyzed using NetChop and HADDOCK predictions. Anti-CSP IgG levels were measured by ELISA in a subset of 10 samples. FindingsHigh polyclonality was detected among P. falciparum infections. A total 27 CSP haplotypes were detected among samples. A significant correlation was detected between the CSP and MSP multiplicity of infection (MOI). No clear clustering of haplotypes was observed by geographic regions. The number of genetic differences in PfCSP between 3D7 and non-3D7 variants does not influence binding interactions to HLA/T cells nor anti-CSP IgG levels. Nevertheless, PfCSP peptide length significantly affects its molecular weight and binding affinity to the HLA. InterpretationsThe presence of multiple non-3D7 strains among P. falciparum infections in Ghana impact the effectiveness of RTS,S. Longer PfCSP peptides may elicit a stronger immune response and should be considered in future version RTS,S. The molecular mechanisms of RTS,S cell-mediated immune responses related to longer CSP peptides warrants further investigations.

bioinformatics↗

bioRxiv2021

Predictions of the SARS-CoV-2 Omicron Variant (B.1.1.529) Spike Protein Receptor-Binding Domain Structure and Neutralizing Antibody Interactions

The genome of the SARS-CoV-2 Omicron variant (B.1.1.529) was released on November 22, 2021, which has caused a flurry of media attention due the large number of mutations it contains. These raw data have spurred questions around vaccine efficacy. Given that neither the structural information nor the experimentally-derived antibody interaction of this variant are available, we have turned to predictive computational methods to model the mutated structure of the spike proteins receptor binding domain and posit potential changes to vaccine efficacy. In this study, we predict some structural changes in the receptor-binding domain that may reduce antibody interaction without completely evading existing neutralizing antibodies (and therefore current vaccines).

molecular biology↗

bioRxiv2019

Ensemble Machine Learning Modeling for the Prediction of Artemisinin Resistance in Malaria

Antiparasitic resistance in malaria is a growing concern affecting many areas of the eastern world. Since the emergence of artemisinin resistance in the late 2000s in Cambodia, research into the underlying mechanisms has been underway. The 2019 Malaria Dream Challenge posited the task of developing computational models that address important problems in advancing the fight against malaria. The first goal was to accurately predict Artemisinin drug resistance levels of Plasmodium falciparum isolates, as quantified by the IC50. The second goal was to predict the parasite clearance rate of malaria parasite isolates based on in vitro transcriptional profiles. In this work, we develop machine learning models using novel methods for transforming isolate data and handling the tens of thousands of variables that result from these data transformation exercises. This is demonstrated by using massively parallel processing of the data vectorization for use in scalable machine learning. In addition, we show the utility of ensemble machine learning modeling for highly effective predictions of both goals of this challenge. This is demonstrated by the use of multiple machine learning algorithms combined with various scaling and normalization preprocessing steps. Then, using a voting ensemble, multiple models are combined to generate a final model prediction.

bioinformatics↗

bioRxiv2019

StrainHub: A phylogenetic tool to construct pathogen transmission networks

In exploring the epidemiology of infectious diseases, networks have been used to reconstruct contacts among individuals and/or populations. Summarizing networks using pathogen metadata (e.g., host species and place of isolation) and a phylogenetic tree is a nascent, alternative approach. In this paper, we introduce a tool for reconstructing transmission networks in arbitrary space from phylogenetic information and metadata. Our goals are to provide a means of deriving new insights and infection control strategies based on the dynamics of the pathogen lineages derived from networks and centrality metrics. We created a web-based application, called StrainHub, in which a user can input a phylogenetic tree based on genetic or other data along with characters derived from metadata using their preferred tree search method. StrainHub generates a transmission network based on character state changes in meta-data, such as place or source of isolation, mapped on the phylogenetic tree. The user has the option to calculate centrality metrics on the nodes including betweenness, closeness, degree, and a new metric, the source/hub ratio. The outputs include the network with values for metrics on its nodes and the tree with characters reconstructed. All of these results can be exported for further analysis.\n\nAvailabilityhttps://github.com/abschneider/StrainHub and strainhub.io

bioinformatics↗