Biology subjects

Kramer, A. M.

Publications and source records attributed to Kramer, A. M..

10 recordsLinked to original sources

bioRxiv2026

Quantum Entanglement as a Physical Determinant of Genomic Instability in TP53

Abstract Mutations in the TP53 tumor suppressor gene are among the most frequent alterations in human cancers, but whether quantum mechanical effects such as DNA entanglement, influence mutation patterns remain unexplored. Here, we applied a coupled quantum harmonic oscillator model to compute von Neumann Entropy (VNE), a proxy for quantum entanglement, across the TP53 sequence. We found that known mutational hot spots exhibited significantly elevated VNE compared to non-hot spot regions, suggesting that local quantum coupling may reflect structural instability or altered repair accessibility. CpG sites, traditionally linked to methylation-driven mutability, did not exhibit elevated VNE, indicating that quantum properties captured by our closed model are distinct from classical biochemical mechanisms. While these computational results raise the possibility that quantum effects could contribute to genomic instability in TP53, we caution that the analysis is limited by the small number of validated hot spots (n=6) and lacks biological validation. our findings are exploratory and should be interpreted carefully. If confirmed in a larger, multi-gene studies with biophysical measurements, a quantum informed framework could open new avenues for predicting mutation hot spots and understanding cancer mutagenesis. We advocate for interdisciplinary efforts bridging quantum physics and oncology to explore these possibilities further.

biophysics↗

bioRxiv2026

Panmap: Scalable phylogeny-guided alignment, genotyping, and placement on pangenomes

Pangenomes capture population-level variation but remain computationally challenging at scale. We present Panmap, a tool that leverages evolutionary structure to place, align, and genotype sequencing reads against mutation-annotated pangenomes containing up to millions of genomes. Panmap introduces a phylogenetically compressed k-mer index that stores only sequence differences along branches, enabling efficient comparison of reads to both sampled genomes and inferred ancestors. This approach reduces index size by up to 600-fold and construction time by over three orders of magnitude relative to existing tools. Panmap places a 100x coverage SARS-CoV-2 sample onto 20,000 genomes in 0.4 seconds and onto 8 million genomes in under two minutes. Furthermore, it enables accurate haplotype identification and abundance estimation in metagenomic samples and sensitive placement of ancient environmental DNA without prior alignment. Our approach makes large-scale pangenomes directly amenable to read mapping, genome assembly, alignment-free phylogenetic placement, and metagenomic analysis.

bioinformatics↗

bioRxiv2024

Integrating predictors of host condition into spatiotemporal multi-scale models of virus shedding

Understanding where and when pathogens occur in the environment has implications for reservoir population health and infection risk. In reservoir hosts, infection status and pathogen shedding are affected by processes interacting across different scales: from landscape features affecting host location and transmission to within-host processes affecting host immunity and infectiousness. While uncommonly done, simultaneously incorporating processes across multiple scales may improve pathogen shedding predictions. In Australia, the black flying fox (Pteropus alecto) is a natural host for the zoonotic Hendra virus, which is hypothesized to cause latent infections in bats. Re-activation and virus shedding may be triggered by poor host condition, leading to virus excretion through urine. Here, we developed a statistical modeling approach that combined data at multiple spatial and temporal scales to capture ecological and biological processes potentially affecting virus shedding. We parameterized these models using existing datasets and compared model performance to under-roost virus shedding data from 2011-2014 in 23 roosts across a 1200-km transect. Our approach enabled comparisons among multiple model structures to determine which variables at which scales are most influential for accurate predictions of virus shedding in space and time. We identified environmental predictors and temporal lags of these features that were important for determining where reservoirs are located and multiple independent proxies for reservoir condition. The best-performing multi-scale model delineated periods of low and high virus prevalence, reflecting observed shedding patterns from pooled under-roost samples. Incorporating regional indicators of food scarcity enhanced model accuracy while incorporating other stress indicators at local scales confounded this signal. This multiscale modeling approach enabled the combination of processes from different ecological scales and identified environmental variables influencing Hendra virus shedding, highlighting how integrating data across scales may improve risk forecasts for other pathogen systems.

ecology↗

bioRxiv2022

Predicting species distributions with environmental time series data and deep learning

Species distribution models (SDMs) are widely used to gain ecological understanding and guide conservation decisions. These models are developed with a wide variety of algorithms - from statistic-based approaches to machine learning approaches - but a requirement almost all share is the use of predictor variables that strongly simplify the temporal variability of driving factors. Conversely, novel architectures of deep learning neural networks allow dealing with fully explicit spatiotemporal dynamics and thus fitting SDMs without the need to simplify the temporal and spatial dimension of predictor data. We present and demonstrate a deep learning based SDM approach that uses time series of spatial data as predictors using distribution data for 74 species from a well-established benchmark dataset. The deep learning approach provided consistently accurate models, directly using time series of predictor data and thus avoiding the use of pre-processed predictor sets that can obscure relevant aspects of environmental variation.

ecology↗

bioRxiv2022

Treenome Browser: co-visualization of enormous phylogenies and millions of genomes

Treenome Browser is a web browser tool to interactively visualize millions of genomes alongside huge phylogenetic trees. Availability and ImplementationTreenome Browser for SARS-CoV-2 can be accessed at cov2tree.org, or at taxonium.org for user-provided trees. Source code and documentation are available at github.com/theosanderson/taxonium and docs.taxonium.org/en/latest/treenome.html. Contactalex.kramer@ucsc.edu, rucorbet@ucsc.edu

bioinformatics↗

bioRxiv2021

Pandemic-scale phylogenetics

1.Phylogenetics has been central to the genomic surveillance, epidemiology and contact tracing efforts during the COVD-19 pandemic. But the massive scale of genomic sequencing has rendered the pre-pandemic tools inadequate for comprehensive phylogenetic analyses. Here, we discuss the phylogenetic package that we developed to address the needs imposed by this pandemic. The package incorporates several pandemic-specific optimization and parallelization techniques and comprises four programs: UShER, matOptimize, RIPPLES and matUtils. Using high-performance computing, UShER and matOptimize maintain and refine daily a massive mutation-annotated phylogenetic tree consisting of all SARS-CoV-2 sequences available in online repositories. With UShER and RIPPLES, individual labs - even with modest compute resources - incorporate newly-sequenced SARS-CoV-2 genomes on this phylogeny and discover evidence for recombination in real-time. With matUtils, they rapidly query and visualize massive SARS-CoV-2 phylogenies. These tools have empowered scientists worldwide to study the SARS-CoV-2 evolution and transmission at an unprecedented scale, resolution and speed.

bioinformatics↗

bioRxiv2021

Economic costs of biological invasions in the United States

The United States has thousands of invasive species, representing a sizable, but unknown burden to the national economy. Given the potential economic repercussions of invasive species, quantifying these costs is of paramount importance both for national economies and invasion management. Here, we used a novel global database of invasion costs (InvaCost) to quantify the overall costs of invasive species in the United States across spatiotemporal, taxonomic, and socioeconomic scales. From 1960 to 2020, reported invasion costs totaled $4.52 trillion (USD 2017). Considering only observed, highly reliable costs, this total cost reached $1.22 trillion with an average annual cost of $19.94 billion/year. These costs increased from $2.00 billion annually between 1960-1969 to $21.08 billion annually between 2010-2020. Most costs (73%) were related to resource damages and losses ($896.22 billion), as opposed to management expenditures ($46.54 billion). Moreover, the majority of costs were reported from invaders from terrestrial habitats ($643.51 billion, 53%) and agriculture was the most impacted sector ($509.55 billion). From a taxonomic perspective, mammals ($234.71 billion) and insects ($126.42 billion) were the taxonomic groups responsible for the greatest costs. Considering the apparent rising costs of invasions, coupled with increasing numbers of invasive species and the current lack of cost information for most known invaders, our findings provide critical information for policymakers and managers.

ecology↗

bioRxiv2021

Spatial spread of white-nose syndrome in North America, 2006-2018

White-nose syndrome has caused massive mortality in multiple bat species and spread across much of North America, making it one of the most destructive wildlife diseases on record. This has also resulted in it being one of the most well-documented wildlife disease outbreaks, making it possible to look for changes in the pattern of spatial spread over time. We fit a series of spatial interaction models to the United States county-level observations of the pathogenic fungus, Pseudogymnoascus destructans, that causes white-nose syndrome. Models included the distance between caves, cave abundance, measures of winter length and winter onset, and species richness of all bats and hibernating bats only. We found that the best supported models included all of these factors, but that the particular structure and most informative covariates changed over the course of the outbreak, with winter length displacing winter onset as the most informative measure of winter conditions, and evidence for the effects total species richness and hibernation varying from year to year. We also found that weather had detectable effects on spread. While the effect sizes for cave abundance and species richness were relatively stable over the length of the outbreak, distance became less important as time went on. These findings indicate that although models produced early in the outbreak captured important and consistent aspects of the spatial spread of white-nose syndrome, there were also changes over time in the factors associated with spread, suggesting that forecasts may be improved by iterative model refinement.

ecology↗

bioRxiv2020

Analysing global economic costs of invasive alien species with the invacost R package

O_LIThe reported costs of invasive alien species from the global database InvaCost are heterogenous and cover different spatio-temporal scales. A standard procedure for aggregating invasive species cost estimates is necessary to ensure the repeatability and comparativeness of studies. C_LIO_LIWe introduce here the invacost R package, an open-source software designed to query and analyse the InvaCost database. We illustrate this package and its framework with cost data associated with invasive alien invertebrates. C_LIO_LIFirst, the invacost package provides updates of this dynamic database directly in the analytical environment R. Second, it helps understand the heteregoneous nature of monetary cost data for invasive species, processes to harmonize the data, and the inherent biases associated with such data. Third, it readily provides complementary methods to investigate the costs of invasive species at different scales, all the while accounting for econometric statistical issues. C_LIO_LIThis tool will be useful for scientists working on invasive alien species, by (i) facilitating access to and use of this multi-disciplinary data resource and (ii) providing a standard procedure which will facilitate reproducibility and comparability among studies, one of the major critics of this topic until now. It should facilitate further interdisciplinary works including economists and invasion ecology researchers. C_LI

ecology↗

bioRxiv2020

Deep learning for time series classification in ecology

Temporal data is ubiquitous in ecology and ecologists often face the challenge of accurately differentiating these data into predefined classes, such as biological entities or ecological states. The usual approach consists of transforming the time series into user-defined features and then using these features as predictors in conventional statistical or machine learning models. Here we suggest the use of deep learning models as an alternative to this approach. Recent deep learning techniques can perform the classification directly from the time series, eliminating subjective and resource-consuming data transformation steps, and potentially improving classification results. We describe some of the deep learning architectures relevant for time series classification and show how these architectures and their hyper-parameters can be tested and used for the classification problems at hand. We illustrate the approach using three case studies from distinct ecological subdisciplines: i) insect species identification from wingbeat spectrograms; ii) species distribution modelling from climate time series and iii) the classification of phenological phases from continuous meteorological data. The deep learning approach delivered ecologically sensible and accurate classifications demonstrating its potential for wide applicability across subfields of ecology.

ecology↗