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bioRxiv2024

Multi-species cryoEM calibration and workflow verification standard

Cryogenic electron microscopy (cryoEM) is a rapidly growing structural biology modality that has been successful in revealing molecular details of biological systems. However, unlike established biophysical and analytical techniques with calibration standards, cryoEM has lacked comprehensive biological test samples. We introduce a cryoEM calibration sample that is a mixture of compatible macromolecules that can be used not only for resolution optimization but also provides multiple reference points for evaluating instrument performance, data quality, and image processing workflows in a single experiment. This combined test specimen provides researchers a reference point for validating their cryoEM pipeline, benchmarking their methodologies, and testing new algorithms.

biochemistry↗

bioRxiv2025

Transient optical clearing using absorbing molecules for ex-vivo and in-vivo imaging

Dynamic imaging is a foundational tool in biology and medicine but is limited by light scattering in live tissues caused by refractive index mismatches, which reduces penetration depth and resolution in biological tissues. Our previous work introduced a novel approach to achieve an optical transparency in live animals by utilizing strongly absorbing molecules. When these absorbing molecules, such as Tartrazine, an FDA approved dye commonly used in foods, dissolve in water, they modify the refractive index of the aqueous medium through Kramers-Kronig relations. This modulation allows reduction of the refractive index mismatch between separate components in tissue with distinct refractive indices, thus mitigating scattering. In this article, we report detailed methodology of our tissue clearing technique using a Tartrazine-based aqueous solution, applied to both ex-vivo and in-vivo samples. This approach reversibly renders live mice bodies transparent in the visible spectrum, enabling clearer and deeper imaging of internal processes and structures. We then utilized an advanced image processing algorithm to improve the contrast and segmentation of organs of interests. We also show high transparency in ex-vivo samples, such as chicken breast, when treated with Tartrazine based solution. This method offers a versatile and accessible approach to enhancing imaging capabilities across various modalities and paves the way for advancement in our understanding of complex biological systems. SUMMARYOptical clearing techniques, such as absorbing molecules solutions, reduce light scattering temporarily to enhance biological imaging and offer biocompatible, reversible in-vivo imaging strategies for deeply embedded organs. This protocol presents detailed experimental procedures as well as advanced image processing to achieve high-quality, dynamic imaging for real-time dynamics in living animals.

bioengineering↗

bioRxiv2025

SCAPE: An AI-Driven Platform for Comprehensive Single-Cell Data Analysis

Single-cell RNA sequencing (scRNA-seq) has transformed the study of cellular heterogeneity, but downstream analysis remains fragmented and technically demanding. Current pipelines often lack analytical diversity, require programming expertise, and offer limited integration of advanced methods. To address these challenges, we developed SCAPE, an AI-driven automated and interactive platform that unifies multi-omics, multi-resource, and multi-modal single-cell data analysis. SCAPE provides platform-independent installation, customizable workflows, and integration of R- and Python-based tools. Beyond Seurat and Scanpy, it incorporates modules for transcription factor and pathway inference, pseudotime trajectory reconstruction, spatial transcriptomics deconvolution, and cell-cell communication analysis. To demonstrate SCAPE, we curated a unified atlas of lung cancer progression in human patients and mouse models, spanning primary tumors and metastatic sites. The platform enabled harmonized integration, regulatory program inference, and spatial mapping, revealing conserved epithelial programs that promote metastatic seeding and organ-specific adaptations driven by microenvironments. Collectively, SCAPE offers an accessible and comprehensive framework for single-cell analysis, providing new insights into cancer progression and broad utility across biological systems.

cancer biology↗

bioRxiv2018

Pou5f3, SoxB1 and Nanog remodel chromatin on High Nucleosome Affinity Regions at Zygotic Genome Activation

The zebrafish embryo is mostly transcriptionally quiescent during the first 10 cell cycles, until the main wave of Zygotic Genome Activation (ZGA) occurs, accompanied by fast chromatin remodeling. At ZGA, homologs of mammalian stem cell transcription factors (TFs) Pou5f3, Nanog and Sox19b bind to thousands of developmental enhancers to initiate transcription. So far, how these TFs influence chromatin dynamics at ZGA has remained unresolved. To address this question, we analyzed nucleosome positions in wild-type and Maternal-Zygotic (MZ) mutants for pou5f3 and nanog by MNase-seq. We show that Nanog, Sox19b and Pou5f3 bind to the High Nucleosome Affinity Regions (HNARs). HNARs are spanning over 600 bp, featuring high in vivo and predicted in vitro nucleosome occupancy and high predicted propeller twist DNA shape value. We suggest a two-step nucleosome destabilization-depletion model, where the same intrinsic DNA properties of HNAR promote both high nucleosome occupancy and differential binding of TFs. In the first step, already prior to ZGA, Pou5f3 and Nanog destabilize nucleosomes on HNAR centers genome-wide. In the second step, post-ZGA, Nanog, Pou5f3 and SoxB1 maintain open chromatin state on the subset of HNARs, acting synergistically. Nanog binds to the HNAR center, while the Pou5f3 stabilizes the flanks. The HNAR model will provide a useful tool for genome regulatory studies in the variety of biological systems.

developmental biology↗

bioRxiv2025

The evolution of public goods altruism

Organisms often behave altruistically by investing resources into public goods production. Because the rewards of public goods are freely available to everyone in a group, they are potentially vulnerable to exploitation by cheaters. Classic kin selection models offer solutions to this public goods dilemma in terms of why public goods investment can be favoured, while game theoretical models offer insights into the conditions where exploitative cheaters can persist. However, existing theory does not provide a unified framework for understanding how much individuals should actually invest in altruism or how this investment strategy relates to why cheaters can coexist with altruists. Here we fill this gap by considering how resource allocation trade-offs shape the generosity of altruists, how this investment decision, in turn, sets the conditions for cheater coexistence, and how local competition impacts these outcomes. We find that, whenever altruists can evolve the optimal level of public goods investment, they exclude cheaters, revealing that stable altruists/cheater coexistence requires overly generous altruists--an outcome frequently driven by mechanistic constraints that limit the range of behaviours in biological systems. Importantly, we show that local competition tends to prevent such coexistence. By capturing key biologically relevant factors, our model provides a compelling framework for interpreting patterns of variation in altruistic behaviour in natural populations, and a means of generating testable predictions.

evolutionary biology↗

bioRxiv2024

CRISPR-GPT: An LLM Agent for Automated Design of Gene-Editing Experiments

Performing effective gene-editing experiments requires a deep understanding of both the CRISPR technology and the biological system involved. Meanwhile, despite their versatility and promise, Large Language Models (LLMs) often lack domain-specific knowledge and struggle to accurately solve biological design problems. We present CRISPR-GPT, an LLM agent system to automate and enhance CRISPR-based gene-editing design and data analysis. CRISPR-GPT leverages the reasoning capabilities of LLMs for complex task decomposition, decision-making, and interactive human-artificial intelligence (AI) collaboration. This system incorporates domain expertise, retrieval techniques, external tools, and a specialized LLM fine-tuned with open-forum discussions among scientists. CRISPR-GPT assists users in selecting CRISPR systems, experiment planning, designing gRNAs, choosing delivery methods, drafting protocols, designing assays, and analyzing data. We showcase the potential of CRISPR-GPT by knocking-out four genes with CRISPR-Cas12a in a human lung adenocarcinoma cell line and epigenetically activating two genes using CRISPR-dCas9 in human melanoma cell line. CRISPR-GPT enables fully AI-guided gene-editing experiment design and analysis across different modalities, validating its effectiveness as an AI co-pilot in genome engineering.

bioinformatics↗

bioRxiv2016

A computational method for the investigation of multistable systems and its application to genetic switches

Genetic switches exhibit multistability, form the basis of epigenetic memory, and are found in natural decision making systems, such as cell fate determination in developmental pathways. Synthetic genetic switches can be used for recording the presence of different environmental signals, for changing phenotype using synthetic inputs and as building blocks for higher-level sequential logic circuits. Understanding how multistable switches can be constructed and how they function within larger biological systems is therefore key to synthetic biology. Here we present a new computational tool, called StabilityFinder, that takes advantage of sequential Monte Carlo methods to identify regions of parameter space capable of producing multistable behaviour, while handling uncertainty in biochemical rate constants and initial conditions. The algorithm works by clustering trajectories in phase space, and iteratively minimizing a distance metric. Here we examine a collection of models of genetic switches, ranging from the deterministic Gardner toggle switch to stochastic models containing different positive feedback connections. We uncover the design principles behind making bistable, tristable and quadristable switches, and find that rate of gene expression is a key parameter. We demonstrate the ability of the framework to examine more complex systems and examine the design principles of a three gene switch. Our framework allows us to relax the assumptions that are often used in genetic switch models and we show that more complex abstractions are still capable of multistable behaviour. Our results suggest many ways in which genetic switches can be enhanced and offer designs for the construction of novel switches. Our analysis also highlights subtle changes in correlation of experimentally tunable parameters that can lead to bifurcations in deterministic and stochastic systems. Overall we demonstrate that StabilityFinder will be a valuable tool in the future design and construction of novel gene networks.

synthetic biology↗

bioRxiv2020

Rearing medium dictates variability across replicates in untreated and arsenic challenged zebrafish larvae

Reproducibility and consistency are hallmarks of scientific integrity. Biological systems are inherently noisy, posing a challenge to reproducibility. This is particularly relevant to the field of environmental toxicology, where many unaccounted experimental parameters can have a marked influence on the biological response to exposure. Here, we extend the use of zebrafish as a robust toxicological model for studying the effects of inorganic arsenic (iAs) on liver biology. We observed that iAs toxicity in this system is not influenced by important parameters including genetic background, rearing container material or rearing volume but the dose response to iAs is influenced by the rearing medium. We compared mortality as a measure of iAs toxicity to embryos cultured in two standard rearing media: egg water made from dehydrated ocean salts dissolved in water and a defined embryo medium which is a pH adjusted, buffered salt solution. Larvae reared in egg water were more susceptible to iAs compared to those reared in embryo medium. This effect was independent of the pH differences between these solutions. These culture conditions did not cause any difference in the global hepatic transcriptome of control zebrafish. Further, no difference in the expression of genes involved in the unfolded protein response (UPR) in larvae exposed to iAs treatment or in a stress independent system to activate UPR genes by transgenic overexpression of activating transcription factor 6 (nAtf6) in hepatocytes was observed. However, the clutch-to-clutch variation in gene expression was significantly greater in larvae reared in egg water compared to those in embryo medium. These data demonstrate that egg water affects reproducibility across replicates in terms of gene expression and exacerbates iAs mediated toxic response. This highlights the importance of rigorous evaluation of experimental conditions to assure reproducibility.

pharmacology and toxicology↗

bioRxiv2020

Normative models of enhancer function

In prokaryotes, thermodynamic models of gene regulation provide a highly quantitative mapping from promoter sequences to gene expression levels that is compatible with in vivo and in vitro bio-physical measurements. Such concordance has not been achieved for models of enhancer function in eukaryotes. In equilibrium models, it is difficult to reconcile the reported short transcription factor (TF) residence times on the DNA with the high specificity of regulation. In non-equilibrium models, progress is difficult due to an explosion in the number of parameters. Here, we navigate this complexity by looking for minimal non-equilibrium enhancer models that yield desired regulatory phenotypes: low TF residence time, high specificity and tunable cooperativity. We find that a single extra parameter, interpretable as the "linking rate" by which bound TFs interact with Mediator components, enables our models to escape equilibrium bounds and access optimal regulatory phenotypes, while remaining consistent with the reported phenomenology and simple enough to be inferred from upcoming experiments. We further find that high specificity in non-equilibrium models is in a tradeoff with gene expression noise, predicting bursty dynamics -- an experimentally-observed hallmark of eukaryotic transcription. By drastically reducing the vast parameter space to a much smaller subspace that optimally realizes biological function prior to inference from data, our normative approach holds promise for mathematical models in systems biology.

biophysics↗

bioRxiv2020

Single-Cell RNA Sequencing of Batch Chlamydomonas Cultures Reveals Heterogeneity in their Diurnal Cycle Phase

The photosynthetic unicellular alga Chlamydomonas (Chlamydomonas reinhardtii) is a versatile reference for algal biology because of the facility with which it can be cultured in the laboratory. Genomic and systems biology approaches have previously been used to describe how the transcriptome responds to environmental changes, but this analysis has been limited to bulk data, representing the average behavior from pools of cells. Here, we apply single-cell RNA sequencing (scRNA-seq) to probe the heterogeneity of Chlamydomonas cell populations under three environments and in two genotypes differing in the presence of a cell wall. First, we determined that RNA can be extracted from single algal cells with or without a cell wall, offering the possibility to sample algae communities in the wild. Second, scRNA-seq successfully separated single cells into non-overlapping cell clusters according to their growth conditions. Cells exposed to iron or nitrogen deficiency were easily distinguished despite a shared tendency to arrest cell division to economize resources. Notably, these groups of cells recapitulated known patterns observed with bulk RNA-seq, but also revealed their inherent heterogeneity. A substantial source of variation between cells originated from their endogenous diurnal phase, although cultures were grown in constant light. We exploited this result to show that circadian iron responses may be conserved from algae to land plants. We propose that bulk RNA-seq data represent an average of varied cell states that hides underappreciated heterogeneity. One-sentence summaryWe show that single-cell RNA-seq (scRNA-seq) can be applied to Chlamydomonas cultures to reveal the that heterogenity in bulk cultures is largely driven by diurnal cycle phases The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantcell.org) is: Matteo Pellegrini (matteop@mcdb.ucla.edu)

genomics↗

bioRxiv2023

csppinet: A Python package for context-specific biological network construction and analysis based on omics data

The analysis of large-scale biological data, particularly omics data, has become essential for understanding complex biological systems. In this study, we present a Python package for the construction of context-specific biological networks based on gene expression activity and protein-protein interaction (PPI) data. The package leverages computational tools and the NetworkX library for network analysis. Through a case study focusing on yeast fermentation with glucose and xylose as carbon sources, we demonstrate the packages capabilities. The context-specific networks derived from these fermentation conditions were compared to highlight the impact of different carbon sources and stages on network dynamics. Hub genes were identified within each network, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to understand their functional implications. The results revealed distinct hub genes and enriched biological processes in each context-specific network. In the glucose-specific network, the exclusively proteins in this network revealed enrichment in terms related to chromosome organization, biological regulation, MAPK signaling pathway and mismatch repair pathway. Conversely, the exclusively proteins in xylose-specific network revealed enrichment in generation of precursor metabolites and energy), mitochondrion organization, response to extracellular stimulus, glutamate metabolic process, cellular response to alcohol, beta-Alanine metabolism, arginine and proline metabolism, glyoxylate and dicarboxylate metabolism and pyruvate metabolism. The developed Python package and context-specific networks provide researchers with a valuable framework to explore complex biological phenomena. By integrating gene expression profiles and PPI data, researchers can gain insights into context-dependent molecular interactions and regulatory mechanisms. These findings contribute to the understanding of cellular behavior and have potential implications in disease mechanisms, biomarker identification, and drug target discovery. The csppinet package is available at https://github.com/lmigueel/csppinet.

bioinformatics↗

bioRxiv2024

Start&Stop - a PhysiCell and PhysiBoSS 2.0 add-on for interactive simulation control

In computational biology, in silico simulators are vital for exploring and understanding the behavior of complex biological systems. Hybrid multi-level simulators, such as PhysiCell and PhysiBoSS 2.0, integrate multiple layers of biological complexity, providing deeper insights into emergent patterns. However, one key limitation of these simulators is the inability to adjust simulation parameters once the simulation has started, which hinders the interactive exploration and adaptation of dynamic protocols ranging from biofabrication to in vitro pharmacological testing. To address this challenge, we introduce the Start & Stop add-on for PhysiCell and PhysiBoSS 2.0. This add-on offers multi-level state preservation and multi-modal stop control, triggered by simulation time or cell conditions, enabling users to pause a simulation, adjust parameters, and then resume from the exact halted state. We validate Start & Stop using two well-established PhysiBoSS 2.0 use cases, a tumor spheroid 3T3 mouse fibroblasts use case under tumor necrosis factor (TNF) stimulation, and a lung cancer cell line invasion simulation, demonstrating that it preserves the simulators original behavior while enabling interactive configuration changes that facilitate the exploration of diverse and adaptive treatment strategies. By enhancing flexibility and user interaction, Start & Stop makes PhysiCell and PhysiBoSS 2.0 more akin to real in vitro scenarios, thus expanding the range of potential simulations and advancing more effective protocol development in a variety of applications.

bioinformatics↗

bioRxiv2025

Local Topological Structures and Global Connectivity Patterns in a Trachelospermum jasminoides: a Pilot Study

Topological approaches to biological systems provide insights into their growth patterns, network connectivity and spatial organization. This perspective explores how biological structures self-organize, maintain stability and adapt to environmental constraints, revealing fundamental principles of efficiency, robustness, resilience and functional optimization. In this pilot study, we analysed the local and global topological properties of a Trachelospermum jasminoides bush (commonly known as star jasmine) using persistent homology, graph theory, spectral analysis and percolation theory. The spatial positions of individual flowers were extracted from an image of the bush and represented as a point cloud to capture their structural distribution and spatial relationships. Using Delaunay triangulation, a connectivity graph revealed a dominant connected component with minimal isolated structures. DBSCAN analysis identified a large number of small, localized clusters, reflecting biological and environmental influences. Most flowers connected to five to six neighbours, forming a uniform network with high clustering. Shortest path analysis showed efficient long-range connectivity, with paths avoiding sparse regions. Spectral analysis indicated smooth percolation without bottlenecks, while percolation analysis simulations revealed resilience up to 18% flower removal, after which connectivity broke down. In sum, we showed that the star jasmine bush topology balances local structural constraints with global connectivity, ensuring efficient resource distribution and structural integrity. By integrating topological data analysis with ecological modeling, we suggested a methodological approach to understanding natural growth networks. These insights can inform broader studies on biological pattern formation, network resilience and plant architecture modeling in ecology, agricultural sciences and biomimetic design.

bioinformatics↗

bioRxiv2025

Force Propagation in Active Cytoskeletal Networks

Conventional materials science establishes clear structure-function relationships: grain boundaries modulate strength by blocking dislocations, and defect density limits conductivity through electron scattering. Active matter presents a fundamentally different challenge, components continuously consume energy to generate forces, creating a dual identity as both structural elements and force-generating machines. This material-machine duality raises a fundamental question: which structural parameters govern functional behavior in systems where constituents actively generate forces rather than merely responding to external inputs? Here we demonstrate that percolation is the critical mechanism that transforms active cytoskeletal networks from energy-dissipating materials into work-performing machines. Using light-controlled microtubule-kinesin networks, we show that increasing bundle length from 0.9 {micro}m to 5 {micro}m triggers a percolation transition that enables global force organization. Networks below the threshold remain incapable of performing coordinated work, while networks above it develop correlation lengths exceeding 240 {micro}m, generate 25-fold stronger forces, and extract 1000-fold more mechanical work, powering transport of live cells across 800 {micro}m distances. Network simulations reveal that introducing just 5-10% longer bundles creates a connected component containing 90% of microtubules, establishing the precise threshold that drives this material-to-machine transition. Our findings establish percolation as the fundamental mechanism governing whether active matter systems function as passive materials or as coordinated machines--providing design principles for both synthetic and biological systems. increasing bundle length from 0.9 {micro}m to 5 {micro}m increasing bundle length from 0.9 {micro}m to 5 {micro}m

biophysics↗

bioRxiv2025

Fundamental Limits of Inferring Dynamical Gene Regulatory Models from Single-Cell Data

A long-standing goal in systems biology is to construct computational models that capture gene regulatory dynamics. On the one hand, graph-based regulatory network models rely on prior knowledge but are constrained by unknown kinetic parameters. On the other hand, recent advancements in single-cell sequencing technologies, particularly RNA velocity and pseudotime analysis, offer data-driven approaches to infer kinetic parameters. However, accurate modeling of biological dynamics requires the estimation of the Jacobian matrix, which captures signal propagation within a regulatory system. Several computational methods have recently emerged to infer Jacobians from single-cell data, including SpliceJAC, scMomentum, and Dynamo. Here, we evaluate these methods by analyzing Jacobian eigenvalues, assessing their ability to capture stability and oscillatory properties. Using simulated ground truth data, we find that methods such as Dynamo and scMomentum generate models that capture the behavioral properties of the cells, however, they do not reliably capture the structural properties. Our results highlight the limitations of dynamic GRN inference, namely, the inability of kinetics to alleviate the need for further structural constrains for the models to adequately reflect the structural properties of the GRN. Developing more robust approaches for Jacobian estimation from single-cell data will be essential for building accurate, large-scale dynamical models of gene regulation. Graphical AbstractOverview of Benchmarking Jacobian-based inference in single-cell RNA Data. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=138 SRC="FIGDIR/small/674509v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@13496e2org.highwire.dtl.DTLVardef@19e0250org.highwire.dtl.DTLVardef@a9db39org.highwire.dtl.DTLVardef@17789e2_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Detecting Mitochondrial Free Radicals with Quantum Sensors: From Organelles to a D. melanogaster Model of Neurodegeneration

This study identifies a distinct free radical signature in a D. melanogaster model of Parkinsons Disease, successfully differentiating Pink1-deficient flies from wild-type controls. This insight was achieved using a robust quantum sensing methodology for the selective detection of free radicals in biological systems. Our approach utilizes optically detected magnetic resonance (ODMR) and magnetic modulation (MM) protocols with nanodiamond nitrogen-vacancy (NV) centres. Selective identification is achieved using the spin probe TEMPOL, a cell-permeable superoxide dismutase 2 (SOD2) mimic that initially quenches the photoluminescence signal. Upon scavenging free radicals like hydroxyl and superoxide, TEMPOL is converted to a diamagnetic adduct, restoring the contrast and thus enabling quantitative detection. The approach was first validated in a chemical system with radical generation confirmed by electron paramagnetic resonance (EPR) spectroscopy. It was then demonstrated across biological scales, from isolated mitochondria and whole glioblastoma cells to the Drosophila model. In these studies, high-resolution respirometry revealed distinct free radical signatures, and findings were compared with NV T1 relaxometry. This work provides new biophysical insight into mitochondrial dysfunction, demonstrating a distinct free radical signature in a neurodegeneration model and connecting it to specific metabolic states.

cell biology↗

bioRxiv2018

Three-dimensional cell geometry controls excitable membrane signaling in Dictyotelium cells

Phosphatidylinositol (3,4,5)-trisphosphate (PtdInsP3) is known to propagate as waves on the plasma membrane and is related to the membrane protrusive activities in Dictyostelium and mammalian cells. While there have been a few attempts to study the three-dimensional dynamics of these processes, most studies have focused on the dynamics extracted from single focal planes. However, the relation between the dynamics and three-dimensional cell shape remains elusive, due to the lack of signaling information about the unobserved part of the membrane. Here we show that PtdInsP3 wave dynamics are directly regulated by the three-dimensional geometry - size and shape - of the plasma membrane. By introducing an analysis method that extracts the three-dimensional spatiotemporal activities on the entire cell membrane, we show that PtdInsP3 waves self-regulate their dynamics within the confined membrane area. This leads to changes in speed, orientation and pattern evolution, following the underlying excitability of the signal transduction system. Our findings emphasize the role of the plasma membrane topology in reaction-diffusion driven biological systems and indicate its importance in other mammalian systems.

biophysics↗

bioRxiv2018

GIMLET: Identifying Biological Modulators in Context-Specific Gene Regulation Using Local Energy Statistics

The regulation of transcription factor activity dynamically changes across cellular conditions and disease subtypes. The identification of biological modulators contributing to context-specific gene regulation is one of the challenging tasks in systems biology, which is necessary to understand and control cellular responses across different genetic backgrounds and environmental conditions. Previous approaches for identifying biological modulators from gene expression data were restricted to the capturing of a particular type of a three-way dependency among a regulator, its target gene, and a modulator; these methods cannot describe the complex regulation structure, such as when multiple regulators, their target genes, and modulators are functionally related. Here, we propose a statistical method for identifying biological modulators by capturing multivariate local dependencies, based on energy statistics, which is a class of statistics based on distances. Subsequently, our method assigns a measure of statistical significance to each candidate modulator through a permutation test. We compared our approach with that of a leading competitor for identifying modulators, and illustrated its performance through both simulations and real data analysis. Our method, entitled genome-wide identification of modulators using local energy statistical test (GIMLET), is implemented with R ([≥] 3.2.2) and is available from github (https://github.com/tshimam/GIMLET).

bioinformatics↗