HyperGraphs.jl - representing high-order relationships in Julia.
Ontology highlight
ABSTRACT: Summary
HyperGraphs.jl is a Julia package that implements hypergraphs. These are a generalisation of graphs that allow us to represent n-ary relationships and not just binary, pairwise relationships. High-order interactions are commonplace in biological systems and are of critical importance to their dynamics; hypergraphs thus offer a natural way to accurately describe and model these systems.Availability and implementation
HyperGraphs.jl is freely available under the MIT license. Source code and documentation can be found at https://github.com/lpmdiaz/HyperGraphs.jl.Supplementary information
Supplementary information is available at Bioinformatics online.
SUBMITTER: Diaz LPM
PROVIDER: S-EPMC9326852 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:To ensure the correctness of network analysis methods, the network (as the input) has to be a sufficiently accurate representation of the underlying data. However, when representing sequential data from complex systems, such as global shipping traffic or Web clickstream traffic as networks, conventional network representations that implicitly assume the Markov property (first-order dependency) can quickly become limiting. This assumption holds that, when movements are simulated on the network, the next movement depends only on the current node, discounting the fact that the movement may depend on several previous steps. However, we show that data derived from many complex systems can show up to fifth-order dependencies. In these cases, the oversimplifying assumption of the first-order network representation can lead to inaccurate network analysis results. To address this problem, we propose the higher-order network (HON) representation that can discover and embed variable orders of dependencies in a network representation. Through a comprehensive empirical evaluation and analysis, we establish several desirable characteristics of HON, including accuracy, scalability, and direct compatibility with the existing suite of network analysis methods. We illustrate how HON can be applied to a broad variety of tasks, such as random walking, clustering, and ranking, and we demonstrate that, by using it as input, HON yields more accurate results without any modification to these tasks.
| S-EPMC4928957 | biostudies-literature
Project description:Flux balance analysis and its variants are widely used methods for predicting steady-state reaction rates in biochemical reaction networks. The exploration of high dimensional networks with such methods is currently hampered by software performance limitations.DistributedFBA.jl is a high-level, high-performance, open-source implementation of flux balance analysis in Julia. It is tailored to solve multiple flux balance analyses on a subset or all the reactions of large and huge-scale networks, on any number of threads or nodes.The code is freely available on github.com/opencobra/COBRA.jl. The documentation can be found at opencobra.github.io/COBRA.jl.ronan.mt.fleming@gmail.com.Supplementary data are available at Bioinformatics online.
| S-EPMC5408791 | biostudies-other
Project description:Synthesis of a novel, stable reagent (1,3-benzothiazol-2-ylsulfonyl)fluoroacetonitrile from readily synthesized ethyl alpha-(1,3-benzothiazol-2-ylsulfanyl)-alpha-fluoroacetate is reported. Aldehydes undergo condensations with (1,3-benzothiazol-2-ylsulfonyl)fluoroacetonitrile in the presence of DBU leading to alpha-fluoro acrylonitriles in high yields and with good Z-stereoselectivity. Lowering of reaction temperature increases the Z selectivity.
| S-EPMC2771215 | biostudies-literature
Project description:BACKGROUND AND OBJECTIVES:Biological systems with intertwined feedback loops pose a challenge to mathematical modeling efforts. Moreover, rare events, such as mutation and extinction, complicate system dynamics. Stochastic simulation algorithms are useful in generating time-evolution trajectories for these systems because they can adequately capture the influence of random fluctuations and quantify rare events. We present a simple and flexible package, BioSimulator.jl, for implementing the Gillespie algorithm, ?-leaping, and related stochastic simulation algorithms. The objective of this work is to provide scientists across domains with fast, user-friendly simulation tools. METHODS:We used the high-performance programming language Julia because of its emphasis on scientific computing. Our software package implements a suite of stochastic simulation algorithms based on Markov chain theory. We provide the ability to (a) diagram Petri Nets describing interactions, (b) plot average trajectories and attached standard deviations of each participating species over time, and (c) generate frequency distributions of each species at a specified time. RESULTS:BioSimulator.jl's interface allows users to build models programmatically within Julia. A model is then passed to the simulate routine to generate simulation data. The built-in tools allow one to visualize results and compute summary statistics. Our examples highlight the broad applicability of our software to systems of varying complexity from ecology, systems biology, chemistry, and genetics. CONCLUSION:The user-friendly nature of BioSimulator.jl encourages the use of stochastic simulation, minimizes tedious programming efforts, and reduces errors during model specification.
| S-EPMC6388686 | biostudies-literature
Project description:BackgroundSemantic interoperability is essential for improving data quality and sharing. The ISO/IEC 11179 Metadata Registry (MDR) standard has been highlighted as a solution for standardizing and registering clinical data elements (DEs). However, the standard model has both structural and semantic limitations, and the number of DEs continues to increase due to poor term reusability. Semantic types and constraints are lacking for comprehensively describing and evaluating DEs on real-world clinical documents.MethodsWe addressed these limitations by defining three new types of semantic relationship (dependency, composite, and variable) in our previous studies. The present study created new and further extended existing semantic types (hybrid atomic and repeated and dictionary composite common data elements [CDEs]) with four constraints: ordered, operated, required, and dependent. For evaluation, we extracted all atomic and composite CDEs from five major clinical documents from five teaching hospitals in Korea, 14 Fast Healthcare Interoperability Resources (FHIR) resources from FHIR bulk sample data, and MIMIC-III (Medical Information Mart for Intensive Care) demo dataset. Metadata reusability and semantic interoperability in real clinical settings were comprehensively evaluated by applying the CDEs with our extended semantic types and constraints.ResultsAll of the CDEs (n = 1142) extracted from the 25 clinical documents were successfully integrated with a very high CDE reuse ratio (46.9%) into 586 CDEs (259 atomic and 20 unique composite CDEs), and all of CDEs (n = 238) extracted from the 14 FHIR resources of FHIR bulk sample data were successfully integrated with high CDE reuse ration (59.7%) into 96 CDEs (21 atomic and 28 unique composite CDEs), which improved the semantic integrity and interoperability without any semantic loss. Moreover, the most complex data structures from two CDE projects were successfully encoded with rich semantics and semantic integrity.ConclusionMDR-based extended semantic types and constraints can facilitate comprehensive representation of clinical documents with rich semantics, and improved semantic interoperability without semantic loss.
| S-EPMC7333279 | biostudies-literature
Project description:Phylogenetic relationships among the major groups of hymenopteran insects were investigated by using comparative sequence information from the mitochondrial 16S rRNA gene. The placement of the ectoparasitic Stephanidae as the sister group to the remaining Apocrita confirmed ectoparasitism as the ground plan biology for the Apocrita. Endoparasitism evolved at least eight times within the Apocrita, and the consequent association with polydnaviruses and virus-like particles evolved at least three times. The Evaniomorpha were consistently placed as basal to the remaining Apocrita but were not resolved as monophyletic. The Gasteruptiidae were resolved as the sister group to the Evaniidae, but the relationship between the Trigonalyoidea and the Evanioidea was unclear. The Proctotrupomorpha (sensu Rasnitsyn) was resolved by topology-dependent permutation tail probability (T-PTP) testing as monophyletic, with strong evidence for a sister group relationship between the Platygastroidea and the Chalcidoidea. Strong evidence was found for the monophyly of the Ichneumonomorpha (Ichneumonidae + Braconidae) and the sister-group relationship between the Aculeata (Vespomorpha) and the Ichneumonomorpha.
| S-EPMC44927 | biostudies-other
Project description:Collodaria are the only group of Radiolaria that has a colonial lifestyle. This group is potentially the most important plankton in the oligotrophic ocean because of its large biomass and the high primary productivity associated with the numerous symbionts inside a cell or colony. The evolution of Collodaria could thus be related to the changes in paleo-productivity that have affected organic carbon fixation in the oligotrophic ocean. However, the fossil record of Collodaria is insufficient to trace their abundance through geological time, because most collodarians do not have silicified shells. Recently, molecular phylogeny based on nuclear small sub-unit ribosomal DNA (SSU rDNA) confirmed Collodaria to be one of five orders of Radiolaria, though the relationship among collodarians is still unresolved because of inadequate taxonomic sampling. Our phylogenetic analysis has revealed four novel collodarian sequences, on the basis of which collodarians can be divided into four clades that correspond to taxonomic grouping at the family level: Thalassicollidae, Collozoidae, Collosphaeridae, and Collophidae. Comparison of the results of our phylogenetic analyses with the morphological characteristics of each collodarian family suggests that the first ancestral collodarians had a solitary lifestyle and left no silica deposits. The timing of events estimated from molecular divergence calculations indicates that naked collodarian lineages first appeared around 45.6 million years (Ma) ago, coincident with the diversification of diatoms in the pelagic oceans. Colonial collodarians appeared after the formation of the present ocean circulation system and the development of oligotrophic conditions in the equatorial Pacific (ca. 33.4 Ma ago). The divergence of colonial collodarians probably caused a shift in the efficiency of primary production during this period.
| S-EPMC3342292 | biostudies-literature
Project description:A Julia-Kocienski approach to trifluoromethyl-substituted alkenes was evaluated in the reactions of 1,3-benzothiazol-2-yl, 1-phenyl-1H-tetrazol-5-yl, and 1-tbutyl-1H-tetrazol-5-yl 2,2,2-trifluoroethyl sulfones with aldehydes. Among the various conditions tested, the best yields were obtained with 1-phenyl-1H-tetrazol-5-yl 2,2,2-trifluoroethyl sulfone, in CsF-mediated, room temperature olefinations in DMSO. Aromatic aldehydes gave (trifluoromethyl)vinyl derivatives in 23-86% yields, with generally moderate stereoselectivity. Straightforward synthesis of the Julia-Kocienski reagent, and conversion to trifluoromethyl-substituted alkenes under mild reaction conditions, are the advantages of this approach.
| S-EPMC4283945 | biostudies-literature
Project description:The order Phyllachorales (Pezizomycotina, Ascomycota) is a group of biotrophic, obligate plant parasitic fungi with a tropical distribution and high host specificity. Traditionally two families are recognised within this order: Phyllachoraceae and Phaeochoraceae, based mostly on morphological and host characteristics. Currently, the position of the order within the class Sordariomycetes is inconclusive, as well as the monophyly of the order, and its internal phylogenetic structure. Here we present a phylogeny of the order Phyllachorales based on sequence data of 29 species with a broad host range resulting from a wide geographical sampling. We inferred Maximum Likelihood and Bayesian phylogenies from data of five DNA regions: nrLSU rDNA, nrSSU rDNA, ITS rDNA, and the protein coding genes RPB2, and TEF1. We found that the order Phyllachorales is monophyletic and related to members of the subclass Sordariomycetidae within Sordariomycetes. Within the order, members of the family Phaeochoraceae form a monophyletic group, and the family Phyllachoraceae is split into two lineages. Maximum Likelihood ancestral state reconstructions indicate that the ancestor of Phyllachorales had a monocotyledonous host plant, immersed perithecia, and a black stroma. Alternative states of these characters evolved multiple times independently within the order. Based on our results we redefine the family Phyllachoraceae and propose the new family Telimenaceae with Telimena erythrinae as type species, resulting in three families in the order. Species of Telimena spp. occur in several monocotyledonous and eudicotyledonous host plants except Poaceae, and generally have enlarged black pseudostroma around the perithecia, a character not present in species of Phyllachoraceae.
| S-EPMC5832958 | biostudies-literature
Project description:High throughput mRNA expression profiling can be used to characterize the response of cell culture models to perturbations such as pharmacologic modulators and genetic perturbations. As profiling campaigns expand in scope, it is important to homogenize, summarize, and analyze the resulting data in a manner that captures significant biological signals in spite of various noise sources such as batch effects and stochastic variation. We used the L1000 platform for large-scale profiling of 978 representative genes across thousands of compound treatments. Here, a method is described that uses deep learning techniques to convert the expression changes of the landmark genes into a perturbation barcode that reveals important features of the underlying data, performing better than the raw data in revealing important biological insights. The barcode captures compound structure and target information, and predicts a compound's high throughput screening promiscuity, to a higher degree than the original data measurements, indicating that the approach uncovers underlying factors of the expression data that are otherwise entangled or masked by noise. Furthermore, we demonstrate that visualizations derived from the perturbation barcode can be used to more sensitively assign functions to unknown compounds through a guilt-by-association approach, which we use to predict and experimentally validate the activity of compounds on the MAPK pathway. The demonstrated application of deep metric learning to large-scale chemical genetics projects highlights the utility of this and related approaches to the extraction of insights and testable hypotheses from big, sometimes noisy data.
| S-EPMC5300121 | biostudies-literature