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172 results for “Genetic Network”
Population genomic evidence that stream networks structure genetic diversity in the narrowly endemic patch-nosed salamander (Urspelerpes brucei)
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Tagged original datasets for 'Genetically Optimized Massively Parallel Binary Neural Networks for Intrusion Detection Systems'
<p>Tagged, non-formatted, original datasets used in 'Genetically Optimized Massively Parallel Binary Neural Networks for Intrusion Detection Systems', T. Murovič, A. Trost.</p> <p>Available from the original authors:</p> <p>1. <a href="https://www.unsw.adfa.edu.au/unsw-canberra-cyber/cybersecurity/ADFA-NB15-Datasets/">https://www.unsw.adfa.edu.au/unsw-canberra-cyber/cybersecurity/ADFA-NB15-Datasets/</a> (UNWS-NB15 dataset)</p> <p>2. <a href="https://www.unb.ca/cic/datasets/nsl.html">https://www.unb.ca/cic/datasets/nsl.html</a> (NSL-KDD dataset)</p>
Analysing landscape effects on dispersal networks and gene flow with genetic graphs
<p>Graph-theoretic approaches have relevant applications in landscape genetic analyses. When species form populations in discrete habitat patches, genetic graphs can be used i) to identify direct dispersal paths followed by propagules or ii) to quantify landscape effects on multigenerational gene flow. However, the influence of their construction parameters remains to be explored. Using a simulation approach, we constructed genetic graphs using several pruning methods (geographical distance thresholds, topological constraints, statistical inference) and genetic distances to weight graph links (F<sub>ST</sub>, D<sub>PS</sub>, Euclidean genetic distances). We then compared the capacity of these different graphs to i) identify the precise topology of the dispersal network and ii) to infer landscape resistance to gene flow from the relationship between cost-distances and genetic distances. Although not always clear-cut, our results showed that methods based on geographical distance thresholds seem to better identify dispersal networks in most cases. More interestingly, our study demonstrates that a subselection of pairwise distances through graph pruning (thereby reducing the number of data points) can counter-intuitively lead to improved inferences of landscape effects on dispersal. Finally, we showed that genetic distances such as the D<sub>PS</sub> or Euclidean genetic distances should be preferred over the F<sub>ST</sub> for landscape effect inference as they respond faster to landscape changes.</p>
Data from: Genetics-based interactions of foundation species affect community diversity, stability, and network structure
We examined the hypothesis that genetics-based interactions between strongly interacting foundation species, the tree Populus angustifolia and the aphid Pemphigus betae, affect arthropod community diversity, stability and species interaction networks of which little is known. In a 2-year experimental manipulation of the tree and its aphid herbivore four major findings emerged: (i) the interactions of these two species determined the composition of an arthropod community of 139 species; (ii) both tree genotype and aphid presence significantly predicted community diversity; (iii) the presence of aphids on genetically susceptible trees increased the stability of arthropod communities across years; and (iv) the experimental removal of aphids affected community network structure (network degree, modularity and tree genotype contribution to modularity). These findings demonstrate that the interactions of foundation species are genetically based, which in turn significantly contributes to community diversity, stability and species interaction networks. These experiments provide an important step in understanding the evolution of Darwin's 'entangled bank', a metaphor that characterizes the complexity and interconnectedness of communities in the wild.
Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer's disease
<p>Connectome, traits and behavior data for APOE234 mice.</p> <ul> <li>1. connectome.zip: mouse brain structural connectivity matrices from diffusion MRI.</li> <li>2. FAGNN_Phenotype.csv: a sheet of trait information of mice used in the study.</li> </ul> <p>columns: winding numbers, total distance, normalized NE time, normalized NE distance, normalized NW time, normalized NW distance, normalized SE time, normalized SE distance, normlaized SW time, normalized SW distance, island latency to first entry, island entries, normalized thigmataxis time, and normalized thigmotaxis distance</p> <div>rows: 4 trials for each day from day 1 to day 5 with 1 probing test each at day 5 and day 8</div> <ul> <li>3. mouse_anatomy.csv: brain region information regarding the connectivity matrix.</li> <li>4. behavior.zip: behavioral data for each mouse from Morris Water Maze experiments.</li> </ul>
Supplementary data for manuscript "Genetic risk converges on regulatory networks mediating early type 2 diabetes"
<p>Supplementary data for manuscript "Genetic risk converges on regulatory networks mediating early type 2 diabetes" Nature 624, 621–629 (2023). <a href="https://doi.org/10.1038/s41586-023-06693-2">https://doi.org/10.1038/s41586-023-06693-2</a></p> <p>Brief description of the included files is given below. Please visit the manuscript website for latest updates: <a href="http://theparkerlab.org/manuscripts/2021_islet-rfx6/">http://theparkerlab.org/manuscripts/2021_islet-rfx6/</a></p>
Genetic networks datasets: ten books by Gustave Roud, "En mer" by Bernard Comment
<p>In genetic criticism, scholarly editing, authorial philology and, more generally, for the study of authorial manuscripts and writing processes it is essential to order and classify the textual witnesses and their relationships. The two datasets documents so-called ‘genetic networks’, representations of the genetic entities (witnesses, publications, dossiers) and of their relationships, modelled according to the GENO 1.0 ontology. The datasets contain genetic networks of the works of two Swiss authors: the main publications of Gustave Roud (1897-1976) and the short story “En mer” by Bernard Comment (1960).</p>
Data from: A global genetic interaction network maps a wiring diagram of cellular function
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Analysing landscape effects on dispersal networks and gene flow with genetic graphs
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Data from: Genetics-based interactions of foundation species affect community diversity, stability, and network structure
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Genetic assignment of individuals to source populations using network estimation tools
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Identification of a genetic network for an ecologically relevant behavioral phenotype in Drosophila melanogaster
<p>Pupation site choice of <i>Drosophila </i>third-instar larvae is critical for the survival of individuals, as pupae are exposed to various biotic and abiotic dangers while immobilized during the 3-4 days of metamorphosis. This singular behavioural choice is sensitive to both environmental and genetic factors. Here we developed a high-throughput phenotyping approach to assay the variation in pupation height in <i>Drosophila melanogaster, </i>while controlling for possibly confounding factors. We find substantial variation of mean pupation height among sampled natural stocks and we show that the <i>Drosophila</i> Genetic Reference Panel (DGRP) captures this variation. Using the DGRP stocks for genome wide association (GWA) mapping, 16 loci involved in determining pupation height could be resolved. The candidate genes in these loci are enriched for high expression in the larval central nervous system. A genetic network could be constructed from the candidate loci, which places <i>scrib</i> at the centre, plus other genes known to be involved in nervous system development, such as <i>Egfr</i> and <i>p53</i>. Using gene disruption lines, we could functionally validate several of the initially identified loci, as well as additional loci predicted from network analysis. Our study shows that the combination of high throughput phenotyping with a genetic analysis of variation captured from the wild can be used to approach the genetic dissection of an environmentally relevant behavioural phenotype.</p>
Approximation of a marine ecosystem model by artificial neural networks designed using a genetic algorithm
<p>Data from the Paper: Approximation of a marine ecosystem model by artificial neural networks designed using a genetic algorithm.</p> <p>Abstract: </p> <p>Marine ecosystem models are important to identify the processes that affects for example the global carbon cycle. Computation of an annually periodic solution (i.e., a steady annual cycle) for these models requires a high computational effort. To reduce this effort, we approximated an exemplary marine ecosystem model by different artificial neural networks. We used a fully connected network, then applied the sparse evolutionary training (SET) procedure, and finally applied a genetic algorithm (GA) to optimize both the network topology. With all three approaches, a direct approximation of the steady annual cycle was not sufficiently accurate. However, using the mass-corrected prediction of the ANN as initial concentration for additional model runs, the results were in very good agreement. In this way, we achieved a runtime reduction by about 15 \%. The result from the SET algorithm were comparable to those of the full network. Further application of the GA may lead to an even higher reduction.</p> <p>Content:</p> <p>Database sqlite <a href="https://zenodo.org/api/files/669d208b-7304-4d31-a6cc-3ad78e3544e9/ANN_Database.db">ANN_Database.db</a></p> <p>zip-files with data: </p> <p><a href="https://zenodo.org/api/files/669d208b-7304-4d31-a6cc-3ad78e3544e9/ANN-Data.zip">ANN-Data.zip</a> structure and weights of used networks</p> <p><a href="https://zenodo.org/api/files/669d208b-7304-4d31-a6cc-3ad78e3544e9/ANN-Results.zip">ANN-Results.zip</a> results obtained with networks</p> <p><a href="https://zenodo.org/api/files/669d208b-7304-4d31-a6cc-3ad78e3544e9/Reference-Results.zip">Reference-Results.zip</a> reference results and training data</p> <p> </p> <p> </p> <p> </p> <p> </p>
Data from: Node-based measures of connectivity in genetic networks
At-site environmental conditions can have strong influences on genetic connectivity, and in particular on the immigration and settlement phases of dispersal. However, at-site processes are rarely explored in landscape genetic analyses. Networks can facilitate the study of at-site processes, where network nodes are used to model site-level effects. We used simulated genetic networks to compare and contrast the performance of 7 node-based (as opposed to edge-based) genetic connectivity metrics. We simulated increasing node connectivity by varying migration in two ways: we increased the number of migrants moving between a focal node and a set number of recipient nodes, and we increased the number of recipient nodes receiving a set number of migrants. We found that two metrics in particular, the average edge weight and the average inverse edge weight, varied linearly with simulated connectivity. Conversely, node degree was not a good measure of connectivity. We demonstrated the use of average inverse edge weight to describe the influence of at-site habitat characteristics on genetic connectivity of 653 American martens (Martes americana) in Ontario, Canada. We found that highly connected nodes had high habitat quality for marten (deep snow and high proportions of coniferous and mature forest) and were farther from the range edge. We recommend the use of node-based genetic connectivity metrics, in particular, average edge weight or average inverse edge weight, to model the influences of at-site habitat conditions on the immigration and settlement phases of dispersal.
Data from: Genetic specificity of a plant-insect food web: implications for linking genetic variation to network complexity
Theory predicts that intraspecific genetic variation can increase the complexity of an ecological network. To date, however, we are lacking empirical knowledge of the extent to which genetic variation determines the assembly of ecological networks, as well as how the gain or loss of genetic variation will affect network structure. To address this knowledge gap, we used a common garden experiment to quantify the extent to which heritable trait variation in a host plant determines the assembly of its associated insect food web (network of trophic interactions). We then used a resampling procedure to simulate the additive effects of genetic variation on overall food-web complexity. We found that trait variation among host-plant genotypes was associated with resistance to insect herbivores, which indirectly affected interactions between herbivores and their insect parasitoids. Direct and indirect genetic effects resulted in distinct compositions of trophic interactions associated with each host-plant genotype. Moreover, our simulations suggest that food-web complexity would increase by 20% over the range of genetic variation in the experimental population of host plants. Taken together, our results indicate that intraspecific genetic variation can play a key role in structuring ecological networks, which may in turn affect network persistence.
Data from: Building genetic networks using relatedness information: a novel approach for the estimation of dispersal and characterization of group structure in social animals
Natal dispersal is an important life history trait driving variation in individual fitness and, therefore, a proper understanding of the factors underlying dispersal behaviour is critical to many fields including population dynamics, behavioural ecology and conservation biology. However, individual dispersal patterns remain difficult to quantify despite many years of research using direct and indirect methods. Here, we quantify dispersal in a single intensively-studied population of the cooperatively breeding chestnut-crowned babbler (Pomatostomus ruficeps) using genetic networks created from the combination of pairwise relatedness data and social networking methods and compare this to dispersal estimates from re-sighting data. Not only does this novel approach identify movements between social groups within our study sites but also provides an estimation of immigration rates of individuals originating outside the study site. Both genetic and re-sighting data indicated that dispersal was strongly female-biased, but the magnitude of dispersal estimates was much greater using genetic data. This suggests that many previous studies relying on mark-recapture data may have significantly underestimated dispersal. An analysis of spatial genetic structure within the sampled population also supports the idea that females are more dispersive, with females having no structure beyond the bounds of their own social group while male genetic structure expands for 750 meters from their social group. Although the genetic network approach we have used is an excellent tool for visualising the social and genetic microstructure of social animals and identifying dispersers, our results also indicate the importance of applying them in parallel with behavioural and life history data.
Data from: Genetics of gene expression responses to temperature stress in a sea urchin gene network
Stress responses play an important role in shaping species distributions and robustness to climate change. We investigated how stress responses alter the contribution of additive genetic variation to gene expression during development of the purple sea urchin, Strongylocentrotus purpuratus, under increased temperatures that model realistic climate change scenarios. We first measured gene expression responses in the embryos by RNA-seq to characterize molecular signatures of mild, chronic temperature stress. We found that an increase from 12 °C to 18 °C caused widespread alterations in gene expression including in genes involved in protein folding, RNA processing, and development. To understand the quantitative genetic architecture of this response, we then focused on a well-characterized gene network involved in endomesoderm and ectoderm specification. Using a breeding design with wild-caught individuals, we measured genetic and gene-environment interaction effects on 72 genes within this network. We found genetic or maternal effects in 33 of these genes, and that the genetic effects were correlated in the network. 14 network genes also responded to higher temperatures, but we found no significant genotype-environment interactions in any of the genes. This absence may be due to an effective buffering of the temperature perturbations within the network. In support of this hypothesis, perturbations to regulatory genes did not affect the expression of the genes that they regulate. Together, these results provide novel insights into the relationship between environmental change and developmental evolution and suggest that climate change may not expose large amounts of cryptic genetic variation to selection in this species.
Data from: Approaches to integrating genetic data into ecological networks
As molecular tools for assessing trophic interactions become common, research is increasingly focused on the construction of interaction networks. Here we demonstrate three key methods for incorporating DNA data into network ecology and discuss analytical considerations using a model consisting of plants, insects, bats and their parasites from the Costa Rican dry forest. The simplest method involves the use of Sanger sequencing to acquire long sequences to validate or refine field identifications, for example of bats and their parasites, where one specimen yields one sequence and one identification. This method can be fully quantified and resolved and these data resemble traditional ecological networks. For more complex taxonomic identifications, we target multiple DNA loci e.g. from a seed or fruit pulp sample in faeces. These networks are also well resolved but gene targets vary in resolution and quantification is difficult. Finally for mixed templates such as faecal contents of insectivorous bats we use DNA metabarcoding targeting two sequence lengths (157bp, 407bp) of one gene region and a MOTU, BLAST and BIN association approach to resolve nodes. This network type is complex to generate and analyse and we discuss the implications of this type of resolution on network analysis. Using these data we construct the first molecular-based network of networks containing 3304 interactions between 762 nodes of 8 trophic functions and involving parasitic, mutualistic, and predatory interactions. We provide a comparison of the relative strengths and weaknesses of these data types in network ecology.
Data from: Scale-dependent genetic structure of the Idaho giant salamander (Dicamptodon aterrimus) in stream networks
The network architecture of streams and rivers constrains evolutionary, demographic, and ecological processes of freshwater organisms. This consistent architecture also makes stream networks useful for testing general models of population genetic structure and the scaling of gene flow. We examined genetic structure and gene flow in the facultatively paedomorphic Idaho giant salamander, Dicamptodon aterrimus, in stream networks of Idaho and Montana, USA. We used microsatellite data to test population structure models by (1) examining hierarchical partitioning of genetic variation in stream networks and (2) testing for genetic isolation by distance along stream corridors versus overland pathways. Replicated sampling of streams within catchments within three river basins revealed that hierarchical scale had strong effects on genetic structure and gene flow. AMOVA identified significant structure at all hierarchical scales (among streams, among catchments, among basins), but divergence among catchments had the greatest structural influence. Isolation by distance was detected within catchments, and in-stream distance was a strong predictor of genetic divergence. Patterns of genetic divergence suggest that differentiation among streams within catchments was driven by limited migration, consistent with a stream hierarchy model of population structure. However, there was no evidence of migration among catchments within basins, or among basins, indicating that gene flow only counters the effects of genetic drift at smaller scales (within rather than among catchments). These results show the strong influence of stream networks on population structure and genetic divergence of a salamander, with contrasting effects at different hierarchical scales.
FIGURE 3. Haplotype network for P in Phylogeny and genetic variation within the widely distributed Bluntnose Minnow, Pimephales notatus (Cyprinidae), in North America
FIGURE 3. Haplotype network for P. notatus. Median-joining haplotype network for the mitochondrial (cytb) gene examined for P. notatus. Each circle represents a different haplotype; circle sizes are proportional to the number of individuals possessing a particular haplotype. Major drainages are represented by different colors. Small red dots represent missing (unsampled or extinct) haplotypes. Lines between circles represent one mutational step, and numbers presented are the number of mutations between haplotypes. Dotted-square-lines depict two major lineages resolved in mtDNA phylogenetic analyses. Dotted-circle depicts a subclade represented in detail (inset). One specimen from P. tenellus was included for comparisons.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.