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1,115 results for “gene network”
Modelling the influence of parental effects on gene network evolution : Data and Program
<p>The C++ software "Simevolv" is a population genetics tool able to simulate the evolution of complex genetic architectures.</p> <p>This version is a development version from which the results of the manuscript "Modelling the influence of parental effects on gene network evolution" (in prep) have been obtained.</p> <p>The "Data" file corresponds to the simulation results used in the above mentioned article.</p>
Fig. 4 in Drainage Network Morphology Influences Population Structure and Gene Flow of the Andean Water Frog (Anura: Telmatobiidae) of the Atacama Desert, Northern Chile.
Fig. 4. Results of the Geneland analysis. A: Bar plot of posterior probability density according to the number of clusters; B: posterior probability maps for the delimited clusters.
Fig. 3 in Drainage Network Morphology Influences Population Structure and Gene Flow of the Andean Water Frog (Anura: Telmatobiidae) of the Atacama Desert, Northern Chile.
Fig. 3. Pairwise FST between localities of Telmatobius pefauri obtained using mitochondrial (A) and microsatellite (B) data. The colour scale corresponding to the values of FST is shown to the right of each matrix. Significant (Bonferroni corrected) comparisons showing p <0.05, p <0.01 and p <0.001 are denoted by *, ** and ***, respectively.
Fig. 2 in Drainage Network Morphology Influences Population Structure and Gene Flow of the Andean Water Frog (Anura: Telmatobiidae) of the Atacama Desert, Northern Chile.
Fig. 2. Median-joining network based on the fragment of the analysed control region. Table 1. Indices of mitochondrial diversity, nuclear diversity, and inbreeding coefficients (FIS) by locality
Fig. 1 in Drainage Network Morphology Influences Population Structure and Gene Flow of the Andean Water Frog (Anura: Telmatobiidae) of the Atacama Desert, Northern Chile.
Fig. 1. Study area, distribution of Telmatobius pefauri. Localities, 1: Socoroma (Socoroma River); 2: Murmuntani; 3: Copaquilla; 4: Chapiquiña; 5: Belén; 6: Lupica; 7: Saxamar. Localities 2 and 3 belong to the Seco River drainage; localities 4–7 belong to the Tignamar River drainage. Basin limits are indicated with dashed lines. The inset map shows the study area (highlighted by a red box) in relation to South America. SAAD = South American Arid Diagonal.
Fig. 5 in Drainage Network Morphology Influences Population Structure and Gene Flow of the Andean Water Frog (Anura: Telmatobiidae) of the Atacama Desert, Northern Chile.
Fig. 5. Scatter plot for the first two principal components obtained in the Principal Components Analysis using SSR data.
ACE gene haplotypes and social networks: Using a biocultural framework to investigate blood pressure variation in African Americans
<p>This dataset contains all processed data used in analyses reported in the manuscript, ACE gene haplotypes and social networks: Using a biocultural framework to investigate blood pressure variation in African Americans. This project is part of a larger study focused on investigating the role of stress, discrimination, genetic variants, and other sociocultural factors in hypertension in African Americans.</p> <p> </p> <p> </p> <p>Variable Names and Descriptions:</p> <p>HHID: Participant Identification number</p> <p>sbp-10: Average of two Systolic Blood Pressure readings without 10 point correction for blood pressure medication (mmHg)</p> <p>sbp: Average of two Systolic Blood Pressure readings with 10 point correction for blood pressure medication (mmHg)</p> <p>dbp-5: Average of two Diastolic Blood Pressure readings without 5 point correction for blood pressure medication (mmHg)</p> <p>dbp: Average of two Diastolic Blood Pressure readings with 5 point correction for blood pressure medication (mmHg)</p> <p>ACE.Genotype: ACE genotype (0= Deletion/Deletion, 1= Insertion/Deletion, 2= Insertion/Insertion)</p> <p>WNK1.Genotype: WNK1 Genotype (0= Deletion/Deletion, 1= Insertion/Deletion, 2= Insertion/Insertion)</p> <p>age: age (years)</p> <p>sex: sex (Male =1, Female = 2)</p> <p>bpmedtake: Whether the participant uses blood pressure medication</p> <p>bmi: Body Mass Index (calculated from height and weight)</p> <p>ALTER GENDER|Answer:Male|Value:0|Count: Number of male alters (social network members)</p> <p>Alter Gender Male: Percentage of alters that are male</p> <p>ALTER GENDER|Answer:Female|Value:1|Count: Number of female alters (social network members)</p> <p>Alter Gender Female: Percentage of alters that are female</p> <p>Closeness_Mean: Average closeness centrality of the network</p> <p>Between_Mean: Average between-ness centrality of the network</p> <p>percentage of family in structural percentage: Percentage of central network positions occupied by family members</p> <p>relationship max close family: The most close (centrally) network member is a family member</p> <p>Average distance: Average distance of individuals in a network</p> <p>Hap B: ACE gene haplotypes</p> <p> </p>
Gene interaction networks from donor and failing heart gene expression
<p>These are the gene-gene interaction networks obtained from gene expression microarray data in the paper "Pathologic gene network rewiring implicates PPP1R3A as a central cardioprotective factor in pressure overload heart failure" by Cordero, Parikh, et al. The dataset contains the following items:</p> <p>* networks.zip: Compressed weighted matrices, in numpy format, for various methods in heart failure and donor cohorts, which are in the file name (ARACNE, JGL [Joint Graphical Lasso], Z-score, Pearson correlation, CLR [Context Likelihood of Relatedness])</p> <p>* wgcna.zip: Compressed weighted matrices, in numpy format, for WGCNA. These are the networks used in the paper.</p> <p>* network_gene_names.txt: Gene names for rows and columns of the gene networks in networks.zip</p> <p>* wgcna</p> <p>Construction of the networks were done as follows for each method:</p> <p>* For Pearson, Z-score, and CLR, we simply computed each relevant statistic in a pairwise manner, no thresholds were applied.</p> <p>* For ARACNE, we performed 500 bootstrapped iterations and searched the MI cutoff automatically using default parameters.</p> <p>* For JGL we performed an Akaike Information Criteria guided model selection by scanning the sparsity and group difference L1 penalty strengths.</p>
Data for: Induction of C4 genes during de-etiolation of Gynandropsis gynandra evolved through changes in cis allowing integration into ancestral C3 gene regulatory networks
<p>C4 photosynthesis has evolved repeatedly and in doing so repurposed existing enzymes to drive a carbon pump that limits the oxygenation reaction of RuBisCO. C4 proteins accumulate to levels matching those of the photosynthetic apparatus, and to allow this gene expression must be modified over evolutionary time. To better understand this rewiring of gene expression we undertook RNA-SEQ and <span>DNaseI</span>-SEQ on de-etiolating seedlings of C4 <em>Gynandropsis gynandra</em> which is evolutionarily proximate to C3 <em>A. thaliana</em>. Changes in chloroplast ultrastructure and C4 gene expression in <em>G. gynandra</em> were coordinated and rapid. C3 and C4 photosynthesis genes showed similar induction patterns, but C4 genes from <em>G. gynandra</em> were more strongly induced than orthologs from <em>A. thaliana</em>. The cistrome of <em>G. gynandra</em> was enriched in TGA, TCP and homeodomain binding sites. Furthermore,<em> in vivo</em> binding data in <em>G. gynandra</em> highlighted TGA and homeodomain as well as light responsive elements such as G- and I-box motifs as being associated with the rapid increase in transcripts derived from C4 genes. Although promoters of <em>PPDK</em> and <em>ASP1</em> from <em>G. gynandra</em> contained distinct light responsive elements, promoters from both <em>A. thaliana</em> and <em>G. gynandra</em> allowed high expression. Deletion analysis of the <em>Ppa6</em> gene from <em>G. gynandra</em> showed that regions containing G- and I-boxes were necessary for high expression. The data support a model in which accumulation of transcripts derived from C4 genes in leaves of <em>G. gynandra</em> is enhanced compared with homologs in <em>A. thaliana</em> because a variety of modifications in <em>cis</em> allowed integration into ancestral transcriptional networks.</p>
Exploring the utility of regulatory network-based machine learning for gene expression prediction in maize
<p>Relevant Data and Code for <em>Exploring the utility of regulatory network-based machine learning for gene expression prediction in maize </em>by Taylor Ferebee and Edward Buckler.</p> <p><strong>Input Data</strong></p> <p>The inputs of the models are enclosed in <em>Input_data-2022-001.zip</em></p> <p><strong>Output Data</strong></p> <p>The outputs of the models are enclosed in <em>Output_Results-2022-001.zip</em></p> <p><strong>Relevant Code </strong></p> <p>The code for all analyses is enclosed in <em>Code_Archive.zip </em></p>
Supplementary material for: PhyloCoalSimulations: A simulator for network multispecies coalescent models, including a new extension for the inheritance of gene flow
<p>We consider the evolution of phylogenetic gene trees along phylogenetic species networks, according to the network multispecies coalescent process, and introduce a new network coalescent model with correlated inheritance of gene flow. This model generalizes two traditional versions of the network coalescent: with independent or common inheritance. At each reticulation, multiple lineages of a given locus are inherited from parental populations chosen at random, either independently across lineages, or with positive correlation according to a Dirichlet process. This process may account for locus-specific probabilities of inheritance, for example.</p> <p>We implemented the simulation of gene trees under these network coalescent models in the Julia package PhyloCoalSimulations, which depends on PhyloNetworks and its powerful network manipulation tools. Input species phylogenies can be read in extended Newick format, either in numbers of generations or in coalescent units. Simulated gene trees can be written in Newick format, and in a way that preserves information about their embedding within the species network. This embedding can be used for downstream purposes, such as to simulate species-specific processes like rate variation across species, or for other scenarios as illustrated in this note. This package should be useful for simulation studies and simulation-based inference methods. The software is available open source with documentation and a tutorial at <a href="https://github.com/cecileane/PhyloCoalSimulations.jl">https://github.com/cecileane/PhyloCoalSimulations.jl</a>.</p>
Synthetic gene expression data with underlying gene network
<p>This is the synthetic gene expression data along with the underlying gene network used in the simulation studies of Hu and Szymczak (2023) for evaluating network-guided random forest.</p> <p>In this dataset we consider the situation of 1000 genes and 1000 samples each for training and testing sets. Each file contains a list of 100 replications of the considered scenario which can be identified via the file name. In particular, we consider 6 different scenarios depending on the number of disease modules and how are the effects of disease genes distributed within the disease module. When there are disease genes, we also consider 3 different levels of effect sizes. The binary responses are then generated via a logistic regression model. More details on these scenarios and the data generation mechanism can be found in Hu and Szymczak (2023).</p> <p>The data is generated by the function <em>gen_data</em> in R package <em>networkRF</em> which can be accessed at https://github.com/imbs-hl/networkRF. To obtain the datasets with 3000 genes, which is the other part of the data used in the simulation studies of Hu and Szymczak (2023), simply modify the <em>num.var</em> argument of the function <em>gen_data.</em> More descriptions on the implementation and the format of the output can be found in the help page of the R package.</p>
Supplementary material for: PhyloCoalSimulations: A simulator for network multispecies coalescent models, including a new extension for the inheritance of gene flow
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The cacao gene atlas: A transcriptome developmental atlas reveals highly tissue-specific and dynamically-regulated gene networks in Theobroma cacao L
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Data from: Gene network topology drives the mutational landscape of gene expression
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Simulations of gene regulatory networks with transcriptional adaptation
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Data for: Induction of C4 genes during de-etiolation of Gynandropsis gynandra evolved through changes in cis allowing integration into ancestral C3 gene regulatory networks
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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>
Mouse and Human Co-expression maps and supplementary material for: "A comparison of human and mouse gene co-expression networks reveals conservation and divergence at the tissue, pathway and disease levels"
<p>Co-expression maps of the human and mouse species derived from microarray data for the first release of the GeneFriend tool.</p> <p>The two co-expression maps have been compared in order to discern similarities and differences between the two species. The results have been described in the manuscript titled: "A comparison of human and mouse gene co-expression networks reveals conservation and divergence at the tissue, pathway and disease levels".</p> <p>The supplementary material of the manuscript have also been included in this repository.</p> <p> </p>
Supplemental Information on the Weighted Gene Co-expression Network Analysis performed for the work "Time-resolved oxidative signal convergence across the algae–embryophyte divide"
<p>Supplemental Information on the Weighted Gene Co-expression Network Analysis (WGNCA) performed for the work "Time-resolved oxidative signal convergence across the algae–embryophyte divide"</p> <p>The results are sorted by the three species analysed: the two algae <em><span>Zygnema circumcarinatum</span></em><span> SAG 698-1b (<em>Zygnema</em>) and <em>Mesotaenium endlicherianum </em></span><span>SAG 12.97 (<em>Mesotaenium</em>); and the bryophyte <em>Physcomitrium patens</em></span><span><em> </em>strain Gransden 2004 (<em>Physcomitrium</em>).</span></p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.
International Brain Laboratory public data
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.