Find research datasets worth reusing

openmit-licenseJul 2024View details →

RNA-Protein Interaction Prediction Using Network-Guided Deep Learning

RNA-protein interactions are critical to various life processes, including fundamental translation and gene regulation. Identifying these interactions is vital for understanding the mechanisms underlying life processes. Then, ZHMolGraph is an advanced pipeline that integrates graph neural network sampling strategy and unsupervised large language models to enhance binding predictions for novel RNAs and proteins. <div> </div>

opencc-by-4.0Jul 2024View details →

Assemblies, synapse clustering and network topology interact with plasticity to explain structure-function relationships of the cortical connectome

Dataset linked to the article with the same title The model itself is very similar to its non-plastic counterpart under the following DOI: <a href="../record/7930275">10.5281/zenodo.7930275</a>, i.e. a 1.5 mm diameter  cortical tissue comprising 211,712 neurons and their connectivity in the front limb and jaw subregions and the dysgranular zone of the Paxinos & Watson rat brain atlas. It's formatted in the open <a href="https://github.com/AllenInstitute/sonata">SONATA</a> standard and contains neuron locations and their properties (such as morphological types, cortical layer, etc.), their detailed morphologies, and synaptic connectivity (with all their anatomical and physiological parameters). The main difference from the non-plastic version is the addition of plasticity related parameters to O1/S1nonbarrel_neurons__S1nonbarrel_neurons__chemical/edges.h5. Extrinsic synaptic connections from the thalamus are included in this release, but for inputs from neurons in the remainder of non-barrel somatosensory cortex please see the non-plastic version of the circuit. Analyzing the model The model can be analyzed in terms of its anatomy, physiology and connectivity using the packages <a href="https://neurom.readthedocs.io/en/stable/">NeuroM</a>, <a href="https://bluebrainsnap.readthedocs.io/en/stable/">BlueBrain SNAP</a> and <a href="https://github.com/BlueBrain/ConnectomeUtilities">ConnectomeUtilities</a>. (see first Jupyter notebook) Simulating the model To simulate the model we'd recommend using out using our open-source simulator <a href="https://github.com/BlueBrain/neurodamus">Neurodamus</a>. The reference version is the branch nbS1-2023, which is archived under the following DOI: <a href="http://doi.org/10.5281/zenodo.8075202">10.5281/zenodo.8075202</a>. Instructions on how to use the simulator are provided on the GitHub page linked above. Briefly, you'll first have to <a href="https://github.com/BlueBrain/neurodamus#install-neurodamus">install Neurodamus</a>. Next, build a "special" executable that include compiled versions of ion channel and synapse models. To do that, follow <a href="https://github.com/BlueBrain/neurodamus#build-special-with-mod-files">these instructions</a>, where mod-files-from-released-circuit is replaced by the location of O1/mods on your system. Finally, <a href="https://github.com/BlueBrain/neurodamus#examples">run a simulation</a>. The specific simulation conditions and stimuli are specified in simulation configuration files. An exemplary simulation configuration is included in this release (simulation_config.zip). Analyzing simulation results Simulation results can be analyzed with <a href="https://bluebrainsnap.readthedocs.io/en/stable/">BlueBrain SNAP</a>, <a href="https://github.com/BlueBrain/ConnectomeUtilities">ConnectomeUtilities</a>, and <a href="https://github.com/BlueBrain/assemblyfire">assemblyfire</a>. Notebooks 2-5 go though these analysis and recreate some of the panels from our article. In most cases the notebooks can be run with the shared HDF5 files and don't require running any simulations. Version 2 Bug fix in simulation_config.json and therefore new version of results (and corresponding notebooks). The underlying circuit model (O1.xz) did not change from v1. -- The development of this dataset was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology.

opencc-by-4.0May 2021View details →

Seasonal trajectories of plant-pollinator interaction networks differ following phenological mismatches along an urbanization gradient - Data and code

Dataset and code used in the article "Seasonal trajectories of plant-pollinator interaction networks differ following phenological mismatches along an urbanization gradient", by A. Fisogni et al., published in Landscape and Urban Planning (2022, 226:104512, <a href="https://www.sciencedirect.com/science/article/pii/S016920462200161X?via%3Dihub">https://doi.org/10.1016/j.landurbplan.2022.104512</a>)

opencc-by-4.0Jan 2023View details →

Geographic range size and species morphology determines the organization of sponge host-guest interaction networks across tropical coral reefs (Raw data)

Datasets for the analysis developed in the Article "Geographic range size and species morphology determines the organization of sponge host-guest interaction networks across tropical coral reefs". For more information, please refer to the original publication. Network_Structural_Index_&_SpogeTraits.csv <- Structural Index for the sponge-dwelling fauna network, sponge accumulated area and sponges’ morphology. NWTA_CoralReefs_Sponges_ interactions.csv <- Relationship between host sponges and guest fauna in the Northwester Atlantic coral reefs NWTA_CoralReefs_Sponge_reacords.csv <- Sponge species incidence records in the Northwester Atlantic coral reefs sponges_morphological_description.csv <- Sponge morphological standardization Network.html <- Interactive sponge-dwelling fauna network Enjoy!

opencc-by-4.0Nov 2020View details →

Predicting placenta transcriptional regulatory interactions based on spatial gene expression data and convolutional neural network

Aims: The dysfunction of placenta development is correlated to the defects of pregnancy and fetal growth. The detailed molecular mechanism of placenta development is not identified in human due to the lack of material in vivo. Image-based reconstructions of GRN are still very underdeveloped. Methods and Results: In this study, first-trimester chorionic villus and decidua tissues were collected. Next, we present a machine-learning system to infer gene interaction networks of the human placenta from immunofluorescence images of trophoblast specific transcription factors obtained by a high-resolution scanner. Conclusions: The experimental results show that deep learning models reveal regulatory roles that have not yet been fully recognized. The spatial expression data reveal new regulatory relationships that traditional experiments have failed to recognize, and has allowed the development of gene regulation networks based on the spatial distribution of gene expression. We demonstrate the effectiveness of this approach in building networks using high-resolution images of the human placenta. Our analysis is of certain significance for further exploration of the development of the placenta and the occurrence of pregnancy-related diseases in the future. The datasets and analysis provide a useful source for the researchers in the field of the maternal-fetal interface and the establishment of pregnancy.

opencc-by-4.0Dec 2020View details →

Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes - Database of Physical gene-gene Interactions in young adult C.elegans.

This repository contains Supplementary Information for manuscript Suriyalaksh et al Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive  of novel ageing genes corresponding to the curation of physical gene-gene interactions for young adult C elegans worms  We manually curated 239,001 regulatory interactions from 289 young adult wild-type (WT) C.elegans datasets, consisting of 126 genes and 495 unique transcription factors (see TableS1_datasets_for_prior.csv for references).  This repository contains 3 different files: TableS1_datasets_for_prior.csv - contains datasets used as sources for physical gene-gene or TF-gene interactions TableS2_physical_priors.xlsx - contains three tabs: ChIPATAC - contains physical TF-gene interactions from 115 L4 or young-adult ChIP-seq datasets from modERN (Kudron et al., 2018) +  ChIP-seq datasets (GSE28350, GSE81521) from  (Hochbaum et. al, 2011, Li et. al, 2016). eY1HATAC- contains  3,501 TF-gene interactions from eY1H assay by Fuxman Bass et al. (2016). motifATAC - contains 202 unique TF DNA recognition motifs using “direct evidence” option from CiS-BP motif database (Weirauch et al., 2014), obtained through RTFBSDB R package (Wang et al., 2016) - see TableS1 TableS3_WT_functional_priors.csv - contains functional knockdown data that we use as gold standard to validate inferred networks in Suriyalaksh et al. (see TableS1_datasets_for_prior.csv for sources) --- Description of methodology to obtain regulatory interactions in TableS2: Regulatory sequences for each gene were acquired from ENSEMBL (Aken et al., 2017), obtained using biomaRt R package (accessed on 31st Oct 2017). This study used WBcel235/ce11 version of the C. elegans genome, and WormBase WS260 genome annotations. For motifs, TFs whose motifs overlapped with an open ATAC-seq region by at least one base pair were kept. For ChIP-seq, TF binding sites that overlapped with an open ATAC-seq region by at least one base pair were kept using bedtools intersect and bedtools merge commands. An interaction from a TF to a gene was inferred by aligning transcription start sites (TSS) using bedtools window commands with 1000 bp window size to the TF-binding locations from ChIP-seq and motifs. For eY1H data, an interaction is included if the TSS site of the target gene overlaps with an open ATAC-seq region by at least one base pair. For gene-gene interactions, of the 298 studies compiled in WormExp v1.0 database (Yang et al, 2016, updated 27/07/16), 98 studies were included in the database spanning 126 different genes (see Table S1 in this repository).

opencc-zeroNov 2023View details →

Non-trophic interactions amplify kelp harvest-induced biomass oscillations and biomass changes in a kelp forest ecological network model

Kelp forests are important marine ecosystems providing habitat for numerous species. Despite over 50 years of mechanical harvesting in the Northeast Atlantic, the indirect impacts of kelp harvesting and associated habitat loss on faunal species within kelp forests remain poorly understood. We investigated the consequences of kelp harvesting by developing an allometric trophic network model for a subtidal Northeast Atlantic kelp forest (dominated by Laminaria hyperborea). Additionally, we designed a novel mechanistic model to explore the non-trophic interactions between kelp and age class 0 Atlantic cod (Gadus morhua) and kelp and European lobster (Homarus gammarus), specifically focusing on the increased survival benefits provided by the kelp habitat. Simulations were conducted over a 50-year period, incorporating harvesting cycles of 2, 5, and 9 years, as well as low and high harvesting intensities. Our findings reveal the complex dynamics resulting from kelp harvesting. The recovery of kelp biomass was observed with 5- and 9-year harvesting cycles, whereas a decline was observed with a 2-year cycle. Furthermore, the non-trophic interaction facilitated a higher pre-harvest biomass for both the European lobster and the Atlantic cod compared to scenarios without this interaction. These results highlight the multitrophic effects of kelp harvesting and emphasize that the recovery of kelp-associated species may not necessarily align with kelp recovery, depending on harvesting intensity and recovery periods. Importantly, our study contributes to a better understanding of the ecological consequences of kelp harvesting and underscores the need for sustainable management practices to mitigate habitat loss in kelp ecosystems.

opencc-by-4.0Nov 2023View details →

Data and code corresponding to the article "Interaction network structure explains species temporal persistence in empirical plant-pollinator communities"

This upload contains the Datasets and code to generate the results of the article "Interaction network structure explains species temporal persistence in empirical plant-pollinator communities".The database comprises two files containing the abundances of plants and pollinators, and one containing the interaction networks among plants and pollinators. The code folder contains the code to generate the results, and to generate the figures of the manuscript.

opencc-zeroNov 2023View details →

Unveiling the genetic networks: Exploring the dynamic interaction of photosynthetic phenotypes in woody plants across varied light gradients

Background: Understanding the mechanisms by which genes control and regulate complex quantitative traits during periods of fluctuating resources remains a challenging and uncertain task in photosynthesis studies. Most studies have focused on the structure of photosynthesis, the photosynthetic response under stress, or the genetic mechanisms involved in photosynthetic effects and neglected the interactive genetic mechanism that governs various traits through significant quantitative trait loci (QTLs). Results In this study, we have developed a differential dynamic system that enables the identification of QTLs based on the photosynthetic phenotypic and genotypic data under varying levels of light intensity gradients. The framework not only allows for the assessment of the direct effects of QTLs on phenotypes but also captures how they influence interactions among phenotypes as light intensities change. We have analyzed the genetic effects and genetic variance, visualized the genetic network associated with photosynthesis interactions, and validated the effectiveness and stability of the DDS framework. Pivotal QTLs were identified individually to uncover the process and pattern of interaction. Through functional annotation, we made an intriguing discovery that seemingly unimportant QTLs can still have significant genetic effects on phenotypic changes through their regulation with other QTLs. Conclusions This finding emphasizes the significance of considering the interactive genetic architecture when seeking to understand the genetic interaction mechanism of photosynthesis in natural populations of woody plants. Moreover, our research provides a novel framework that can be extended to explore the interactive genetic architecture among organisms, contributing to a deeper understanding of stress resistance mechanisms in woody plants.

opencc-zeroDec 2023View details →

Plant interaction networks reveal the limits of our understanding of diversity maintenance

Species interactions are key drivers of biodiversity and ecosystem stability. Current theoretical frameworks for understanding the role of interactions make many assumptions which, unfortunately, do not always hold in natural, diverse communities. This mismatch extends to annual plants, a common model system for studying coexistence, where interactions are typically averaged across environmental conditions and transitive competitive hierarchies are assumed to dominate. We quantify interaction networks for a community of annual wildflowers in Western Australia across a natural shade gradient at local scales. Whilst competition dominated, intraspecific and interspecific facilitation were widespread in all shade categories. Interaction strengths and directions varied substantially despite close spatial proximity and similar levels of local species richness, with most species interacting in different ways under different environmental conditions. Contrary to expectations, all networks were predominantly intransitive. These findings encourage us to rethink how we conceive of and categorise the mechanisms driving biodiversity in plant systems.

opencc-by-4.0Aug 2023View details →

Decoding host-microbiome interactions through co-expression network analysis within the non-human primate intestine

Supplementary Table Captions: Supplementary Table S9. Evaluation and parameter determination of host and microbiome RNA read classification using simulation datasets Supplementary Table S10. 40 pathways significantly upregulated in the cecum as compared to the transverse colon Supplementary Table S11. Host-microbiome gene co-expression network edges Supplementary Table S12. Host-host gene co-expression network edges Supplementary Table S13. Microbiome-microbiome gene co-expression network edges Supplementary Table S14. List of genes included in each gene module identified from the gene co-expression network Supplementary Table S15. Results of enrichment analysis for each gene module identified from the gene co-expression network Supplementary Table S16. The top 32 bacterial species in terms of expression abundance based on metatranscriptome profiles Supplementary Table S17. Number of microbiome RNA reads annotated by the KEGG database Supplementary Table S18. Results of enrichment analysis of gene modules for each parameter Supplementary Table S19. Evaluation of modules in each parameter of Newman algorithm Supplementary Table S20. Evaluation of modules in each parameter of Louvain algorithm Supplementary Table S21. Evaluation of modules in each parameter of Leiden algorithm Supplementary Table S22. Evaluation of modules in each parameter of WGCNA

opencc-by-4.0Jun 2021View details →

Forest cover and connectivity have pervasive effects on the maintenance of evolutionary distinct interactions in seed dispersal networks

This Data set contain 29 table of weighted interaction network between plants (columns) and frugivore birds from the Brazilian Atlantic Forest used in the manuscript "Forest cover and connectivity have pervasive effects on the maintenance of evolutionary distinct interactions in seed dispersal networks" published in Oikos Journal.

opencc-zeroMay 2022View details →

Stable species and interactions in plant-pollinator networks deviate from core position in fragmented habitats

Species and their interactions are more dynamic over time and space in fragmented habitats than in continuous habitats. In fragmented habitats, the low nestedness of mutualistic networks may be related to the position change of stable (high persistence over time/space) species and interactions in the networks. Previous studies have shown that stable species and interactions tend to be in the core position of mutualistic networks. However, in fragmented habitats, it remains unknown whether stable species or interactions still tend to be in the core position. To address this gap, here we evaluated the correlation between the position of proximity to the network core and the temporal/spatial stability of species and interactions, using the observation of 42 plant-pollinator networks conducted in a fragmented island landscape over 3 years. We showed that temporally/spatially stable species and interactions deviated from the network core to varying degrees. Temporally stable plants were most likely to deviate from the network core, followed by pollinators and interactions, while only spatially stable pollinators tend to deviate from the network core. When unstable species (present in few time/space points, typically specialists) and interactions occupy the network core, they cannot interact with most species in the network as generalists do, resulting in the decrease of network nestedness. Therefore, from the perspective of position and stability, stable species and interactions deviate from the network core in fragmented habitats, which is an important reason for the decrease of nestedness in mutualistic networks. Our study suggests that protecting plants that occupy the core in large plant-pollinator networks is essential for maintaining the network persistence in fragmented habitats.

opencc-by-4.0Jun 2022View details →

Data for RAPPPID: Towards Generalisable Protein Interaction Prediction with AWD-LSTM Twin Networks

Data for RAPPPID, a method for the Regularised Automative Prediction of Protein-Protein Interactions using Deep Learning. These datasets are in a format that RAPPPID is ready to read. Comparatives Dataset These datasets were derived from the STRING v11 H. sapiens dataset, according to the C1, C2, and C3 procedures outlined by Park and Marcotte, 2012. Negative samples are sampled randomly from the space of proteins not known to interact. See <a href="https://doi.org/10.1101/2021.08.13.456309">Szymborski & Emad</a> for details. Repeatability Datasets The following datasets are all derived from STRING in the manner as the comparatives dataset, but three different random seeds are used for drawing proteins. References Park,Y. and Marcotte,E.M. (2012) Flaws in evaluation schemes for pair-input computational predictions. Nat Methods, 9, 1134–1136. Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, N. T., Morris, J. H., Bork, P., Jensen, L. J., and Mering, C. (2019). String v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research, 47(D1), D607–D613. Szymborski,J. and Emad,A. (2021) RAPPPID: Towards Generalisable Protein Interaction Prediction with AWD-LSTM Twin Networks. bioRxiv https://doi.org/10.1101/2021.08.13.456309

opencc-by-4.0Sep 2022View details →

Dataset of pollinator functional traits and interaction networks in neotropical mangroves: effects of patch size and surrounding land use

This is the dataset of the manuscript entitled "Pollinator functional traits and interaction networks in neotropical mangroves: effects of patch size and surrounding land use", which was submitted for publication. The dataset include the functional traits of 162 insect pollinator species and 315 interactions with the mangrove species Avicennia germinans, Conocarpus erectus, Laguncularia racemosa, and Rhizophora mangle. The manuscript evaluates the effects of mangrove patch size and surrounding land use on pollinator functional diversity and plant-pollinator interactions in seven mangrove patches from the Colombian Caribbean region. Data variables are pollinator order, family, species, functional traits (pollinator guilds, body size, feeding preference, sociality, and nesting site) and frequency, interacting mangrove species, mangrove patch name, coordinates and size (ha), surrounding land use areas (urban areas, croplands, conserved dry forest, degraded vegetation areas, beach and water) and landscape diversity (Shannon H').

opencc-by-4.0Jun 2024View details →

Results from Interpreting Cis-Regulatory Interactions from Large-Scale Deep Neural Networks for Genomics

Open the record for dataset details and reuse information.

opencc-by-4.0Mar 2019View details →

Gene interaction networks from donor and failing heart gene expression

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: * 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]) * wgcna.zip: Compressed weighted matrices, in numpy format, for WGCNA. These are the networks used in the paper. * network_gene_names.txt: Gene names for rows and columns of the gene networks in networks.zip * wgcna Construction of the networks were done as follows for each method: * For Pearson, Z-score, and CLR, we simply computed each relevant statistic in a pairwise manner, no thresholds were applied. * For ARACNE, we performed 500 bootstrapped iterations and searched the MI cutoff automatically using default parameters. * For JGL we performed an Akaike Information Criteria guided model selection by scanning the sparsity and group difference L1 penalty strengths.

opencc-by-4.0May 2019View details →

Network analysis highlights increased generalisation and evenness of plant-pollinator interactions after conservation measures

DATASET used in the article entitled “Network analysis highlights increased generalisation and evenness of plant-pollinator interactions after conservation measures”. We supply weighted and binary matrices used for plant-pollinator network analyses, before and after the implementation of conservation measures. We also supply the list of plant and pollinator species recorded in this study.

opencc-by-4.0Jun 2022View details →

Network of hotspot interactions cluster tau amyloid folds

Datafiles associated with Network of hotspot interactions cluster tau amyloid folds.   The alascan files contain the averaged and raw outputs of the in silico alanine scan conducted on tau fibrils. The combo aggregation file contains ThT fluorescence data from the VQIVYK/VEVKSE alanine mutants and coaggregation experiments. The incorporation_vs_insilico file contains the results of the alanine scan top and bottom hits compared to the results of the incorporation experiment of alanine mutants on that position.   Version 2: Files added providing data for how edge and center chains contribute to the total energetics, as well as a per-layer energy contribution to total deltaREU in the alanine scan of the PHF fibril.