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295 results for “Structure prediction”
Data from: Drivers of the spatial scale that best predict primate responses to landscape structure
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Data from: Stream flow alone does not predict population structure of diving beetles across complex tropical landscapes
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Data from: Does population structure predict the rate of speciation? A comparative test across Australia's most diverse vertebrate radiation
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Early life learning ability predicts adult social structure, with potential implications for fitness outcomes in the wild
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Data from: Montane refugia predict population genetic structure in the Large-blotched Ensatina salamander
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Data from: Relating trophic resources to community structure: a predictive index of food availability
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Data from: Geographic range size and latitude predict population genetic structure in a global survey
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Data from: Ocean circulation model predicts high genetic structure in a long-lived pelagic developer
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Data from: A trait-based approach to predict population genetic structure in bees
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Screening of 6000 compounds for uncoupling activity: input parameters, the predicted uncoupling activity, as well as the results in respect to structural alerts
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Supplementary materials for Khan et al 2007: Predicting Odor Pleasantness from Odorant Structure: Pleasantness as a Reflection of the Physical World
<p>Supplementary materials for Predicting Odor Pleasantness from Odorant Structure: Pleasantness as a Reflection of the Physical World</p> <p>Rehan M. Khan, Chung-Hay Luk, Adeen Flinker, Amit Aggarwal, Hadas Lapid, Rafi Haddad, Noam Sobel</p> <p>Journal of Neuroscience 12 September 2007, 27 (37) 10015-10023; DOI: 10.1523/JNEUROSCI.1158-07.2007</p>
Supplementary Structural Models (SARS-CoV-2 Spike-RBD:ACE2 complex and TMPRSS2) - SARS-CoV-2 spike protein predicted to form complexes with host receptor protein orthologues from a broad range of mammals
<p>Structural Models (PDB) of SARS-CoV-2 Spike RBD bound to ACE2 receptors of 215 animals.</p> <p>Structural model of Human TMPRSS2.</p> <p>Modelled using the FunMod pipeline and referenced in the preprint</p> <p><a href="https://www.biorxiv.org/content/10.1101/2020.05.01.072371v5">SARS-CoV-2 spike protein predicted to form complexes with host receptor protein orthologues from a broad range of mammals</a></p> <p> </p>
Data from: Repertoire-wide gene structure analyses: a case study comparing automatically predicted and manually annotated gene models
The location and modular structure of eukaryotic protein-coding genes in genomic sequences can be automatically predicted by gene annotation algorithms. These predictions are often used for comparative studies on gene structure, gene repertoires, and genome evolution. However, automatic annotation algorithms do not yet correctly identify all genes within a genome, and manual annotation is often necessary to obtain accurate gene models and gene sets. As manual annotation is time-consuming, only a fraction of the gene models in a genome is typically manually annotated, and this fraction often differs between species. To assess the impact of manual annotation efforts on genome-wide analyses of gene structural properties, we compared the structural properties of protein-coding genes in seven diverse insect species sequenced by the i5k initiative. Our results show that the subset of genes chosen for manual annotation by a research community (3.5-7% of gene models) may have structural properties (e.g., lengths and exon counts) that are not necessarily representative for a species' gene set as a whole. Nonetheless, the structural properties of automatically generated gene models are only altered marginally (if at all) through manual annotation. Major correlative trends, for example a negative correlation between genome size and exonic proportion, can be inferred from either the automatically predicted or manually annotated gene models alike. Vice versa, some previously reported trends did not appear in either the automatic or manually annotated gene sets, pointing towards insect-specific gene structural peculiarities. In our analysis of gene structural properties, automatically predicted gene models proved to be sufficiently reliable to recover the same gene-repertoire-wide correlative trends that we found when focusing on manually annotated gene models only. We acknowledge that analyses on the individual gene level clearly benefit from manual curation. However, as genome sequencing and annotation projects often differ in the extent of their manual annotation and curation efforts, our results indicate that comparative studies analyzing gene structural properties in these genomes can nonetheless be justifiable and informative.
Kinship does not predict the structure of a shark social network
<p>Genetic relatedness in animal societies is often a factor that drives the structure of social groups. In the marine world, most studies which have investigated this question have focused on marine mammals such as whales and dolphins. For sharks, recent studies have demonstrated preferential associations among individuals from which social communities emerge. Assortment patterns have been found according to phenotypic or behavioural traits but the role of genetic relatedness in shaping the social structure of adult shark populations has, to the best our knowledge, never been investigated. Here, we used a social network analysis crossed with DNA microsatellite genotyping to investigate the role of the genetic relatedness in the social structure of a blacktip reef shark (<i>Carcharhinus melanopterus</i>) population. Based on data from 156 groups of sharks, we used generalized affiliation indices to isolate social preferences from non-social associations, controlling for the contribution of sex, size, gregariousness, spatial and temporal overlap on social associations, to test for the influence of genetic relatedness on social structure. A double permutation procedure was employed to confirm our results and account for issues arising from potentially elevated type I and type II error rates. Kinship was not a predictor of associations and affiliations among sharks at the dyad or community levels as individuals tended to associate independently of the genetic relatedness among them. The lack of parental care in this species may contribute to the breakdown of family links in the population early in life, thereby preventing the formation of kin-based social networks.</p>
Predicting Hydrophobicity by Learning Spatiotemporal Features of Interfacial Water Structure: Combining Molecular Dynamics Simulations with Convolutional Neural Networks
<p>Files for reproducing results from Kelkar et al. (JPCB 2020) - Predicting Hydrophobicity by Learning Spatiotemporal Features of Interfacial Water Structure: Combining Molecular Dynamics Simulations with Convolutional Neural Networks</p> <p> </p> <p>This folder contains simulations starter files and also plug-and-play datasets to test ML algorithms on molecular dynamics (MD) simulation data.</p> <p> </p> <p>All analysis scripts can also be found on GitLab on this link: https://gitlab.com/atharva-kelkar/kelkar_et_al_jpcb_2020</p>
Data from: Dispersal capacity predicts both population genetic structure and species richness in reef fishes
Dispersal is a fundamental species characteristic that should directly affect both rates of gene flow among spatially distributed populations and opportunities for speciation. Yet no single trait associated with dispersal has been demonstrated to affect both micro- and macroevolutionary patterns of diversity across a diverse biological assemblage. Here, we examine patterns of genetic differentiation and species richness in reef fishes, an assemblage of over 7,000 species comprising approximately one-third of the extant bony fishes and over one-tenth of living vertebrates. In reef fishes, dispersal occurs primarily during a planktonic larval stage. There are two major reproductive and parental investment syndromes among reef fishes, and the differences between them have implications for dispersal: (1) benthic guarding fishes lay negatively buoyant eggs, typically guarded by the male parent, and from these eggs hatch large, strongly swimming larvae; in contrast, (2) pelagic spawning fishes release small floating eggs directly into the water column, which drift unprotected before small weakly swimming larvae hatch. Using phylogenetic comparative methods, we show that benthic guarders have significantly greater population structure than pelagic spawners and additionally that taxonomic families of benthic guarders are more species rich than families of pelagic spawners. Our findings provide a compelling case for the continuity between micro- and macroevolutionary processes of biological diversification and underscore the importance of dispersal-related traits in influencing the mode and tempo of evolution.
Data from: Autocorrelation structure at rest predicts value correlates of single neurons during reward-guided choice
Correlates of value are routinely observed in the prefrontal cortex (PFC) during reward-guided decision making. In previous work (Hunt et al., 2015), we argued that PFC correlates of chosen value are a consequence of varying rates of a dynamical evidence accumulation process. Yet within PFC, there is substantial variability in chosen value correlates across individual neurons. Here we show that this variability is explained by neurons having different temporal receptive fields of integration, indexed by examining neuronal spike rate autocorrelation structure whilst at rest. We find that neurons with protracted resting temporal receptive fields exhibit stronger chosen value correlates during choice. Within orbitofrontal cortex, these neurons also sustain coding of chosen value from choice through the delivery of reward, providing a potential neural mechanism for maintaining predictions and updating stored values during learning. These findings reveal that within PFC, variability in temporal specialisation across neurons predicts involvement in specific decision-making computations.
Data from: Knowledge-based prediction of protein backbone conformation using a structural alphabet
Libraries of structural prototypes that abstract protein local structures are known as structural alphabets and have proven to be very useful in various aspects of protein structure analyses and predictions. One such library, Protein Blocks, is composed of 16 standard 5-residues long structural prototypes. This form of analyzing proteins involves drafting its structure as a string of Protein Blocks. Predicting the local structure of a protein in terms of protein blocks is the general objective of this work. A new approach, PB-kPRED is proposed towards this aim. It involves (i) organizing the structural knowledge in the form of a database of pentapeptide fragments extracted from all protein structures in the PDB and (ii) applying a knowledge-based algorithm that does not rely on any secondary structure predictions and/or sequence alignment profiles, to scan this database and predict most probable backbone conformations for the protein local structures. Though PB-kPRED uses the structural information from homologues in preference, if available. The predictions were evaluated rigorously on 15,544 query proteins representing a non-redundant subset of the PDB filtered at 30% sequence identity cut-off. We have shown that the kPRED method was able to achieve mean accuracies ranging from 40.8% to 66.3% depending on the availability of homologues. The impact of the different strategies for scanning the database on the prediction was evaluated and is discussed. Our results highlights the usefulness of the method in the context of proteins without any known structural homologues. A scoring function that gives a good estimate of the accuracy of prediction was further developed. This score estimates very well the accuracy of the algorithm (R2 of 0.82). An online version of the tool is provided freely for non-commercial usage at http://www.bo-protscience.fr/kpred/.
Data from: miR-122, small RNA annealing and sequence mutations alter the predicted structure of the Hepatitis C virus 5′ UTR RNA to stabilize and promote viral RNA accumulation
Annealing of the liver-specific microRNA, miR-122, to the Hepatitis C virus (HCV) 5′ UTR is required for efficient virus replication. By using siRNAs to pressure escape mutations, 30 replication-competent HCV genomes having nucleotide changes in the conserved 5′ untranslated region (UTR) were identified. In silico analysis predicted that miR-122 annealing induces canonical HCV genomic 5′ UTR RNA folding, and mutant 5′ UTR sequences that promoted miR-122-independent HCV replication favored the formation of the canonical RNA structure, even in the absence of miR-122. Additionally, some mutant viruses adapted to use the siRNA as a miR-122-mimic. We further demonstrate that small RNAs that anneal with perfect complementarity to the 5′ UTR stabilize and promote HCV genome accumulation. Thus, HCV genome stabilization and life-cycle promotion does not require the specific annealing pattern demonstrated for miR-122 nor 5′ end annealing or 3′ overhanging nucleotides. Replication promotion by perfect-match siRNAs was observed in Ago2 knockout cells revealing that other Ago isoforms can support HCV replication. At last, we present a model for miR-122 promotion of the HCV life cycle in which miRNA annealing to the 5′ UTR, in conjunction with any Ago isoform, modifies the 5′ UTR structure to stabilize the viral genome and promote HCV RNA accumulation.
Improving predictions of critical shear stress in gravel bed rivers: identifying the onset of sediment transport and quantifying sediment structure: Dataset
<p>This dataset accompanies the paper: Hodge RA, Voepel HE, Yager EM, Leyland J, Johnson JPL, Sear DA, Ahmed S. Improving predictions of critical shear stress in gravel bed rivers: identifying the onset of sediment transport and quantifying sediment structure. In review for Earth Surface Processes and Landforms. </p> <p>These data are from Figure 3 to 6. The aim of this part of the paper was to assess different methods for measuring the grain-scale sediment structure of a gravel-bed river. The approaches used are direct measurements, terrestrial laser scanning, and CT scanning. </p> <p> </p>
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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.
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.