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528 results for “gene prediction”
Harnessing underutilized gene bank diversity and genomic prediction of cross usefulness to enhance resistance to Phytophthora cactorum in strawberry
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Gene prediction for: A reference genome for ecological restoration of the sunflower sea star, Pycnopodia helianthoides
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Data from: Does the number of functional olfactory receptor genes predict olfactory sensitivity and discrimination performance in mammals?
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Data from: Strength of selection on Trpc2 gene predicts accessory olfactory bulb form in bat vomeronasal evolution
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Convergent evolution and predictability of gene copy numbers associated with diets in mammals
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Data for: CRISPR spacers acquired from plasmids primarily target backbone genes, making them valuable for predicting potential hosts and host range
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Genomics of new ciliate lineages provides insight into the evolution of obligate anaerobiosis - single gene datasets for phylogenomic analysis of anaerobic ciliates (SAL, Ciliophora), protein datasets for mitochondrial pathways prediction, and mitochondrial genomes
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Mimulus cardinalis plasticity analyses and R scripts for: Spatial variation in high temperature-regulated gene expression predicts evolution of plasticity with climate change in the scarlet monkeyflower
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The telomere regulatory gene POT1 responds to stress and predicts performance in nature: implications for telomeres and life history evolution
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Predicting plasmid contigs from assemblies using single copy marker genes, plasmid genes, kmers
<p>Introduction: Antimicrobial resistant (AMR) genes in bacteria are often carried on plasmids. Since these plasmids can spread the AMR genes between bacteria, it is important to know if the genes are located on highly transferable plasmids or in the more stable chromosomes. Whole genome sequence (WGS) analysis makes it easy to determine if a strain contains a resistance gene, however, it is not easy to determine if the gene is located on the chromosome or on a plasmid as genome sequence assembly generally results in 50-300 DNA fragments (contigs). With our newly developed prediction tool, we analyze the composition of these contigs to predict their likely source, plasmid or chromosomal. This information can be used to determine if a resistant gene is chromosomally located or on a plasmid. The tool is optimized for 19 different bacterial species, including Campylobacter, E. coli, and Salmonella, and can also be used for metagenomic assemblies.</p> <p>Methods: The tool identifies the number of chromosomal marker genes, plasmid replication genes and plasmid typing genes using CheckM and DIAMOND Blast, and determines pentamer frequencies and contig sizes per contig. A prediction model was trained using Random Forest on an extensive set of plasmids and chromosomes from 19 different bacterial species and validated on separate test sets of known chromosomal and plasmid contigs of the different bacteria. Results: Prediction of plasmid contigs was nearly perfect when calculated based on number of correctly predicted bases, with up to 99% specificity and 99% sensitivity. Prediction of small contigs remains difficult, since these contigs consists primarily of repeated sequences present in both plasmid and chromosome, e.g. transposases.</p> <p>Conclusion: The newly developed tool is able to determine if contigs are chromosomal or plasmid with a very high specificity and sensitivity (up to 99%) and can be very useful to analyze WGS data of bacterial genomes and their antimicrobial resistance genes.</p> <p>Plasmid databases can be downloaded from: http://klif.uu.nl/download/plasmid_db/</p> <p>Data used for training can be downloaded here: http://klif.uu.nl/download/plasmid_db/trainingsets2/</p>
Data from: DNA methylation predicts immune gene expression in introduced house sparrows (Passer domesticus)
Populations undergoing range expansions are often faced with novel selective pressures, and to cope with such challenges, populations must either adapt quickly or exhibit phenotypic plasticity. This latter option allows for rapid phenotypic adjustments and persistence in novel environments, and thus could be advantageous at range‐edges. Our previous research on house sparrows in Kenya—a site of ongoing range expansion— and a growing literature suggests that invasion success is facilitated by epigenetic regulation of gene expression. Previously, we found (i) differences in the expression of a microbial surveillance gene (i.e. Toll‐like receptor 4—TLR4), and (ii) extensive variation in genome‐wide DNA methylation among house sparrows across Kenya. Here, our goal was to investigate whether these two observations are related, specifically whether DNA methylation within a target sequence upstream of the TLR4 transcription start site is associated with variation in TLR4 expression. We found that DNA methylation in the aforementioned region was quite variable among individuals, and variation at one CpG site predicted differences in TLR4 expression. Moreover, we found genetic variation within the same sequence upstream of the TLR4 exon, but this variation did not predict TLR4 expression. To our knowledge, this is the first study to demonstrate an association between DNA methylation and the expression of an ecologically relevant trait in a range‐expanding vertebrate.
Data from: Ecological interactions and coexistence are predicted by gene expression similarity in freshwater green algae
Phenotypic variation controls the species interactions which determine whether or not species coexist. Long-standing hypotheses in ecology and evolution posit that phenotypic differentiation enables coexistence by increasing the size of niche differentiation. This hypothesis has only been tested using macroscopic traits to date, but niche differentiation, particularly of microscopic organisms, also occurs at the molecular and metabolic level. We examined how phenotypic variation that arises at the level of gene expression over evolutionary time affects phytoplankton species interactions and coexistence. We predicted that similarity in gene expression among species would decline with phylogenetic distance, and that reduced similarity in gene expression would weaken competition, increase facilitation and promote coexistence. To test this, we grew eight species of freshwater green algae in monocultures and bicultures for 46 days in a laboratory microcosm experiment. We quantified the strength of species interactions by: (i) fitting Lotka–Volterra models to time-series densities and estimating interaction coefficients, and (ii) calculating relative densities that compare species' steady-state densities in biculture to those in monoculture. We used Illumina high throughput sequencing to quantify the expression of 1253 families of homologous genes, including a set of 17 candidate genes that we hypothesized a priori to be involved in competition or facilitation. Synthesis. We found that closely related species had greater similarity in gene expression than did distantly related species, but as gene expression became more similar, species experienced weaker competition or greater facilitation, and were more likely to coexist. We identified gene functional categories that were uniquely differentially regulated in association with particular species interaction types. Contrary to common thinking in ecology and evolution, similarity in gene expression, and not differentiation, was associated with weaker competition, facilitation and coexistence.
Data from: Behavioral tactic predicts preoptic-hypothalamic gene expression more strongly than developmental morph in fish with alternative reproductive tactics
Reproductive success relies on the coordination of social behaviors, such as territory defense, courtship, and mating. Species with extreme variation in reproductive tactics are useful models for identifying the neural mechanisms underlying social behavior plasticity. The plainfin midshipman (Porichthys notatus) is a teleost fish with two male reproductive morphs that follow widely divergent developmental trajectories and display alternative reproductive tactics (ARTs). Type I males defend territories, court females, and provide paternal care, but will resort to cuckoldry if they cannot maintain a territory. Type II males reproduce only through cuckoldry. We sought to disentangle gene expression patterns underlying behavioral tactic, in this case ARTs, from those solely reflective of developmental morph. Using RNA-sequencing, we investigated differential transcript expression in the preoptic area-anterior hypothalamus (POA-AH) of courting type I males, cuckolding type I males, and cuckolding type II males. Unexpectedly, POA-AH differential expression was more strongly coupled to behavioral tactic than morph. This included a suite of transcripts implicated in hormonal regulation of vertebrate social behavior. Our results reveal that divergent expression patterns in a conserved neuroendocrine center known to regulate social-reproductive behaviors across vertebrate lineages may be uncoupled from developmental history to enable plasticity in the performance of reproductive tactics.
Data from: Urban landscape genetics: canopy cover predicts gene flow between white-footed mouse (Peromyscus leucopus) populations in New York City
In this study, I examine the influence of urban canopy cover on gene flow between 15 white-footed mouse (Peromyscus leucopus) populations in New York City. Nm calculated from F_ST and recent migration estimated in BayesAss+, but not historic migration estimated in Migrate-n, exhibited significant isolation-by-distance (IBD). Gene flow was also associated with "effective distances" between populations that were calculated based on percent canopy cover using two different approaches: 1) isolation-by-effective-distance (IED) that calculates the single best pathway to minimize passage through high-resistance (i.e. low canopy cover) areas, and 2) isolation-by-resistance (IBR), an implementation of circuit theory that identifies all low-resistance paths through the landscape. IBR, but not IED, models were still significantly associated with all three measures of gene flow after factoring out the influence of IBD using partial Mantel tests. In cases where both IBR and IED explained gene flow independently of IBD, an additional partial Mantel test indicated that the IBR models still explained gene flow after factoring out IED. The IBR models that explained the most variation in recent migration after factoring out IBD (r = 0.70 – 0.90) included landscape cells with at least 60-80% canopy cover as low resistance habitat. These results have implications for understanding the impacts of urbanization trends on native wildlife, as well as for urban reforestation efforts that aim to improve urban ecosystem processes.
Data from: Do freshwater ecoregions and continental shelf width predict patterns of historical gene flow in the freshwater fish Poecilia butleri?
We examined historical patterns of gene flow in the freshwater fish Poecilia butleri in western Mexico. We tested the hypothesis that the boundaries between four freshwater ecological communities (ecoregions) might have limited the movement of P. butleri because changes in species compositions might restrict establishment between adjacent ecoregions, even in situations where a physical barrier is absent. Hence, we predicted that boundaries between ecoregions should correspond to phylogeographical breaks in P. butleri. We also tested the hypothesis that the width of the continental shelf affected historical gene flow in P. butleri because a broad continental shelf provides a greater opportunity for rivers to coalesce during historical episodes of low sea levels as opposed to a narrow continental shelf that should restrict the potential for gene flow among adjacent rivers. Hence, we predicted greater amounts of historical gene flow among neighbouring river basins in the region of western Mexico where the continental shelf is wider, whereas, in the region where the continental shelf is narrower, we expected to detect limited levels of historical gene flow. We analyzed mitochondrial DNA sequence data (cytochrome b) taken from 264 individuals of P. butleri collected from 34 locations distributed across four different ecoregions in western Mexico. To examine patterns of phylogenetic diversification and historical gene flow in P. butleri, we employed several analytical approaches, including traditional tree-based phylogenetic analyses (likelihood and parsimony), haplotype network reconstruction, analyses of molecular variance, and spatial analysis of molecular variance. We found genetic breaks coinciding with two out of three different ecoregion boundaries, suggesting limited historical gene flow. In addition to different species compositions between these adjacent ecoregions, geological features such as the Trans-Mexican Volcanic Belt and the mountainous topography in south-western Mexico, likely contributed to these observed genetic breaks. By contrast, no genetic break was evident between two other ecoregions, a result that partially rejects our first hypothesis. Several results were consistent with our second hypothesis. Changes in the width of the continental shelf in western Mexico are associated with the observed patterns of historical gene flow. Our results indicate that the interactions among multiple geological and biological factors affect the spatial patterns of genetic diversity of widespread freshwater species.
Supplementary Information for the manuscript: "Gene expression evolution is predicted by stronger selection at more pleiotropic genes"
<p>This repository contains the supplementary information for the manuscript "<em>Gene expression evolution is predicted by stronger selection at more pleiotropic genes</em>" (<a href="https://doi.org/10.1101/2024.07.22.604294">https://doi.org/10.1101/2024.07.22.604294</a>).</p> <p>The supplementary data "data.tar.gz" is related to the Github repository <a href="https://github.com/charlesrocabert/Koch-et-al-Gene-expression-evolution-is-predictable-and-driven-by-indirect-selection-pressures">https://github.com/charlesrocabert/Koch-et-al-Predictability-of-Gene-Expression</a>.</p> <h2>Content of the repository</h2> <ul> <li><strong>Script S1. </strong>BSFG estimates (50.9 MB).</li> <li><strong>Script S2.</strong> WGCNA analysis and results G1 (99.6 MB).</li> <li><strong>Data S1.</strong> Datasets resulting from the global genomics analysis of the output of the transcriptomics pipeline (5.9 MB).</li> <li><strong>Data S2.</strong> VCF file containing the 566,296 quality-checked SNPs (1.1 GB).</li> <li><strong>Data S3.</strong> VCF file containing the 358,142 SNPs with a call rate higher or equal to 50% (964.3 MB).</li> <li><strong>Data S4.</strong> VCF file containing the imputed genotypes (964.3 MB).</li> <li><strong>Data S5.</strong> Results of the imputation tests (479.5 kB).</li> <li><strong>Data S6.</strong> VCF file containing imputed genotypes where SNPs with a minor allele frequency lower than 0.05 have been filtered out (MAF $\leq$ 0.05) (176.0 MB).</li> <li><strong>Data S7.</strong> List of the 1,273 significant eQTL associations (for gene expression levels or relative fitness as phenotypes) (61.2 kB).</li> <li><strong>Data S8.</strong> List of allele frequency changes (AFCs) for every markers in HD environment, for lines L1, L2, L3, L5, L6, Mx1 and Mx2 (32.4 MB).</li> <li><strong>Data S9.</strong> List of all SNPs indicating if their AFC is significantly higher in each line and their degree of parallelism (2.4 MB).</li> <li><strong>Data S10.</strong> Excel file containing the results of the gene functional enrichment analysis of the hub and eQTL carrier genes (33.7 KB).</li> <li><strong>data.tar.gz.</strong> Dataset mandatory to re-run the genomics analysis (see <a href="https://github.com/charlesrocabert/Koch-et-al-Gene-expression-evolution-is-predictable-and-driven-by-indirect-selection-pressures">https://github.com/charlesrocabert/Koch-et-al-Predictability-of-Gene-Expression</a>) (7.8 GB).</li> </ul> <p> </p>
DeepMRG: a multi-label deep learning classifier for predicting bacterial metal resistance genes
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Extended Data Fig. 2-27 Geographical information of bioinformatic predicted samples based on the analysis of 16S rRNA gene in four PE degrading bacteria
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Gene Signature predicts autoimmune toxicity in metastatic melanoma
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Dataset for paper "GRN-Transformer: Predicting Single Cell Gene Regulatory Network based on Axial Transformer"
<p>Dataset for paper "GRN-Transformer: Predicting Single Cell Gene Regulatory Network based on Axial Transformer"</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.