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29,889 results for “Gene expression”
Figure 2 in Expression analysis of phosphate induced genes in contrasting maize genotypes for phosphorus use efficiency
Figure 2. Phylogenetic analysis based on nucleotide sequences of plant phosphate transporters. Plant phosphate transporters were assembled using ClustalX, and NJ-plot was used to develop the tree. Abbreviations are shown for respective transporters: ZmPTs: Zea mays phosphate transporters;AtPT: Arabidopsis thaliana phosphate transporters;LePT: Lycopersicon esculentum phosphate transporters; OsPT: Oryza sativa phosphate transporters; HvPT: Hordeum vulgare phosphate transporters; SbPT: Sorghum bicolor phosphate transporters.
Figure 1. A – P in Expression analysis of phosphate induced genes in contrasting maize genotypes for phosphorus use efficiency
Figure 1. A – P-efficient and P-inefficient maize plants grown in the Cerrado under low Pi conditions.B – Dry weight of maize genotypes. C – Root/shoot ratio of maize plants. D and E – Phosphorus content. F – Anthocyanin concentration. G and H – Units of APA activity. B to H, The maize plants were grown in hydroponics culture in the presence (250 µM Pi - gray bar) or absence (0 µM Pi – black bar) of phosphate harvested after 15 days in treatment. Each bar is the mean of three replicates with a standard deviation.
Figure 3. A in Expression analysis of phosphate induced genes in contrasting maize genotypes for phosphorus use efficiency
Figure 3. A – Northern blot analysis of phosphate starvation-induced ZmPTs genes in maize genotypes. B – Expression of ZmPTs in a plant grown in different phosphorus concentrations. C – Suppression of the ZmPTs expression by Pi resupply. D – Expression of ZmPT genes using RNA isolated from different root parts. E – Expression of ZmPTs in different maize plants under Pi starvation. F – Effect of duration of phosphate starvation on ZmPTs genes expression in maize genotypes. G – Expression of ZmPT homologs in roots of two sorghum genotypes. Total RNA isolated from different times of hydroponically grown plants supplied with half-strength modified Hoagland's solution containing 250 µM phosphate (+) or no phosphate (-) for different days or different concentrations as indicated. All the blots were probed with 32P labeled ZmPTs. The panel below the Northern blots is the ethidium bromide-stained gel prior to blotting showing the RNA integrity and uniformity of loading.
Práctica de transcriptómica: expresión diferencial de genes aplicado a la producción de alimentos / Practical Transcriptomics: Differential gene expression applied to food production
<p>Data for the eLearning tutorial Practical Transcriptomics: Differential gene expression applied to food production</p> <p>Datos para el tutorial eLearning Práctica de transcriptómica: expresión diferencial de genes aplicado a la producción de alimentos</p>
Aberrant gene expression prediction benchmark based on GTEx v8
<p>This repository contains the aberrant gene expression prediction benchmark data as well as the necessary expected gene expression across tissues and tissue-specific isoform contribution scores for AbExp prediction.<br> </p> <p>The aberrant gene expression prediction benchmark data (aberrant_expression_prediction_benchmark.parquet) contains the following columns:</p> <ul> <li>individual: GTEx individual</li> <li>gene: Ensembl gene identifier</li> <li>tissue: GTEx tissue</li> <li>tissue_type: GTEx tissue type</li> <li>mu: OUTRIDER-estimated expected gene expression</li> <li>theta: OUTRIDER-estimated gene dispersion</li> <li>counts: Raw gene expression count</li> <li>normalized_counts: OUTRIDER-normalized gene expression count</li> <li>l2fc: log2 fold change between observed and expected gene expression count</li> <li>zscore: z-score of gene expression, obtained by quantile-mapping the OUTRIDER-estimated distribution to the standard normal distribution</li> <li>nominal_pvalue: OUTRIDER-estimated <em>p</em>-value of being an expression outlier</li> <li>FDR: FDR-adjusted <em>p</em>-value of being an expression outlier</li> <li>is_in_benchmark: Whether this observation is part of the aberrant gene expression prediction benchmark</li> <li>is_underexpressed_outlier: Whether this observation is an underexpression outlier at FDR < 5%. This is the benchmark prediction label.</li> </ul> <p><br>The isoform proportions table (gtex_v8_isoform_proportions.tsv) contains the following columns:</p> <ul> <li>gene: Ensembl gene identifier</li> <li>tissue_type: GTEx tissue type</li> <li>tissue: GTEx tissue</li> <li>transcript: Ensembl transcript identifier</li> <li>mean_transcript_proportions: mean transcript proportions across individuals in GTEx v8</li> <li>median_transcript_proportions: median transcript proportions across individuals in GTEx v8</li> <li>sd_transcript_proportions: standard deviation of transcript proportions across individuals in GTEx v8</li> </ul> <p><br>The expected gene expression table (gtex_v8_expected_expression.tsv) contains the following columns:</p> <ul> <li>gene: Ensembl gene identifier</li> <li>tissue_type: GTEx tissue type</li> <li>tissue: GTEx tissue</li> <li>gene_is_expressed: Whether the gene is expressed in the tissue</li> <li>median_expression: median OUTRIDER-estimated expected gene expression (mu) across individuals</li> <li>expression_dispersion: OUTRIDER-estimated gene dispersion (theta)</li> </ul>
Per-gene per-strain data: expression divergence between strains and alleles in F1s in wild C. elegans
<p>This dataset comprises p<span>er-gene per-strain data (used to perform all analyses and generate all figures), including regulatory pattern and inheritance mode classifications and underlying statistical differential expression results</span>.</p> <p>This is supplemental data for the linked preprint/publication describing insights derived from comparing gene expression (RNA-seq) between seven wild <em>C. elegans</em> strains and the laboratory reference strain N2, as well as the allelic expression of the wild and N2 alleles in F1s of crosses between all these wild strains and the reference strain.</p> <p>The PDF file <code>column_names_descriptions_worm_ase_data_pergene_perstrain.pdf</code> and excel spreadsheet <code>column_names_descriptions_worm_ase_data_pergene_perstrain.xlsx</code> serve as READMEs for the data file by providing details of the data held in each column of the data file <code>worm_ase_data_pergene_perstrain.txt.gz</code></p> <p>If you use this dataset (we hope someone does!), please cite the latest version of the accompanying preprint/publication.</p> <p>To query each gene in a user-friendly, visual format, see our shiny app <a href="https://wildworm.biosci.gatech.edu/ase/" target="_blank" rel="noopener">https://wildworm.biosci.gatech.edu/ase/</a></p>
Raw data for Figures in: LAP2alpha facilitates myogenic gene expression by preventing nucleoplasmic lamin A/C from spreading to active chromatin regions, Ferraioli et al., Nucleic Acids Res. 2024
<p>These datasets represent raw data for the preparation of Figures in:</p> <p><span>Ferraioli S, Sarigol F, Prakash C, Filipczak D, <strong>Foisner R</strong>, Naetar N. (2024) </span>LAP2alpha facilitates myogenic gene expression by preventing nucleoplasmic lamin A/C from spreading to active chromatin regions<span>. <em>Nucleic Acids Res.</em>2024 Sep 4:gkae752. doi: 10.1093/nar/gkae752.</span></p>
Dataset for "Diets supplemented with Saccharina latissima influence the expression of genes related to lipid metabolism and oxidative stress modulating rainbow trout (Oncorhynchus mykiss) fillet composition" (doi.org/10.1016/j.fct.2020.111332)
<p>Dataset corresponding to the following article:</p> <p>Ferreira, M., Larsen, B.K., Granby, K., Cunha, S.C., Monteiro, C., Fernandes, J.O., Nunes, M.L., Marques, A., Dias, J., Cunha, I., Castro, L.F.C., Valente, L.M.P., 2020. Diets supplemented with <em>Saccharina latissima </em>influence the expression of genes related to lipid metabolism and oxidative stress modulating rainbow trout (<em>Oncorhynchus mykiss</em>) fillet composition. Food Chem. Toxicol. 140, 111332. <a href="https://doi.org/10.1016/j.fct.2020.111332">https://doi.org/10.1016/j.fct.2020.111332</a></p>
Elucidating gene expression adaptation of phylogenetically divergent coral holobionts under heat stress
<p>As coral reefs struggle to survive under climate change, it is crucial to know whether they have the capacity to withstand changing conditions, particularly increasing seawater temperatures. Thermal tolerance requires the integrative response of the different components of the coral holobiont (coral host, algal photosymbiont, and associated microbiome). Here, using a controlled thermal stress experiment across three divergent Caribbean coral species, we attempt to dissect holobiont member metatranscriptome responses from coral taxa with different sensitivities to heat stress and use phylogenetic ANOVA to study the evolution of gene expression adaptation. We show that coral response to heat stress is a complex trait derived from multiple interactions among holobiont members. We identify host and photosymbiont genes that exhibit lineage-specific expression level adaptation and uncover potential roles for bacterial associates in supplementing the metabolic needs of the coral-photosymbiont duo during heat stress. Our results stress the importance of integrative and comparative approaches across a wide range of species to better understand coral survival under the predicted rise in sea surface temperatures.</p>
RNA-seq data of "Transcriptome analyses of leaves reveal that hexanoic acid priming differentially regulate gene expression in contrasting Coffea arabica cultivars"
<p>This dataset represent FASTQ gziped files from the study "Transcriptome analyses of leaves reveal that hexanoic acid priming differentially regulate gene expression in contrasting <em>Coffea arabica</em> cultivars" (<a href="https://doi.org/10.3389/fsufs.2021.735893">https://doi.org/10.3389/fsufs.2021.735893</a>). Sequencing was done using an Illumina Novaseq 6000 instrument, paired-sequencing (2 X150 bp). Sample details are also available at https://www.ebi.ac.uk/ena/browser/view/ERA6282544.</p> <p> </p> <p>All filenames have the following naming scheme:</p> <p>LCS7609_DS_AAA_leafBBB_(R1 or R2).fq.gz</p> <p>AAA stands for the abbreviations:</p> <p>- CC (Coffea arabica cv Catuai control)</p> <p>- CHx (Coffea arabica cv Catuai exposed to Hexanoic acid)</p> <p>- OC (Coffea arabica cv Obatã control)</p> <p>- OHx (Coffea arabica cv Obatã exposed to Hexanoic acid)</p> <p>BBB stands for the number of biological replicate (1, 2 or 3).</p> <p> </p> <p> </p> <p> </p>
Significance of the relationship between BIRC5 and the expression of other genes in AML patients
<p>BIRC5 expression levels were examined in relationship to the expression of other genes in the MILE dataset.</p>
Online Supplemental Tables - An atlas of genome-wide gene expression and metabolite associations and possible mediation effects towards body mass index
<p>Summary statistics of metabolite-gene expression associations and mediation analyses of effects on body mass index.</p> <p>The corresponding publication is currently under revision.</p> <ul> <li><strong>Online Supplemental Table 1</strong>: Gene expression-metabolite association summary statistics from 97 metabolites and metabolite ratios and up to 15175 genes calculated seperately in the LIFE-Adult, LIFE-Heart, LIFE-AMI and the Sorb studies. Associations were adjusted for six covariates.</li> <li><strong>Online Supplemental Table 2</strong>: Random-effects meta-analyzed gene expression-metabolite association summary statistics. P-Values were adjusted for multiple testing using a hierarchical adjustment procedure both on local (within phenotypes) and on global (across phenotypes) level.</li> <li><strong>Online Supplemental Table 3</strong>: Single-study association results needed for checking mediation analysis assumptions and for calculating mediation statistics. Only gene expression probe-metabolite pairs that associated significantly at hierarchical FDR=5% in the gene-expression-metabolite association meta-analysis qualified for these associations.The following Associations were tested: gene expression probes ~ metabolites, log-BMI ~ metabolites log-BMI ~ gene expression, log-BMI ~ gene expression + metabolite. Associations were adjusted for six covariates. P-values</li> <li><strong>Online Supplemental Table 4</strong>: Meta-analyzed association results needed for checking mediation analysis assumptions and for calculation mediation statistics.</li> <li><strong>Online Supplemental Table 5: </strong>Mediation analysis summary statistics. Mediations of gene expression effects (exposure) via metabolite effects (mediatior) and of metabolite effects (exposure) via gene expression effects (mediator) on body mass index (outcome) were tested.</li> </ul> <p> </p>
Gene expression and splicing counts from the Yepez, Gusic et al study - fibroblast, hg19, strand-specific, low seq depth
<p><strong>File description:</strong></p> <ol> <li> <p>geneCounts: gene-level counts </p> </li> <li> <p>k_j: split counts spanning from one exon to another.</p> </li> <li> <p>k_theta: non-split counts covering a splice site</p> </li> <li> <p>n_psi3: total split counts from a given acceptor site</p> </li> <li> <p>n_psi5: total split counts from a given donor site</p> </li> <li> <p>n_theta: total split and non-split counts for a given splice site</p> </li> <li> <p>Sample annotation describing each sample from the dataset</p> </li> <li> <p>Description file with global information from the dataset</p> </li> </ol> <p>The gene counts were originated using the GTF file from release 34 of GENCODE <a href="https://www.gencodegenes.org/human/release_34">https://www.gencodegenes.org/human/release_34</a>, and the split and non-split counts contain only the annotated junctions from the same release.</p> <p><strong>Use: </strong>The count matrices are intended to help researchers that are interested in using RNA-Seq data with the purpose of diagnostics. Researchers can merge their own dataset with the downloaded ones, provided the tissue, genome build, strand, and paired-end specifications match. Afterwards, DROP can be used to compute expression and splicing outliers (<a href="https://github.com/gagneurlab/drop">https://github.com/gagneurlab/drop</a>).</p> <p><strong>Number of samples:</strong> 127<br> <strong>Tissue:</strong> Fibroblast<br> <strong>Organism:</strong> Homo sapiens<br> <strong>Genome assembly:</strong> hg19<br> <strong>Gene annotation:</strong> gencode34</p> <p><strong>Median mapped reads:</strong> 71 million<br> <strong>Disease</strong> (ICD-10: N): E88: 84, NONE: 12, F89: 6, G31: 3, R27: 3, E72: 3, G40: 2, R16: 2, K72: 2, P94: 2, E77: 1, E75: 1, G71: 1, G93: 1, Q78: 1, G82: 1, R29: 1, Q02: 1<br> <strong>Strand specific:</strong> True<br> <strong>Paired end:</strong> True</p> <p><strong>Dataset contact:</strong> Vicente Yepez, yepez at in.tum.de; Christian Mertes, mertes at in.tum.de; Julien Gagneur, gagneur at in.tum.de; Holger Prokisch, prokisch at helmholtz-muenchen.de</p> <p><strong>Citation:</strong> Cite both the resource using Zenodo's citation and the publication under References</p> <p> </p>
Gene expression and splicing counts from the Yepez, Gusic et al study - fibroblast, hg19, strand-specific, high seq depth
<p><strong>File description:</strong></p> <ol> <li> <p>geneCounts: gene-level counts </p> </li> <li> <p>k_j: split counts spanning from one exon to another.</p> </li> <li> <p>k_theta: non-split counts covering a splice site</p> </li> <li> <p>n_psi3: total split counts from a given acceptor site</p> </li> <li> <p>n_psi5: total split counts from a given donor site</p> </li> <li> <p>n_theta: total split and non-split counts for a given splice site</p> </li> <li> <p>Sample annotation describing each sample from the dataset</p> </li> <li> <p>Description file with global information from the dataset</p> </li> </ol> <p> </p> <p>The gene counts were originated using the GTF file from release 34 of GENCODE <a href="https://www.gencodegenes.org/human/release_34">https://www.gencodegenes.org/human/release_34</a>, and the split and non-split counts contain only the annotated junctions from the same release.</p> <p><strong>Use: </strong>The count matrices are intended to help researchers that are interested in using RNA-Seq data with the purpose of diagnostics. Researchers can merge their own dataset with the downloaded ones, provided the tissue, genome build, strand, and paired-end specifications match. Afterwards, DROP can be used to compute expression and splicing outliers (<a href="https://github.com/gagneurlab/drop">https://github.com/gagneurlab/drop</a>).</p> <p><strong>Number of samples:</strong> 135<br> <strong>Tissue:</strong> Fibroblast<br> <strong>Organism:</strong> Homo sapiens<br> <strong>Genome assembly:</strong> hg19<br> <strong>Gene annotation:</strong> gencode34</p> <p><strong>Median mapped reads:</strong> 116 million<br> <strong>Disease</strong> (ICD-10: N): E88: 112, G31: 8, NONE: 5, K72: 2, G71: 2, E72: 1, G93: 1, I42: 1, F82: 1, E75: 1, F89: 1<br> <strong>Strand specific:</strong> True<br> <strong>Paired end:</strong> True<br> <strong>Dataset contact:</strong> Vicente Yepez, yepez at in.tum.de; Christian Mertes, mertes at in.tum.de; Julien Gagneur, gagneur at in.tum.de; Holger Prokisch, prokisch at helmholtz-muenchen.de</p> <p><strong>Citation:</strong> Cite both the resource using Zenodo's citation and the publication under References</p> <p> </p>
Leaf gene expression trajectories during the growing season are consistent between sites and years in American beech
<p>Transcriptomics, the quantification of gene expression, provides a versatile tool for ecological monitoring. Here, we show that through genome-guided profiling of transcripts mapping to 33,042 loci, gene expression differences can be discerned among multi-year and seasonal leaf samples collected from American beech trees at two latitudinally separated sites. Despite a bottleneck imposed due to large-scale post-Columbian deforestation, the SNP-based population genetic background analysis has yielded sufficient variation to account for differences between populations and among individuals. Our time series of expression analyses during spring-summer and summer-fall transitions for two consecutive years involved 4197 differentially expressed protein coding genes. A global comparison of 12 seasons has revealed that spring gene expression sets the pace for the rest of the growing season. Using \textit{Populus} orthologs of the differentially expressed genes, we reconstructed a protein-protein interactome as a representation of the leaf physiological states of trees during the seasonal transitions. Gene set enrichment analysis revealed GO terms that highlight molecular functions and biological processes possibly influenced by abiotic forcings such as recovery from drought and response to excess precipitation. Further, based on 324 co-regulated transcripts, we focused on a subset of terms that could be putatively attributed to phenological shifts due to late spring. Our conservative results indicate that extended transcriptome-based monitoring of forests can capture ranges of responses arising from other factors including air quality, chronic disease as well as herbivore outbreaks that require activation and/or downregulation of genes collectively tuning reaction norms needed for the survival of long living trees such as the American beech.</p>
CORE: Gene Expression-Cancer Knowledge Base
<p>This repository contains the Gene Expression-Cancer Knowledge Base generated by the CORE system.<br> The 'schema.owl' file contains the KB schema, whereas the 'data.ttl' file contains the actual data.</p>
Gene expression in monocytes, neutrophils and whole blood after stroke
<p>Dataset from:</p> <p>Carmona-Mora, P., Knepp, B., Jickling, G.C. <em>et al.</em> Monocyte, neutrophil, and whole blood transcriptome dynamics following ischemic stroke. <em>BMC Med</em> <strong>21</strong>, 65 (2023). https://doi.org/10.1186/s12916-023-02766-1</p> <p>All methods available in the publication above.</p> <p>Abstract</p> <p>Background After ischemic stroke (IS), peripheral leukocytes infiltrate the damaged region and modulate the response to injury. Peripheral blood cells display distinctive gene expression signatures post IS and these transcriptional programs reflect changes in immune responses to IS. Dissecting the temporal dynamics of gene expression after IS improves our understanding of immune and clotting responses at the molecular and cellular level that are involved in acute brain injury and may assist with time-targeted, cell-specific therapy.</p> <p>Methods The transcriptomic profiles from peripheral monocytes, neutrophils, and whole blood from 38 ischemic stroke patients and 18 controls were analyzed with RNAseq as a function of time and etiology after stroke. Differential expression analyses were performed at 0-24 h, 24-48 h, and >48 h following stroke.</p> <p>Results Unique patterns of temporal gene expression and pathways were distinguished for monocytes, neutrophils and whole blood with enrichment of interleukin signaling pathways for different timepoints and stroke etiologies. Compared to control subjects, gene expression was generally up-regulated in neutrophils and generally down- regulated in monocytes over all times for cardioembolic, large vessel and small vessel strokes. Self-Organizing Maps identified gene clusters with similar trajectories of gene expression over time for different stroke causes and sample types. Weighted Gene Co- expression Network Analyses identified modules of co-expressed genes that significantly varied with time after stroke and included hub genes of immunoglobulin genes in whole blood.</p> <p>Conclusions Altogether, the identified genes and pathways are critical for understanding how the immune and clotting systems change over time after stroke. This study identifies potential time- and cell-specific biomarkers and treatment targets.</p> <p>clinical_parameters_MON.txt: Clinical parameters from cohort used from monocyte samples.</p> <p>clinical_parameters_NEU.txt: Clinical parameters from cohort used from neutrophil samples.</p> <p>clinical_parameters_WB.txt: Clinical parameters from cohort used from whole blood samples.</p> <p>MON_gene_counts_filtered-WGCNA.txt: Filtered counts of each annotated gene from monocyte samples, cohort used for WGCNA analyses, (TPM normalized, non-log, filtered features where maximum <=40 reads were excluded).</p> <p>NEU_gene_counts_filtered-WGCNA.txt: Filtered counts of each annotated gene from neutrophil samples, cohort used for WGCNA analyses, (TPM normalized, non-log, filtered features where maximum <=40 reads were excluded).</p> <p>WB_gene_counts_filtered-WGCNA.txt: Filtered counts of each annotated gene from whole blood samples, cohort used for WGCNA analyses, (TPM normalized, non-log, filtered features where maximum <=80 reads were excluded).</p> <p>MON_gene_raw_counts.txt: raw counts for cohort used of monocyte samples.</p> <p>NEU_gene_raw_counts.txt: raw counts for cohort used of neutrophil samples.</p> <p>WB_gene_raw_counts.txt: raw counts for cohort used of whole blood samples.</p> <p>MON_Time_Course_filtered_normalized_counts_ready.txt: matrix counts of each annotated gene used for differential expression analyses of time points in monocyte samples. (TPM normalized, filtered features where maximum <=30 reads were excluded).</p> <p>NEU_Time_Course_filtered_normalized_counts_ready.txt: matrix counts of each annotated gene used for differential expression analyses of time points in neutrophil samples. (TPM normalized, filtered features where maximum <=30 reads were excluded).</p> <p>WB_Time_Course_filtered_normalized_counts_ready.txt: matrix counts of each annotated gene used for differential expression analyses of time points in whole blood samples. (TPM normalized, filtered features where maximum <=30 reads were excluded).</p> <p> </p> <p>All methods to generate the above files are available in the publication:</p> <p>Carmona-Mora, P., Knepp, B., Jickling, G.C. <em>et al.</em> Monocyte, neutrophil, and whole blood transcriptome dynamics following ischemic stroke. <em>BMC Med</em> <strong>21</strong>, 65 (2023). https://doi.org/10.1186/s12916-023-02766-1</p> <p> </p>
Data from: Fungal symbionts generate water-saver and water-spender plant drought strategies via diverse effects on host gene expression
<p><em>Panicum</em> <em>hallii</em> var <em>hallii</em> HAL2 plants were inoculated individually with six foliar fungal endophytes or fungus-free controls and subjected to 5% or 20% soil moisture treatments. The fungi were selected for their previously observed effects on plant drought physiology, inducing either a "water saver" or a "water spender" strategy in the host. Plants were grown in enclosed microcosms to prevent cross-contamination and each treatment and control included 6 replicates. All fungi were Ascomycetes isolated from plants in central Texas. Plants were monitored for height, wilt, water loss, and survival. At the harvest, we also measured biomass and leaf colonization by the fungi and flash-froze leaf tissue for transcriptomic analyses. Both plant response and gene expression data are provided.</p>
A benchmark of gene expression tissue-specificity metrics
<p>Supplementary figures and data to the paper "A benchmark of gene expression tissue-specificity metrics"</p> <p><em>Briefings in Bioinformatics</em>, Volume 18, Issue 2, March 2017, Pages 205–214, <a href="https://doi.org/10.1093/bib/bbw008">https://doi.org/10.1093/bib/bbw008</a></p> <p>Previously published at FigShare, republishing because of access problems for some researchers.</p>
Data for: Weaker selection on genes with treatment-specific expression consistent with a limit on plasticity evolution in Arabidopsis thaliana
<p>Differential gene expression between environments often underlies phenotypic plasticity. However, environment-specific expression patterns are hypothesized to relax selection on genes, and thus limit plasticity evolution. We collated over 27 terabases of RNA-sequencing data on <em>Arabidopsis thaliana</em> from over 300 peer-reviewed studies and 200 treatment conditions to investigate this hypothesis. Consistent with relaxed selection, genes with more treatment-specific expression have higher levels of nucleotide diversity and divergence at nonsynonymous sites but lack stronger signals of positive selection. This result persisted even after controlling for expression level, gene length, GC content, the tissue specificity of expression, and technical variation between studies. Overall, our investigation supports the existence of a hypothesized trade-off between the environment specificity of a gene's expression and the strength of selection on said gene in <em>A. thaliana</em>. Future studies should leverage multiple genome-scale datasets to tease apart the contributions of many variables in limiting plasticity evolution.</p>
ScienceDex guides
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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.