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11,177 results for “identify”
Genome-phenotype-environment associations identify signatures of selection in a panmictic population of threespine stickleback
<p>Adaptive genetic divergence occurs when selection imposed by the environment causes the genomic component of the phenotype to differentiate. However, genomic signatures of natural selection are usually identified without information on which trait is responding to selection by which selective agent(s). Here we integrate whole-genome-sequencing with phenomics and measures of putative selective agents to assess the extent of adaptive divergence in threespine stickleback occupying the highly heterogeneous lake Mývatn, NE Iceland. We find negligible genome-wide divergence, yet multiple traits (body size, gill raker structure and defence traits) were divergent along known ecological gradients (temperature, predatory bird densities and water depth). SNP-based heritability of all measured traits was high (h<sup>2</sup> = 0.42 – 0.65), indicating adaptive potential for all traits. Whilst environment-association analyses identified thousands of loci putatively involved in selection, related to genes linked to neuron development and protein phosphorylation, only loci linked to pelvic spine length were concurrently linked to environmental variation (water depth) – supporting the conclusion that divergence in pelvic spine length occurred in face of gene flow. Our results suggest that whilst there is substantial genetic variation in the traits measured, phenotypic divergence of Mývatn stickleback is mostly weakly associated with environmental gradients, potentially as a result of substantial gene flow. Our study illustrates the value of integrative studies that combine genomic assays of multivariate trait variation with landscape genomics.</p>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Nathan Upham, <a href="https://orcid.org/0000-0001-5412-9342">https://orcid.org/0000-0001-5412-9342</a>. Claims were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Identifying serpentine minerals by their chemical compositions with machine learning (dataset and python code)
<p>The dataset and python code for the manuscript of Identifying serpentine minerals by their chemical compositions with machine learning (submitted to American Geologist)</p>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Dean A. Hendrickson, <a href="https://orcid.org/0000-0001-7835-0295">https://orcid.org/0000-0001-7835-0295</a>. Claims were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Anne Dubéarnès, <a href="https://orcid.org/0000-0002-5754-9235">https://orcid.org/0000-0002-5754-9235</a>. Claims were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Raw Data for the article: High-Resolution Secretome Analysis of Chemical Hypoxia Treated Cells Identifies Putative Biomarkers of Chondrosarcoma
<p>Chondrosarcoma is the second most common bone tumor, accounting for 20% of all cases. Little is known about the pathology and molecular mechanisms involved in the development and in the metastatic process of chondrosarcoma. As a consequence, there are no approved therapies for this tumor and surgical resection is the only treatment currently available. Moreover, there are no available biomarkers for this type of tumor, and chondrosarcoma classification relies on operator-dependent histopathological assessment. Reliable biomarkers of chondrosarcoma are urgently needed, as well as greater understanding of the molecular mechanisms of its development for translational purposes. Hypoxia is a central feature of chondrosarcoma progression. The hypoxic tumor microenvironment of chondrosarcoma triggers a number of cellular events, culminating in increased invasiveness and migratory capability. Herein, we analyzed the effects of chemically-induced hypoxia on the secretome of SW 1353, a human chondrosarcoma cell line, using high-resolution quantitative proteomics. We found that hypoxia induced unconventional protein secretion and the release of proteins associated to exosomes. Among these proteins, which may be used to monitor chondrosarcoma development, we validated the increased secretion in response to hypoxia of glyceraldehyde 3-phosphate dehydrogenase (GAPDH), a glycolytic enzyme well-known for its different functional roles in a wide range of tumors. In conclusion, by analyzing the changes induced by hypoxia in the secretome of chondrosarcoma cells, we identified molecular mechanisms that can play a role in chondrosarcoma progression and pinpointed proteins, including GAPDH, that may be developed as potential biomarkers for the diagnosis and therapeutic management of chondrosarcoma.</p>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Thomas Edward Saunders, <a href="https://orcid.org/0000-0003-1780-0314">https://orcid.org/0000-0003-1780-0314</a>. Claims were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Identifying the best approximating model in Bayesian phylogenetics: Bayes factors, cross-validation or wAIC?
<p>There is still no consensus as to how to select models in Bayesian phylogenetics, and more generally in applied Bayesian statistics. Bayes factors are often presented as the method of choice, yet other approaches have been proposed, such as cross-validation or information criteria. Each of these paradigms raises specific computational challenges, but they also differ in their statistical meaning, being motivated by different objectives: either testing hypotheses or finding the best-approximating model. These alternative goals entail different compromises, and as a result, Bayes factors, cross-validation and information criteria may be valid for addressing different questions. Here, the question of Bayesian model selection is revisited, with a focus on the problem of finding the best-approximating model. Several model selection approaches were re-implemented, numerically assessed and compared: Bayes factors, cross-validation (CV), in its different forms (k-fold or leave-one-out), and the widely applicable information criterion (wAIC), which is asymptotically equivalent to leave-one-out cross validation (LOO-CV). Using a combination of analytical results and empirical and simulation analyses, it is shown that Bayes factors are unduly conservative. In contrast, cross-validation represents a more adequate formalism for selecting the model returning the best approximation of the data-generating process and the most accurate estimates of the parameters of interest. Among alternative CV schemes, LOO-CV and its asymptotic equivalent represented by the wAIC, stand out as the best choices, conceptually and computationally, given that both can be simultaneously computed based on standard MCMC runs under the posterior distribution.</p>
Using the Uniqueness of Global Identifiers to Determine the Provenance of Python Software Source Code
<p>A replication package for the paper "Using the Uniqueness of Global Identifiers to Determine the Provenance of Python Software Source Code", Journal of Empirical Software Engineering.</p>
Data for: Attributes of CloudSat identified echo objects
<p>This data set contains a collection of attributes associated with CloudSat identified echo objects (or contiguous regions of radar/dBZ echo) from15 June 2006 till 17 January 2013. CloudSat is a NASA satellite that carries a 94 GHz (3 mm) nadir pointing cloud profiling radar (CPR). CloudSat makes approximately 14 orbits per day with an equator passing time of 0130 and 1330 local time. Echo objects were identified using CloudSat's 2B-GEOPROF product that includes 2D arrays (alongtrack x vertical) of the radar reflectivity factor and gaseous attenuation correction. Also included in the product is a "cloud mask" with values ranging between 0 and 40 with higher values indicating a greater likelihood of cloud detection.</p> <p>An EO was defined as a contiguous region of cloud mask greater than or eaqual to 20, consisting of at least three pixels with their edges and not merely their corners touching. Each echo object (EO) is assigned multiple attributes. The geographic attributes include minimum, mean, and maximum latitude and longitude, minimum and maximium location along the CloudSat orbit track, and the underlying surface altitude and land mask data, which allows the EOs to be catagorized as occuring over land, sea, or the coast. The geometric attributes include top, mean, and bottom height, width, and the total number of pixels within the EO. Attributes describing the internal structure of the EO are also available including the number of pixels and cells (i.e., group of pixels) greater than 0 dBZ and -17 dBZ. Finally, the time of day of occurance was also recorded to compare the statistics of EOs ocurring during the daytime versus nighttime. In total, we identified 15,181,193 EOs from 15 June 2006 to 17 January 2013. After 17 April 2011, data were only collected during the day due to a battery failure onboard CloudSat. Each attribute is organized as a 1D array where the size of the array corresponds to the number of EOs. This organization allows subsets of EOs to be easily identified using simple "where" statements when writing code.</p> <p>The attributes were used to identify cloud types and analyze global cloud climatology according to season, surface type, and region (i.e., Riley 2009; Riley and Mapes 2009). The varability of EOs across the MJO was also analyzed (Riley et al. 2011).</p>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Andrea Acurio, <a href="https://orcid.org/0000-0002-1792-7107">https://orcid.org/0000-0002-1792-7107</a>. Claims were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Precise modulation of transcription factor levels identifies features underlying dosage sensitivity
<p><strong>Processed data and code for "Precise modulation of transcription factor levels reveals drivers of dosage sensitivity," Naqvi et al 2022.</strong></p> <p><strong>Count/expression data</strong></p> <ul> <li>all.sub.150bpclust.greater2.500bp.merge.ATAC.counts.fulldep.3h.24h.txt.gz - ATAC-seq counts from all samples (SOX9 titration and depletion) over all reproducible ATAC-seq peak regions</li> <li>all.sub.150bpclust.greater2.500bp.merge.k27ac.txt.gz - H3K27ac ChIP-seq counts from SOX9 depletion samples over all reproducible peak regions</li> <li>all.sub.150bpclust.greater2.500bp.merge.SOX9titr.V5.counts.txt.gz - V5 (SOX9) ChIP-seq counts from partial SOX9 titration (100%, 60%, 30%, 0%) over all reproducible peak regions</li> <li>all.sub.150bpclust.greater2.500bp.merge.SOX9titr.TWIST1.in.counts.tab.txt.gz - TWIST1 and input ChIP-seq counts from partial SOX9 titration (100%, 60%, 30%, 0%) over all reproducible peak regions</li> <li>rna.salmon.7rep.txi.counts.txt.gz - RNA-seq counts from SOX9 titration samples </li> <li>rna.salmon.7rep.txi.abundance.txt.gz - RNA-seq TPM values from SOX9 titration samples </li> <li>slam.tcreadcount.txt.gz - SLAM-seq T-C conversion-containing read counts (representing newly transcribed mRNAs) from SOX9 depletion samples</li> <li>slam.readcount.txt.gz - SLAM-seq read counts (representing all mRNAs) from SOX9 depletion samples</li> </ul> <p><strong>Metadata</strong></p> <ul> <li>all.protcod.gene.features.txt.gz - Features of interest for all protein-coding genes</li> <li>all.sub.150bpclust.greater2.500bp.merge.features.txt.gz - Features of interest for all reproducible peak regions</li> <li>atac_depletion_3h_24h_design.txt - design matrix for ATAC-seq SOX9 depletion samples</li> <li>atac_titration_48h_design.txt - design matrix for ATAC-seq SOX9 titration samples</li> <li>Homo_sapiens.GRCh38.cdna.all.txid2gene.id.symbol.type.txt.gz - Ensembl transcript types (for filtering to protein-coding genes in various analyses)</li> <li>k27_depletion_3h_24h_design.txt - design matrix for H3K27ac ChIP-seq SOX9 depletion samples</li> <li>v5_sox9titration_design.txt - design matrix for V5 (SOX9) ChIP-seq SOX9 partial titration samples</li> <li>twist1_sox9titration_design.txt - design matrix for TWIST1 ChIP-seq SOX9 partial titration samples</li> <li>rna_titration_48h_design.txt - design matrix for RNA-seq SOX9 titration samples</li> <li>slam_depletion_3h_24h_design.txt - design matrix for SLAM-seq SOX9 depletion samples</li> <li>facialgwas_snpia_ld0.5.hg38.bed - SNPs in LD (r2 > 0.5) with any of the facial GWAS lead SNPs in Supplementary Table 2 of Naqvi, Hoskens, et al, Annu Rev. Hum Genet. Genom. 2022. </li> <li>facialgwas_prsendo_7e5_either_snpia_ld0.5.hg38.bed - SNPs in LD (r2 > 0.5) with the subset of the same facial GWAS SNPs that show significant (p-value < 7e-05, ~corresponding to Bonferonni-corrected p-value of 0.01) association with the PRS endophenotype GWAS in either US or UK cohort. </li> </ul> <p><strong>Scripts</strong></p> <ul> <li>atac_deseq_fitmodels_bs_parallel.R - R code for fitting bootstrapped Hill equations to all SOX9-dependent REs (computationally intensive, so has been coded for parallelization over multiple cores) <ul> <li>Input: all.sub.150bpclust.greater2.500bp.merge.ATAC.counts.fulldep.3h.24h.txt.gz, atac_titration_48h_design.txt</li> <li>Output: enh_linear_sig_aic_bsmat.txt, enh_linear_sig_aic_bsmat_enhind.txt</li> </ul> </li> <li>atac_deseq_fitmodels.R - R code for fitting Hill equations (no bootstrap) to all SOX9-dependent REs <ul> <li>Input: all.sub.150bpclust.greater2.500bp.merge.ATAC.counts.fulldep.3h.24h.txt.gz, atac_titration_48h_design.txt</li> <li>Output: enh_linear_sig_aic.rds</li> </ul> </li> <li>atac_k27_depletion_deseq.R - R code for DESeq2 analysis of ATAC and H3K27ac ChIP SOX9 depletion (3h and 24h) <ul> <li>Input: all.sub.150bpclust.greater2.500bp.merge.ATAC.counts.fulldep.3h.24h.txt.gz, all.sub.150bpclust.greater2.500bp.merge.k27ac.txt.gz, atac_depletion_3h_24h_design.txt</li> <li>Output: atac_depletion_3h_24h_deseq.txt, k27_depletion_3h_24h_deseq.txt </li> </ul> </li> <li>v5_twist1_sox9titration_deseq.R - R code for DESeq2 analysis of V5 (SOX9) and TWIST1 ChIP in partial SOX9 titration (100%, 60%, 30%, 0%) <ul> <li>Input: all.sub.150bpclust.greater2.500bp.merge.SOX9titr.V5.counts.txt.gz, all.sub.150bpclust.greater2.500bp.merge.SOX9titr.TWIST1.in.counts.tab.txt.gz, v5_sox9titration_design.txt, twist1_sox9titration_design.txt</li> <li>Output: v5_sox9titration_deseq.txt, twist1_sox9titration_deseq.txt </li> </ul> </li> <li>drm.R - Modified version of code from drc() package to prevent errors, install drc() with this version to avoid errors</li> <li>group_comparisons.Rmd - R code to compare computed parameters (i.e. ED50, Hill) between sets of REs/genes utilizing bootstrap information <ul> <li>Input: enh_linear_sig_aic_bsmat_enhind.txt, enh_linear_sig_aic_bsmat.txt.gz, enh_linear_sig_aic.rds, gene_linear_sig_aic_bsmat_enhind.txt, gene_linear_sig_aic_bsmat.txt.gz, gene_linear_sig_aic.rds, all.sub.150bpclust.greater2.500bp.merge.features.txt.gz, all.protcod.gene.features.txt.gz</li> <li>Uses: summarize_bs_helper.R</li> </ul> </li> <li>plot_re_gene_fits.Rmd - R code for plotting individual RE/gene counts and Hill/linear fits <ul> <li>Input: all.sub.150bpclust.greater2.500bp.merge.ATAC.counts.fulldep.3h.24h.txt.gz, atac_titration_48h_design.txt, rna.salmon.7rep.txi.counts.txt.gz, rna.salmon.7rep.txi.abundance.txt.gz, rna_titration_48h_design.txt</li> </ul> </li> <li>rna_deseq_fitmodels_bs_parallel.R - R code for fitting bootstrapped Hill equations to all SOX9-dependent genes (computationally intensive, so has been coded for parallelization over multiple cores) <ul> <li>Input: rna.salmon.7rep.txi.counts.txt.gz, rna.salmon.7rep.txi.abundance.txt.gz, rna_titration_48h_design.txt</li> <li>Output: gene_linear_sig_aic_bsmat.txt, gene_linear_sig_aic_bsmat_enhind.txt</li> </ul> </li> <li>rna_deseq_fitmodels.R - R code for fitting Hill equations (no bootstrap) to all SOX9-dependent genes <ul> <li>Input: rna.salmon.7rep.txi.counts.txt.gz, rna.salmon.7rep.txi.abundance.txt.gz, rna_titration_48h_design.txt</li> <li>Output: gene_linear_sig_aic.rds</li> </ul> </li> <li>slam_depletion_deseq.R - R code for DESeq2/sva analysis of SLAM-seq SOX9 depletion (3h and 24h) <ul> <li>Input: slam.tcreadcount.txt.gz, slam_depletion_3h_24h_design.txt</li> <li>Output: slam_depletion_3h_24h_deseq.txt</li> </ul> </li> <li>summarize_bs_helper.R - Helper functions for group_comparisons.Rmd</li> </ul> <p><strong>Intermediate/output files (some files are gzipped to save space, the Rscripts that output them won't gzip but they expect gzipped input when indicated)</strong></p> <ul> <li>atac_depletion_3h_24h_deseq.txt.gz - DESeq2 output of ATAC SOX9 depletion (3h and 24h)</li> <li>enh_linear_sig_aic_bsmat_enhind.txt - index to name file for SOX9-dependent RE bootstrap output</li> <li>enh_linear_sig_aic_bsmat.txt.gz - SOX9-dependent RE bootstrap output</li> <li>enh_linear_sig_aic.rds - Parameters from Hill equation fit on all SOX9-dependent REs (no bootstrap) (RDS file)</li> <li>gene_linear_sig_aic_bsmat_enhind.txt - index to name file for SOX9-dependent gene bootstrap output</li> <li>gene_linear_sig_aic_bsmat.txt.gz - SOX9-dependent gene bootstrap output</li> <li>gene_linear_sig_aic.rds - Parameters from Hill equation fit on all SOX9-dependent gene (no bootstrap) (RDS file)</li> <li>k27_depletion_3h_24h_deseq.txt.gz - DESeq2 output of H3K27ac ChIP-seq SOX9 depletion (3h and 24h)</li> <li>slam_depletion_3h_24h_deseq.txt.gz - DESeq2 output of SLAM-seq SOX9 depletion (3h and 24h)</li> <li>v5_sox9titration_deseq.txt.gz - DESeq2 output of V5 (SOX9) ChIP-seq in partial SOX9 titration (100%, 60%, 30%, 0%)</li> <li>twist1_sox9titration_deseq.txt.gz - DESeq2 output of TWIS1 ChIP-seq in partial SOX9 titration (100%, 60%, 30%, 0%)</li> </ul> <p><strong>chromatin_predictions.tar.gz (self-contained folder for chromatin-based predictions of gene expression change) contains:</strong></p> <ul> <li>ABC_6conc.sh - Bash script to calculate predicted gene expression change based on ATAC-seq fold-change at each of five SOX9 concentrations (warning: creates a number of very large additional intermediate output files). Requires as input all files in this folder except for all.sub.150bpclust.greater2.500bp.merge.ABC.5Mb.power-0.7.norm.6conc.all.total.txt</li> <li><strong> </strong>all.sub.150bpclust.greater2.500bp.merge.ABC.5Mb.power-0.7.norm.6conc.all.total.txt - ATAC-based predicted fold-change of all genes each of five SOX9 concentrations (78, 52, 25, 8, 0, in that order), relative to 100% SOX9</li> <li>all.sub.150bpclust.greater2.500bp.merge.ATAC.DMSO.counts.txt - ATAC-seq counts over all reproducible peak regions in updepleted samples</li> <li>all.sub.150bpclust.greater2.500bp.merge.bed - bed file of all reproducible peak regions</li> <li>all.sub.150bpclust.greater2.500bp.merge.deseq.allconc.lfc.txt - DESeq2 output from ATAC SOX9 titration, comparing each lowered SOX9 concentration to 100% SOX9</li> <li>all.sub.150bpclust.greater2.500bp.merge.k27ac.dmso.counts.txt - H3K27ac ChIP-seq counts over all reproducible peak regions in updepleted samples</li> <li>hg38_refGene_TSS_collapsed.bed - collapsed TSSs for all genes</li> <li>hg38.genome - genome file</li> </ul> <p> </p>
Multiplex imaging of breast cancer lymph node metastases identifies prognostic single-cell populations independent of clinical classifiers
<p>This repository contains the raw IMC data of ZTMA 26 as continuation of dataset <strong>10.5281/zenodo.7494413.</strong> The zip files starting with ZTMA contain the raw IMC measurements (mcd and txt) of those parts of the TMA. The TMA measurements are split up into parts in order to avoid huge files.</p> <p>Additionally, this repository contains the metadata of the patients analyzed in this study, the panel information and the single-cell data that was extracted from the multiplexed images together with the associated metadata in SingleCellExperiment format for analysis in R.</p> <p>The analysis.zip folder contains files that were written out during the analysis according to the scripts in https://github.com/BodenmillerGroup/BC_LN_metastses.</p> <p>The single-cell and other data outputs from CellProfiler can be found in the cpout.zip file.</p> <p>The IF_whole_sections.zip file contains the IF images of the primary breast cancer sections (czi files) and the extracted single-cell data.</p>
Multiplex imaging of breast cancer lymph node metastases identifies prognostic single-cell populations independent of clinical classifiers
<p>This repository contains the raw IMC data of ZTMA 21 and 25 of the matched primary breast cancer and lymph node metastasis study presented in Fischer and Jackson et al., 2023. The code that was used to process and analyze this data can be found at https://github.com/BodenmillerGroup/BC_LN_metastses.</p> <p>The zip files starting with ZTMA contain the raw IMC measurements (mcd and txt) of the respective parts of the TMA. The TMA measurements are split up into parts in order to avoid huge files.</p>
Urban flask measurements of CO2ff and CO to identify emission sources at different site types in Auckland, New Zealand
<p>As part of the CarbonWatch-NZ research programme, air samples were collected at 28 sites around Auckland, New Zealand to determine the atmospheric ratio (R<sub>CO</sub>) of excess (local enhancement over background) carbon monoxide to fossil CO<sub>2</sub> (CO<sub>2</sub>ff). Sites were categorised into seven types (background, forest, industrial, suburban, urban, downwind, and motorway) to observe R<sub>CO</sub> around Auckland. Flasks from motorway sites observed R<sub>CO</sub> of 14 ± 1 ppb/ppm and were used to evaluate traffic R<sub>CO</sub>. The similarity between suburban (14 ± 1 ppb/ppm) and traffic R<sub>CO</sub> suggests that traffic dominates suburban CO<sub>2</sub>ff emissions during daytime hours, the period of flask collection. The lower urban R<sub>CO</sub> (11 ± 1 ppb/ppm) suggests that urban CO<sub>2</sub>ff emissions are comprised of more than just traffic, with contributions from residential, commercial, and industrial sources, all with a lower R<sub>CO</sub> than traffic. Finally, the downwind sites were believed to best represent R<sub>CO</sub> for Auckland City overall (11 ± 1 ppb/ppm). We demonstrate that the initial discrepancy between the downwind R<sub>CO</sub> and Auckland's estimated daytime inventory R<sub>CO</sub> (15 ppb/ppm) can be attributed to an overestimation in inventory traffic CO emissions. After revision based on our observed motorway R<sub>CO</sub>, the revised inventory R<sub>CO</sub> (12 ppb/ppm) is consistent with our observations. </p>
Identifying temporal dynamics in post-release survival of a restored large ungulate
<p>Wildlife translocation is an important conservation tool for restoring species and reducing global biodiversity decline; however, this practice is challenging for wildlife, and translocated individuals must adjust to their release landscapes for restoration success. The period following release is a vulnerable time for wildlife and determining when animals acclimate following translocation improves long-term estimates of population dynamics and allows researchers to efficiently tailor translocation design and post-release management to each population's needs.</p> <p>We evaluated demographic acclimation and investigated changes in post-release survival of a population of 106 elk (<em>Cervus canadensis</em>) translocated to Missouri, U.S.A., from 2011 to 2013 using generalized survival modeling. We defined the acclimation period as the duration of time prior to stabilization in mortality risk relative to time from release, and we evaluated acclimation duration from flexible plots of estimated mortality hazards across time. We simultaneously investigated factors contributing to post-release survival and compared survival rates and mortality sources observed during acclimation and post-acclimation periods.</p> <p>Elevated mortality risk for translocated elk spanned a 5-month acclimation period following release, with an additional one-year period reflecting reduced, but still elevated, mortality hazards. Baseline hazards during the initial 5-month acclimation period were approximately four times higher than the subsequent 4.4 years following release.</p> <p>Release cohort, but not age class or sex, was associated with post-release mortality risk, and mortality sources within the acclimation period tended to be undetermined with greater frequency relative to mortalities occurring after the acclimation period.</p> <p><em><strong>Synthesis and applications.</strong></em> Our results highlight the utility of flexible modeling approaches to evaluate dynamic post-release responses of translocated populations. Translocated elk demonstrated a pronounced demographic acclimation period that spanned five months following release. Changes in mortality sources and the timing of stabilization in mortality hazards suggest that demographic acclimation in translocated elk was associated with adjustments in physiological stress and movement rates that stabilize over a similar post-release duration. Improving post-release monitoring and assessment of restored populations by evaluating temporal dynamics of manifold biological and ecological responses of translocated animals.</p>
SomaScan dataset used to identify protein biomarkers for distinguishing between bacterial and viral infections in febrile children
<p>Protein profiles of children with confirmed bacterial infections (DB), confirmed viral infections (DV) in addition to healthy controls (HC). Protein profiles generated through the SomaScan 1.3k assay (SomaLogic, Colorado, USA).</p> <p>Data has been normalised already, including batch effect correction using COCONUT (https://cran.r-project.org/web/packages/COCONUT/COCONUT.pdf) and log2 transformed. </p> <p>Accompanying the protein abundance values is a separate .csv file containing information about the proteins, including UniProt ID and Entrez gene IDs associated with the proteins.</p>
Data from: One model to rule them all: Identifying priority bat habitats from multi‐species habitat suitability models
<p>Bats are important components of global ecosystems, providing essential ecosystem services with substantial economic benefit. Yet North American bat populations have been negatively affected by numerous factors (e.g., disease, habitat loss, and wind energy development) with compounding effects. Bats use habitats at a variety of scales, from small, isolated patches to large, contiguous corridors. Landscape‐level research is necessary to identify important habitats, patches, and corridors to strategically target management interventions. We created habitat suitability models (HSMs) for hoary bats (<em>Lasiurus cinereus</em>), eastern red bats (<em>L. borealis</em>), and tri‐colored bats (<em>Perimyotis subflavus</em>) across Illinois, USA using species-specific landscape and climate variables. With the 3 models from this study and a previously published HSM for Indiana bats (<em>Myotis sodalis</em>), we stacked binary HSMs, thereby identifying priority conservation areas across Illinois. Species exhibited different distributional patterns and habitat preferences across Illinois. Multi‐species HSMs highlight high quality habitat (i.e., ecologically important habitat that provides preferred resources for roosting, foraging, and raising young) in southern Illinois and along river riparian areas. This approach identified priority conservation areas mainly following hydrologic zones, which allows managers to strategically target restoration and conservation measures, invest funds in habitat likely to have high return‐on‐investment, and assist with decisions that affect bats (e.g., siting wind turbines and purchasing mitigation lands). </p>
Massively parallel screens to identify splice disruptive variants in human disease genes
<p>Splicing is a critical step in mRNA maturation with roles in gene regulation and proteome diversification. Splice disruptive variants (SDVs) are implicated in diverse human diseases, and 10-33% of exonic variants may disrupt splicing. However, identifying SDVs remains challenging due to the degeneracy and redundancy of the underlying sequence code. Experimental splicing measurements from patient cells or mini-gene assays can detect SDVs but have traditionally been low-throughput. </p> <p>Massively parallel reporter assays (MPSAs) systematically measure splicing impacts at scale and could clarify variant pathogenicity and inform models of splicing regulation. In this assay, complex barcoded libraries of mutant exons are synthesized, cloned into minigene constructs, and transfected into human cells. Splicing outcomes of each mutation are quantified en masse using targeted RNA-seq of minigene-derived transcripts, and I developed custom python package to process the resulting data.</p> <p>In Chapter 2, I apply this assay to the pituitary transcription factor gene <em>POU1F1</em> (in collaboration Dr. Sally Camper’s lab). Mutations in <em>POU1F1</em> cause combined pituitary hormone deficiency (CPHD), a clinically and genetically heterogenous disorder with prevalence ~1:4000. We targeted exon 2, which has two alterative isoforms (alpha and beta) using competing splice acceptors that encode mutually antagonistic proteins. We measured the splicing effects of 1,070 SNVs across the exon and surrounding introns and identified 96 SDVs - 14 of which were synonymous substitutions. Our measurements were concordant with six nearby heterozygous missense and synonymous variants seen in unrelated hypopituitarism patients. This map identifies a putative splice silencer motif that represses the use of the normally lowly expressed beta isoform.</p> <p>In Chapter 3, I apply a MPSA to a critical developmental renal transcription factor gene, <em>WT1</em> (in collaboration with clinical nephrologist Dr. Jen Lai Yee). Mutations in <em>WT1 </em>are implicated in nephrotic syndrome and sexual differentiation phenotypes. I focus on exon 9 which is alternatively spliced at competing donor sites resulting in two isoforms (KTS+ and KTS-). KTS+ and KTS- are normally expressed in ~2:1 ratio, but perturbation of the ratio can lead to Frasier’s syndrome – a rare nephrotic syndrome. We tested 518 SNVs for splicing defects and identified 8 known Frasier’s Syndrome variants as well as 16 additional variants that similarly lowered the KTS ratio. We also detected 19 variants increasing the KTS ratio - two of which have been observed in patients with sexual differentiation phenotypes.</p> <p>Although MPSAs can measure splicing effects of hundreds of variants simultaneously, the current scale of variant discovery via exome and genome sequencing demands efficient and accurate computational approaches to identify splice disruptive variants genome-wide. To evaluate the state of the art within contemporary splice prediction algorithms, in Chapter 4 I employed the results of five high throughput splicing assays and one literature curated variant set. A unique advantage of MPSAs over typical training and validation datasets is that they avoid bias towards essential splice site variants. I found the latest deep learning tools, SpliceAI and Pangolin, were most concordant with the measured splicing effects. However, all tools showed less agreement with exonic splicing outcomes compared to intronic. Some tools’ predictions, like SpliceAI’s, were sensitive to specified annotation files. Thus, there is still room for improvement within the next generation of splice prediction algorithms which future MPSA studies may facilitate.</p>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Diego Esteban Martínez Revelo, <a href="https://orcid.org/0000-0002-4446-4902">https://orcid.org/0000-0002-4446-4902</a>. Claims were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
ScienceDex guides
Understand access before you commit
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.