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1,297 results for “differential analysis”
Reproducible in-silico omics analyses - GSE37703: Differential analysis of HOXA1 in adult cells dataset
<p>GSE37703: Differential analysis of HOXA1 in adult cells at isoform resolution by RNA-Seq’ for quantification by Kallisto and differential abundance with Sleuth dataset used for the "Reproducible in-silico omics analyses across clouds and clusters" paper.</p>
Differential methylation analysis in neuropathological confirmed dementia with Lewy bodies
<p>Authors: Paolo Reho, Sara Saez-Atienzar, Sultana Solaiman, Zalak Shah, Ruth Chia, Karri Kaivola, Bryan J. Traynor, Bension S. Tilley, Steve M. Gentleman, Angela K. Hodges, Dag Aarsland, Edwin S. Monuki, Kathy L. Newell, Randy Woltjer, Marilyn S. Albert, Ted M. Dawson, Liana S. Rosenthal, Juan C. Troncoso, Olga Pletnikova, Geidy E. Serrano, Thomas G. Beach, Hariharan P. Easwaran, Sonja W. Scholz</p> <p>Epigenome-wide association study investigating epigenetic modulations in a cohort of neuropathologically confirmed dementia with Lewy bodies cases and neurologically healthy controls.</p>
Processed datasets and codes for differential expression analysis on polulation-level RNA-seq data
<p>This version includes codes and data necessary to reproduce all results in our response to the correspondences ("Response to 'Neglecting normalization impact in semi‑synthetic RNA‑seq data simulation generates artificial false positives' and 'Winsorization greatly reduces false positives by popular differential expression methods when analyzing human population samples'") (<a href="https://doi.org/10.1186/s13059-024-03232-8">https://doi.org/10.1186/s13059-024-03232-8</a>).</p> <p>It also includes a README file to guide the reproduction of the results in our original publication and resources for the goodness of fit test in the original publication, "Exaggerated False Positives by Popular Differential Expression Methods When Analyzing Human Population Samples" (<a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02648-4">https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02648-4</a>).</p>
A novel UPLC-MS metabolomic analysis-based strategy to monitor the course and extent of iPSC differentiation to hepatocytes
<p>ms2 raw data, peak tables generated in Quantitative Analysis Software from Agilent and Matlab functions for QC-SVRC, data clean-up and analysis for the publication with title "Monitoring the differentiation of iPSC to hepatocytes by means of UPLC-MS metabolomics".</p>
Input data for Differential NicheNet analysis performed in the liver atlas paper Guilliams et al., Cell 2022
<p>Input data for Differential NicheNet analysis performed in the liver atlas paper Guilliams et al., Cell 2022</p> <p>See https://github.com/saeyslab/NicheNet_LiverCellAtlas and https://www.sciencedirect.com/science/article/pii/S0092867421014811</p>
Differential expression analysis to aluminum toxicity in Citrus x limonia Osbeck
<p>Here we deliver the Differential gene expression on genes response to aluminum toxicity in <em>Citrus</em> x<em> limonia. </em>Root apices of ‘Mandarin’ lime plants grown for 60 days in nutrient solutions either with 1480 mM Al<sup>3+</sup> or 0 mM Al<sup>3+</sup> were analyzed by RNA-seq.</p> <p>Clean reads were mapped to the sweet orange (<em>Citrus sinensis</em>) genome (Xu et al. 2013). Gene expression levels were calculated by CPM (Counts per million) reads. We used HTSeq ver. 0.6.1 (Anders et al. 2015) CPM estimation. The differentially expressed genes (DEGs) here reported by NOIseq ver. 2.16.0 (Tarazona et al. 2016). </p> <p> </p> <p><strong>Results:</strong></p> <p><br> Number of differentially expressed (DE) features (Probability > 0.7): 3,351</p> <p>Up-regulated (M > 0): 1,664<br> Down-regulated (M < 0): 1,687</p> <p>All software were run on OmicsBox interface.</p> <p><strong>References:</strong></p> <p>Anders S., Pyl PT. and Huber W. (2015). HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics (Oxford, England), 31(2), 166-9.</p> <p>OmicsBox - Bioinformatics made easy. BioBam Bioinformatics (Version 2.0.36). March 3, 2019. www.biobam.com/omicsbox.</p> <p>Tarazona S., Furio-Tari P., Turra D., Pietro AD., Nueda MJ., Ferrer A. and Conesa A. (2015). Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package. Nucleic acids research, 43(21), e140.</p> <p>Xu Q, Chen L-L, Ruan X, et al (2013) The draft genome of sweet orange (Citrus sinensis). Nat Genet 45:59–66.</p> <p> </p> <p>Legend: </p> <p>Regulation - UP or DOWN = differentially expressed genes, UPregulated or DOWNregulated</p> <p>Citrus_40_Al_2 - Normalized CPM for root apexes under 1480 mM Al<sup>3+</sup> </p> <p>Citrus_0_Al_1 - Normalized CPM for root apexes under 0 mM Al<sup>3+</sup></p>
A comparative phylogenomic analysis of birds reveals heterogeneous differentiation processes among Neotropical Savannas
<p>The main objective of this study is to evaluate biogeographic hypotheses of diversification and connection between isolated savannas north (Amazonian savannas) and south (Cerrado core) of the Amazon River. To achieve our goal, we employed genomic markers (genotyping by sequencing) to evaluate the genetic structure, population phylogenetic relationships, and historical range shifts of four Neotropical passerines with peri-Atlantic distributions: the Narrow-billed Woodcreeper (<em>Lepidocolaptes angustirostris</em>), the Plain-crested Elaenia (<em>Elaenia cristata</em>), the Grassland Sparrow (<em>Ammodramus humeralis</em>), and the White-banded Tanager (<em>Neothraupis fasciata</em>). The population genetic analyses indicated that landscape (e.g., geographic distance, landscape resistance, and percentage of tree cover) and climate metrics explained divergence among populations in most species, but without indicating a differential role between current and historical factors. Our results did not fully support the hypothesis that isolated populations at Amazonian savannas have been recently derived from the Cerrado core domain. Intraspecific phylogenies and gene flow analyses supported multiple routes of connection between the Cerrado and Amazonian savannas, rejecting the hypothesis that the Atlantic corridor explains the peri-Atlantic distribution. Our results reveal that the biogeographic history of the region is complex and cannot be explained by simple vicariant models.</p>
Genome-wide population structure and admixture analysis reveals weak differentiation among Ugandan goat breeds
<p><strong>Summary</strong></p> <p>Uganda is endowed with a large population of goats from predominantly indigenous breeds reared in diverse production systems, whose existence is threatened by crossbreeding with exotic Boer goats. Knowledge about the genetic characteristics and relationships among these Ugandan goat breeds and the potential admixture of the exotic breed Boer is still limited. Using a medium density single nucleotide polymorphism (SNP) panel, we assessed the genetic diversity, population structure and admixture in six Ugandan goat breeds. Samples from five indigenous Ugandan goat breeds including Mubende (n=29), Kigezi (n=29), Small East African (n=29), Sebei (n=29) and Karamojong (n=15), and the exotic breed Boer (n=13) from different agro-ecological regions of Uganda were genotyped using the GoatSNP50 BeadChip. Analysis of genotype data revealed high levels of polymorphism with the proportion of polymorphic SNPs ranging from 0.885 in Kigezi to 0.928 in Sebei. The overall mean genetic diversity indices across breeds for <em>H<sub>O</sub></em> and <em>H<sub>E</sub></em> was 0.355±0.147 and 0.384±0.143 respectively. Principle components, genetic distances and ADMIXTURE analyses revealed weak population sub-structuring among the breeds. Principle components separate Kigezi and weakly Small East African from other indigenous goats. Sebei and Karamojong are tightly entangled together while Mubende occupies a more central position with high admixture from all other local breeds. The Boer breed showed a unique cluster from the Ugandan indigenous goat breeds. The results reflect common ancestry but also some level of geographical differentiation. ADMIXTURE and four population test analyses further revealed gene-flow from Boer to Ugandan indigenous goat breeds and varying levels of admixture among the Ugandan indigenous breeds. Generally, moderate to high levels of genetic variability were observed in the Ugandan goat breeds. Our findings provide useful insight to devise strategies to maintain genetic diversity in local goat breeds from Uganda and to design appropriate breeding programs to exploit within breed diversity and heterozygote advantage in cross-breeding schemes.</p>
Correlation-based Analysis of the Influence of Bound Constraint Handling Methods on Population Dynamics in Differential Evolution
<p>The dataset is based on the average values collected over 5 independent runs, considering the largest common number of<br>iterations for LSHADE algorithm coupled with ’sat’, ’midT’, ’midB’, ’unif’, ’beta’, ’mir’, ’tor’, ’expC_R’, ’expC_T’, ’expC_B’, ’vectR’, ’vectT’, ’vectB’, ’mahalanobis’ correction methods, on BBOB function f3, 4, 5, 16, 23, instance 1</p> <p>Plots for the averaged values are included for measures 'pop_size', 'best', 'error', 'prob_infeas', 'genMutatedComponent', 'genSuccessMutants','meanImprovements', 'varPop', 'avgF', 'avgCR', 'extension', 'shape', 'eccentricity', 'kl_unif'</p>
Simulated RNA-seq data for differential splicing analysis with covariates
<p>The repository includes alignments of simulated RNA-seq data for evaluating differential splicing detection with covariates. Starting from an empirical transcript expression matrix trained on an RNA-seq data set from lung fibroblasts (GenBank A# SRR493366) and using GENCODE v.41 as reference, 11.5 million 100 bp long paired-end reads were generated per sample, from 2,000 genes with two or more expressed isoforms. RNA-seq data was simulated for one ‘condition’, with values ‘control’, ‘disease’ and ‘stage2’, with one covariate, ‘biological sex’, with values ‘M’ and ‘F’. 5 samples each were simulated for each (condition x sex) category. Changes were simulated in the expression (DE) and/or the splicing ratio (DS) of genes as follows. Changes in expression (DE) were simulated by either halving or doubling the expression level of the gene. Changes in splicing ratios (DS) were simulated by swapping the expression levels of the gene’s top two transcript isoforms. All RNA-seq data was mapped to the hg38 genome with the spliced alignment tool STAR v2.7.10a.</p> <p> <em><u>Pairwise comparison alignment set</u></em>: Differences due to ‘condition’ between two states, ‘control’ and ‘disease’, were simulated at 600 genes, including 200 DE, 200 DS and 200 DE+DS genes. Differences in ‘biological sex’ (covariate) were represented as changes in 300 genes, including 100 from each of the DS, DE and DE+DS categories. Hence, the target gene set for differential splicing ratio (DSR)<em> pairwise comparisons </em>consists of the pooled 200 DS and 200 DS+DE genes differentially spliced between the ‘control’ and ‘disease’ states, while for differential splicing abundance (DSA)<em> pairwise comparisons </em>the target gene set is the set of 600 modified genes, 200 in each of the DS, DE and DS+DE categories.</p> <p> <em><u>Multiway (3-way) comparison alignment set:</u></em> Additional changes between ‘disease’ and ‘stage2’ were made to 100 of the previously modified genes, as well as to a set of 200 additional genes not encountered previously, for each of the categories DE, DS and DE+DS. Therefore, for <em>DSR three-way comparisons</em>, the target gene set represents the 800 genes simulated as being DS or DE+DS between any of the ‘control’, ‘disease’ and ’stage2’ categories, while for the <em>multi-way DSA comparisons</em> the target is the full set of 1,200 genes (400 DE, 400 DS and 400 DE+DS) simulated to have changed between any of the 'control', 'disease’ and ‘stage2’ states.</p> <p> <em><u>Further details:</u></em> See the ‘key’ directories in each package for the gene lists.</p>
Single cell multiomic analysis identifies key genes differentially expressed in innate lymphoid cells from COVID-19 patients
<p>Innate lymphoid cells (ILCs) are enriched at mucosal surfaces where they respond rapidly to environmental stimuli and contribute to both tissue inflammation and healing. To gain insight into the role of ILCs in the pathology and recovery from COVID-19 infection, we employed a multi-omic approach consisting of Abseq and targeted mRNA sequencing to respectively probe the surface marker expression, transcriptional profile and heterogeneity of ILCs in peripheral blood of patients with COVID-19 compared with healthy controls. We found that the frequency of ILC1 and ILC2 cells was significantly increased in COVID-19 patients. Moreover, all ILC subsets displayed a significantly higher frequency of CD69-expressing cells, indicating a heightened state of activation. ILC2s from COVID-19 patients had the highest number of significantly differentially expressed (DE) genes. The most notable genes DE in COVID-19 vs healthy participants included a) genes associated with responses to virus infections and b) genes that support ILC self-proliferation, activation and homeostasis. In addition, differential gene regulatory network analysis revealed ILC-specific regulons and their interactions driving the differential gene expression in each ILC. Overall, this study provides mechanistic insights into the characteristics of ILC subsets activated during COVID-19 infection.</p>
Table with results of differential gene expression analysis in the controlled environment for fin tissues.
<p>Differences between transcriptomes of three Cottus fish lineages were assessed under controlled, laboratory conditions. Two tissues were investigated: fins and livers. Present table shows results of the differential gene expression analysis performed on fin tissues of Cottus fish. Base-mean, Log-2-fold change. standard error, statistics and associated p-values and FDR-corrected p-values are given for every contrast possible in our experimental design.</p>
Table with results of differential gene expression analysis in the controlled environment for liver tissues.
<p>Differences between transcriptomes of three Cottus fish lineages were assessed under controlled, laboratory conditions. Two tissues were investigated: fins and livers. Present table shows results of the differential gene expression analysis performed on liver tissues of Cottus fish. Base-mean, Log-2-fold change. standard error, statistics and associated p-values and FDR-corrected p-values are given for every contrast possible in our experimental design.</p>
A time-course analysis using Differential Static Light Scattering (DSLS) of purified HTT1-3144 Q23 - 2019/01/28
<p><strong>Project: </strong>Biophysical investigation of purified HTT protein samples</p> <p><strong>Experiment: </strong>A time-course analysis using Differential Static Light Scattering (DSLS) of purified HTT<sup>1-3144</sup>Q23 </p> <p><strong>Date completed:­ </strong>2019/01/28</p> <p><strong>Rationale: </strong>Time and resources in the HD field have been primarily focussed on understanding HTT aggregation looking as caspase cleavage products spanning aa. 1-586 or exon 1 spanning aa. 1-90. However, we know that HTT protein purified in its apo form is able to self-associate into larger oligomeric species and that monomer, dimer and larger species are found following FLAG-affinity chromatography as determined by size-exclusion chromatography (SEC) and SEC-multi-angle light scattering (SEC-MALS). This experiment aimed to begin to investigate how HTT self-associates and aggregates over time in a range of different conditions. </p>
Dataset for the paper "Network-Based Differential Abundance Analysis: Bridging Community Interactions and Host-Microbiome Dynamics."
<p>The files with extension rds are files that contain simulated data and the tsv files contain original data along with their meta data.</p>
RNA-Seq analysis to identify differentially expressed genes in top and bottom leaves under Alternaria brassicicola infection
<p>The broccoli plants were infected with Alternaria brassicicola and RNA samples were extracted for control and inoculated plants at 10 days post inoculation. </p>
curatedPCaData supplementary data table for differential gene expression analysis
<p>This tab-separated plaintext file contains differential gene expression analyses reported for the curatedPCaData data resource publication.</p>
Apparent differential phenotypic responses by kelp forest grazers to disease-driven removal of sea star predators; [Data: Tegula shell morphology, GSI, stable isotope analysis]
Open the record for dataset details and reuse information.
Data from: Spider webs, stable isotopes and molecular gut content analysis: multiple lines of evidence support trophic niche differentiation in a community of Hawaiian spiders
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Two-step mixed model approach to analyzing differential alternative RNA splicing: Datasets and R scripts for analysis of alternative splicing
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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.