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1,297 results for “differential analysis”
Single cell multiomic analysis identifies key genes differentially expressed in innate lymphoid cells from COVID-19 patients
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Transcriptome-wide alternative mRNA splicing analysis reveals post-transcriptional regulation of neuronal differentiation
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Data from: High-resolution analysis of red deer (<em>Cervus elaphus</em>) management units in a Central European region of high human population density reveals severe effects on genetic diversity and differentiation
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A comparative phylogenomic analysis of birds reveals heterogeneous differentiation processes among Neotropical Savannas
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Training data for "Differential exon usage analysis"
<p>In RNA-Seq, we usually want to know the differentially expressed genes, as explained in several Galaxy Training Material tutorials. Sometimes,the question is more "which exons are differentially expressed". The process to identify differentially expressed exons is really similar to the one for differentially expressed genes.</p> <p>In this tutorial, we identify exons regulated by the <em>Pasilla</em> gene using RNA-Seq data from <a href="https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/tutorial.html#brooks2011conservation">Brooks <em>et al.</em> 2011</a>.</p>
Data from: Using sperm morphometry and multivariate analysis to differentiate species of gray Mazama
There is genetic evidence that the two species of Brazilian gray Mazama, Mazama gouazoubira and Mazama nemorivaga, belong to different genera. This study identified significant differences that separated them into distinct groups, based on characteristics of the spermatozoa and ejaculate of both species. The characteristics that most clearly differentiated between the species were ejaculate colour, white for M. gouazoubira and reddish for M. nemorivaga, and sperm head dimensions. Multivariate analysis of sperm head dimension and format data accurately discriminated three groups for species with total percentage of misclassified of 0.71. The individual analysis, by animal, and the multivariate analysis have also discriminated correctly all five animals (total percentage of misclassified of 13.95%), and the canonical plot has shown three different clusters: Cluster 1, including individuals of M. nemorivaga; Cluster 2, including two individuals of M. gouazoubira; and Cluster 3, including a single individual of M. gouazoubira. The results obtained in this work corroborate the hypothesis of the formation of new genera and species for gray Mazama. Moreover, the easily applied method described herein can be used as an auxiliary tool to identify sibling species of other taxonomic groups.
Data from: Differential gene expression analysis of symbiotic and aposymbiotic Exaiptasia anemones under immune challenge with Vibrio coralliilyticus
Anthozoans are a class of Cnidarians that includes scleractinian corals, anemones and their relatives. Despite a global rise in disease epizootics impacting scleractinian corals, little is known about the immune response of this key group of invertebrates. To better characterize the anthozoan immune response, we used the model anemone Exaiptasia pallida to explore the genetic links between the anthozoan-algal symbioses and immunity in a two-factor RNA-Seq experiment using both symbiotic and aposymbiotic(menthol-bleached) Exaiptasia pallida exposed to the bacterial pathogen Vibrio coralliilyticus. Multivariate and univariate analyses of Exaiptasia gene expression demonstrated that exposure to live Vibrio coralliilyticus had strong and significant impacts on transcriptome-wide gene expression for both symbiotic and aposymbiotic anemones, but we did not observe strong interactions between symbiotic state and Vibrio exposure. There were 4,164 significantly differentially expressed (DE) genes for Vibrio exposure, 1,114 DE genes for aposymbiosis, and 472 DE genes for the additive combinations of Vibrio and aposymbiosis. KEGG enrichment analyses identified 11 pathways - involved in immunity (5), transport and catabolism (4) and cell growth and death (2) - that were enriched due to both Vibrio and/or aposymbiosis. Immune pathways showing strongest differential expression included complement, coagulation, nucleotide-binding and oligomerization domain (NOD), and Toll for Vibrio exposure and coagulation and apoptosis for aposymbiosis.
Data from: Machine learning-based differential network analysis: a study of stress-responsive transcriptomes in Arabidopsis thaliana
Machine learning (ML) is an intelligent data mining technique that builds a prediction model based on the learning of prior knowledge to recognize patterns in large-scale data sets. We present an ML-based methodology for transcriptome analysis via comparison of gene coexpression networks, implemented as an R package called machine learning–based differential network analysis (mlDNA) and apply this method to reanalyze a set of abiotic stress expression data in Arabidopsis thaliana. The mlDNA first used a ML-based filtering process to remove nonexpressed, constitutively expressed, or non-stress-responsive "noninformative" genes prior to network construction, through learning the patterns of 32 expression characteristics of known stress-related genes. The retained "informative" genes were subsequently analyzed by ML-based network comparison to predict candidate stress-related genes showing expression and network differences between control and stress networks, based on 33 network topological characteristics. Comparative evaluation of the network-centric and gene-centric analytic methods showed that mlDNA substantially outperformed traditional statistical testing–based differential expression analysis at identifying stress-related genes, with markedly improved prediction accuracy. To experimentally validate the mlDNA predictions, we selected 89 candidates out of the 1784 predicted salt stress–related genes with available SALK T-DNA mutagenesis lines for phenotypic screening and identified two previously unreported genes, mutants of which showed salt-sensitive phenotypes.
Data from: Bucking the trend: genetic analysis reveals high diversity, large population size and low differentiation in a deep ocean cetacean
Understanding the genetic structure of a population is essential to its conservation and management. We report the level of genetic diversity and determine the population structure of a cryptic deep ocean cetacean, the Gray's beaked whale (Mesoplodon grayi). We analysed 530 bp of mitochondrial control region and 12 microsatellite loci from 94 individuals stranded around New Zealand and Australia. The samples cover a large area of the species distribution (~6000 km) and were collected over a 22-year period. We show high genetic diversity (h=0.933–0.987, π=0.763–0.996% and Rs=4.22–4.37, He=0.624–0.675), and, in contrast to other cetaceans, we found a complete lack of genetic structure in both maternally and biparentally inherited markers. The oceanic habitats around New Zealand are diverse with extremely deep waters, seamounts and submarine canyons that are suitable for Gray's beaked whales and their prey. We propose that the abundance of this rich habitat has promoted genetic homogeneity in this species. Furthermore, it has been suggested that the lack of beaked whale sightings is the result of their low abundance, but this is in contrast to our estimates of female effective population size based on mitochondrial data. In conclusion, the high diversity and lack of genetic structure can be explained by a historically large population size, in combination with no known exploitation, few apparent behavioural barriers and abundant habitat.
Comparative analysis of statistical methods used for detecting differential expression in label-free mass spectrometry proteomics - Data Supplement
<p>This the is Data Supplement for the article "Comparative analysis of statistical methods used for detecting differential expression in label-free mass spectrometry proteomics" submitted to the Journal of Proteomics 2015.</p>
FIGURE 4. Discriminant function analysis depicting morphological differentiation within the C. vittatus-hansenae group. Close grey circle indicates C. vittatus Group II. Open circle denotes C. hansenae Group I. Closed black circle represents C. hansenae Group II in Re-evaluating the taxonomic status of Chiromantis in Thailand using multiple lines of evidence (Amphibia: Anura: Rhacophoridae)
FIGURE 4. Discriminant function analysis depicting morphological differentiation within the C. vittatus-hansenae group. Close grey circle indicates C. vittatus Group II. Open circle denotes C. hansenae Group I. Closed black circle represents C. hansenae Group II.
Single-cell Roadmap dataset "Cardiac differentiation roadmap for analysis of plasticity and balanced lineage commitment" (Snabel et al.)
<p>View the temporal single-cell transcriptomics data (UMAP, PCA, Heatmaps and Violin plots) using the Shiny App interface of iSEE (<a href="https://doi.org/10.12688/f1000research.14966.1">doi:10.12688/f1000research.14966.1</a>) for easy visualization of the single-cell data described in "Single-cell roadmap of cardiac differentiation identifies roles for ZNF711 and retinoic acid in balanced epicardial and cardiomyocyte lineage commitment" (Snabel et al., bioRXiv).</p> <p>For instructions on how to use this data, please visit https://github.com/Rebecza/scRoadmap_CardiacDiffs/.</p>
Comparative analysis of differential gene expression indicates divergence in ontogenetic strategies of leaves in two conifer genera
<p><em>Juniperus flaccida</em> (drooping juniper) and <em>Pinus cembroides</em> (pinyon pine) are conifers native to North America, spanning Mexico and the Southwestern region of the United States. Although from two different lineages, both species exhibit heteroblastic growth. Morphologically, their leaves undergo a change between the juvenile and adult life stage. <em>J. flaccida</em> leaves appear needle-like at youth and scale-like at maturity, whereas the <em>P. cembroides</em> will transition from needle-like leaves to brown scale-like leaves. The objective was to perform a comparative transcriptomic analysis to quantify and examine differential expression in juvenile and adult individuals from both species. RNA from twelve samples was sequenced on HiSeq 1500 (100bp PE) and analyzed with available software. Because there are no reference genomes for these species, they were assembled<em> de novo </em>from the RNA-Seq reads. Following assembly, the coding regions were identified and redundant transcripts were removed. Quality filtered reads were aligned to the reference transcriptomes (one for each species), counts were generated from the alignment files, and differential expression analysis was performed with DESeq2 via Kallisto. Up and down-regulated genes (padj<0.1) across both age classes (juvenile vs adult) were observed in each species and compared.</p>
Differential analysis of gene regulation at transcript resolution by RNA-Seqcount table
<p>Expression profiling by high throughput sequencing</p>
Time-series transcriptome analysis identified differentially expressed genes in broiler chicken infected with mixed Eimeria species
<p>Coccidiosis caused by the <em>Eimeria</em> species is a highly problematic disease in the chicken industry. Here, we used RNA sequencing to observe the time-dependent host responses of <em>Eimeria</em>-infected chickens to examine the genes and biological functions associated with immunity to the parasite. Transcriptome analysis was performed at three time points: 4, 7, and 21 days post-infection (dpi). Based on the changes in gene expression patterns, we defined three groups of genes that showed differential expression. This enabled us to capture evidence of endoplasmic reticulum stress at the initial stage of <em>Eimeria</em> infection. Furthermore, we found that innate immune responses against the parasite were activated at the first exposure; they then showed gradual normalization. Although the cytokine-cytokine receptor interaction pathway was significantly operative at 4 dpi, its downregulation led to an anti-inflammatory effect. Additionally, the construction of gene co-expression networks enabled identification of immunoregulation hub genes and critical pattern recognition receptors after <em>Eimeria</em> infection. Our results provide a detailed understanding of the host-pathogen interaction between chicken and <em>Eimeria</em>. The clusters of genes defined in this study can be utilized to improve chickens for coccidiosis control.</p>
On taming the effect of transcript level intra-condition count variation during differential expression analysis: a story of dogs, foxes and wolves: Bowtie2 counts and kallisto abundances
<p>Intra [1] and inter [2-5] study RNA-seq read datasets representing the varying brain compartments of foxes (n=24), as well as dogs (n=14) and wolves (n=6), as described in Lobo <em>et al.</em>, (2022) (under review), were mapped to the dog reference transcriptome [6], which contained 26,107 annotated transcripts (Ensembl CanFam3.1, release 92) [7], using Bowtie2 v.2.3.4.1 [8] and using kallisto v0.46.1 [9]. Count data obtained following each mapping approach for each dataset had high correlations (Lobo <em>et al.</em>, Figure S2). Bowtie2 counts were subsequently used in multiple differential analysis experiments in order to explore the effects of intra-condition count variation on the detection of differentially expressed transcripts. The individual count and abundance datasets for each corresponding RNA-seq dataset are available here.</p> <p> </p> <p>A preprint of Lobo et al., 2022, currently under review for PLOS ONE, is available [10]. The preprint however does not contain reviewer requested information on simulations as this, along with other additions including an additional author RL, has been subsequently added during the review process. These additions will be made available following review via a link to the final paper. </p> <p> </p> <p>Related software to this project are:<br> 1. <a href="http://sourceforge.net/projects/cstone/">CStone</a> <br> 2. <a href="http://sourceforge.net/projects/csreadgen/">CSReadGen</a><br> 3. <a href="https://sourceforge.net/projects/cview/">CView</a> <br> 4. <a href="https://sourceforge.net/projects/chimsim/">ChimSim</a><br> 5. <a href="https://sourceforge.net/projects/tvscript/">TVScript</a> <</p> <p> </p> <p>General details of the projects involved are available: <a href="https://cibio.up.pt/en/projects/is-hybridization-between-wolves-and-dogs-shaping-the-evolutionary-trajectory-of-wolf-populations-in-human-dominated-landscapes/">dog-wolf</a> and <a href="https://cibio.up.pt/en/projects/de-novo-based-sequence-assembly-of-next-generation-sequence-data-without-chimeras-improved-annotation-gene-expression-profiles-and-haplotype-br-reconstruction/">chimerism</a>.</p> <p> </p> <p><strong>References</strong></p> <p>1. Wang X, Pipes L, Trut L, Herbeck Y, Vladimirova A, Gulevich R, et al. Genomic responses to selection for tame/aggressive behaviors in the silver fox (Vulpes vulpes). Proc Natl Acad Sci. 2018;115: 10398–10403. doi:10.1073/pnas.1800889115</p> <p> </p> <p>2. Roy M, Kim N, Kim K, Chung WH, Achawanantakun R, Sun Y, et al. Analysis of the canine brain transcriptome with an emphasis on the hypothalamus and cerebral cortex. Mamm Genome. 2013;24: 484–499. doi:10.1007/s00335-013-9480-0</p> <p> </p> <p>3. Fushan AA, Turanov AA, Lee SG, Kim EB, Lobanov A V, Yim SH, et al. Gene expression defines natural changes in mammalian lifespan. Aging Cell. 2015;14: 352–365. doi:10.1111/acel.12283</p> <p> </p> <p>4. Hoeppner MP, Lundquist A, Pirun M, Meadows JRS, Zamani N, Johnson J, et al. An improved canine genome and a comprehensive catalogue of coding genes and non-coding transcripts. PLoS One. 2014;9(3):91172. doi:10.1371/journal.pone.0091172</p> <p> </p> <p>5. Albert FW, Somel M, Carneiro M, Aximu-Petri A, Halbwax M, Thalmann O, et al. A Comparison of Brain Gene Expression Levels in Domesticated and Wild Animals. Akey JM, editor. PLoS Genet. 2012;8:e1002962. doi:10.1371/journal.pgen.1002962</p> <p> </p> <p>6. Hoeppner MP, Lundquist A, Pirun M, Meadows JRS, Zamani N, Johnson J, et al. An improved canine genome and a comprehensive catalogue of coding genes and non-coding transcripts. PLoS One. 2014;9(3):91172. doi:10.1371/journal.pone.0091172</p> <p> </p> <p>7. Yates AD, Achuthan P, Akanni W, Allen J, Allen J, Alvarez-Jarreta J, et al. Ensembl 2020. Nucleic Acids Res. 2020;48: D682–D688. doi:10.1093/NAR/GKZ966</p> <p> </p> <p>8. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012. doi:10.1038/nmeth.1923</p> <p> </p> <p>9. Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 2016 345. 2016;34: 525–527. doi:10.1038/nbt.3519</p> <p> </p> <p>10. Lobo D, Godinho R, Archer JP. On taming the effect of transcript level intra-condition count variation during differential expression analysis: a story of dogs, foxes and wolves. bioRxiv. 2022; 2022.01.24.477470. doi:10.1101/2022.01.24.477470</p>
Data and analysis for: Differential effects of ankle constraints on foot placement control between normal and split belt treadmills
<p>Here we compared the effect of ankle moment constraints (LesSchuh; a shoe with a narrow ridge along the length of the shoe's sole), on a single and a split-belt treadmill. To this end we considered the foot placement model as proposed by Wang and Srinivasan (2014), and used the R^2 of this model as an outcome measure. In addition, step width, stride frequency and toe-out angles have been computed. The results have been written up in our publication in the journal of biomechanics.<br> <br> Wang, Y., & Srinivasan, M. (2014). Stepping in the direction of the fall: the next foot placement can be predicted from current upper body state in steady-state walking. <em>Biology letters</em>, <em>10</em>(9), 20140405.</p>
Peptidoform analysis of IP-MS data allows detection of differentially present bait proteoforms
<p>Datasets supporting iPTMs manuscript (https://doi.org/10.1101/2024.01.23.576810)</p>
Data for: High-throughput micro-CT analysis identifies sex-dependent biomarkers of erosive arthritis in TNF-Tg mice and differential response to anti-TNF therapy
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Dataset for Improved differential expression analysis of miRNA-seq data by modeling competition to be counted
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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.