Find research datasets worth reusing

opencc-by-4.0Aug 2022View details →

Paired differential gene expression and splicing analyses results of 199 baseline vs. case comparisons across 100 datasets

This dataset contains results from paired differential expression and differential splicing analyses as well as gene-set over-representation analysis results for 199 baseline vs. case comparisons across 100 randomly curated datasets with accompanying metadata (<a href="https://doi.org/10.1186/s12915-023-01724-w" target="_blank" rel="noopener">article</a>). All results were computed using the R package <a href="https://github.com/shdam/pairedGSEA">pairedGSEA</a>, which utilized DESeq2 (Love et al., 2014), DEXSeq (Anders et al., 2012), and fgsea (Korotkevich et al., 2019). See limma results here: <a href="https://doi.org/10.5281/zenodo.8162214">https://doi.org/10.5281/zenodo.8162214</a> Each .RDS file contains a list with four objects: A 'metadata' object with the metadata of the respective raw data, a 'genes' object with gene-level differential splicing and expression results, a 'gene_set' object with over-representation results, and 'experiment' with the experiment title. The filenames follow this pattern: "[dataset ID]_[GEO accession number]_[Manually assigned comparison title].RDS". All datasets were obtained from a local copy of the ARCHS4 v11 database of  transcript counts (Lachmann et al., 2018).

opencc-by-4.0Jul 2023View details →

TCGA Gene Expression Datasets

Abstract: The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. These datasets contain gene expression profiles of bladder urothelial carcinoma (BLCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), glioblastoma multiforme (GBM), head & neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), and lower grade glioma (LGG). The gene expression profiles for BLCA, CESC, HNSC, KIRC, and LGG were measured experimentally using the Illumina HiSeq 2000 RNA Sequencing platform by the University of North Carolina TCGA genome characterization center.  The gene expression profile of the GBM dataset was measured experimentally using the Affymetrix HT Human Genome U133a microarray platform by the Broad Institute of MIT and Harvard University cancer genomic characterization center. Inspiration: This dataset was uploaded to UBRITE for GTKB project.  Instruction: The log2(x+1) normalization was removed, and z-normalization was performed on the BLCA, CESC, HNSC, KIRC, and LGG datasets. The log2(x) normalization was removed, and z-normalization was performed on the GBM dataset. Acknowledgments: Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8. The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764. U-BRITE last update: 07/13/2023

opencc-by-4.0Aug 2023View details →

CollecTRI Data for Investigation of SETBP1 gene expression and transcription factor activity across human tissues

Here we provide the human CollecTRI prior (accessed May 2023) for inference of TF activity across 31 GTEx tissues using multivariate linear modeling method decoupleR. The `human_prior_tri.csv` includes 1,178 unique TFs (referred to as the source) that target 6,627 unique genes (referred to as targets) to give us 42,595 interactions in the CollecTRI prior input. Interactions are represented as a + or - 1 (mor).

opencc-by-4.0Sep 2023View details →

Simultaneous estimation of gene regulatory network structure and RNA kinetics from single cell gene expression

Supplemental Data 1 is single-cell response to rapamycin count data first sequenced in this work and deposited in GEO with accession GSE242556. It is a 173348 rows × 5847 columns TSV.GZ file where the first row is a header, the first 5843 columns are integer gene counts, and the final 4 columns ('Gene', 'Replicate', 'Pool', and 'Experiment') are cell-specific metadata. Supplemental Data 2 is bulk response to rapamycin count data first sequenced in this work. It is a 33 rows × 5847 columns TSV.GZ file where the first row is a header, the first 5843 columns are integer gene counts, and the final 4 columns ('Oligo', 'Time', 'Replicate', and 'Sample_barcode') are sample-specific metadata. Supplemental Data 3 is single-cell count data published as GSE125162 and re-analyzed with the pipeline used for single-cell quantification in this work. It is a 65068 rows × 5850 columns TSV.GZ file where the first row is a header, the first 5843 columns are integer gene counts, and the final 7 columns ('Condition', 'Sample', 'Genotype_Group', 'Genotype_Individual', 'Genotype', 'Replicate', 'Cell_Barcode') are cell-specific metadata. Supplemental Data 4 is the four deep learning models trained in this work. It is a TAR.GZ file containing the final biophysical transcription/decay model, the pre-trained decay model, the velocity prediction model, and the count prediction model. Each model file is an h5 file containing a pytorch model that can be loaded with supirfactor\_dynamical.read(). Supplemental Data 5 is the prior knowledge network used to constrain the models for TF interpretability. It is a 1574 rows × 204 columns [Genes x TFs] TSV.GZ file where the first row is a header with TF names, the first column is an index of gene names, and TF-gene interactions are indicated by non-zero values in the matrix. There are 2799 TF-gene interactions. Supplemental Table 6 is the oligonucleotide sequences used in this work. It is a TSV file with a header row. Supplemental Table 7 is the yeast strains used in this work. It is a TSV file with a header row. Supplemental Table 8 is gene metadata used in this work (e.g. Ribosomal Protein gene labels, etc). It is a TSV file with a header row. Supplemental Table 9 is FY4/5 growth curve data generated in this work. It is a 20 rows × 7 columns TSV file where the first row is a header with replicate IDs, the first column is an index of times in minutes, and values are cell densities in YPD culture, in units of 10$^6$ cells / mL. Supplemental Data 10 is a TAR.GZ file containing the yeast SacCer3 genome, modified to add UTR sequences, that was used to generate transcripts for kallisto pseudoalignment in this work.

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Leaf litter, soil, and periphyton gene expression along freshwater to marine gradients in Everglades National Park (FCE LTER), Florida, USA, January 2021 and April 2021

We collected leaf litter, periphyton, and soil along freshwater to marine gradients at SRS-2, SRS-4, SRS-6, TS/PH-2, TS/Ph-3, TS/Ph-7a, and TS/Ph-10. Samples were collected in January and April of 2021 to understand how microbial communities respond to and influence the breakdown of organic matter along freshwater to marine transects. Data collection for this project is complete. For each site and litter pair we collected a subset of 2-3 g wet mass of litter, a grab sample of soil, and a grab sample of periphyton for each site. All subsamples were preserved at -20°C until extraction, which took place up to a year after initial collection. Samples were sent to Novogene (Novogene Co. Ltd., Beijing, China) for the total RNA extraction followed by metatranscriptome sequencing. We selected n = 12 genes/gene families encoding for focal enzymes to investigate which are important to the breakdown of organic matter: Dioxygenases (associated with aerobic respiration), Sulfatases (associated with the release of sulfates from complex molecules), sulfite reductases (associated with sulfite reduction), methyl coenzyme M reductase and formylmethanofuran (associated with methanogenesis), nitrite reductases (associated with nitrite reduction), cellobiosidase, glucosidase, and xylosidase (associated with cellulose breakdown), phenol oxidase (associated with lignin breakdown), acid phosphatase (associated with phosphate acquisition in acidic environments), and alkaline phosphatase (associated with phosphate acquisition in basic environments). For each gene/family of interest, we searched all annotated transcripts for all entries corresponding to that gene/family and combined all values for a total expression. We selected n = 6 monophyletic microbial functional groups, representing sulfate reducers, sulfate oxidizers, methane oxidizers, methanogens, nitrite oxidizers, and ammonia oxidizers associated with sulfate and methane cycling. We filtered all annotated transcripts for all specie

openCC (other)Jul 2024View details →

opencc-by-4.0Sep 2019View details →

DS7_LH_ Lodola et al_Sci Adv_2019_Gene expression Studies

RT-PCR on ECFCs seeded on P3HT and subjected to Light stimulation.

opencc-by-4.0Aug 2020View details →

Extensive qPCR analysis reveals altered gene expression in middle ear mucosa from cholesteatoma patients

Abstract The middle ear is a small and hard to reach compartment, limiting the amount of tissue that can be extracted and the possibilities for studying the molecular mechanisms behind diseases like cholesteatoma. In this paper 14 reference gene candidates were evaluated in the middle ear mucosa of cholesteatoma patients and two different control tissues. ACTB and GAPDH were shown to be the optimal genes for the normalisation of target gene expression when investigating middle ear mucosa in multiplex qPCR analysis. Validation of reference genes using c-MYC expression confirmed the suitability of ACTB and GAPDH as reference genes and showed an upregulation of c-MYC in middle ear mucosa during cholesteatoma. The occurrence of participants of the innate immunity, TLR2 and TLR4, were analysed in order to compare healthy middle ear mucosa to cholesteatoma. Analysis of TLR2 and TLR4 showed variable results depending on control tissue used, highlighting the importance of selecting relevant control tissue when investigating causes for disease. It is our belief that a consensus regarding reference genes and control tissue will contribute to the comparability and reproducibility of studies within the field.

opencc-zeroAug 2020View details →

Illuminating the impact of diel vertical migration on visual gene expression in deep-sea shrimp

Diel vertical migration (DVM) of marine animals represents one of the largest migrations on our planet. Migrating fauna are subjected to a variety of light fields and environmental conditions that can have notable impacts on sensory mechanisms, including an organism's visual capabilities. Among deep-sea migrators are oplophorid shrimp, that vertically migrate hundreds of meters to feed in shallow waters at night. These species also have bioluminescent light organs that emit light during migrations to aid in camouflage. The organs have recently been shown to contain visual proteins (opsins) and genes that infer light sensitivity. Knowledge regarding the impacts of vertical migratory behavior, and fluctuating environmental conditions, on sensory system evolution is unknown. In this study, the oplophorid Systellaspis debilis was either collected during the day from deep waters or at night from relatively shallow waters to ensure sampling across the vertical distributional range. De novo transcriptomes of light sensitive tissues (eyes/photophores) from the Day/Night specimens were sequenced and analyzed to characterize opsin diversity and visual/light interaction genes. Gene expression analyses were also conducted to quantify expression differences associated with DVM. Our results revealed an expanded opsin repertoire among the shrimp and differential opsin expression that may be linked to spectral tuning during the migratory process. This study sheds light on the sensory systems of a bioluminescent invertebrate and provides additional evidence for extraocular light sensitivity. Our findings further suggest opsin coexpression and subsequent fluctuations in opsin expression may play an important role in diversifying the visual responses of vertical migrators.

opencc-by-4.0Oct 2020View details →

Xpresso: Predicting gene expression levels from genomic sequences

Xpresso: Predicting gene expression levels from genomic sequences More info at: Publication: https://doi.org/10.1016/j.celrep.2020.107663 Website: https://xpresso.gs.washington.edu/ Github: https://github.com/vagarwal87/Xpresso

opencc-by-sa-4.0Nov 2020View details →

mRNA expression data of genes related to mitochondrial quality control in hepatopancreas of the two marine bivalves, Mytilus edulis and Crassostrea gigas, during short-term hypoxia/reoxygenation stress

Coastal environments commonly experience strong oxygen fluctuations. Resulting hypoxia/reoxygenation stress can negatively affect mitochondrial functions, since oxygen deficiency impairs ATP generation, whereas a surge of oxygen causes mitochondrial damage by oxidative stress mechanisms. Marine intertidal bivalves are adapted to fluctuating oxygen conditions, yet the underlying molecular mechanisms that sustain mitochondrial integrity and function during oxygen fluctuations are not yet well understood. We used targeted mRNA expression analysis to determine the potential involvement of the mitochondrial quality control mechanisms in responses to short-term hypoxia (24 h at <0.01% O2) and subsequent reoxygenation (1.5 h at 21% O2) in two hypoxia-tolerant marine bivalves, the Pacific oysters Crassostrea gigas and the blue mussels Mytilus edulis. To test these hypotheses, We focused on the transcript levels of the following marker genes: for mitochondrial fission and fusion - mfn2 (encoding mitofusin 2),  opa1 (mitochondrial dynamin-like 120kDa protein), dnm1l (dynamin-1-like protein), mff  (mitochondrial fission factor), fis1 (mitochondrial fission protein 1); for protein and DNA quality control - tsfm (encoding mitochondrial translation elongation factor Ts), lonp1 (mitochondrial Lon protease),  spg7 (paraplegin), oma1 (mitochondrial metalloendopeptidase OMA1), clpB (mitochondrial caseinolytic matrix peptidase chaperone subunit B), atp23 (mitochondrial inner membrane protease ATP23), twnk (mitochondrial twinkle mtDNA helicase); and for mitophagy -  mieap (encoding mitochondrial eating protein), hyou1 (hypoxia upregulated protein 1), prkn (parkin), pink1 (PTEN- induced kinase 1), and pgam5 (mitochondrial serine/threonine protein phosphatase PGAM5). The revealed species-specific differences in the expression of the mitochondrial quality control pathways shed light on the potentially important mechanisms of mitochondrial protection against H/R-induced damage that might contribute to hypoxia tolerance in marine bivalves.

opencc-zeroNov 2020View details →

Data from: Populus euphratica WRKY1 binds the promoter of PeHA1 to enhance gene expression and salt tolerance

Plasma membrane proton pumps play a crucial role in maintaining ionic homeostasis in salt-resistant Populus euphratica under saline conditions. High levels of NaCl (200 mM) induced PeHA1 expression in P. euphratica roots and leaves. We isolated a 2022-bp promoter fragment upstream of the translational start of PeHA1 from P. euphratica. The promoter-reporter construct PeHA1-pro::GUS was transferred to tobacco plants, demonstrating that β-glucuronidase activities increased in root, leaf, and stem tissues under salt stress. DNA affinity purification sequencing revealed that PeWRKY1 protein targeted the PeHA1 gene. We assessed the salt-induced transcriptional response of PeWRKY1 and its interaction with PeHA1 in P. euphratica. PeWRKY1 binding to the PeHA1 W-box in promoter region was verified by a yeast one-hybrid assay, electrophoretic mobility shift assay, luciferase reporter assay, and virus-induced gene silencing. Transgenic tobacco plants overexpressing PeWRKY1 had improved expression of NtHA4, which has a cis-acting W-box in the regulatory region, and H+ pumping activity in both in vivo and in vitro assays. We conclude that salt stress upregulated PeHA1 transcription due to the binding of PeWRKY1 to the W-box in the promoter region of PeHA1. Thus, we conclude that enhanced H+ pumping activity enabled salt-stressed plants to retain Na+ homeostasis.

opencc-by-4.0Nov 2020View details →

Predicting placenta transcriptional regulatory interactions based on spatial gene expression data and convolutional neural network

Aims: The dysfunction of placenta development is correlated to the defects of pregnancy and fetal growth. The detailed molecular mechanism of placenta development is not identified in human due to the lack of material in vivo. Image-based reconstructions of GRN are still very underdeveloped. Methods and Results: In this study, first-trimester chorionic villus and decidua tissues were collected. Next, we present a machine-learning system to infer gene interaction networks of the human placenta from immunofluorescence images of trophoblast specific transcription factors obtained by a high-resolution scanner. Conclusions: The experimental results show that deep learning models reveal regulatory roles that have not yet been fully recognized. The spatial expression data reveal new regulatory relationships that traditional experiments have failed to recognize, and has allowed the development of gene regulation networks based on the spatial distribution of gene expression. We demonstrate the effectiveness of this approach in building networks using high-resolution images of the human placenta. Our analysis is of certain significance for further exploration of the development of the placenta and the occurrence of pregnancy-related diseases in the future. The datasets and analysis provide a useful source for the researchers in the field of the maternal-fetal interface and the establishment of pregnancy.

opencc-zeroDec 2020View details →

Biogeographic parallels in thermal tolerance and gene expression variation under temperature stress in a widespread bumble bee

Global temperature changes have emphasized the need to understand how species adapt to thermal stress across their ranges. Genetic mechanisms may contribute to variation in thermal tolerance, providing evidence for how organisms adapt to local environments. We determine physiological thermal limits and characterize genome-wide transcriptional changes at these limits in bumble bees using laboratory-reared Bombus vosnesenskii workers. We analyze bees reared from latitudinal (35.7–45.7°N) and altitudinal (7–2154 m) extremes of the species' range to correlate thermal tolerance and gene expression among populations from different climates. We find that critical thermal minima (CTMIN) exhibit strong associations with local minimums at the location of queen origin, while critical thermal maximum (CTMAX) was invariant among populations. Concordant patterns are apparent in gene expression data, with regional differentiation following cold exposure, and expression shifts invariant among populations under high temperatures. Furthermore, we identify several modules of co-expressed genes that tightly correlate with critical thermal limits and temperature at the region of origin. Our results reveal that local adaptation in thermal limits and gene expression may facilitate cold tolerance across a species range, whereas high temperature responses are likely constrained, both of which may have implications for climate change responses of bumble bees.

opencc-zeroNov 2019View details →

Data from: Has gene expression neofunctionalization in the fire ant antennae contributed to queen discrimination behavior?

Queen discrimination behavior in the fire ant Solenopsis invicta maintains its two types of societies: colonies with one (monogyne) or many (polygyne) queens, yet the underlying genetic mechanism is poorly understood. This behavior is controlled by two supergene alleles, SB and Sb, with ~600 genes. Polygyne workers, having either the SB/SB or SB/Sb genotype, accept additional SB/Sb queens into their colonies but kill SB/SB queens. In contrast, monogyne workers, all SB/SB, reject all additional queens regardless of genotype. Because the SB and Sb alleles have suppressed recombination, determining which genes within the supergene mediate this differential worker behavior is difficult. We hypothesized that the alternate worker genotypes sense queens differently because of the evolution of differential expression of key genes in their main sensory organ, the antennae. To identify such genes, we sequenced RNA from four replicates of pooled antennae from three classes of workers: monogyne SB/SB, polygyne SB/SB, and polygyne SB/Sb. We identified 81 differentially expressed protein-coding genes with 13 encoding potential chemical metabolism or perception proteins. We focused on the two odorant perception genes: an odorant receptor SiOR463 and an odorant binding protein SiOBP12. We found that SiOR463 has been lost in the Sb-genome. In contrast, SiOBP12 has an Sb-specific duplication, SiOBP12b', which is expressed in the SB/Sb worker antennae, while both paralogs are expressed in the body. Comparisons with another fire ant species revealed that SiOBP12b' antennal expression is specific to S. invicta and suggests that queen discrimination may have evolved, in part, through expression neofunctionalization.

opencc-zeroSep 2019View details →

Data from: Effects of multiple climate change stressors on gene expression in blue rockfish (Sebastes mystinus)

Global climate change is predicted to increase the co-occurrence of high pCO2 and hypoxia in upwelling zones worldwide. Yet, few studies have examined the effects of these stressors on economically and ecologically important fishes. Here, we investigated short-term responses of juvenile blue rockfish (Sebastes mystinus) to independent and combined high pCO2 and hypoxia at the molecular level, using changes in gene expression and metabolic enzymatic activity to investigate potential shifts in energy metabolism. Fish were experimentally exposed to conditions associated with intensified upwelling under climate change: high pCO2 (1200 μatm, pH~7.6), hypoxia (4.0 mg O2/L), and a combined high pCO2/hypoxia treatment for 12 h, 24 h or two weeks. Muscle transcriptome profiles varied significantly among the three treatments, with limited overlap among genes responsive to both the single and combined stressors. Under elevated pCO2, blue rockfish increased expression of genes encoding proteins involved in the electron transport chain and muscle contraction. Under hypoxia, blue rockfish up regulated genes involved in oxygen and ion transport and down regulated transcriptional machinery. Under combined high pCO2 and hypoxia, blue rockfish induced a unique set of ionoregulatory and hypoxia responsive genes not expressed under the single stressors. Thus, high pCO2 and hypoxia exposure appears to induce a non-additive transcriptomic response that cannot be predicted from single stressor exposures alone, further highlighting the need for multiple stressor studies at the molecular level. Overall, lack of a major shift in cellular energetics indicates that blue rockfish may be relatively resistant to intensified upwelling conditions in the short term.

opencc-zeroDec 2018View details →

Data from: Differential gene expression in relation to mating system in Peromyscine rodents

Behaviors that increase an individual's exposure to pathogens are expected to have important effects on immunoactivity. Because sexual reproduction typically requires close contact among conspecifics, mating systems provide an ideal opportunity to study the immunogenetic correlates of behaviors with high versus low risks of pathogen exposure. Despite logical links between polygynandrous mating behavior, increased pathogen exposure, and greater immunoactivity, these relationships have seldom been examined in nonhuman vertebrates. To explore interactions among these variables in a different lineage of mammals, we used RNAseq to study the gene expression profiles of liver tissue—a highly immunoactive organ—from sympatric populations of the monogamous California mouse (Peromyscus californicus) and two polygynandrous congeners (P. maniculatus and P. boylii). Differential expression and co‐expression analyses revealed distinct patterns of gene activity among species, with much of this variation associated with differences in mating system. This tendency was particularly pronounced for MHC genes, with multiple MHC Class I genes being upregulated in the two polygynandrous species, as expected if exposure to sexually transmitted pathogens varies with mating system. Our results underscore the role of mating behavior in influencing patterns of gene expression and highlight the use of emerging transcriptomic tools in behavioral studies of free‐living animals.

opencc-zeroAug 2014View details →

Data from "Corset: enabling differential gene expression analysis for de novo assembled transcriptomes"

This dataset contains de novo transcriptome assemblies for three publicly available RNA-seq dataset (SRA055442, SRR453566-SRR453571 and GSE37704 ). For each assembly we also provide a table with the read counts per contig, the output from corset (clusters and counts), and the results from a genome-based analysis. This dataset was used to assess the performance of the corset software. More detail is provided in the paper: Nadia M Davidson and Alicia Oshlack, Corset: enabling differential gene expression analysis for de novo assembled transcriptomes, Genome Biology 2014, 15:410. http://genomebiology.com/2014/15/7/410/abstract

opencc-zeroJul 2015View details →

Cancer Gene Expression Datasets

The attached files contain two datasets termed GCM and Acute Leukemia datasets. They have been used in our submitted paper termed 'Semi-supervised Nonnegative Matrix Factorization for Cancer Classification'. The file names imply its datasets. The details are listed as follows: GCM_Test.res       --- The testing gene expression data of the GCM dataset GCM_Test.cls       --- The ground-truth classes of the testing samples of the GCM dataset GCM_Training.res   --- The training gene expression data of the GCM dataset GCM_Training.cls   --- The ground-truth classes of the training samples of the GCM dataset GCM.mat            --- The .mat file contains the training and testing data and their classes information. The GCM dataset were downloaded from the website http://www.broadinstitute.org/cgi-bin/cancer/datasets.cgi.   Leukemia dataset Acute_Leukemia_feature.txt          --- The feature names Acute_Leukemia_sampleLabel.txt      --- The ground-truth classes of all samples Acute_Leukemia_sampleName.txt       --- The sample names Acute_Leukemia1.txt                 --- The file is the gene expression of the first 1048 samples Acute_Leukemia2.txt                 --- The file is the gene expression of the subsequent 1048 samples Acute_Leukemia.mat                  --- The .mat file contains gene expression data of all  the samples and their classes. The Leukemia dataset were obtained from the website :  http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13159

opencc-by-sa-4.0Sep 2016View details →

Gene expression data and proliferation rates for NCI-60

Data set dimensions: 57 rows x 54357 columns   This is a data set contains RMA normalized log expression values for 54356 genes identified with their ENSEMBL ID (columns 1-54356) for 57 cancer cell lines and their respective proliferation rates (column 54357). Gene expression was obtained from the GSE29682 GEO HuEx 1.0 ST microarray data.