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opencc-by-4.0Mar 2024View details →

Supplementary datasets for manuscript titled: Seasonal tissue-specific gene expression in wild crown-of-thorns starfish reveals reproductive and stress-related transcriptional systems

Supplementary datasets for manuscript titled: Seasonal tissue-specific gene expression in wild crown-of-thorns starfish reveals reproductive and stress-related transcriptional systems

opencc-by-4.0Nov 2020View details →

Tissue heterogeneity is prevalent in gene expression studies

This archive contains results associated with the publication Tissue heterogeneity is prevalent in gene expression studies. Gregor Sturm, Markus List and Jitao David Zhang.   <ul> <li>expr.tissuemark.affy.roche.symbols.gmt: The tissue signatures from the BioQC publication used in this study</li> <li>gtex_v6_gini_solid.gmt: The cross-platform cross-species validated tissue signatures produced in this study</li> <li>heterogeneity_results.tsv.gz: Signature scores and heterogeneity calls for each tested signature</li> <li>heterogeneity_fractions.tsv: Fraction of heterogeneous and severely heterogeneous samples per tissue</li> </ul>

opencc-by-4.0Dec 2022View details →

The spatial landscape of gene expression isoforms in tissue sections

This upload provides raw in situ sequencing (ISS) data used to validate Spatial Isoform Transcriptomics (SiT), as well as R scripts required for SiT analysis. GenePlots.zip and Reads.zip are ISS data generated and collected by the CARTANA ISS service. The following data description is cited from the report provided by CARTANA ISS service: "Folder "Reads" contains coordinates and gene information of segmented spots. The coordinates are in pixel unit. Scaling factor is 0.32 um/pixel. (0,0) is at northwest (top-left corner). With Low/High Threshold, we refer to the quality thresholding. Our technology is fluorescence based, i.e. with the thresholding one can balance how certain the signals are. Files ending with _LowThreshold: reads not matching with any known barcode were already discarded. Files ending with _HighThreshold: has information only about spots that passed additional quality check. Folder "GenePlots" has plotted images in static .png format, fully zoomed out. LowThreshold and HighThreshold follow the same thresholding strategy as in reads files."   SiT-master.zip is a download of the GitHub repository <a href="https://github.com/ucagenomix/SiT">https://github.com/ucagenomix/SiT</a>, providing figures and analysis scripts for SiT.   Related SiT data are deposited through GEO, accession number <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE153859">GSE153859</a>

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-4.0Mar 2024View details →

Cre Expression in Control Tissues of the PTF1a Pig

Immunohistochemistry (IHC) analysis of the stomach, colon, lung, muscle, and duodenum tissues from PTF1a-Cre pig reveals an absence of Cre protein expression.

opencc-by-4.0Nov 2024View details →

Distinct tissue-dependent composition and gene expression of human fetal innate lymphoid cells

Countmatrix and metadata for gene expression in bulk NK cells and CD304+ ILC3s isolated from human fetal liver, lung, intestine, and skin.

opencc-by-4.0Oct 2021View details →

Gene expression and splicing counts from 49 tissues from GTEx v6p genome build hg19 - non-strand specific

Dataset description: 49 folders, each corresponding to one tissue from GTEx v6p and containing the following files: <ol> <li> geneCounts: gene-level counts  </li> <li> k_j: split counts spanning from one exon to another. </li> <li> k_theta: non-split counts covering a splice site </li> <li> n_psi3: total split counts from a given acceptor site </li> <li> n_psi5: total split counts from a given donor site </li> <li> n_theta: total split and non-split counts for a given splice site </li> <li> Sample annotation describing each sample from the dataset </li> <li> Description file with global information from the dataset </li> </ol> The gene counts were originated using the GTF file from <a href="http://www.gencodegenes.org/human/release_29lift37.html">release 29 of GENCODE</a>, and the split and non-split counts contain only the annotated junctions from the same release. Statistics are reported only for GENCODE-annotated introns and splice sites, in compliance with the regulations of the GTEx consortium. For a description of the samples, methods, and protocols, see the GTEx publication specified below. Use: The count matrices are intended to help researchers that are interested in using RNA-Seq data with the purpose of diagnostics. Researchers can merge their own dataset with the downloaded ones, provided the tissue, genome build, strand, and paired-end specifications match. Afterwards, the <a href="https://github.com/gagneurlab/drop">Detection of RNA outliers Pipeline (DROP) </a> can be used to compute gene expression and splicing outliers. Organism: Homo sapiens Genome assembly: hg19 Gene annotation: gencode29 Strand specific: FALSE Paired end: TRUE Protocol: poly(A) enrichment Contact: Vicente A. Yepez, yepez at in.tum.de; Christian Mertes, mertes at in.tum.de; Julien Gagneur, gagneur at in.tum.de Citation: Write the following in the "Data availability" section of the manuscript or similar replacing the three citations by the ones from the References section below: <blockquote> The count matrices for the GTEx samples <cite GTEx publication, see below> were downloaded from Zenodo (doi: 10.5281/zenodo.5596755) and were generated through DROP <cite DROP, see below> using the release 29 of the GENCODE annotation <cite GENCODE, see below>. </blockquote> Also, write the following in the Acknowledgements section:   <blockquote> The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The raw data used for the analyses described in this manuscript were obtained from the GTEx Portal on June 12, 2017, under accession number dbGaP  phs00424.v6.p1. </blockquote>

opencc-by-4.0Feb 2022View details →

Gene expression and splicing counts from 49 tissues from GTEx v8 genome build hg38 - non-strand specific

Dataset description: 49 folders, each corresponding to one tissue from GTEx v8 and containing the following files: <ol> <li> geneCounts: gene-level counts  </li> <li> k_j: split counts spanning from one exon to another. </li> <li> k_theta: non-split counts covering a splice site </li> <li> n_psi3: total split counts from a given acceptor site </li> <li> n_psi5: total split counts from a given donor site </li> <li> n_theta: total split and non-split counts for a given splice site </li> <li> Sample annotation describing each sample from the dataset </li> <li> Description file with global information from the dataset </li> </ol> The gene counts were originated using the GTF file from <a href="https://www.gencodegenes.org/human/release_29.html">release 29 of GENCODE</a>, and the split and non-split counts contain only the annotated junctions from the same release. Statistics are reported only for GENCODE-annotated introns and splice sites, in compliance with the regulations of the GTEx consortium. For a description of the samples, methods, and protocols, see the GTEx publication specified below. Use: The count matrices are intended to help researchers that are interested in using RNA-Seq data with the purpose of diagnostics. Researchers can merge their own dataset with the downloaded ones, provided the tissue, genome build, strand, and paired-end specifications match. Afterwards, the <a href="https://github.com/gagneurlab/drop">Detection of RNA outliers Pipeline (DROP) </a> can be used to compute gene expression and splicing outliers. Organism: Homo sapiens Genome assembly: hg38 Gene annotation: gencode29 Strand specific: FALSE Paired end: TRUE Protocol: poly(A) enrichment Contact: Vicente A. Yepez, yepez at in.tum.de; Christian Mertes, mertes at in.tum.de; Julien Gagneur, gagneur at in.tum.de Citation: Write the following in the "Data availability" section of the manuscript or similar replacing the three citations by the ones from the References section below: <blockquote> The count matrices for the GTEx samples <cite GTEx publication, see below> were downloaded from Zenodo (doi: 10.5281/zenodo.6078397) and were generated through DROP <cite DROP, see below> using the release 29 of the GENCODE annotation <cite GENCODE, see below>. </blockquote> Also, write the following in the Acknowledgements section:   <blockquote> The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The raw data used for the analyses described in this manuscript were obtained from the GTEx Portal on June 12, 2017, under accession number dbGaP  phs000424.v8.p2. </blockquote>

opencc-by-4.0Dec 2020View details →

Host and pathogen gene expression profiles in Necrotizing Soft Tissue Infections

Data underlying the article Analysis of host-pathogen gene association networks reveals patient-specific response to streptococcal and poly-microbial necrotizing soft tissue infections Sanjeevan Jahagirdar1, Lorna Morris2, Nirupama Benis3, Oddvar Oppegaard4, Mattias Svenson5, Ole Hyldegaard6, Steinar Skrede4,7, Anna Norrby-Teglund5, INFECT Study group, Vitor A. P. Martins dos Santos1,2, Edoardo Saccenti1* Contains Data described in 4.3.2 Sample Selection and in Figure 6: INFECT_DualRNASeq_norm_counts_Human.txt  Normalised counts from Kallisto mapping to GRCh38 (release 91) for 81 NSTI patients INFECT_DualRNASeq_relative_abundance_Bacteria.txt Relative abundance for all species from HumanN2 mapping for 81 NSTI patients BStrep_filtered_D1 - INFECT_DualRNASeq_relative_abundance_Bacteria.txt filtered for samples classified as Streptococcal from Thänert et al (2019) taken on the day of admission (day 1). BPoly_filtered_D1 - INFECT_DualRNASeq_relative_abundance_Bacteria.txt filtered for samples classified as polymicrobial from Thänert et al (2019) taken on the day of admission (day 1). HStrep_filtered_D1 - INFECT_DualRNASeq_norm_counts_Human.txt filtered for samples classified as Streptococcal from Thänert et al (2019) taken on the day of admission (day 1). HPoly_filtered_D1 - INFECT_DualRNASeq_norm_counts_Human.txt filtered for samples classified as polymicrobial from Thänert et al (2019) taken on the day of admission (day 1).

Fig. 4 in Expressed sequence tags in venomous tissue of Scorpaena plumieri (Scorpaeniformes: Scorpaenidae)

Fig. 4. Sequence alignment of putative lectin from Scorpaena plumieri. Alignment of a lectin-like EST in silico translated sequence from S. plumieri (ClustalW2 EBI) with fish-egg lectin from Oplegnathus fasciatus (BAL618145), Dicentrarchus labrax (CBK52298), Maylandia zebra (XP_004574029), and Oreochromis niloticus (XP003443389). The recombinant clone was isolated with antibody fraction derived from S. plumieri venom. * identifies and identical residue;: identifies a conserved residue. Underlined residues represent invariable sites, underlined IRLS = N-acetylation site.

Fig. 3 in Expressed sequence tags in venomous tissue of Scorpaena plumieri (Scorpaeniformes: Scorpaenidae)

Fig. 3. The classification of EST from Scorpaena plumieri based on their putative fractions. Three-hundred fifty-six EST edited sequences were initially analyzed with Blast and Swiss protein databanks. The consensus sequence was attributed a function based on the strongest match.

Fig. 2 in Expressed sequence tags in venomous tissue of Scorpaena plumieri (Scorpaeniformes: Scorpaenidae)

Fig. 2. Agarose gel electrophoresis of DNA isolated from clones. White colonies containing insert were grown and the plasmidial DNA isolated and digested with EcoRI enzyme. An aliquot from each clone (1-27) was electrophoresed on 1% Agarose gel and stained with ethidium bromide.

Fig. 1 in Expressed sequence tags in venomous tissue of Scorpaena plumieri (Scorpaeniformes: Scorpaenidae)

Fig. 1. Agarose- formaldehyde electrophoresis of RNA from Scorpaena plumieri. A) 1) 2 µg of E. coli tRNA; 2) 2 µg de rRNA de Rattus norvegicus; 3) and 4) 2 µg total RNA from S. plumieri spine gland. B) 1) 2 µg de total RNA from S. plumieri; 2) the same sample incubated 2 h a 37ºC before electrophoresis.

opencc-by-4.0Sep 2019View details →

Figure 3 in Validation of reference genes for quantitative expression analysis by qPCR in various tissues of date mussel (Lithophaga lithophaga)

Figure 3. Pairwise variation (V-value) of candidate reference genes in date mussel (L. lithophaga) using geNorm.

opencc-by-4.0Sep 2016View details →

A benchmark of gene expression tissue-specificity metrics

Supplementary figures and data to the paper "A benchmark of gene expression tissue-specificity metrics" Briefings in Bioinformatics, Volume 18, Issue 2, March 2017, Pages 205–214, <a href="https://doi.org/10.1093/bib/bbw008">https://doi.org/10.1093/bib/bbw008</a> Previously published at FigShare, republishing because of access problems for some researchers.

opencc-by-4.0Oct 2020View details →

Human tissue gene expression TPM values for the advanced forensic biology course

Gene expression per tissue This dataset comes from the <a href="https://www.gtexportal.org/home/">Genotype-Tissue Expression (GTEx)</a> database that gathers gene expression data from various human tissues. Specifically, the GTEx Analysis v7 version was used. The file is called "<a href="https://zenodo.org/api/files/9f872792-6c96-4226-b659-ce0807da66e8/GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_median_tpm.tsv">GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_tpm.tsv</a>"  and contains tabulated-separated values of median TPM gene expression by tissue (TPM: transcript per million).   Genes with a favored sucutaneous-adipose expression profile This file contains 195 genes that have a statistically (p < 0.01) favored expression profile in subcutaneous adipose tissue compared to other tissues.   https://zenodo.org/api/files/7342bbfa-0e4d-49e7-b916-5eff5b638c33/genes_with_a_subcutaneous_adipose_favored_expression.tsv

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Data from: Ocean acidification induces subtle shifts in gene expression and DNA methylation in mantle tissue of the Eastern oyster (Crassostrea virginica)

Early evidence suggests that DNA methylation can mediate phenotypic responses of marine calcifying species to ocean acidification (OA). Few studies, however, have explicitly studied DNA methylation in calcifying tissues through time. Here, we examined the phenotypic and molecular responses in the extrapallial fluid and mantle (fluid and tissue at the calcification site) in adult eastern oyster (Crassostrea virginica) exposed to experimental OA over 80 days. Oysters were reared under three experimental pCO2 treatments ('control', 580 μatm; 'moderate OA', 1000 μatm; 'high OA', 2800 μatm) and sampled at 6 time points (24 hours - 80 days). We found that high OA initially induced an increase in the pH of the extrapallial fluid (pHEPF) relative to the external seawater that peaked at day 9, but then diminished over time. Calcification rates were significantly lower in the high OA treatment compared to the other treatments. To explore how oysters regulate their extrapallial fluid, gene expression and DNA methylation were examined in the mantle-edge tissue of oysters from days 9 and 80 in the control and high OA treatments. Mantle tissue mounted a significant global molecular response (both in the transcriptome and methylome) to OA that shifted through time. Although we did not find individual genes that were significantly differentially expressed under OA, the pHEPF was significantly correlated with the eigengene expression of several co-expressed gene clusters. A small number of OA-induced differentially methylated loci were discovered, which corresponded with a weak association between OA-induced changes in genome-wide gene body DNA methylation and gene expression. Gene body methylation, however, was not significantly correlated with the eigengene expression of pHEPF-correlated gene clusters. These results suggest that OA induces a subtle response in a large number of genes in C. virginica, but also indicate that plasticity at the molecular level may be limited. Our study highlights the need to reassess our understanding of tissue-specific molecular responses in marine calcifiers, as well as the role of DNA methylation and gene expression in mediating physiological and biomineralization responses to OA.

opencc-zeroOct 2020View details →

opencc-by-4.0Oct 2015View details →

Mouse and Human Co-expression maps and supplementary material for: "A comparison of human and mouse gene co-expression networks reveals conservation and divergence at the tissue, pathway and disease levels"

Co-expression maps of the human and mouse species derived from microarray data for the first release of the GeneFriend tool. The two co-expression maps  have been compared in order to discern similarities and differences between the two species. The results have been described in the manuscript titled: "A comparison of human and mouse gene co-expression networks reveals conservation and divergence at the tissue, pathway and disease levels". The supplementary material of the manuscript have also been included in this repository.

opencc-by-4.0Feb 2022View details →

Raw Data for the article: miRNA expression analysis in the human heart: Undifferentiated progenitors vs. bioptic tissues-Implications for proliferation and ageing

In developed countries, cardiovascular diseases are currently the first cause of death. Cardiospheres (CSs) and cardiosphere-derived cells (CDCs) have been found to have the ability to regenerate the myocardium after myocardial infarction (MI). In recent years, much effort has been made to gain insight into the human heart repair mechanisms, in which miRNAs have been shown to play an important role. In this regard, to elucidate the involvement of miRNAs, we evaluated the miRNA expression profile across human heart biopsy, CSs and CDCs using microarray and next-generation sequencing (NGS) technologies. We identified several miRNAs more represented in the progenitors, where some of them can be responsible for the proliferation or the maintenance of an undifferentiated state, while others have been found to be downregulated in the undifferentiated progenitors compared with the biopsies. Moreover, we also found a correlation between downregulated miRNAs in CSs/CDCs and patient age (eg miR-490) and an inverse correlation among miRNAs upregulated in CSs/CDCs (eg miR-31).