Find research datasets worth reusing

Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data

Data used to test the robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data, described in <a href="https://doi.org/10.1186/s13059-020-1949-z">Holland et al. 2020</a>. The folder data contains raw data and the folder output contains intermediate and final results of all analyses.  The associated analyses code and more information are available on <a href="https://github.com/saezlab/FootprintMethods_on_scRNAseq">GitHub</a>.   Abstract Background Many functional analysis tools have been developed to extract functional and mechanistic insight from bulk transcriptome data. With the advent of single-cell RNA sequencing (scRNA-seq), it is in principle possible to do such an analysis for single cells. However, scRNA-seq data has characteristics such as drop-out events and low library sizes. It is thus not clear if functional TF and pathway analysis tools established for bulk sequencing can be applied to scRNA-seq in a meaningful way. Results To address this question, we perform benchmark studies on simulated and real scRNA-seq data. We include the bulk-RNA tools PROGENy, GO enrichment, and DoRothEA that estimate pathway and transcription factor (TF) activities, respectively, and compare them against the tools SCENIC/AUCell and metaVIPER, designed for scRNA-seq. For the in silico study, we simulate single cells from TF/pathway perturbation bulk RNA-seq experiments. We complement the simulated data with real scRNA-seq data upon CRISPR-mediated knock-out. Our benchmarks on simulated and real data reveal comparable performance to the original bulk data. Additionally, we show that the TF and pathway activities preserve cell type-specific variability by analyzing a mixture sample sequenced with 13 scRNA-seq protocols. We also provide the benchmark data for further use by the community. Conclusions Our analyses suggest that bulk-based functional analysis tools that use manually curated footprint gene sets can be applied to scRNA-seq data, partially outperforming dedicated single-cell tools. Furthermore, we find that the performance of functional analysis tools is more sensitive to the gene sets than to the statistic used.   For questions related to the data please write an email to christian.holland@bioquant.uni-heidelberg.de or use the <a href="https://github.com/saezlab/FootprintMethods_on_scRNAseq/issues">GitHub issue system</a>.

Data for "Lone-pair effect on carrier capture in Cu2ZnSnS4 solar cells"

Data for "Lone-pair effect on carrier capture in Cu2ZnSnS4 solar cells" Dataset required for the analysis for "<a href="https://pubs.rsc.org/en/content/articlelanding/2019/ta/c8ta10130b">Lone-pair effect on carrier capture in Cu2ZnSnS4 solar cells</a>". The dataset consists of: <ul> <li>Crystal structures upon which first-principles (DFT) calculations were carried out in the original publication (DFT_calculations.tar)</li> <li>Python script to reproduce figures and data in the publication (data_process.zip)</li> </ul>

Bulk RNA-Seq PBMC data of SLE patients and healthy volunteers/ profiling of 29 individual immune cell types as well as PBMCs of healthy donors

This Zenodo project contains processed gene expression data from two publicly available data sets. It includes the gene expression data of peripheral blood mononuclear cells (PBMCs) of systemic lupus erythematosus (SLE) patients as well as healthy volunteers (GSE122459). The project also comprises the bulk RNA-Seq profiling of 29 immune cell types as well as PBMCs of healthy individuals (GSE107011). In both cases, the raw RNA-Seq data was downloaded, aligned and processed. The gene expression data is available in form of a count matrix (GSE107011) or count matrix and transcript-per-million (TPM) values (GSE122459). For the latter, an annotation file is attached. Further details are provided in the information file.

Data set for "Pathway-, layer- and cell-type-specific thalamic input to mouse barrel cortex"

Data set for: Sermet BS, Truschow P, Feyerabend M, Mayrhofer JM, Oram TB, Yizhar O, Staiger JF, Petersen CCH (2019) Pathway-, layer- and cell-type-specific thalamic input to mouse barrel cortex. eLife 8: e52665. https://doi.org/10.7554/eLife.52665 There are 2 files in this upload: 1. The file named "2019_Sermet_eLife.pdf" is the Open Access pdf file of the manuscript published in eLife. 2. The file named "Sermet_data_code.zip" (~5 GB) is a zipped version of a folder "Sermet_data_code" (~5 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. When unzipped, the folder contains 8 Matlab '.m' files with analysis code and one '.mat' data file. In order to run the analysis of the data set, you need to execute 'PopPlot.m'.

opencc-by-4.0Mar 2020View details →

Dataset related to article "TNF-Stimulated Gene-6 Is a Key Regulator in Switching Stemness and Biological Properties of Mesenchymal Stem Cells."

Mesenchymal stem cells (MSCs) are well established to have promising therapeutic properties. TNF-stimulated gene-6 (TSG-6), a potent tissue-protective and anti-inflammatory factor, has been demonstrated to be responsible for a significant part of the tissue-protecting properties mediated by MSCs. Nevertheless, current knowledge about the biological function of TSG-6 in MSCs is limited. Here, we demonstrated that TSG-6 is a crucial factor that influences many functional properties of MSCs. The transcriptomic sequencing analysis of wild-type (WT) and TSG-6-/- -MSCs shows that the loss of TSG-6 expression leads to the perturbation of several transcription factors, cytokines, and other key biological pathways. TSG-6-/- -MSCs appeared morphologically different with dissimilar cytoskeleton organization, significantly reduced size of extracellular vesicles, decreased cell proliferative rate, and loss of differentiation abilities compared with the WT cells. These cellular effects may be due to TSG-6-mediated changes in the extracellular matrix (ECM) environment. The supplementation of ECM with exogenous TSG-6, in fact, rescued cell proliferation and changes in morphology. Importantly, TSG-6-deficient MSCs displayed an increased capacity to release interleukin-6 conferring pro-inflammatory and pro-tumorigenic properties to the MSCs. Overall, our data provide strong evidence that TSG-6 is crucial for the maintenance of stemness and other biological properties of murine MSCs.   Some dataset of this research are in prism format, to ensure open access we attach a pdf instruction about this format and a link where downoladed it

opencc-by-4.0Mar 2020View details →

Dataset related to article "NKp46-expressing human gut-resident intraepithelial Vδ1 T cell subpopulation exhibits high antitumor activity against colorectal cancer"

γδ T cells account for a large fraction of human intestinal intraepithelial lymphocytes (IELs) endowed with potent antitumor activities. However, little is known about their origin, phenotype, and clinical relevance in colorectal cancer (CRC). To determine γδ IEL gut specificity, homing, and functions, γδ T cells were purified from human healthy blood, lymph nodes, liver, skin, and intestine, either disease-free, affected by CRC, or generated from thymic precursors. The constitutive expression of NKp46 specifically identifies a subset of cytotoxic Vδ1 T cells representing the largest fraction of gut-resident IELs. The ontogeny and gut-tropism of NKp46+/Vδ1 IELs depends both on distinctive features of Vδ1 thymic precursors and gut-environmental factors. Either the constitutive presence of NKp46 on tissue-resident Vδ1 intestinal IELs or its induced expression on IL-2/IL-15-activated Vδ1 thymocytes are associated with antitumor functions. Higher frequencies of NKp46+/Vδ1 IELs in tumor-free specimens from CRC patients correlate with a lower risk of developing metastatic III/IV disease stages. Additionally, our in vitro settings reproducing CRC tumor microenvironment inhibited the expansion of NKp46+/Vδ1 cells from activated thymic precursors. These results parallel the very low frequencies of NKp46+/Vδ1 IELs able to infiltrate CRC, thus providing insights to either follow-up cancer progression or to develop adoptive cellular therapies.   This dataset is created with fcs files form, in order to guarantee the access we attach a pdf information about

Data_Figure 2_Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration

Data of figure 2 from Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration Dataset (doi: 10.1007/s00018-019-03227-w) contains the original figure as TIF-format (10.1007_s00018-019-03227-w_CMLS_Fig2). Corresponding raw data obtained from a) Migration potential as four files in CSV format (31003A-179400_ date_examiner_17BHSD12_16_1_1-4. b) mRNA content analyzed by RT-PCR provided as ten files in CSV format (31003A-179400_date_examiner_17BHSD12_1_1-2_1-6) and proliferation investigation on xCELLigence provided as six files in CSV format (31003A-179400_date_examiner_17BHSD12_9_2_1-6). All further experiment related information and subsequent data analysis provided as four meta-data-files (31003A-179400_ date_examiner_17BHSD12_16/1/9_dataset_M_1) as TXT format.

Data_Figure 6_Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration

Data of figure 6 from Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration Dataset (doi: 10.1007/s00018-019-03227-w) contains the original figure as TIF-format (10.1194_jlr.M092908_Fig. 6). Corresponding raw data obtained from a1/2) cellomics HTC array scan analysis provided as six files in CSV format (31003A-179400_Date_examiner_17BHSD12_8_6_1-6), b1/2) oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) provided as 10 files in CSV format (31003A-179400_20190521_MT_17BHSD12_10_3-4_1-5); c 1/2 ), cellomics HTC array scan analysis provided as 12 files in CSV format (31003A-179400_Date_examiner_17BHSD12_8_7-8_1-8). d) Western blot and densitometry provided as eight files in CSV format (31003A-179400_Date_examiner_17BHSD12_2_3-4_1-5). All further experiment related information protocols and subsequent data analysis provided as meta-data-files (31003A-179400_date_examiner_17BHSD12_8/10/2_dataset_M_1) as TXT format and (31003A-179400_date_examiner_17BHSD12_2_dataset_M_2-3) as PNG format.

Data_Figure 7_Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration

Data of figure 7 from Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration Dataset (doi: 10.1007/s00018-019-03227-w) contains the original figure as TIF-format (10.1194_jlr.M092908_Fig. 7). Corresponding raw data obtained from a1/2) Western blot and densitometry provided as eight files in CSV format (31003A-179400_date_examiner_17BHSD12_2_5-6_1-6); b) mRNA content analyzed by RT-PCR provided as four files in CSV format (31003A-179400_date_examiner_17BHSD12_1_6_1-4); c 1/2) cellomics HTC array scan analysis provided as 11 files in CSV format (31003A-179400_Date_examiner_17BHSD12_8_9-10_1-6); d1/2); Western blot and densitometry provided as eight files in CSV format (31003A-179400_date_examiner_17BHSD12_2_7-8_1-6). All further experiment related information protocols and subsequent data analysis provided as meta-data-files (31003A-179400_date_examiner_17BHSD12_2/1/8_dataset_M_1) as TXT format and (31003A-179400_date_examiner_17BHSD12_2_dataset_M_2-3) as PNG format.

Data_Figure 5_Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration

Data of figure 5 from Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration Dataset (doi: 10.1007/s00018-019-03227-w) contains the original figure as TIF-format (10.1194_jlr.M092908_Fig. 5). Corresponding raw data obtained from oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) provided as 10 files in CSV format (31003A-179400_20190521_MT_17BHSD12_10_1-2_1-5). All further experiment related information and subsequent data analysis provided as two meta-data-file: (31003A-179400_20190521_MT_17BHSD12_10_1-2_1) as TXT format.

Data_Figure 4_Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration

Data of figure 4 from Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration Dataset (doi: 10.1007/s00018-019-03227-w) contains the original figure as TIF-format (10.1194_jlr.M092908_Fig. 4). Corresponding raw data obtained from: a1/2) Migration potential as five files in CSV format (31003A-179400_date_examiner_17BHSD12_16_3_1-5); b 1/2) Migration potential as four files in CSV format (31003A-179400_date_examiner_17BHSD12_16_4_1-4); c1/2/3) mRNA content analyzed by RT-PCR provided as four files in CSV format (31003A-179400_date_examiner_17BHSD12_1_4_1-4); cellomics HTC array scan analysis provided as three files in CSV format (31003A-179400_Date_examiner_17BHSD12_8_3-4_1-4); d) Migration potential as five files in CSV format (31003A-179400_ date_examiner_17BHSD12_16_5_1-5); e) mRNA content analyzed by RT-PCR provided as six files in CSV format (31003A-179400_date_examiner_17BHSD12_1_5_1-6); f) Migration potential as four files in CSV format (31003A-179400_date_examiner_17BHSD12_16_6_1-4), cellomics HTC array scan analysis provided as three files in CSV format (31003A-179400_Date_examiner_17BHSD12_8_5_1-5); g) ELISA measurement provided as four files in CSV format (31003A-179400_date_examiner_17BHSD12_20_1_1-4). All further experiment related information protocols and subsequent data analysis provided as 10 meta-data-files (31003A-179400_date_examiner_17BHSD12_8/16/1/20_dataset_M_1) as TXT format.

Data_supplemental figure 2_Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration

Data of supplemental figure 2 from Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration Dataset (doi: 10.1007/s00018-019-03227-w) contains the original figure as TIF format (10.1194_jlr.M092908_Fig. S2). Corresponding raw data obtained from cellomics HTC array scan analysis provided as seven files in CSV format (31003A-179400_Date_examiner_17BHSD12_8_11-12_1-4) All further experiment related information protocols as meta-data-files (31003A-179400_date_examiner_17BHSD12_8_11-12_M_1) as TXT format.

opencc-by-4.0May 2015View details →

Prediction of nucleosome dyads for the K562 cell line in the hg19 genome assembly

<a href="https://andre-rendeiro.com/2015/05/12/predicting_dyads_from_mnase">Predicting dyads from MNase-seq data</a> I needed the location of nucleosomal dyads in the K562 cell line (ENCODE tier 1 line). Surprisingly, although plenty of MNase-seq data for that cell line is available, no nucleosome and dyad prediction exists. Running NuMap I found the <a href="http://www-hsc.usc.edu/~valouev/NuMap/NuMap.html">NuMap</a> software by Anton Valouev to do exactly what I intended. Since it is in a somewhat obscure page and this seemed to be the only place where this software was, I have <a href="https://github.com/afrendeiro/NuMap">uploaded it into a Github repository</a> for the sake of preservation (<a href="https://github.com/orphancode/NuMap">https://github.com/orphancode/NuMap</a>). Predicting dyads from MNase-seq data with NuMap seemed trivial: I downloaded the <a href="http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeSydhNsome/">K562 MNase-seq data set</a> (11 replicates ~85Gb!!), combined all replicates and ran NuMap on the data(instructions on the Github README). From NuMap output there are <a href="https://www.dropbox.com/s/asmp7bi40lrvtjb/K562_dyads.bed?dl=0">dyad positions in bed format</a> and you can also produce several metrics to evaluate how good the prediction was. Distograms & phasograms Valouev describes two measurements of the frequencies of distances between MNase-seq reads. The frequency of distances between reads mapping to opposite strands can be used to build a “distogram”, which ilustrates the expected nucleosome length (147 bp) - this is consistent across most eukaryotic cells. The frequency of distances between reads mapping to the same strand gives a measurement of the distance between nucleosomes, as they’re separated by some linker DNA - (Valouev calls this plot a “phasogram”). This measurement, on the other hand tends to be species and cell-type specific. K562 predictions: The expected 147 bp nucleosome length in K562 cells. The average distance between dyads in K562 cells seems to be 185 bp. References: Valouev, A., Johnson, S. M., Boyd, S. D., Smith, C. L., Fire, A. Z., Sidow, A. (2011). Determinants of nucleosome organization in primary human cells. Nature, 474(7352), 516–520. <a href="http://doi.org/10.1038/nature10002">http://doi.org/10.1038/nature10002</a>

opencc-by-4.0Feb 2020View details →

leaf anatomy, vascular traits and nanomechanical cell-wall properties in European beech provenances

The file contains leaf anatomical data (thickness of individual leaf parenchyma layers), vascular traits of leaf midrib (vessel area and density and derived parameters), and nanomechanical properties of xylem cell walls (modulus of elasticity, adhesion, energy dissipation and deformation), which were studied in 15 provenances of European beech, originating from sites distributed across the whole range of the species. The trial plot (locality Tale in central Slovakia) was established in 1998 with 2-years-old seedling within the international provenance experiment with beech coordinated by the Institute of Forest Genetics of Thuenen Institute Grosshansdorf. Leaf anatomy was studied using light microscopy, while fluorescent microscopy was used to acquire vascular traits and atomic-force microscopy for nanomechanical cell-wall traits. Sun leaves were collected from 4 trees per provenances, 1 leaf per tree was analyzed. AFM was done in a subset of 8 provenances. The aim of the study was assessing geographical trends of the studied traits and their association with climate at the sites of origin to reveal potential adaptive variation patterns.

openother-openMay 2020View details →

Integrative modeling results of in-cell architecture of an actively transcribing-translating expressome

Repository containing good-scoring models, input files, modeling and analysis protocols for the integrative modeling of the M. pneumoniae expressome from in-cell cryo-electron tomography and crosslinking mass spectrometry using IMP.

opencc-by-3.0Nov 2017View details →

Fig. 34. Disco-submarginal cell. A in The genus Enicospilus Stephens, 1835 (Hymenoptera, Ichneumonidae, Ophioninae) in Saudi Arabia, with twelve new species records and the description of five new species

Fig. 34. Disco-submarginal cell. A. Enicospilus psammus (after Gauld & Mitchell 1978). B. E. perlatus (after Shestakov 1926).

opencc-by-3.0Apr 2016View details →

Fig. 2. Fore wing discosubmarginal cell. A in The British species of Enicospilus (Hymenoptera: Ichneumonidae: Ophioninae)

Fig. 2. Fore wing discosubmarginal cell. A. Enicospilus undulatus (Gravenhorst, 1829). B. E. ramidulus (Linnaeus, 1758). C. E. merdarius (Gravenhorst, 1829). D. E. repentinus (Holmgren, 1860).

opencc-by-4.0May 2020View details →

Interactive visualization of the comparison between the neoplastic cells and the neural cells

Interactive visualization of the comparison between the neoplastic cells and the neural cells. In order to visualize graphs, the Hipathia Bioconductor package must be installed (https://www.bioconductor.org/packages/release/bioc/html/hipathia.html). Then, the function visualize.pathways() must be used, specifying the path to supplementary data (the file “Supplementary Figure 5”) in your computer. The function invokes a local web server that provides the functionality of the HiPathia web application. The user can navigate across the pathways and focus on specific circuits that are up- or down-activated.

opencc-by-4.0Sep 2019View details →

DS4_LH_ Lodola et al_Sci Adv_2019_Cell Viability

Cell Viability and tubulogenesis assays in dark and light conditions.

DS4_LH_Tullii et al._ACS Appl. Mater. Interfaces_2019_cell viability

cell viability assay on HEK cells and neurons plated on P3HT flat and pillars