Find research datasets worth reusing

Efficient statistical method for single-cell QTL analysis

This upload contains data objects associated with our paper "Efficient statistical method for single-cell QTL analysis" introducing the SAIGE-QTL method (preprint available soon!).

opencc-zeroMar 2024View details →

dryad36/100

The bone marrow endothelial progenitor cell response to septic infection: Dataset

An early increase in the level of endothelial progenitor cells (EPCs) in the systemic circulation occurs in patients with septic infection/sepsis. The significance and underlying mechanisms of this response remain unclear. This study investigated the bone marrow EPC response in adult mice with septic infection induced by intravenous injection (i.v.) of Escherichia coli. For in vitro experiments, sorted marrow stem/progenitor cells (SPCs) including lineage(lin)-stem cell growth factor receptor(c-kit)+stem cell antigen-1(Sca-1)-, lin-c-kit+, and lin- cells were cultured with or without lipopolysaccharides (LPS) and recombinant murine vascular endothelial growth factor (VEGF) in the absence and presence of anti-Sca-1 crosslinking antibodies. In a separate set of experiments, marrow lin-c-kit+ cells from green fluorescence protein (GFP)+ mice, i.v. challenged with heat-inactivated E. coli or saline for 24 h were subcutaneously implanted in Matrigel plugs for 5 weeks. Marrow lin-c-kit+ cells from Sca-1 knockout (KO) mice challenged with heat-inactivated E. coli for 24 h were cultured in the Matrigel medium for 8 weeks. The marrow pool of EPCs bearing the lin-c-kit+Sca-1+VEGF receptor 2 (VEGFR2)+ (LKS VEGFR2+) and LKS CD133+VEGFR2+ surface markers expanded rapidly following septic infection, which was supported by both proliferative activation and phenotypic conversion of marrow stem/progenitor cells. An increase in marrow EPCs and their reprogramming for enhancing angiogenic activity correlated with cell-marked upregulation of Sca-1 expression. Sca-1 coupled with ras-related C3 botulinum toxin substrate 2 (Rac2) in signaling the marrow EPC response. Septic infection caused a substantial increase in plasma levels of IFN-γ, VEGF, G-CSF, and SDF-1. The early increase in circulating EPCs was accompanied by their active homing and incorporation into pulmonary microvasculature. These results demonstrate that the marrow EPC response is a critical component of the host defense system. Sca-1 signaling plays a pivotal role in the regulation of EPC response in mice with septic infection.

dryad36/100

Data from: Preparation of isolated guard cells, containing cell walls, from Vicia faba

Stomatal movement, initiated by specialized epidermal cells known as guard cells (GCs), plays a pivotal role in plant gas exchange and water use efficiency. Despite protocols existing for isolating GCs through proplasting for carrying out biochemical, physiological, and molecular studies, protocols for isolating GCs with their cell walls still intact have been lacking in the literature. In this paper, we introduce a method for the isolation of complete GCs from Vicia faba and show their membrane to remain impermeable through propidium iodide staining. This methodology enables further in-depth analyses into the cell wall composition of GCs, facilitating our understanding of structure-function relationship governing reversible actuation within cells.

opencc-zeroDec 2023View details →

Identification of clinically relevant T cell receptors for personalized T cell therapy using combinatorial algorithms

Single-cell RNA (scRNA) and single-cell TCR (scTCR)-sequencing data data for patients number 11, 12 and 14 from the article " Identification of clinically relevant T-cell receptors for personlized T-cell therapy".  Each compressed file contains two subfolders. One is for the scRNA-seq (GEX-sequencing) and the other for the scTCR-seq (VDJ-sequencing).  In the original article, scRNA-seq and scTCR-seq were aligned to the GRCh38 reference genome using cellranger count (10X Genomics, version 3.0.1) and vdj (10X Genomics, version 3.1.0) respectively. The subsequent data processing was performed using Seurat library (V.4.3.0) on R Statistical Software (V.4.0.3).

Isosceles paper simulated ovarian cell line ONT data (bulk RNA-Seq)

<div> Simulated ovarian cell line ONT data (bulk RNA-Seq) for the Isosceles paper - more details can be found in the <a href="https://github.com/Genentech/Isosceles_Paper" target="_blank" rel="noopener">Isosceles_Paper</a> repository. </div>

Investigating the causal association between immune cell phenotypes and allergic diseases and non-allergic asthma using conventional Two-sample and Bayesian weighted Mendelian randomization

Investigating the causal association between immune cell phenotypes and allergic diseases and non-allergic asthma using conventional Two-sample and Bayesian weighted Mendelian randomization

Accelerated Perovskite Solar Cells Ageing Dataset

The dataset contains accelerated ageing test traces (time vs. MPPT PCE/ maximum power point tracking power conversion efficiency) for perovskite solar cells with SAM (self-assembled monolayer)-based and NiOx-based hole transport layers at various temperatures in .csv format. The next step to process and analyze the data, can be accessed here: https://github.com/noortitan/AcceleratedCyclePSCs/.

Results of the mechanical characterization by tensile tests and the in vitro cell-biomaterial interaction analyses by WST-1 and Live/Dead of 3D-printed scaffolds generated by PLA, PCL, FF, FD and GelMA

Dataset containing the quantitative results of the mechanical characterization, and the in vitro cell-biomaterial interaction analyses with neural cells after 72 hours and 7 days of cell culture of the following 3D printed scaffolds: <ul> <li>Polylactic acid (PLA)</li> <li>Polycaprolactone (PCL)</li> <li>Conductive Filaflex (FF)</li> <li>Flexdym (FD)</li> <li>GelMA (G)</li> </ul> The Live/Dead results were quantified using ImageJ software to determine area fractions corresponding to live (green) and dead (red) cells.

opencc-zeroApr 2024View details →

Replication Data for: Precision Oncology, Cell Signaling and Targeted Therapy: A Holistic Approach to Molecular Cancer Therapeutics

In recent decades, there has been a deluge in the large-scale production of anticancer agents, primarily due to advances in genomic technologies enabling precise targeting of oncogenic pathways involved in disease progression. This initiated a paradigm shift in cancer research and therapeutics based on the ability to study molecular changes throughout the genome. It provided a unique opportunity in the field of translational cancer research and have led to the concept of precision medicine in cancer therapy, raising hopes of developing better diagnostic and therapeutic means for the management of cancer. The purpose of this article is to briefly review the tools and techniques involved in precision oncology research and their applications in the field of cancer treatment.

Data and Software of "Development of a Geometric Modeling Strategy for the Generation of Representative Unit Cells in 2D Braids"

<h1>Id: Data of following publication</h1> title = "Development of a Geometric Modeling Strategy for the Generation of Representative Unit Cells in 2D Braids", journal = "Composite Structures", volume =" 348", pages = "118503", year = "2025", doi = "<a href="https://doi.org/10.1016/j.compstruct.2024.118503" target="_blank" rel="noopener">10.1016/j.compstruct.2024.118503</a>", author = "José Rothkegel, Benjamin Renson, Michael Bruyneel, Ludovic Noels" Data doi on 10.5281/zenodo.10829042 <h1>pyRVE</h1> <h2>Python Code for Geometrical Generator for Braided Composites RVE</h2> pyRVE is a code written in Python using the GMSH API that generates the Representative Unit Cell (RUC) of braided composites. It allows the generation of the RUC of triaxial braided for Diamond and Regular patterns. <h2>Requirements</h2> To run, it requires: <ul> <li>The GMSH Python API, which must be built with OpenCascade support. <ul> <li>Choose a local installation directory; <code>CMAKE_INSTALL_PREFIX=$HOME/local/gmsh</code>, and <code>GMSHPY_INSTALL_DIRECTORY=$HOME/local/gmsh</code> e.g.;</li> <li>Make that directory part of your <code>export PYTHONPATH=$HOME/local/gmsh/lib:$PYTHONPATH</code>.</li> <li>After compiling use <code>make install</code>.</li> </ul> </li> <li>The CM3 app dG3D if the final RVE homogenized solution is needed (<a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a>).</li> <li>Make sure that the latest version of OpenCascade (OCCT) is used. Current used version in occt-V7.8.0.</li> </ul> <h2>Usage</h2> <h3>File Structure</h3> A typical run case must have a file structure, where: <ul> <li><code>brd</code>: the files <code>.brd</code> and <code>.brep</code> are located here. The <code>.brd</code> is a backup of the <code>braidClass</code> instance used in the model saved using <code>pickle</code>, the <code>.brep</code> is the Boundary Representation file that can be opened with GMSH.</li> <li><code>csv</code>: the <code>.csv</code> file saved here is the initial output of the code. It contains the actually used dimensions and the final cover factor of the braid.</li> <li><code>data</code>: It contains <code>.csv</code> files with the material properties and the dimensions of the tows. The original model dimensions are read from here.</li> <li><code>dir</code>: In the case of running the RVE homogenization, the directions of the tow fibers are stored here. They are saved for post processing.</li> <li><code>msh</code>: the mesh file <code>.msh</code> obtained after the geometry geneartion is stores here.</li> <li><code>png</code>: in the case of automatic post processing, png files are stored here.</li> <li><code>res</code>: this folder is used to store the homogenization results. They have to be moved here.</li> <li><code>stp</code>: if acitvated, a <code>.stp</code> file of the geometry is stored here</li> <li><code>svg</code>: the projection of the geometry on the x-y plane is stored here.</li> <li><code>vtk</code>: A copy of the mesh file without the matrix mesh is sotred here as a `.vtk`` file.</li> </ul> <h3>How to Run</h3> We will consider the current file structure to run the example in 000_Base. To run the code, it can be called from the command prompt as <div> <pre><code>python3 ../../source/mainRVE.py --name --pattern <pattern></code></pre> </div> In this case, the <code>--name</code> refers to the index that will be given to the model, where <code></code> must be changed to an integer and <code>--pattern</code> refers to the wanted pattern to be used, where <code><pattern></code> must be changed to either <code>dia</code> or <code>reg</code>. <blockquote> Note: <code><code>--name</code>cat</code> can also be used to reproduce the regular pattern benchmark of the paper. In that case, the volume fraction of fiber in the tows is hard coded as the provided value in the reference (i.e. 0.86). For other cases, the volume fraction is evaluated from the tow cross-sections. Note: <code>mainRVE.py</code> must be accesible from the directory where the case is being run. This example shows the usage of the current file structure. </blockquote> <h3>All Command Line Options</h3> The code can be run using further options that serve different purpouses, some serving pre processing needs and other serving run administration. The different command line options are: <ul> <li>Required: <ul> <li><code>--name</code> : it gives a suffix to the run model. It is usually an integer.</li> <li><code>--pattern</code> : indicates the type of pattern to be used to build the geometry. The two current options are <code>dia</code> for diamond and <code>reg</code> for regular.</li> </ul> </li> <li>Optional <ul> <li><code>-dG3D</code>: it indicates that the homogenization of the generated RUC is to be perfomed.</li> <li><code>-GMSH</code> : it indicates that GMSH must be open upon competion of the generation of the mesh.</li> <li><code>-loadModel</code> : it will try to load a premade model. It will ignore <code>--pattern</code>.</li> <li><code>--rndPrm</code> : it will generate randomized geometrical parameters. It can be used to generate batches of results. It takes an argument that can be <code>2</code>, <code>4</code> or <code>6</code>. Currently, <code>2</code> gives a random value for <code>s_axial</code> and <code>theta</code>, <code>4</code> randomizes the same as <code>2</code> and adds <code>h_axial</code> and <code>h_bias</code>, and <code>6</code> randomizes the same as <code>4</code> and adds <code>w_axial</code> and <code>w_bias</code>.</li> </ul> </li> <li>Pre-Processing <ul> <li><code>-refCF</code>: it tells the code to generate a grid of values for <code>s_axial</code> and <code>theta</code> where only the cover factor is obtained. It is meant for posterior graphing purposes.</li> </ul> </li> </ul> <h3>Examples</h3> Following the run options, a few examples are indicated <ul> <li>A basic mesh generation run for the basic data, considering a regular pattern, for a model named 2:</li> </ul> <div> <pre><code>python3 ../../source/mainRVE.py --name 2 --pattern reg</code></pre> </div> <ul> <li>The generation of the cover factor data and export, considering a regular pattern:</li> </ul> <div> <pre><code>python3 ../../source/mainRVE.py --pattern reg -refCF</code></pre> </div> <ul> <li>A run for the modified basic data, where the 2 parameters are modified randomly, considering a regular pattern, for a model named 2:</li> </ul> <div> <pre><code>python3 ../../source/mainRVE.py --name 2 --pattern reg --rndPrm 2</code></pre> </div> <ul> <li>A run, where model 2 already exists in <code>brd</code> folder but not the <code>.msh</code> and <code>.vtk</code> files:</li> </ul> <div> <pre><code>python3 ../../source/mainRVE.py --name 2 -loadModel </code></pre> </div> <h2>Code Structure</h2> The code is implemented into Python files, where <code>mainRVE.py</code> runs the whole code. The files are: <ul> <li>Braid: <ul> <li><code>braidClass.py</code> :</li> <li><code>bzrPairClass.py</code> :</li> </ul> </li> <li>Geometry <ul> <li><code>bezrClass.py</code> :</li> <li><code>bilnClass.py</code> :</li> <li><code>patchClass.py</code> :</li> <li><code>pntSetClass.py</code> :</li> <li><code>pointClass.py</code> :</li> <li><code>sctnClass.py</code> :</li> <li><code>stripeClass.py</code> :</li> <li><code>surfClass.py</code> :</li> <li><code>surfOffClass.py</code> :</li> </ul> </li> <li>Material: <ul> <li><code>chamis.py</code> :</li> </ul> </li> <li>Tools: <ul> <li><code>dataIO.py</code> :</li> <li><code>postDirection.py</code> :</li> <li><code>tool.py</code> :</li> <li><code>toolData.py</code> :</li> </ul> </li> <li><code>curveClass.py</code> :*</li> </ul>

Extraembryonic gut endoderm cells undergo programmed cell death during development (source data and custom code)

Despite a distinct developmental origin, extraembryonic cells in mice contribute to gut endoderm and converge to transcriptionally resemble their embryonic counterparts. Notably, extraembryonic progenitors share a non-canonical epigenome, raising several pertinent questions, including whether this landscape is reset to match the embryonic regulation and if these cells persist into later development. Here, we developed a two-color lineage tracing strategy to track and isolate extraembryonic cells over time. We find that extraembryonic gut cells display substantial memory of their developmental origin including retention of their original DNA methylation landscape and resulting transcriptional signatures. Furthermore, we show that extraembryonic gut cells undergo programmed cell death and neighboring embryonic cells clear their remnants via non-professional phagocytosis. By midgestation, we no longer detect extraembryonic cells in the wild type gut while they persist and differentiate further in p53 mutant embryos. Our study provides key insights into the molecular and developmental fate of extraembryonic cells inside the embryo.

opencc-zeroApr 2024View details →

SeuratExtend Tutorial: Curated Example Datasets for Single-Cell Analysis

This repository contains example datasets specifically curated for the SeuratExtend tutorial, aimed at facilitating advanced analyses and visualization techniques in single-cell genomics. The datasets have been derived from publicly available data obtained from the 10X Genomics website and have undergone careful preprocessing to serve specific tutorial goals. The collection includes the following datasets: <ol> <li> Myeloid Subset from PBMC 10k Dataset: This subset focuses on myeloid cells extracted from the larger PBMC 10k dataset, showcasing a preprocessed SeuratObject stored as an RDS file. The data serve as a primary example for demonstrating the capabilities of SeuratExtend differentiation trajectory analysis. </li> <li> Velocyto LOOM File of Myeloid Subset from PBMC 10k Dataset: Accompanying the first dataset, this Velocyto-generated LOOM file represents a subset of the same myeloid cells, focusing on RNA velocity analyses. It provides a dynamic perspective on gene expression changes over time, enriching the tutorial with advanced single-cell transcriptomics insights. </li> <li> SCENIC-Processed PBMC 3k Dataset: An outcome of running the SCENIC workflow on the PBMC 3k dataset, this LOOM file represents a refined dataset highlighting gene regulation networks. It serves as an advanced example for users interested in exploring gene regulatory mechanisms using SeuratExtend. </li> </ol> Each dataset has been subsetted and processed, making them ideal for users ranging from beginners to advanced researchers in the field of single-cell genomics. The provided data are intended for educational and tutorial purposes, allowing users to gain hands-on experience with real-world single-cell analysis scenarios.

Fluorescence Microscopy Images of Hela Cell infected with Plasmodium Berghei parasite expressing mCherry in cytoplasm

The purpose of our experiments was to delve into the liver stage development of the P. berghei parasite and examine the host-parasite interactions using HeLa cells. This research is primarily focused on in vitro analysis and does not extend to in vivo applications. Our study investigated the integration of fluorescent microscopy with artificial intelligence to  track and predict the developmental milestones of Plasmodium liver stage development.  This is the dataset used in our study.

opencc-zeroApr 2024View details →

dryad36/100

Data from: Time or distance: flexible coding of Hippocampal cells

Analysis of neuronal activity in the hippocampus of behaving animals has revealed cells acting as "Time Cells," which exhibit selective spiking patterns at specific time intervals since a triggering event, and "Distance Cells," which encode the traversal of specific distances. Other neurons exhibit a combination of these features, alongside place selectivity. This study aims to investigate how the task performed by animals during recording sessions influences the formation of these representations. The data used here was originally curated in a treadmill running study conducted by Kraus et al. (2013) in which rats were trained to run at different velocities. The rats were recorded in two trial contexts: a "fixed time" condition, where the animal ran on the treadmill for a predetermined duration before proceeding, and a "fixed distance" condition, where the animal ran a specific distance on the treadmill. Our findings indicate that the type of experimental condition significantly influenced the encoding of hippocampal cells. Specifically, distance-encoding cells dominated in fixed-distance experiments, whereas time-encoding cells dominated in fixed-time experiments. These results underscore the flexible coding capabilities of the hippocampus, which are shaped by over-representation of salient variables associated with reward conditions. Data used in Absramson et al., 2023, "Time or distance: flexible coding of Hippocampal cells", originally curated by Kraus et al., Neuron, 78(6), 1090-1101, 2013.

Dataset for "Understanding Wavelength-dependent Synergies between Morphology and Photonic Design in TiO2-based Solar Powered Redox Cells"

Open the record for dataset details and reuse information.

opencc-by-4.0Dec 2023View details →

Gene expression landscape of cutaneous squamous cell carcinoma progression

Data files associated with "Gene expression landscape of cutaneous squamous cell carcinoma progression" by Bencomo and Lee. The README.md file has descriptions for each file.Funding support: This work was supported by a Scholar-Innovator Award from the Harrington Discovery Institute and a Discovery Boost grant from the American Cancer Society (CSL).

Transcriptomic atlas reveals organ-specific disease tolerance in sickle cell mice. Dataset for HbSS livers injected or not with heme

The objective of this experiment was to explore the transcriptome of the HbSS Townes mouse model of sickle cell disease. Townes model mice carry several human hemoglobin knock-in genes replacing the endogenous mouse genes and may be useful in studying sickle cell disease. All mice were genotyped, age- and sex-matched littermates. All HbAA (control, normal human hemoglobin) vs HbSS (sickle cell disease, mutated human hemoglobin) mice were used for experimentations at 6-8 weeks of age, to limit intra-group heterogeneity. Hemin (Ferriprotoporphyrin IX) was purchased from Frontiers Scientific and injected intravenously (iv.) in a retroorbital sinus at a concentration of 24 µmol/kg. Control mice received PBS instead. Mice were anesthetized with isoflurane 2-3% for injections, blood collection and sacrifice. All mice were sacrificed by cervical dislocation, 4 hours after injection. The results of livers (indicated foie) from HbSS mice injected or not with heme are presented here.  The results of livers (indicated foie) from HbAA mice injected or not with heme can be found at number 10.5281/zenodo.10963640.  Thirty μm-thick frozen tissue sections of livers were cut as above and homogenized in 200μL of 1-Thioglycerol/Homogenization Solution (Maxwell® 16 LEV simplyRNA Tissue Kit Promega AS1280). The quality and quantity of mRNA were evaluated using a 2100 bioanalyzer with TNA 6000 NanoKits (all Agilent Technologies, Palo Alto, CA, USA). RNA Integrity Numbers superior to 7 were eligible for subsequent reverse transcription into cDNA. RNAseq was performed at the GenomIC plateform Cochin Institute INSERM U1016. After RNA extraction, RNA quality (RNA integrity number) was estimated. 1μg of high-quality total RNA sample (RIN &gt;7) was processed to build up the libraries, using TruSeq Stranded mRNA kit (Illumina) according to manufacturer instructions. Briefly, purified poly-A containing mRNA molecules were fragmented and reverse-transcribed using random primers. Replacement of dTTP by dUTP during second strand synthesis allowed us to achieve strand specificity. Addition of a single A base to the cDNA was followed by ligation of Illumina adapters. Libraries were quantified by qPCR using KAPA Library Quantification Kits for Illumina Libraries (KapaBiosystems, Wilmington, MA). Library profiles were assessed using DNA High Sensitivity LabChip kits on an Agilent Bioanalyzer. Libraries were sequenced on an Illumina Nextseq 500 instrument using 75 base-lengths read V2 chemistry in a paired-end mode. After sequencing, primary analysis based on AOZAN software (ENS, Paris), was applied to demultiplex and control the quality of the raw data (based of FastQC modules / version 0.11.5). The dataset here represents 4 groups of mice, 4 mice per group as follows: HbAA PBS, HbAA heme, HbSS PBS, HbSS heme.

Raman spectra from "Discrimination of immune cell activation using Raman micro-spectroscopy in an in-vitro & ex-vivo model"

The uploaded files are data from Chaudhary et al, 2021 (Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, Discrimination of immune cell activation using Raman micro-spectroscopy in an in-vitro & ex-vivo model, https://doi.org/10.1016/j.saa.2020.119118). There are two files in .mat format. In one (Preprocessed.mat) the data has been completely pre-processed according to the methods described in the paper. In the second (Unpreprocessed.mat) the data has been calibrated using the methods described in the paper, but has not received further pre-processing. Within both files there are datasets for the spectral measurement from each cell (‘spectra’), together with the treatment which was applied to each sample (‘treatment’) and the wavenumber at which the spectral measurements were made (‘wavenumber’).

Transcriptomic atlas reveals organ-specific disease tolerance in sickle cell mice. Dataset for HbAA heart injected or not with heme

The objective of this experiment was to explore the transcriptome of the HbSS Townes mouse model of sickle cell disease.Townes model mice carry several human hemoglobin knock-in genes replacing the endogenous mouse genes and may be useful in studying sickle cell disease.All mice were genotyped, age- and sex-matched littermates. All HbAA (control, normal human hemoglobin) vs HbSS (sickle cell disease, mutated human hemoglobin) mice were used for experimentations at 6-8 weeks of age, to limit intra-group heterogeneity. Hemin (Ferriprotoporphyrin IX) was purchased from Frontiers Scientific and injected intravenously (iv.) in a retroorbital sinus at a concentration of 24 µmol/kg. Control mice received PBS instead. Mice were anesthetized with isoflurane 2-3% for injections, blood collection and sacrifice. All mice were sacrificed by cervical dislocation, 4 hours after injection. The results of heart (indicated coeur) from HbAA mice injected or not with heme are presented here .  The results of heart (indicated coeur) from HbSS mice injected or not with heme can be found at number 10.5281/zenodo.10964159 Thirty μm-thick frozen tissue sections of kidneys were cut as above and homogenized in 200μL of 1-Thioglycerol/Homogenization Solution (Maxwell® 16 LEV simplyRNA Tissue Kit Promega AS1280). The quality and quantity of mRNA were evaluated using a 2100 bioanalyzer with TNA 6000 NanoKits (all Agilent Technologies, Palo Alto, CA, USA). RNA Integrity Numbers superior to 7 were eligible for subsequent reverse transcription into cDNA. RNAseq was performed at the GenomIC plateform Cochin Institute INSERM U1016. After RNA extraction, RNA quality (RNA integrity number) was estimated. 1μg of high-quality total RNA sample (RIN &gt;7) was processed to build up the libraries, using TruSeq Stranded mRNA kit (Illumina) according to manufacturer instructions. Briefly, purified poly-A containing mRNA molecules were fragmented and reverse-transcribed using random primers. Replacement of dTTP by dUTP during second strand synthesis allowed us to achieve strand specificity. Addition of a single A base to the cDNA was followed by ligation of Illumina adapters. Libraries were quantified by qPCR using KAPA Library Quantification Kits for Illumina Libraries (KapaBiosystems, Wilmington, MA). Library profiles were assessed using DNA High Sensitivity LabChip kits on an Agilent Bioanalyzer. Libraries were sequenced on an Illumina Nextseq 500 instrument using 75 base-lengths read V2 chemistry in a paired-end mode. After sequencing, primary analysis based on AOZAN software (ENS, Paris), was applied to demultiplex and control the quality of the raw data (based of FastQC modules / version 0.11.5). The dataset here represents 4 groups of mice, 4 mice per group as follows: HbAA PBS, HbAA heme, HbSS PBS, HbSS heme.

Data: A Marine Group A isolate relies on other growing bacteria for cell wall formation

<div> Raw data underlying Microscopic pictures for the paper "A Marine Group A isolate relies on other growing bacteria for cell wall formation". Data is subdivided by each figure in the paper.  </div>