Find research datasets worth reusing

opencc-by-4.0Aug 2024View details →

Genome Sizes of Bacterial Species Detected in Cell-Free DNA of Patients with Acute Leukemia and Sepsis, Including Those Undergoing Bone Marrow Transplantation

Next Generation Sequencing (NGS) analysis of Cell-Free DNA provides valuable insights into a spectrum of pathogenic species (particularly bacterial) in blood. Patients with Sepsis often face problems like delays in treatment regimens (combination or cocktail of antibiotics) due to the long turnaround time (TAT) of classical and standard blood culture procedures. NGS gives results with lower TAT along with high-depth coverage. The use of NGS may be a possible solution to deciding treatment regimens for patients without losing precious time and more accurately possibly saving lives. Our curated dataset is of bacterial species or strains detected along with their genome size in 107 AML patients diagnosed with Sepsis clinically. Cell-free DNA profiles of patients were built and sequencing was done in Illumina (NovaSeq and NextSeq). Bioinformatic analysis was performed using two classification algorithms namely kraken2 and kaiju. For kraken2  based classification reference bacterial index developed by Carlo Ferravante et al (Zenodo 2020)  (link: https://zenodo.org/records/4055180) was used, while for kaiju-based classification reference database named "nr_euk" dated "2023-05-10" (link: https://bioinformatics-centre.github.io/kaiju/downloads.html) was used. Genome size annotation is important in metagenomics since for the use of depth of coverage (abundance), genome size is required. In metagenomic classification algorithms like kraken/kraken2 and kaiju output computes reads assigned only and not abundance. In kaiju, the problem is more complicated since the reference database does not have a fasta file but only an index file from which alignment is done.  To address the above challenges to compute "depth of coverage" or simply abundance, we build a Genome size annotator tool (https://github.com/patkarlab/Genome-Size-Annotation) which provides genome size for each species detected given its taxid is available. In this tool, the NCBI Datasets tool, NCBI Genome API check tool, and Data Mining from AI search engines like perplexity.ai are used.  We have curated two datasets Kraken2 dataset named "FINAL METAGENOMIC DATA MASTERSHEET - kraken_genome_annotation" Kaiju dataset named "FINAL METAGENOMIC DATA MASTERSHEET - kaiju_genome_annotation" *Please note that for kraken2 curated dataset, we used data mining from the AI search engine perplexity.ai while for kaiju we did not use perplexity, ai, and any species whose genome size was not found was labeled "NA"

openother-openJul 2021View details →

MSVermet/Cell-Nuclei-Detection-And-Segmentation-CENet-UNet: Cell nuclei detection and segmentation

Fully automated nuclei detection and segmentation with CE-Net and U-Net on the PanNuke dataset. Repository includes code and dataset.

opencc-by-4.0Sep 2022View details →

Additional files of scTensor paper "scTensor detects many-to-many cell-cell interactions from single cell RNA-sequencing data"

Complex biological systems are described as a multitude of cell-cell interactions (CCIs). Recent single-cell RNA-sequencing studies focus on CCIs based on ligand-receptor (L-R) gene co-expression. However, the analytical methods are still not mature; such methods cannot detect CCIs and the related L-R pairs simultaneously or also are not appropriate to detect many-to-many CCIs. In this work, we propose scTensor, a novel method for extracting representative triadic relationships (or hypergraphs), which include ligand-expression, receptor-expression, and related L-R pairs. Through extensive studies with simulated and empirical datasets, we have shown that scTensor could detect some hypergraphs, which cannot be detected by conventional methods, especially when those CCIs are many-to-many relationships.

opencc-by-4.0Jun 2023View details →

Datasets underlying the paper Zero-mode waveguide nanowells for single-molecule detection in living cells

Different datasets underlying the paper Zero-mode waveguide nanowells for single-molecule detection in living cells. The repository contains .zip archives, mostly containing a readme file with additional information. <pre>Cell imaging experiments.zip contains the raw image files acquired on arrays of version 1 or version 2 using a Nikon TI inverted microscope and used in figures 4-6. </pre> Gla_0127_14.zip contains SEM images of the fabrication of arrays of version 1 Gla_29_Pd_1.zip contains SEM images of the fabrication of arrays of version 2 SM experiments.zip contains the raw single-molecule fluorescence data acquired on an array of version 1 using a PicoQuant Microtime microscope together with the analysis files. FDTD simulations.zip contains the simulation files for the use in the software Lumerical

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Evaluating somatic cell count, the California mastitis test, and infrared thermography for subclinical mastitis detection in meat ewes

Open the record for dataset details and reuse information.

publicJul 2025View details →

dryad36/100

The cell adhesion molecule Sdk1 shapes assembly of a retinal circuit that detects localized edges

Nearly 50 different mouse retinal ganglion cell (RGC) types sample the visual scene for distinct features. RGC feature selectivity arises from its synapses with a specific subset of amacrine (AC) and bipolar cell (BC) types, but how RGC dendrites arborize and collect input from these specific subsets remains poorly understood. Here we examine the hypothesis that RGCs employ molecular recognition systems to meet this challenge. By combining calcium imaging and type-specific histological stains we define a family of circuits that express the recognition molecule Sidekick 1 (Sdk1) which include a novel RGC type (S1-RGC) that responds to local edges. Genetic and physiological studies revealed that Sdk1 loss selectively disrupts S1-RGC visual responses which result from a loss of excitatory and inhibitory inputs and selective dendritic deficits on this neuron. We conclude that Sdk1 shapes dendrite growth and wiring to help S1-RGCs become feature selective.

opencc-zeroJun 2021View details →

opencc-by-4.0Dec 2023View details →

Datasets associated with the manuscript "Differential detection workflows for multi-sample single-cell RNA-seq data"

In this Zenodo repository, we share the data that is required to reproduce all the analyses from our publication "Differential detection workflows for multi-sample single-cell RNA-seq data". This repository includes all* input data, intermediate results and final outputs that are represented in our manuscript. For a more elaborate description of the data, we refer to the companion GitHub. https://github.com/statOmics/DD_benchmarks for the benchmarks and https://github.com/statOmics/DD_cases for the case studies, respectively.

opencc-by-4.0Dec 2023View details →

Multimodal Epigenetic Sequencing Analysis (MESA) of Cell-free DNA for Non-invasive Colorectal Cancer Detection

Processed data (feature-by-sample matrices) of non-disruptive bisulfite-free methylation sequencing for cfDNA samples from 4 clinical cohorts (Cohort 1, Cohort 2, Cohort 3, and cfTAPS dataset). Codes used to repeat the results in our paper can be found https://rpubs.com/LiYumei/926228 and https://github.com/ChaorongC/MESA.

opencc-by-4.0Dec 2023View details →

Dataset and Code for Manuscript "Cell sorting based on pulse shapes from angle resolved detection of scattered light"

Dataset and code for the cell cycle analysis and cluster selection for sorting: <ul> <li>ReadMe file with explanations of the data set and analysis</li> <li>Python scripts for converting the data and to reproduce the sort cluster selection</li> <li>binary data files containing pulse shapes and wavelet transform coefficients</li> <li>FSC data files containing the respective common flow cytometry parameters</li> <li>text files with event indices that represent the gating</li> </ul>

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Data from: ESCRT-III-dependent adhesive and mechanical changes are triggered by a mechanism detecting alteration of Septate Junction integrity in Drosophila epithelial cells

Barrier functions of proliferative epithelia are constantly challenged by mechanical and chemical constraints. How epithelia respond to and cope with disturbances of barrier functions to allow tissue integrity maintenance is poorly characterized. Cellular junctions play an important role in this process and intracellular traffic contribute to their homeostasis. Here, we reveal that, in Drosophila pupal notum, alteration of the bi- or tricellular septate junctions (SJs) triggers a mechanism with two prominent outcomes. On one hand, there is an increase in the levels of E-cadherin, F-Actin and non-muscle Myosin II in the plane of adherens junctions. On the other hand, β-integrin/Vinculin-positive cell contacts are reinforced along the lateral and basal membranes. We found that the weakening of SJ integrity, caused by the depletion of bi- or tricellular SJ components, alters ESCRT-III/Vps32/Shrub distribution, reduces degradation, and instead favours recycling of SJ components, an effect that extends to other recycled transmembrane protein cargoes including Crumbs, its effector β-Heavy Spectrin Karst, and β-integrin. We propose a mechanism by which epithelial cells, upon sensing alterations of the septate junction, reroute the function of Shrub to adjust the balance of degradation/recycling of junctional cargoes and thereby compensate for barrier junction defects to maintain epithelial integrity.

opencc-zeroMar 2024View details →

opencc-by-4.0Mar 2022View details →

TestDataset for Oneat networks for detection and prediction of dividing Hela cells for different imaging modalities

Here we present the tif files to be used to test the Oneat networks for detection and prediction of division events for Hela cells imaged under different imaging modalities and presented originally here: https://zenodo.org/record/6139958#.Yjcjl3rMJD8

opencc-by-4.0Apr 2024View details →

Fig. 7 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Fig. 7 Confusion matrix of predictions by YOLOv4 models. YOLO,You Only Look Once (model)

opencc-by-4.0Aug 2024View details →

Benchmarking Illumina RNA-seq fusion transcript detection methods - cancer cell lines RNA-seq

Cancer cell line RNA-seq data (reads or names of reads from CCLE data) used for benchmarking Illumina-based fusion detection methods as used in: Haas, B.J., Dobin, A., Li, B. et al. Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods. Genome Biol 20, 213 (2019). https://doi.org/10.1186/s13059-019-1842-9   For CCLE data, direct sharing of fastq files was not possible.  CCLE data must be obtained from:      https://portals.broadinstitute.org/ccle/home Instead, the identifiers for the reads leveraged as part of our study are made available, and these reads can be extracted from the CCLE fastq files directly once obtained from the primary source. For the non-CCLE data, the exact reads leveraged by our study are made directly available here in fastq format.

opencc-by-4.0Oct 2021View details →

Trained network for cell-cycle slowdown detection - DetecDiv (id03)

<pre>Trained network for cell-cycle slowdown detection. Related to the dataset: <a href="https://doi.org/10.5281/zenodo.5553796">doi.org/10.5281/zenodo.5553796</a></pre> ------------------------------------------ Author(s): Théo, ASPERT Contact email: theo.aspert@gmail.com Affiliation: IGBMC, Université de Strasbourg Funding bodies: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02.

dryad36/100

A longitudinal study of DNA and RNA viruses plasma detection in allogeneic hematopoietic stem cell transplant recipients

Background: Viral infections are among the most common complications after allogeneic hematopoietic stem cell transplantation (allo-HSCT) and can be associated with transient or sustained viremia. Besides viruses that are common causes of infection, metagenomics revealed the presence of several novel viruses and variants that are overlooked in clinical routine and represent potential sources of unrecognized systemic infections. Our aim was to describe the prevalence and the dynamics of 17 DNA and 3 RNA viral infections using (r(RT-)PCR) assays on plasma samples of adult allo-HSCT recipients over a one-year period after HSCT. Methods: 109 adult patients that received a first allo-HSCT from 1st March 2017 to 31st January 2019 we included in this longitudinal observational monocentric cohort study. 17 DNA and 3 RNA viral species were screened with qualitative and/or quantitative r(RT)-PCR assays performed on plasma samples collected at five time-points (day 0 and 30 days, 3 months, 6 months and one year after HSCT). Results: TTV was the most prevalent with an increasing prevalence to 96% of patients at 3 months. HPgV-1 prevalence ranged from 26 to 36% of patients. TTV and HPgV-1 plasma viral load peaked at month 3 (TTV: median 3.29E5 copies/ml [range, 3.37E2 to 4.06E9 copies/ml]; HPgV-1: median 1.18E6 copies/ml [range, 2.61E3 to 4.49E7 copies/ml]). Among Polyomaviridae, BKPyV, JCPyV, MCPyV, HPyV6 and 7 were detected in ≥10% of patients at ≥1 time-point. HPyV6 and HPyV7 prevalence reached 27% and 12% of patients at month 3. Among those, 41% and 63% had quantifiable viral loads, with median viral loads above 1E3copies/ml and results may suggest HPyV6 sustained viremia. Co-detections were frequent, in particular at 3 months with ≥2 viruses detected in 72% of patients. Conclusion: Our study confirms that TTV and HPgV-1 infections are highly prevalent and that infection may be sustained up to one year after allo-HSCT. Our systematic and large strategy of screening also revealed diverse and numerous co-detections, and that several novel Polyomaviridae (MCPyV, HPyV6/7) that are overlooked in clinical routine are as or more frequently detected compared to classical culprits. Our results underscores the need for further studies investigating the clinical impact of classical culprits together with other viruses in particular novel Polyomaviridae and HPgV-1.

opencc-zeroJan 2023View details →

restrictedIPD-UNDECIDEDFeb 2026View details →

The Detection Of Circulating Tumor Cells (CTC) In Patients With NSCLC Undergoing Definitive Radiotherapy Or Chemoradiotherapy

ClinicalTrials.gov study NCT02135679. IPD Sharing: Not stated. Countries: 1. Publications: 1.

controlledIPD-YESFeb 2026View details →

Evaluation of Low-cost Techniques for Detecting Sickle Cell Disease and β-thalassemia in Nepal and Canada

ClinicalTrials.gov study NCT05506358. IPD Sharing: YES. Countries: 2. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

A Comparison of the RPS Adeno Detector IV to Viral Cell Culture at Detecting Adenoviral Conjunctivitis

ClinicalTrials.gov study NCT00921895. IPD Sharing: Not stated. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →

Study for the Prediction of Active Rejection in Organs Using Donor-derived Cell-free DNA Detection

ClinicalTrials.gov study NCT03984747. IPD Sharing: NO. Countries: 1. Publications: 1.