Find research datasets worth reusing

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-4.0Aug 2024View details →

Data from: Molecular landscapes of glioblastoma cell lines revealed a group of patients that do not benefit from WWOX tumor suppressor expression

Supporting data for the article "Molecular landscapes of glioblastoma cell lines revealed a group of patients that do not benefit from WWOX tumor suppressor expression", published in Frontiers in Neuroscience (DOI: 10.3389/fnins.2023.1260409).

opencc-by-4.0Nov 2024View details →

DT-LEMBAS cell line specific models

This contains 33 ensembles of 50 DT-LEMBAS models each. The models were trained on 33 cell line transcriptomic data from the L1000 dataset.

opencc-by-4.0Dec 2020View details →

BRG1 shRNA knockdown in ALCL cell lines

To determine the consequences of Brg1 expression in ALCL, cells were transfected with shRNA to Brg1 leading to a decrease in Brg1 transcript and protein levels. RNA was isolated 72 hours later from the shRNA5 (TRCN0000015551) expressing cells and sequencing conducted to examine changes to the transcriptome mediated by Brg1. Following differential gene expression analysis of the scrambled shRNA versus shRNA5, the transcriptome was identified as being dramatically altered showing both downregulation (2,527 mRNA transcripts: of a cumulative 26,833 genes, adj. p ≤ 0.05) and upregulation (2,900 mRNA transcripts, of a cumulative 26,833 genes, adj. p ≤ 0.05) of genes. When analysing the the 15,285 protein-coding genes alone, 1,934 (13%) were downregulated while 2,797 (18%) were upregulated (adj. p ≤ 0.05). To establish which physiological processes are most affected by the activity of BRG1, pathway enrichment analyses were undertaken. The input comprised of the significant (adj. p ≤ 0.05) protein-coding targets, where the absolute log2 fold change of expression was ± 1.5. Transcripts whose expression was either induced (n=33) or repressed (n=243) by BRG1 were investigated. Reactome analysis suggests that BRG1 plays roles in upregulating genes associated with the cell cycle, while suppressing genes associated with epigenetic regulation.

opencc-by-4.0May 2021View details →

Annotated quantitative phase microscopy cell dataset of various adherent cell lines for segmentation purposes

This dataset contains microscopic images of multiple cell lines captured by quantitative phase microscopy (QPI) without use of any fluorescent labeling and a manually annotated ground truth for subsequent use in segmentation algorithms. Dataset also includes images reconstructed according to the methods described below in order to ease further segmentation.  Our data consist of quantitative phase microscopy images: <ul> <li>244 labelled images of PC-3 (7,907 cells), 205 labelled PNT1A (9,288 cells), 25 labelled images of G361, 25 labelled images of A2050 and 25 labelled images of HOB cells , in the paper designated as "labelled", and</li> <li>1,819 unlabelled images with a mixture of 22Rv1, A2058, A2780, DU145, Fadu, G361, HOB and LNCaP used for pretraining, in the paper designated as "unlabelled".</li> </ul> <ul> <li>See Vicar et al. XXXX 2021  DOI XXX (TBA after publishing)</li> <li>Code using this dataset is available at <a href="https://github.com/tomasvicar/Deep-QPI-Cell-Segmentation">github.com/tomasvicar/Deep-QPI-Cell-Segmentation</a></li> </ul> Materials and methods  A set of adherent cell lines of various origins, tumorigenic potential, and morphology were used in this paper (PC-3, PNT1A, 22Rv1, DU145, LNCaP, A2058, A2780, Fadu, G361, HOB). PC-3, PNT1A, 22Rv1, DU145, LNCaP, A2780, and G361 cell lines were cultured in RPMI-1640 medium, A2058, FaDu, and HOB cell lines were cultured in DMEM-F12 medium, all supplemented with antibiotics (penicillin 100 U/ml and streptomycin 0.1 mg/ml), and with 10% fetal bovine serum (FBS). Prior to microscopy acquisition, the cells were maintained at 37 °C in a humidified (60%) incubator with 5%  CO\textsubscript{2} (Sanyo, Japan). For acquisition purposes, the cells were cultivated in the Flow chamber µ-Slide I Luer Family (Ibidi, Martinsried, Germany). To maintain standard cultivation conditions during time-lapse experiments, cells were placed in the gas chamber H201 - for Mad City Labs Z100/Z500 piezo Z-stage (Okolab, Ottaviano NA, Italy). For the acquisition of QPI, a coherence-controlled holographic  microscope (Telight,  Q-Phase) was used. Objective Nikon Plan 10×/0.3 was used for hologram acquisition with a CCD camera (XIMEA MR4021MC). Holographic data were numerically reconstructed with the Fourier transform method (described in Slaby, 2013 and phase unwrapping was used on the phase image. QPI datasets used in this paper were acquired during various experimental setups and treatments. In most cases, experiments were conducted with the time-lapse acquisition. The final dataset contains images acquired at least three hours apart. Folder structure and file and filename description labelled (labelled): cells with segmentation labels, e.g. 00001_PC3_img.tif - 32bit tiff image (in pg/um2 values) 00001_PC3_mask.png - 8bit image with mask with unique grayscale value corresponding to single cell in FOV. unlabelled (unlabelled): 11 varying cell lines, total 1819 FOVs,  32bit tiff image (in pg/um2 values)

opencc-zeroAug 2021View details →

STonKGs Cell Line Model

The fine-tuned model trained on cell line annotations.

opencc-by-4.0Apr 2024View details →

A computational workflow for cell line profiling by Imaging Mass Cytometry.

Imaging Mass Cytometry Data as 32-bit single TIFF with computational analysis from the manuscript:  A computational workflow for cell line profiling by Imaging Mass Cytometry. Breast cancer cell lines SKBR3 MCF7 HCC1143 IMC data and CellProfiler pipelines.zip Elongated cell lines HeLa SKOV3 BJ IMC data and CellProfiler pipelines.zip: Small cell lines A431 HT29 BxPC3 IMC data and CellProfiler pipelines.zip U937 PMA-differentiated cells IMC data and CellProfiler pipeline.zip A431 Cisplatin Study IMC data and CellProfiler pipeline.zip Contains 1 folder per cell line or drug treatment of single TIFF 32-bit markers exported from MCD/txt files (including Xe131 channel) and their respective cpproj. pipeline file for IMC Cell Line Profiler workstream reproducible analysis IMC Cell Line Profiler high dimensional and correlation analysis R scripts.zip Contains three adaptable R scripts for high dimensional analysis, correlation analysis and combination of both scripts for Machine Learning classified datasets. Breast cancer cell lines nuclear state classification by CellProfiler Analyst MLs.zip Contains SQLite databases, properties files, training datasets, nuclear classes visual rendering, and classifier model files with outputs for two machine learning classifiers (Random Forest and Fast Gentle Boosting) per breast cancer cell line for CellProfiler Analyst workflow reproducibility. A431 Cisplatin Study IMC data nuclear state classification by CellProfiler Analyst MLs.zip Contains SQLite databases, properties files, training datasets, classifier model with outputs for Fast Gentle Boosting and Random Forest per treatment for CellProfiler Analyst workflow reproducibility. IMC Cell Line Profiler pseudo-color images with Ki-67 marker Cytoplasm marker and Cell-ID nuclei (Fig2 Fig3), visual nuclei and whole-cell segmentation contours rendered images (Fig4). Non-compensated and compensated multiTIFF 32-bit cells lines with Cellprofiler masks SCE objects and FCS files and Datatables.zip Contains publicly available compensation matrix (<a href="https://zenodo.org/records/7575859">https://zenodo.org/records/7575859</a>) , R compensation script (Compensation IMC data with CATALYST.R), compensated and non-compensated multiTIFF stacks 32-bit per cell line experiment, exported CellProfiler 16-bit masks per cell line dataset, R single cell experiment script (Conversion IMC data to Single Cell Experiments Objects and FCS.R) with inputs and outputs (fcs files, sce files, panel files, metadata files),R conversion single cell experiment to datatable script (Conversion SCE to Datatable and analysis.R). Step-by-step guide to assist users with the IMC Cell Line Profiler computational workflow.

opencc-by-4.0May 2019View details →

CRISPR knockout of EZH1 in AML cell line

We wanted to ensure the specificity of the EZH1 antibody that I was working with in previous posts, as well as creating a useful reagent to use in future experiments by generating a CRISPR knockout line of EZH1.

opencc-by-4.0Jun 2023View details →

Long read isoforms with A-to-I edits for H1975 cell line

FLAIR2 was used to determine isoform models sequenced from H1975 cells using R2C2 cDNA nanopore sequencing. RNA sequence variants observed in the data were called with longshot and integrated into FLAIR2 isoform models.

opencc-by-4.0Jul 2023View details →

Drug-Cell Line-Gene heterogeneous graph from NCI60

Dataset for "Attention-Guided Gene Importance Assessment in Drug-Cell-Gene Heterogeneous Network for Drug Response Deciphering".  # information - drug_cell_gene.csv.gz: Adjacency matrix of drugs, cell lines, and genes from NCI60.  - train.csv: training data. Contains drug name and cell line name - val.csv: validation data. Contains drug name and cell line name - test.csv: test data. Contains drug name and cell line name - train_values.npy: train value. Contains drug response values (Z-score) - val_values.npy: validation value. Contains drug response values (Z-score) - test_values.npy: test value. Contains drug response values (Z-score)

opencc-by-4.0Aug 2023View details →

Transcriptomic profiles of MCF7-derived tamoxifen resistant cell lines

We report mRNA profiles of human breast cancer cell lines, MCF7 parental, and MCF7-derived tamoxifen-resistant cell lines MCF7-TR1 and MCF7-TR2. These cells have been previously described in  Ines Barone, Lauren Brusco, Guowei Gu, Jennifer Selever, Amanda Beyer, Kyle R. Covington, Anna Tsimelzon, Tao Wang, Susan G. Hilsenbeck, Gary C. Chamness, Sebastiano Andò, and Suzanne A.W. Fuqua. Loss of Rho GDIα and Resistance to Tamoxifen via Effects on Estrogen Receptor α. J Natl Cancer Inst. 2011 Apr 6; 103(7): 538–552. PMID: 21447808

opencc-by-4.0Oct 2023View details →

Functional anaysis of miR-143-3p/KSR2 interaction and oncogenic function in JURKAT and ALL-SIL T-cell acute lymphoblastic leukemia cell lines

1. FCS files from GFP competition assay performed in ALL-SIL and JURKAT cell lines upon transduction with hsa-mir-143 expression vector (pCDH miR-143-3p) or empty vector (pCDH EV) as control.2. Uncropped chemiluminescent immunoblot in JURKAT and ALL-SIL cell lines transduced with hsa-mir-143 expression vector (pCDH miR-143-3p) or empty vector (pCDH EV) as control. Upper band is KSR2 protein (~100 kDa) and lower band is loading control GAPDH protein (~37 kDa). Order of samples on the membrane: JURKAT pCDH miR-143-3p replicate 1, pCDH EV replicate 1, pCDH EV replicate 2, pCDH miR-143-3p replicate 2, pCDH EV replicate 3, pCDH miR-143-3p replicate 3, ALL-SIL pCDH miR-143-3p replicate 1, pCDH miR-143-3p replicate 2, pCDH EV replicate 1, pCDH miR-143-3p replicate 3, pCDH EV replicate 2, pCDH EV replicate 3.3. RT-qPCR amplification data for relative quantification of KSR2 expression in reference to ACTB and GAPDH in JURKAT and ALL-SIL cell lines expressing deadCas9-KRAB system for transcriptional repression, upon transduction with sgRNA targeting KSR2 transcription start site vector (KSR2 sgRNA1 and KSR2 sgRNA2) or non-targeting sgRNA vector (NT) as control.4. FCS files from GFP competition assay performed in ALL-SIL and JURKAT cell lines expressing deadCas9-KRAB system for transcriptional repression, upon transduction with sgRNA targeting KSR2 transcription start site vector (KSR2 sgRNA1 and KSR2 sgRNA2) or non-targeting sgRNA vector (NT) as control.

controlledIPD-YESFeb 2026View details →

Pembrolizumab (MK-3475) as First-line Therapy for Advanced Merkel Cell Carcinoma (MK-3475-913)

ClinicalTrials.gov study NCT03783078. IPD Sharing: YES. Countries: 8. Publications: 1.

Nivolumab in Combination With Ipilimumab (Part 1); Nivolumab Plus Ipilimumab in Combination With Chemotherapy (Part 2) as First Line Therapy in Stage IV Non-Small Cell Lung Cancer

ClinicalTrials.gov study NCT02659059. IPD Sharing: Not stated. Countries: 2. Publications: 1.

Study of Front Line Therapy With Nivolumab and Salvage Nivolumab + Ipilimumab in Patients With Advanced Renal Cell Carcinoma

ClinicalTrials.gov study NCT03117309. IPD Sharing: Not stated. Countries: 1. Publications: 2.

Vaccine Therapy in Patients With Stages IIIB/IV Non-Small Cell Lung Cancer Who Have Finished First-Line Chemotherapy

ClinicalTrials.gov study NCT00534209. IPD Sharing: Not stated. Countries: 1. Publications: 2.

controlledIPD-YESFeb 2026View details →

A Study of Carboplatin-Paclitaxel/Nab-Paclitaxel Chemotherapy With or Without Pembrolizumab (MK-3475) in Adults With First Line Metastatic Squamous Non-small Cell Lung Cancer (MK-3475-407/KEYNOTE-407)

ClinicalTrials.gov study NCT03875092. IPD Sharing: YES. Countries: 1. Publications: 2.

First-line Treatment of Participants With Stage IV Squamous Non-Small Cell Lung Cancer With Necitumumab and Gemcitabine-Cisplatin

ClinicalTrials.gov study NCT00981058. IPD Sharing: Not stated. Countries: 26. Publications: 4.

Cilengitide and Cetuximab in Combination With Platinum-based Chemotherapy as First-line Treatment for Subjects With Advanced Non Small Cell Lung Cancer (NSCLC)

ClinicalTrials.gov study NCT00842712. IPD Sharing: Not stated. Countries: 8. Publications: 1.