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21,150 results for “tumor”
Data of FigS7, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of FigS7, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_FigS7.PNG). The Corresponding raw data and subsequent data analysis obtained contains one file in txt-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_1_M .txt) and one file as csv-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_12_1.csv).</p>
Dataset supporting the paper: Deciphering Oxygen Distribution and Hypoxia Profiles in the Tumor Microenvironment: A Data-Driven Mechanistic Modeling Approach
<p>The necessary image files for the paper titled "Deciphering Oxygen Distribution and Hypoxia Profiles in the Tumor Microenvironment: A Data-Driven Mechanistic Modeling Approach"</p>
The expression level of microRNA in invasive and nonivasive gonadotrph pituitary tumors
<p>The data include the normalized read counts from smallRNA sequencing of 20 RNA samples from gonadotroph pituitary tumors<br>The quality of small RNA fractions was assessed using Agilent 2100 Bioanalyzer with Small RNA Kit Chip (Agilent) and measured with Qubit RNA HS Assay Kits (Thermo Fisher Scientific). One μg of total RNA was used for sequencing library construction with an Ion Total RNA-Seq Kit v2 (Thermo Fisher Scientific), according to the manufacturer’s protocol. Ion Xpress™ RNA-Seq Barcode Kit was used for hybridization and ligation of RNA adapters that allows for multiplexed sequencing. RNA reverse transcription and subsequent cDNA purification and library size selection were performed using Nucleic Acid Binding Beads. cDNA was PCR-amplified, followed by DNA purification and size selection. The amount and size distribution of the amplified DNA was determined using Bioanalyzer 2100 using a High Sensitivity DNA Kit (Agilent). The length of miRNA ligation products in barcoded libraries ranged between 94 and114 bp. Template preparation for clonal amplification of up to four<br>miRNA libraries at a concentration of 18pM and loading of the PI chip were performed using Ion Chef Instrument, with Ion PI™ Hi-Q™ Chef Kit (Thermo Fisher Scientific). Ion Proton Sequencer (Thermo Fisher Scientific) was used for sequencing. Unmapped bam files were converted into fastq files with a bamToFastq script from bedtools. Read mapping to known human miRNAs (according to miRBase v.22) and reads quantification were performed using miRDeep2.14. Data normalization was performed using DESeq2. </p>
Robust estimation of cancer and immune cell-type proportions from bulk tumor ATAC-Seq data.
<p>Bulk ATAC-seq data of tumour samples result in an averaged signal across different cell-types (cancer, stromal, vascular and immune cells). We propose a deconvolution framework called EPIC-ATAC (<a href="https://doi.org/10.7554/eLife.94833.1">https://doi.org/10.7554/eLife.94833.1</a>), which relies on newly identified cell-type specific ATAC-Seq marker peaks and reference profiles for all major cancer-relevant cell-types to predict the proportions of each cell-type.</p> <p>To evaluate EPIC-ATAC, we generated a bulk ATAC-Seq dataset from peripheral blood mononuclear cells (PBMCs) samples, from which the number of cells in each cell-type has been estimated using flow cytometry, as ground truth for cell proportions. The data provided in this Zenodo deposit correspond to:</p> <p>- The raw counts matrix for each peak called in this ATAC-Seq dataset: PBMC_counts.txt</p> <p>- The normalized (TPM-like) counts matrix for each peak called in this ATAC-Seq dataset: PBMC_counts_norm.txt</p> <p>- The cell fractions of each cell type in each sample: PBMC_cell_fractions.txt</p> <p>- The peaks called in each sample using MACS2 (*narrow.peaks): *_normalized.narrowPeak</p> <p>- Bed files listing ATAC-Seq fragments for each sample: *.bed</p> <p>We also evaluated EPIC-ATAC on multiple pseudobulks generated from single-cell ATAC-Seq data. We provide rds files containing the pseudobulks data used in our work for the evaluation of EPIC-ATAC. The rds files are located in the zip file "pseudobulks.zip".</p> <p>The file "additional_data.zip" contains additional files used to generate the reference profiles in EPIC-ATAC and to reproduce the main analyses performed in the manuscript: <a href="https://doi.org/10.7554/eLife.94833.1">https://doi.org/10.7554/eLife.94833.1</a>. These files are required to run the code available on the following GitHub repository: GfellerLab/EPIC-ATAC_manuscript. </p>
Simulated tumor and healthy sample
<p>This dataset provides tumor and corresponding healthy sample from a simulation of somatic indels and SNVs using Venters genome. The reference genome (UCSC hg18) used for CRAM compression is delivered as well. Simulation was done as described here: <a href="https://doi.org/10.1101/741256">https://doi.org/10.1101/741256</a>.</p>
Common biochemical and topological properties of metabolic genes recurrently dysregulated in tumors
<p>Although tumors exhibit numerous metabolic alterations, it’s unclear if common objectives and constraints underlie diverse metabolic changes. Here we interpret cancer gene expression, copy number variation, and survival data using a computational model, MetOncoFit. MetOncoFit evaluates142 metabolic features that can impact tumor fitness, including enzyme catalytic activity, pathway association, network topological attributes, and reaction flux. Meta-analysis of tumor databases using MetOncoFit revealed that metabolic enzymes with high catalytic activity were frequently up-regulated in many tumors and associated with poor survival. MetOncoFit also identified metabolites that were hot-spots of dysregulation. MetOncoFit illuminates how enzyme activity and metabolic network architecture influences tumorigenesis.</p>
Conventional therapy induces tumor immunoediting and modulates the immune contexture in colorectal cancer
<p>Cancer immunotherapies for patients with colorectal cancer (CRC) continue to lag behind other solid cancer types with the exception of 4% of patients with microsatellite-instable tumors. Thus, there is an urgent need to broaden the clinical benefit of checkpoint blockers to CRC by combining conventional therapies to sensitize tumors to immunotherapy. However, the impact of conventional drugs on immunoediting and hence, imposing positive selection towards less immunogenic variants, and on the tumor immune contexture in CRC remains elusive.</p> <p>In this study, we performed comprehensive multimodal profiling using longitudinal samples from metastatic CRC patients undergoing neoadjuvant therapy with mFOLFOX6 and Bevacizumab. Exome-sequencing, RNA-sequencing and multiplexed immunofluorescence imaging was carried out on tumor samples obtained before and after therapy and the data was analyzed using established methods. The results of the analysis were extrapolated to publicly available datasets (TCGA and CPTAC). In order to identify a surrogate marker, an explainable artificial intelligence method was developed using a transformer-based analytical pipeline for the identification of features in H&E images associated with specific biological processes, followed by manual evaluation of highly informative tiles by a pathologist.</p> <p>We expect that the results of this project will provide a deeper understanding of the tumor-immune interactions and will allow the development of more robust combinatorial therapeutic strategies for MSS CRC.</p>
A molecular taxonomy of tumors independent of tissue-of-origin
<p>This tarball contains the pre-processed data in .Rda files required to compile our manuscript entitled "A molecular taxonomy of tumors independent of tissue-of-origin"</p>
Escape from NK cell tumor surveillance by NGFR-induced lipid remodeling in melanoma
<p>Metabolomics Data used in the publication</p> <p>1) raw files obtained by the FGCZ (<a href="https://fgcz.ch/">Functional Genomics Center Zurich</a>):</p> <p>QCpools (technical replicate of all sample pooled: p2947_o5292_SOP3_DDA5_pos_QCpool_cells</p> <p>Sample of M010817 CMVTOEV cells not induced: p2947_o5292_SOP3_DDA5_pos_EV_cells_noninduced_sample</p> <p>Sample of M010817 CMVTOEV cells induced: p2947_o5292_SOP3_DDA5_pos_EV_cells_induced_sample</p> <p>Sample of M010817 CMVTONGFR cells not induced: p2947_o5292_SOP3_DDA5_pos_p75_cells_noninduced_sample</p> <p>Sample of M010817 CMVTONGFR cells induced: p2947_o5292_SOP3_DDA5_pos_p75_cells_induced_sample</p> <p>2) method description LC-MS</p> <p>LC-MS-based lipidomic analysis was performed with M010817 CMVTOEV and CMVTONGFR cells pretreated with 1 μg/ml doxycycline for 24 h. Cells were detached with PBS 2 mM EDTA, washed and resuspended in 1-butanol/methanol (1:1). Cells were vortexed for 15 sec and subsequently sonicated on ice with a pulse of 3x 10 sec and 1x 30 sec at an amplitude of 15% using a SONOPULS HD 2070 Ultrasonic Homogenizer (Bandelin). Cell extracts were centrifuged at 16’000 g, 20°C for 10 min to remove macromolecules and subsequently diluted (1:3) with water/methanol (1:2) solution. The dilution was vortexed and centrifuged (16,000 x g, 20 °C, 10 min). 100 μl of the supernatant was transferred to a glass vial with narrowed bottom (Total Recovery Vials, Waters) for LC-MS injection. <br> Lipids were separated on a nanoAcquity UPLC (Waters) equipped with a HSS T3 capillary column (150 μm x30mm, 1.8 μm particle size, Waters), applying a gradient of 5 mM ammonium acetate in water/acetonitrile 95:5 (A) and 5 mM ammonium acetate in isopropanol/acetonitrile 90:10 (B) from 5% B to 100% B over 10 min. The following 5 min conditions were kept at 100% B, followed by 5 min reequilibration to 5% B. The injection volume was 1 μL. The flow rate was constant at 2.5 μl/min. The UPLC was coupled to QExactive mass spectrometer (Thermo) by a nanoESI source. MS data was acquired using positive polarization and data-dependent acquisition (DDA). Full scan MS spectra were acquired in profile mode from 80-1200 m/z with an automatic gain control target of 1e6, an Orbitrap resolution of 70`000, and a maximum injection time of 200 ms. The 5 most intense charged (z = +1 or +2) precursor ions from each full scan were selected for collision induced dissociation fragmentation. Precursor was accumulated with an isolation window of 0.4 Da, an automatic gain control value of 5e4, a resolution of 17`500, a maximum injection time of 50 ms and fragmented with a normalized collision energy of 20, 30 and 40 (arbitrary unit). Generated fragment ions were scanned in the linear trap. Minimal signal intensity for MS2 selection was set to 500.</p> <p> </p> <p>3) csv result files were generated by the FGCZ (<a href="https://fgcz.ch/">Functional Genomics Center Zurich</a>)</p> <p>adducts found: adducts_p2947_o5292_new_10K_20210910</p> <p>identifications found: identifications_p2947_o5292_new_10K_20210910</p> <p>abundances of features: measurements_p2947_o5292_new_10K_20210910</p> <p>Metaboanalyst file statistics: Metabo_p2947_o5292_10k_cells_CVcleaned_20210916</p> <p>Metaboanalyst file enrichment: Metabo_Enrichment_p2947_o5292_10k_cells_20210910 _p75</p> <p>4) method description data analysis</p> <p>Data sets were evaluated with Progenesis QI software (Nonlinear Dynamics), which aligns the ion intensity maps based on a reference data set, followed by a peak picking on an aggregated ion intensity map. Detected ions were identified based on accurate mass, detected adduct patterns and isotope patterns by comparing with entries in the LipidMaps Data Base (LM) and KEGG database. Considered adducts were M+H, M+NH4, 2M+H M+H-H2O. A mass accuracy tolerance of 5 ppm was set for the searches. Fragmentation patterns were considered for 600 the identifications of metabolites. Putative identifications were further ranked based on Mass error (observed mass – exact mass), isotope similarity (observed versus theoretical).</p> <p> </p>
Dataset for "Implementing a Functional Precision Medicine Tumor Board for Acute Myeloid Leukemia"
<p><strong>Article: Implementing a Functional Precision Medicine Tumor Board for Acute Myeloid Leukemia</strong></p> <p><em>Cancer Discovery</em>, <strong>DOI:</strong> 10.1158/2159-8290.CD-21-0410</p> <p> </p> <p>Data Types:</p> <p>1. Clinical summary</p> <p>2. Drug response data</p> <p>3. Exome-sequencing data</p> <p>4. RNA-sequencing data</p> <p> </p> <p><strong>Updates:</strong></p> <p>- <strong>FILE</strong>: File_3.2. <strong>DATE</strong>: 28.11.2022.</p> <p> </p> <p><strong>1. Clinical summary</strong></p> <p><strong>File_0: </strong>Common sample annotation including patient and sample IDs, stage of the disease, tissue type and availability of different data types.</p> <p><strong>File_1.1: </strong>Clinical data for 186 AML patients including clinical diagnosis, disease classification, gender, age at diagnosis, treatments, cytogenetic and molecular details. The description of the variables/column titles is given below the clinical data.</p> <p><strong>File_1.2</strong>: Description of the clinical variables in File_1.1.</p> <p> </p> <p><strong>2. Drug response data for 164 AML patient samples and 17 healthy samples</strong></p> <p><strong>File_2: </strong>Drug library details for 515 chemical compounds. The compound collection includes drugs names, drug class defined by molecular targets or mode of action, concentration range used for drug testing, supplier information, solvent information and vendor information.</p> <p><strong>File_3.1.: </strong>Drug response data including selective drug sensitivity scores (sDSS) for 515 compounds across 181 samples (164 AML patient samples and 17 healthy control samples). The DSS is modified area under the curve values and are calculated as shown in Yadav et al publication (1). The selective drug sensitivity scores (sDSS) is healthy control normalized DSS that gives estimated cancer-selective drug responses. The higher the sDSS values indicate drug sensitivities and negative sDSS values represent drug resistance.</p> <p><strong>File_3.2.: </strong>Drug response data including drug sensitivity scores (DSS) and selective drug sensitivity scores (sDSS) for 515 compounds across 181 samples (164 AML patient samples and 17 healthy control samples). The data is identical to the Supplementary Table 7 in the manuscript.</p> <p><em>Note: We recommend using selective DSS values instead of raw values (% inhibition, IC50, DSS). </em></p> <p><em>Note: If the value is missing, </em><em>the drug was not tested for </em><em>that</em><em> given sample</em><em>.</em></p> <p><strong>File_4: </strong>Drug sensitivity and resistance testing (DSRT) assay details for 181 samples (164 AML patient samples and 17 healthy control samples). The information includes medium (MCM or CM) used for the drug testing, % cell viability after 72 h without drug testing and blast cell percentage of each sample.</p> <p><em>Note: Column E is </em><em>the ratio of luminescence values at 72 h and 0 h. The fold change in the cell viability without drug treatment was calculated as % cell viability. That is why the value could be more than 100% e.g. 70% cell viability meaning that 30% cells died during 72 h and 300% cell viability meaning that cells grew 3 times in 72 h incubation period.</em></p> <p> </p> <p><strong>3. Exome-sequencing data for 225 AML patient samples</strong></p> <p><em>Note: The number of samples in the manuscript is 226. The correct number used in the analyses is 225.</em></p> <p>Mutation data. The cancer specific gene list was prepared by combining AML related genes from TCGA(2) (n=23), InToGen(3) (n=32), Papaemmanuil et al.(4) (n=111) and Census database(5) (n=616). Out of these genes, we found 340 genes as mutated across 225 AML patient samples. The mutation was called with P-values less than 0.05.</p> <p><strong>File_5: </strong>VAF (variant allele frequency) of 340 cancer-specific genes across 225 AML patient samples. The VAF was calculated using paired skin samples as a control from the same AML patient.</p> <p><strong>File_6:</strong> Binary data for 57 cancer specific genes frequently mutated (a given mutation detected in 5 or more samples) across 225 AML patient samples.</p> <p> </p> <p><strong>4. RNA-sequencing data for 163 AML patient samples and 4 healthy</strong></p> <p>CPM (count per million) data: The CPM values are batch corrected values used for direct comparison of gene expression.</p> <p><strong>File_7:</strong> Log2CPM values for 18,202 protein coding genes across 167 samples (163 AML patient samples and 4 healthy CD34+ samples).</p> <p><strong>File_8: </strong>Raw read count data RNA-seq library information for all 60,619 genes across 167 samples (163 AML patient samples and 4 healthy CD34+ samples). The raw read count data was used to calculate differential gene expression.</p> <p><strong>File_9: </strong>RNA-seq library information including RNA extraction method and sequencing library preparation information for 167 samples (163 AML patient samples and 4 healthy CD34+ samples).</p> <p> </p> <p><strong>References</strong></p> <p>1. Yadav B, Pemovska T, Szwajda A, Kulesskiy E, Kontro M, Karjalainen R<em>, et al.</em> Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies. Scientific Reports <strong>2014</strong>;4:5193.</p> <p>2. Ley TJ, Miller C, Ding L, Raphael BJ, Mungall AJ, Robertson A<em>, et al.</em> Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med <strong>2013</strong>;368(22):2059-74.</p> <p>3. Gonzalez-Perez A, Perez-Llamas C, Deu-Pons J, Tamborero D, Schroeder MP, Jene-Sanz A<em>, et al.</em> IntOGen-mutations identifies cancer drivers across tumor types. Nature Methods <strong>2013</strong>;10(11):1081-2.</p> <p>4. Papaemmanuil E, Gerstung M, Bullinger L, Gaidzik VI, Paschka P, Roberts ND<em>, et al.</em> Genomic classification and prognosis in acute myeloid leukemia. New England Journal of Medicine <strong>2016</strong>;374(23):2209-21.</p> <p>5. Tate JG, Bamford S, Jubb HC, Sondka Z, Beare DM, Bindal N<em>, et al.</em> COSMIC: the Catalogue Of Somatic Mutations In Cancer. Nucleic Acids Research <strong>2019</strong>;47(D1):D941-D7.</p> <p> </p>
Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces (Figures)
<p>High resolution figures related to the below manuscript:</p> <p>Atul Deshpande, Melanie Loth, et al., <a href="https://doi.org/10.1101/2022.06.02.490672">Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces</a>. <em>bioRxiv</em> 2022. doi:10.1101/2022.06.02.490672</p>
Tumor MHC Class I Expression Associates with Interleukin-2 Response in Melanoma
<p>The processed multiplexed IF data and sample ID corresponding to the raw data in record (https://zenodo.org/record/4300912#.Y-WGBuzMKY-). All code used to produce the results of this study are available at <a href="https://github.com/cBio-MSKCC/Halo_Melanoma_IL2">https://github.com/cBio-MSKCC/Halo_Melanoma_IL2</a>.</p>
Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors
<p>Data required to reproduce the results/figures of the "<strong>Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors</strong>" project. </p>
Longitudinal observational study of pediatric patients with primary brain tumors: establishment of a hospital-based registry.
<p>Although tumors of the central nervous system (CNS) represent 2 % of all malignancies in general, they cause a disproportionately large morbidity and mortality and are the second most common form of cancer in children and the major solid tumor in childhood in the U.S., occurring in 21.3% of all children with malignant disease. The treatment of brain tumors in children and adolescents has evolved significantly in recent decades. Nowadays, most children with a diagnosis of brain tumor are treated properly and achieve prolonged survival. In order to obtain an overview of the impact of brain tumors, specialized registries, which provide information on all types of brain tumors, have emerged in several countries. Following on the pioneering Japanese and American experiences of specialized national records of brain tumors, other specialized registries were opened in European countries. This project aims to initiate a registry of the epidemiological profile of patients treated for CNS tumors in the Pediatric Cancer Center (CPC) of our hospital from January 2000 to December 2013, at diagnosis and during follow-up, updating information periodically. This data will be recorded in an electronic database capable of storing, retrieving and presenting information of interest. Prospectively recorded epidemiological data of patients diagnosed from January 2014 will be accrued, maintaining the database active to continuously record information on patients with CNS tumors treated in the CPC HIAS. Thus, creating a hospital registry of pediatric patients with CNS tumors. To this end, an instrument of data collection will be created using Google Apps (Google Inc., 2014), a digital platform with capacity for storage, creation and editing of documents and collaboration in real time over the cloud.</p>
CyclomicsSeq: Accurate detection of circulating tumor DNA using nanopore consensus sequencing
<p>CyclomicsSeq is a protocol designed to produce and sequence long DNA concatemers with a linear repetition to acquire high accuracy consensus reads. In this dataset, we used CyclomicsSeq for sequencing TP53 in cell-free DNA of healthy individuals and of head and neck cancer patients and for sequencing synthetic TP53 DNA sequences that mimic the length of cell-free DNA. This dataset contains data (mainly base calls of the backbone and the insert) of 32 nanopore sequencing runs. <br> </p>
Lactate Increases Stemness of CD8+ T Cells to Augment Anti-Tumor Immunity
<p>The immunological role of lactate in antitumor immunity is not well understood. In this study, we report lactate treatment significantly augments antitumor efficacy of immune checkpoint blockade or T cell vaccine therapy in multiple tumor models. Single cell transcriptomics and flow cytometry analysis revealed an increased subpopulation of stem-like TCF-1-expressing CD8<sup>+</sup> T cells upon lactate treatment.</p>
Immunofluorescence staining of a human kidney (#1, tumor area) obtained by MELC
<p>19 marker MELC run in a human peri-tumor kidney sample (#1). Each image shows the same field of view, sequentially stained with the depicted fluorescence labelled antibodies. Images contain 2024 x 2024 pixels and are generated using an inverted wide field fluorescence microscope with a 20 x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Scale bar 100 µm.</p> <p>All raw images were aligned based on the reference phase contrast image taken at the beginning of measurement. Each fluorescence MELC image was processed by background subtraction and illumination correction, based on bleaching images. Then an “Extended Depth of Field” algorithm was applied to the 3D fluorescence stack in each cycle. Images were at last normalized in ImageJ, where a rolling ball algorithm was used for background estimation, edges were removed (accounting for the maximum allowed shift during the autofocus procedure) and fluorescence intensities were stretched to the full intensity range (16 bit -> 2<sup>16</sup>).</p>
Integrative spatial omics reveals distinct tumor-promoting multicellular niches and immunosuppressive mechanisms in African American and European American patients with TNBC (Spatial Transcriptomic 10X Visium portion)
<p>Racial disparities in triple-negative breast cancer (TNBC) outcomes have been reported. However, the biological mechanisms underlying these disparities remain unclear. We integrated imaging mass cytometry and spatial transcriptomics, to characterize the tumor microenvironment (TME) of African American (AA) and European American (EA) patients with TNBC. The TME in AA patients was characterized by interactions between endothelial cells, macrophages, and mesenchymal-like cells, which were associated with poor patient survival. In contrast, the EA TNBC-associated niche is enriched in T-cells and neutrophils suggestive of an exhaustion and suppression of otherwise active T cell responses. Ligand-receptor and pathway analyses of race-associated niches found AA TNBC to be “immune cold” and hence immunotherapy resistant tumors, and EA TNBC as ‘inflamed’ tumors that evolved a distinctive immunosuppressive mechanism. Our study revealed the presence of racially distinct tumor-promoting and immunosuppressive microenvironments in AA and EA patients with TNBC, which may explain the poor clinical outcomes.</p> <p> </p> <p>This dataset contains the 10X Visium Spatial Transcriptomic data of TNBC patients. There are two cohorts.</p> <p> </p> <p><strong>Baylor Scott and White (BSW) cohort</strong>: <strong>10x.visium.tar.gz</strong>, containing 10 patients with TNBC from Baylor Scott and White affiliated Hospital. </p> <p>Each sample is made of Space Ranger processed spot-separated gene expression data (processed to HDF5 AnnData file). There are also H&E images, and spot coordinate files available. </p> <p> </p> <p>For <strong>Georgia validation cohort</strong>, 400 genes used for validation of ESG signatures (associated with BA-Community 1 and WA-Community-1) were obtained and provided by Ritu Aneja's lab. These 400 genes' spot-based expression data across Black and White TNBC patients are provided. See file <strong>georgia.validation.visium.tar.gz</strong>. Expression was normalized by total counts per spot, followed by log-normalization by Giotto.</p> <p> </p> <p>As well in our paper, we integrated a published racial TNBC cohort for deriving some of initial results in the paper. This refers to the Bassiouni et al (Cancer Research) paper in Carpten's group. <strong>GSM_giotto_processed.tar.gz</strong> refers to this dataset, which we deposit here. The data were normalized by Giotto using standard procedure.</p>
Data for methylome sequencing: Enriching and Profiling Methylomes for Tumor Classification and Liquid Biopsies
<p>We benchmarked and demonstrated the versatility of FLEXseq (Fragment Ligation EXclusive methylation sequencing) across different sample types: genomic DNA from the K562 (leukemia) cell line, DNA mix-in titrations of four immune cell types (B cells, T cells, monocytes, and neutrophils), DNA titrations of three cancer cell lines (breast invasive carcinoma [BRCA], colon adenocarcinoma [COAD], and glioblastoma [GBM]) mixed with those four immune cell mixtures separately, input titrations of cell-free (cf) DNA from one plasma sample and DNA from formalin-fixed paraffin-embedded (FFPE) tissues, cfDNA from 106 cerebrospinal fluids (CSF) and 42 other body fluids, and DNA from 37 FFPE tissues.</p> <p>We sequenced all the samples mentioned above using FLEXseq. Paired-end reads were quality and length trimmed with cutadapt version 3.5, and all high-quality sequencing reads were then aligned to the hg38 reference genome using Bismark v0.23.0. We then filtered out reads with unmethylated cytosine in the non-CpG context with filter_non_conversion function. Next, we used the bismark_methylation_extractor function to extract the methylation calls (removing single-nucleotide polymorphisms [SNP]).</p> <p>We also used the bam2pat function from wgbs_tools, to convert bam files into .pat files for deconvolution, keeping reads covering at least three CpG sites. The .pat files preserve fragment-level data and were de-identified by removing SNPs using the mask_pat function. </p> <p>We used CNVkit (v0.9.10) to analyze and visualize genome-wide copy numbers. Our inputs into CNVkit were Bismark/Bowtie 2 aligned BAM files deduplicated by Bismark based on end positions and fragment lengths. We then generated log2copy ratio plots for all body fluid and FFPE samples based on the pooled reference and visualized them across all bins using the DNAcopy R package.</p> <p> </p>
Single-cell datasets for cell cycle plasticity underlies fractional resistance to palbociclib in ER+/HER2- breast tumor cells
<p>There are 7 files uploaded in the data.</p><p>tumor_preprocessed.h5ad: Full primary tumor dataset post-feature selection and standardization across three treatment conditions (0, 10, and 100 nM palbociclib). AnnData object format. 14 cell cycle features, phase labels and other cell metadata, and two PHATE dimensions for manifold visualization.</p><p>T47D_preprocssed.h5ad: Full dataset of main text T47D dataset post-feature selection and standardization across three treatment conditions (0, 10, and 100 nM palbociclib). AnnData object format. 14 cell cycle features, phase labels and other cell metadata, and two PHATE dimensions for manifold visualization.</p><p>sketched_integrated.h5ad: After downsample 6,000 (2,000 per condition) from T47D_preprocessed and tumor_preprocessed, we integrate the two datasets into one joint latent space using TRANSACT. Now included in the data are the consensus component columns ('0',..,'13'). AnnData object.</p><p>sketched_integrated_df.csv: sketched_integrated.h5ad in .csv format.</p><p>T47D_replicate_preprocessed: Replicate experimental dataset of T47D for supplementary analysis post-feature selection and standardization across three treatment conditions (0, 10, and 100 nM palbociclib). 15 cell cycle features (same 14 but with CDK6).</p><p>sketched_rep.h5ad: Representative downsample of the T47D_replicate_preprocessed. Selecting 6,000 cells (2,000 for each of the three treatment conditions) using kernel herding sketching. AnnData object.</p><p>sketched_rep_df.csv: Same data as sketched_rep.h5ad in csv format.</p><p>T47D_triplicate_preprocessed.h5ad: T47D biological replicate sample collected in triplicate form (three wells for 0, 10, and 100 nM of palbociclib). Wells were joined and the data were sketched down to 20,000 per condition.</p><p>T47D_triplicate_preprocessed.h5ad: T47D triplicate in .csv form.</p><p>tumor_2_preprocessed.h5ad: An additional tumor sample from a new patient with the same treatment conditions of palbociclib. Sketched down to 2,000 cells per condition.</p><p>tumor_2_preprocessed.csv: The additional tumor sample in .csv form.</p><p> </p><p>Further description of sketched_integrated: This is the joint dataset between the T47D and primary tumor, after subsampling using kernel herding sketching. This is a dataset consisting of T47D and primary tumor cells resected from a consented patient. The samples were imaged using iterative indirect immunofluorescent imaging (4i) to get proteomic measurements on a single-cell level. The T47D and tumor samples were gathered, cultured, and imaged separately. Each sample was treated with three conditions of CDK4/6 inhibitor palbociclib (control, 10 nM, and 100 nM). Then, we used kernel sketching to representatively downsample each dataset, selecting 2,000 from each of the three treatment conditions (6,000 cells from each of the two sources). We used an integration method called TRANSACT to integrate the two datasets into one shared, latent space. The dataset here is consisting of these 12,000 cells. The columns ('0','1',...'13') are the principal vectors of the joint latent space. After that, there are the columns of the standardized proteomic measurements of different cell cycle effectors, and biological annotations of interest. The standardization is done for each data source separately. Well refers to the treatment condition. 'prb_ratio' is a marker of if a cell is still proliferating or arrested, found by selecting the upper modality of pRB/RB values. 'phase' are cell cycle phase labels found by unsupervised clustering done on a handful of known cell cycle markers.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.