11743 results for "cancer rna-seq"

in 135.83 milliseconds.

• Transcriptomic Profiling of Bulk Tissue and Laser-Capture Microdissected Neurons of Postmortem Human Brains in the Superior Temporal Gyrus

  We performed RNA-Seq on bulk tissue and laser-capture microdissected (LCM) neurons from 59 postmortem human brains in the superior temporal gyrus (STG) to investigate Autism Spectrum Disorder (ASD)-associated transcriptomic changes. We examined post-mortem brain tissue from 27 subjects with ASD and 32 neuro-typical controls. Raw RNA-Seq data in FASTQ format are provided.

  Study phs003208

• RNA-seq derived from metastatic Castration-Resistant Prostate Cancer

  Patients with progressive, metastatic castration-resistant prostate cancer (mCRPC) underwent metastatic tumor biopsy. 118 total samples including 14 paired samples (baseline and later progression). Various organ sites including soft tissue & bone were present. Published in DOI: 10.1200/JCO.2017.77.6880 Journal of Clinical Oncology 36, no. 24 (August 20, 2018) 2492-2503.

  Dataset EGAD00001008991

• Single-cell RNA-seq profiling of patient derived organoids

  The aim of this study was to profile developement of resistance to cancer treatments using organoids. Two patient derived colorectal cancer organoids were profiled using single cell RNAseq after treatment with a combination of different anticancer drugs. The organoids were profiled both after 4 weeks of treatment and after a drug holiday of 2 weeks.

  Study EGAS50000001025

• Precise reconstruction of the tumor microenvironment using bulk RNA-seq and a unique machine learning-based algorithm trained on artificial transcriptomes

  Cellular deconvolution algorithms virtually reconstruct tissue composition by analyzing the gene expression of complex tissues. Here, we present the decision tree machine learning algorithm, Kassandra, trained on a broad collection of > 9,400 tissue and blood sorted cell RNA profiles to accurately reconstruct the tumor microenvironment (TME). Bioinformatic correction for technical and biological variability, aberrant cancer cell expression inclusion, and the accurate quantification and normalization of transcript expression increased the stability and robustness of Kassandra. Performance was validated on over 4,000 H&E tissue slides and more than 1,000 normal and tumor tissues by comparison with cytometric, immunohistochemical or single-cell RNA-seq measurements. Kassandra accurately deconvolved stromal and immune elements of blood and tumors, revealing the role of the TME in tumor pathogenesis. Digital TME reconstruction revealed that the presence of PD1-positive CD8+ T cells strongly correlated with immunotherapy response and increased the predictive potential of established biomarkers, indicating that Kassandra could potentially be utilized in future clinical applications.

  Dataset EGAD00001008776

• Transcriptomic Characterization of Human Innate T Cells

  We performed low input RNA-seq and single-cell RNA-seq on 7 lymphocyte populations with the objective of finding the common transcriptional programs shared among innate T cells. In brief, we drew blood from healthy donors and isolated CD4+ T cells, CD8+ T cells, Natural Killer cells, invariant natural killer T (iNKT) cells, mucosal-associated invariant T (MAIT) cells, and two γδ T cell populations: Vδ1 and Vδ2. We performed low input RNA-seq on samples with 1000 cells each, for 6 healthy individuals, in duplicate. We performed single-cell RNA-seq with DNA-barcoded hashing antibodies to identify the cell type of origin for each cell. We found a common transcriptional program across lymphocytes that is gradually turned on as lymphocytes have a more "innateness" state, promoting effector functions and sacrificing proliferative capacity. Additionally, we performed low input RNA-seq for Vδ3-expressing γδ T cells and δ/αβ T cells for one healthy individual in duplicate (1000 cells per sample). This revealed δ/αβ T cells are transcriptionally more adaptive-like and Vδ3 more innate-like.

  Study phs002007

• Epigenomic dataset of Human Hepatocellular Carcinoma for EpiHK

  This dataset is generated for EpiHK. It contains Histone PTM ChIP-seq, WGBS, RNA-seq of Human Hepatocellular Carcinoma and tumor adjacent normal tissue sample.

  Study EGAS50000000039

• Melanoma Brain Metastasis Single-Cell RNA Sequencing Atlas

  Melanoma brain metastasis (MBM) frequently occurs in patients with advanced melanoma, yet our understanding of the underlying salient biology is rudimentary. Here, we performed single-cell/nucleus RNA-seq in 22 treatment-naive MBM (scRNA-seq n=5; snRNA-seq n=17) and 10 extracranial melanoma metastases (ECM; all snRNA-seq), and matched spatial single-cell transcriptomics (n=16) and T cell receptor (TCR)-seq (n=5). Key observations were validated in matched bulk RNA- and ATAC-seq of isogenic MBM/ECM cell culture models (n=24; including triplicates (labeled as *-A, -B and -C) of 4 MBM- and 4 ECM-derived patient cell lines with 2 matched pairs). This work provides comprehensive insights into MBM biology and serves as a foundational resource for further discovery and therapeutic exploration.

  Study phs002944

• CCA RNA-seq data (60 CCA, 6 normal, 16 cell-line)

  Fastq files for RNA-seq for 60 CCAs, 6 normal bile duct tissues, 14 CCA cell-lines (including replicates), and 2 normal cholangiocyte cell-lines (including replicates). RNA was extracted using the Qiagen RNeasy Mini kit. Illumina Tru-Seq Stranded Total RNA kit (Illumina, San Diego, California, USA) was used to prepare RNA libraries from 1 µg of total RNA. Paired-end 150 bp sequencing was performed using Illumina HiSeq4000 sequencer with the paired-end 150 bp read option.

  Dataset EGAD00001011998

• Asan Medical Center Data Access Committee

  This DAC is responsible for reviewing and approving data access requests to protect the sensitive information of patients. The dataset includes RNA-seq data from CRLM patients treated with FOLFOX, bevacizumab, and atezolizumab, and supports research in gene expression analysis and therapeutic response. Data access requests require an approved ethical research plan, limited to projects related to cancer biology and therapy development.

  Dac EGAC50000000439

• RNA-seq Phase Ib of olaparib and capivasertib

  RNA-seq Phase Ib of olaparib and capivasertib

  Dataset EGAD00001006840

• RNA-seq from melanoma biopsies

  RNA-seq data for melanoma biopsies at baseline and after treatment

  Dataset EGAD00001009059

• RNA-seq from normal human tissues

  RNA-seq from normal human tissues (2 x 75 bp)

  Dataset EGAD00001001057

• RNA-seq from normal human tissues

  RNA-seq from normal human tissues (2 x 250 bp)

  Dataset EGAD00001001922

• RNA-seq

  Bulk RNA-seq data of 8 RNA samples were generated. Libraries were prepared using the Stranded mRNA Library Prep, Ligation Kit (Illumina) following manufacturer’s recommendations and sequenced on a NextSeq 2000 (2x50 bp, Illumina).

  Dataset EGAD00001010308

• Analysis of Treg gene expression patterns by RNA-Seq in patients with rheumatoid arthritis (RA) and healthy controls (HCs) : Comparison between young and elderly individuals

  We compared the gene expression of CD4+CD45RA-CD25hiCD127lo Treg cells from PBMCs between young and elderly RA patients and HCs by RNA-seq

  Study JGAS000693

• Whole genome sequencing, SNP array and RNA-seq of uveal melanomas

  We conducted whole genome sequencing and DNA SNP array of 12 uveal melanoma genomes and their matched DNA from blood. We also conducted RNA-seq of the 12 tumour samples.

  Study EGAS00001000472

• RNA-seq data from hypothalamic tissue from individuals with Prader-Willi syndrome and age-matched controls.

  RNA-seq data (bam files) from the hypothalamus of 4 individuals with Prader-Willi syndrome and 4 age-matched control individuals. Detailed information about the study design, case-control matching and RNA-seq data processing is provided in the accompanying publication [Bochukova et al (2018) Cell Reports].

  Dataset EGAD00001004034

• Alpha synucleinopathy spectrum - bulk RNA seq of prefrontal cortex

  Dataset corresponding to the bulk RNA seq dataset from prefrontal cortex for a total of N=44 samples. Tissue samples correspond to patients with different alpha synucleinopathies. Specifically, idiopathic Parkinson's disease (PD, N=20) monogenic PD caused by LRRK2 mutations (LRRK2-PD, N=7), multiple system atrophy (MSA, N=6) and neurologically healthy controls (N=11). RNA seq was carried out using ribosomal depletion. The totality of these samples were also sequenced using single nucleus RNA seq, and available within the same study as a separate dataset.

  Dataset EGAD50000000430

• Alpha synucleinopathy spectrum - single nucleus RNA seq of prefrontal cortex

  Single nucleus RNA seq dataset from prefrontal cortex of a total of N=46 individuals with alpha synucleinopathies and healthy controls. Tissue samples correspond to patients with Parkinson's disease (PD, N=20) monogenic PD caused by LRRK2 mutations (LRRK2-PD, N=7), multiple system atrophy (MSA, N=6) and neurologically healthy controls (N=13). RNA seq was carried out employing 10X Genomics Chromium. These samples were also sequenced using conventional bulk tissue RNA seq, and available within the same study as a separate dataset.

  Dataset EGAD50000000431

• RNA seq reads

  RNA seq reads constituting of FASTQ paired end reads from 5 FHD/FHDL patients

  Dataset EGAD00001003790

• Paired-end RNA-seq analysis of GBM, additional patient.

  Dataset contains one paired-end RNA-seq sample.

  Dataset EGAD00001011990

• WGS, RNA-seq and methyl-seq data for multiple tumour clones from a single glioblastoma case.

  10 bams of WGS data from HiSeqXTen platform; 10 bams of RNA-seq data from HiSeq2500 platform; 7 bams of TruSeq Methyl Capture EPIC sequencing data from HiSeq4000 platform

  Dataset EGAD00001004773

• Integrated diagnosis based on transcriptome analysis in suspected pediatric sarcomas

  We performed RNA-sequencing in 47 patients with suspected pediatric sarcomas, to evaluate the potential utility of RNA-seq in the diagnostic process of pediatric sarcoma.

  Study JGAS000284

• Whole-genome landscape of adult T-cell leukemia/lymphoma

  Whole-genome sequencing (WGS) data of tumor-normal pairs from 139 ATL patients and RNA sequencing (RNA-seq) data of tumors from 28 ATL patients.

  Study EGAS00001005237

• RNA-seq FASTQ files studied in Generation of hypoimmunogenic induced pluripotent stem cells by CRISPR-Cas9 system and detailed evaluation for clinical application

  This data set contains 12 paired fastq files (RNA-seq) from one wild-type iPS cell, two HLA-KO iPS clones and three CD14+ monocytes differentiated from them. The RNA-seq libraries were generated with Illumina TruSeq Stranded Total RNA library prep kit. The libraries were sequenced with Illumina NovaSeq 6000 in paired-end mode.

  Dataset EGAD50000001700

• RNA-seq data of proliferative vitreoretinal diseases and healthy human retinal pigment epithelium

  This dataset contains RNA-seq data generated from surgical specimens of proliferative vitreoretinal diseases (including proliferative vitreoretinopathy and epiretinal membranes) and from retinal pigment epithelial cells isolated from post-mortem donor eyes. Total RNA was extracted and cDNA libraries were prepared using the Ovation Solo RNA-seq Kit (Tecan). Sequencing was performed on the Illumina NovaSeq 6000 platform. Raw FASTQ files are included to enable downstream analyses such as gene expression profiling.

  Dataset EGAD50000001780

• mapped Bam files from whole transcriptome RNA-seq

  In this study we characterized genomic alterations in two to five metachronous bladder tumors from 29 patients initially diagnosed with early stage disease. Fourteen patients (32 tumors) had non progressive disease (NPD) and 15 patients (34 tumors) had progressive disease (PD). Whole exome sequencing (WES, ~50x mean read depth and whole transcriptome RNA-seq was performed (RNA was not advalible for 4 tumors). Data provided here consist of 71 mapped Bam files form whole transcriptome RNA-seq.

  Dataset EGAD00001002718

• unmapped Bam files from whole transcriptome RNA-seq

  In this study we characterized genomic alterations in two to five metachronous bladder tumors from 29 patients initially diagnosed with early stage disease. Fourteen patients (32 tumors) had non progressive disease (NPD) and 15 patients (34 tumors) had progressive disease (PD). Whole exome sequencing (WES, ~50x mean read depth and whole transcriptome RNA-seq was performed (RNA was not advalible for 4 tumors). Data provided here consist of 71 unmapped Bam files form whole transcriptome RNA-seq.

  Dataset EGAD00001002717

• Diagnostic utility of whole genome sequencing in adults with B-other acute lymphoblastic leukemia - RNA

  Genomic profiling at diagnosis of B-cell precursor Acute Lymphoblastic Leukemia (BCP-ALL) in adults is used to guide disease classification, risk stratification and treatment decisions. Patients for which diagnostic screening fails to identify disease defining or risk stratifying lesions are classified as B-other ALL. We screened a cohort of 652 BCP-ALL cases enrolled in UKALL14 to identify and perform whole genome sequencing (WGS) on paired tumor-normal samples. For 52 B-other patients we compared WGS findings to data from clinical and research cytogenetics. WGS identifies a cancer associated event in 51/52 cases, this includes an established subtype defining genetic alteration in 5/52 that were previously missed by standard-of-care genetics. Of the 47 true B-other ALL we identified a recurrent driver in 87% (41).  Complex karyotype by cytogenetics emerges as a heterogeneous group, underlied by distinct genetic alterations associated with either favorable (DUX4-r) or poor outcomes (MEF2D-r, IGK::BCL2). For a subset of 31 cases, we integrate findings from RNA-sequencing (RNA-seq) analysis to include fusion gene detection, and classification by gene expression. Compared to RNA-seq, WGS was sufficient to detect and resolve recurrent genetic subtypes, however RNA-seq can provide orthogonal validation of findings. In conclusion, we demonstrate that WGS can identify clinically relevant genetic abnormalities missed by standard-of-care testing and identify leukemia driver events in virtually all cases of B-other ALL.

  Dataset EGAD00001009305

• IPF Core Biopsy Study RNA-Seq Fastqs

  RNA-Seq fastq files for 97 Idiopathic Pulmonary Fibrosis samples.

  Dataset EGAD00001007568

• Bulk RNA-seq osteosarcoma

  We profiled 23 patient osteosarcoma tumor samples by bulk RNA-seq. The raw fastqs are provided.

  Dataset EGAD00001011371

• RNA-Seq dataset

  10 RNA-Seq bam files including 4 cases with fusion and 6 controls without fusion.

  Dataset EGAD00001004360

• RNA-Seq and WXS from 6 glioblastoma patients

  RNA-Seq and WXS from 6 glioblastoma patients

  Dataset EGAD00001003293

• RNA-Seq files for SJOS study

  RNA-Seq files for SJOS study

  Dataset EGAD00001003154

• Single-cell RNA-seq data of angioimmunoblastic T-cell lymphoma

  Single-cell RNA-seq data of angioimmunoblastic T-cell lymphoma

  Dataset EGAD00001011361

• Ewings Sarcoma RNA-seq drug sensitivity

  Illumina RNA-Seq will be performed on four Ewing's sarcoma cell lines and two control cell lines. RNA was extracted from all the lines using a basic Trizol extraction protocol.

  Dataset EGAD00001000337

• All available datasets of DEEP

  All available datasets of DEEP. There are several Reference Epigenomes available. For metadata check out www.deep.dkfz.de One reference epigenome represents a set of 6 ChIP-Seqs, WGBS, RNA-Seq, smallRNA-Seq and sometimes DNase1 and/or NOME-Seq.

  Study EGAS00001001608

• StemNet - in vitro differentiation of human hepatocytes

  Integrative epigenomic and transcriptomic characterization of hepatocyte-like cells differentiated in vitro from human induced pluripotent stem cells in comparison with primary human hepatocytes. This study comprises single cell RNA-seq, bulk mRNA-seq, ATAC-seq and RRBS.

  Study EGAS00001004201

• Epigenetic, transcriptome and TF analysis of human NK cell and T cells

  RNA-seq 10 subsets; 5 donors. ATAC-seq 9 subsets, 4 donors; Histone modification profiling 10 subsets, 2 donors all using human NK cell and T cell subsets. TF ChIP-seq Bcl11b, Bach2, Runx2, Gata3, PLZF. Illuminia sequencing platform, ATAC-seq is Paired-end, RNA-seq/ChIP-seq is Single-end

  Dataset EGAD00001008449

• Extracellular RNA Profiling of Serum, Plasma, and Urine of Healthy Subjects

  This study was conducted to generate a reference dataset of extracellular RNA (exRNA) from standard human biofluids. Small RNA-seq data was obtained from plasma, serum, and urine of healthy subjects.

  Study phs003054

• Raw sequencing data from chromosome conformation capture, RNA sequencing and chromatin assays in human primary monocytes

  This dataset contains raw sequencing data from eQTL Capture Hi-C, ATAC-seq and RNA-seq experiments in monocytes isolated from 34 healthy male donors, as well as from Hi-C, 4C-seq and CTCF ChIP-seq in subsets of individuals. The data are released as paired-end Illumina FASTQ files. Hi-C and Capture Hi-C used DpnII digestion and Tn5-based low-input library preparation. In Capture Hi-C, this was followed by targeted capture with custom-designed biotinylated RNA probes targeting ~1100 eQTL regions. ATAC-seq was performed on PFA-fixed cells using a digitonin-based protocol. 4C-seq and CTCF ChIP-seq were performed using standard protocols.

  Dataset EGAD50000001116

• Human Epidermis scRNA

  Single cell RNA-seq analysis of human skin.

  Dataset EGAD00010001620

• Exome reads and RNA-seq

  Exome sequencing for 26 patients with matched blood RNA-seq for 41 patients

  Dataset EGAD00001002722

• GCLJ RNA-Seq data

  RNA-Seq data from 6 Giant Cell Lesions of the Jaw (GCLJ) samples.

  Dataset EGAD00001004320

• nccRCC RNA-Seq data of consented samples

  nccRCC RNA-Seq data of consented samples

  Dataset EGAD00001001022

• Single-cell RNA-seq of PBMC from 2 patients iGRAN-Low and 2 patients iGRAN-High

  Single-cell RNA-seq of PBMC from 2 patients iGRAN-Low and 2 patients iGRAN-High [Publication title] CXCL8 secreted by immature granulocytes inhibits wildtype hematopoiesis in chronic myelomonocytic leukemia

  Study EGAS50000000556

• Longitudinal RNA-seq (whole blood) in a twin cohort

  The dataset consists of 232 RNA-seq samples (whole blood) obtained from healthy female from the TwinsUK adult registry cohort. The samples were obtained at two time points separated on average by 22 months.

  Study EGAS00001001763

• RNA-seq data: Histone mutations in human Pre-Leukemic HSC and AML

  RNA-seq data from TEX cells transduced with HIST1H3H WT, HIST1H3H K27M, HIST1H3F WT, HIST1H3F K27I and Luc2 control in triplicate. In addition, RNA-Seq was performed on untransduced TEX cells. Libraries [rRNA-depleted stranded (HMR)] were sequenced on an Illumina Hiseq 4000 platform to generate 100 bp paired-end reads.

  Dataset EGAD00001004437

• Next-Generation Sequencing of RNA and DNA Isolated from Paired Fresh-Frozen and Formalin-Fixed Paraffin-Embedded Samples of Human Cancer and Normal Tissue

  Formalin-fixed, paraffin-embedded (FFPE) tissues are an invaluable resource for clinical research. However, nucleic acids extracted from FFPE tissues are fragmented and chemically modified making them challenging to use in molecular studies. We analysed 23 fresh-frozen (FF), 35 FFPE and 38 paired FF/FFPE specimens, representing six different human tissue types (bladder, prostate and colon carcinoma; liver and colon normal tissue; reactive tonsil) in order to examine the potential use of FFPE samples in next-generation sequencing (NGS) based retrospective and prospective clinical studies. Two methods for DNA and three methods for RNA extraction from FFPE tissues were compared and were found to affect nucleic acid quantity and quality. DNA and RNA from selected FFPE and paired FF/FFPE specimens were used for exome and transcriptome analysis. Preparations of DNA Exome-Seq libraries was more challenging (29.5 % success) than that of RNA-Seq libraries, presumably because of modifications to FFPE tissue-derived DNA. Libraries could still be prepared from RNA isolated from two-decade old FFPE tissues. Data were analysed using the CLC Bio Genomics Workbench and revealed systematic differences between FF and FFPE tissue-derived nucleic acid libraries. In spite of this, pairwise analysis of DNA Exome-Seq data showed concordance for 70-80 % of variants in FF and FFPE samples stored for fewer than three years. RNA-Seq data showed high correlation of expression profiles in FF/FFPE pairs (Pearson Correlations of 0.90 +/- 0.05), irrespective of storage time (up to 244 months) and tissue type. A common set of 1,494 genes was identified with expression profiles that were significantly different between paired FF and FFPE samples irrespective of tissue type. Our results are promising and suggest that NGS can be used to study FFPE specimens in both prospective and retrospective archive-based studies in which FF specimens are not available.

  Study EGAS00001000737

• RNA Seq OMELib

  The transcriptional landscape of haematopoietic cells will be characterized by RNA Seq following amplification of RNA/cDNA and preparation of libraries by PTA(primary template-directed amplification). Samples have been sourced from the Cambridge Biobank.

  Dataset EGAD00001015252

• The Finland-United States Investigation of NIDDM Genetics (FUSION) Study - Islet Expression and Regulation by RNAseq and ATACseq

  This study evaluates gene expression and its regulation in human pancreatic islets, a tissue relevant in the study of genetic risk factors contributing to diabetes. We obtained pancreatic tissue sections and purified pancreatic islets from deceased donors and processed these biological samples to generate various data types. We generated spatial transcriptomic data from pancreatic tissue samples using the 10X Genomics VISIUM technology with sequencing on the Illumina NovaSeq platform. We also generated multiple data types from the purified pancreatic islets using genome-wide SNP chips, bulk RNA-Seq, microRNA (miRNA)-Seq, whole genome sequence, DNA methylation (methyl)-Seq, cap analysis of gene expression (CAGE)-Seq, single cell RNA-seq, and single nuclei ATAC-seq approaches. These data include ATAC-seq of two islet subjects, RNA-seq of 31 additional subjects, genome-wide chip genotypes, and imputed genotypes of the 33 subjects released with phs001188.v1. For genotyping, 500-1000 islet equivalents (IEQ) were cultured as in Gershengorn (Science, 2004, PMID: 15564314); genomic DNA isolated from islet cultures. For RNA analyses, 2500-5000 IEQ from each islet source were used for bulk or single-cell RNA isolation. Messenger RNA was isolated with trizol extraction and 12-plex libraries were generated using the Illumina TruSeq directional mRNA-seq library protocol. Bulk RNA sequencing was performed on HiSeq2000/HiSeq2500/Hiseq4000/NovaSeq6000 sequencers using paired-end reads at the NIH Intramural Sequencing Center (NISC). miRNA libraries were prepared from total RNA from 68 samples, pooled and sequenced 50bp single-end reads on Illumina HiSeq2500. CAGE libraries were prepared from total RNA samples using the nAnT-iCAGE protocol at DNAFORM, Japan. CAGE libraries were sequenced at the NIH Intramural Sequencing Center (NISC) on the HiSeq2000 sequencer. Genotyping on the Illumina Omni2.5M array was performed at the NHGRI Genomics Core facility. Genotypes were imputed using the HRC.r1.1.2016 reference panel. To assess regions of open chromatin in islets, we performed bulk ATAC-seq on HiSeq2000 sequencers using paired-end reads at NISC. Single-nuclei ATAC-seq libraries were prepared using single-cell-combinatorial-indexing (sci-) ATAC-seq protocol and sequenced on Illumina NextSeq using paired-end reads. scRNA-seq libraries were generated using the 10X Genomics platform and sequenced on Illumina HiSeq3000 at the Genomics Technology Core of the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS). Greater than 90% of the loci associated with T2D through genome-wide association studies occur in non-coding regions, suggesting a strong regulatory component to disease susceptibility. Therefore, there is a critical need to understand the full spectrum of genetic variation and regulatory element usage in T2D-relevant tissues. To that end, this study contains multiple genomic and transcriptomic data sets from pancreas tissue sections and pancreatic islets, a resource that will contribute to our efforts to investigate the genetic risk factors for type 2 diabetes.

  Study phs001188

• RNA-Seq for academic use only

  RNA-Seq transcriptome data is only for academic use.

  Dataset EGAD00001009675

• Human adipocyte bulk RNA-seq dataset

  The dataset contains raw RNA-seq data of human adipocytes from 13 individuals.

  Dataset EGAD00001008335

• RNA-seq for IgA BMPC project

  7 RNA-seq samples in total: CD19n_IgAn (x2) , CD19n_IgAp (x2) , CD19p_IgAn (x1), CD19p_IgAp (x2)

  Dataset EGAD00001006862

• Diffuse Intrinsic Pontine Glioma

  This study contains DNA and RNA sequencing data for 5 DIPG patients. All patients have tumor DNA data, 4 have matched normal DNA data and 3 have tumor RNA seq data

  Study EGAS00001006353

• RNA-Seq data for paper titled 'Genomic Landscape of Pediatric Myelodysplastic Syndromes'

  When available (25 primary MDS, 12 MDS/MPN, and 6 AML-MRC cases), high quality RNA (stranded-total) was submitted for RNA-seq. RNA was extracted from bulk myeloid cells which was used as the tumor population. Files uploaded are mapped BAM files.

  Dataset EGAD00001003782

• scRNA-seq of monochorionic dizygotic twin chimera PBMC

  Single cell RNA-seq of PBMC of monochorionic dizygotic twin chimeras. 10x chromium v3.1 was used to generate barcoded scRNA library.

  Study EGAS50000000097

• SF11612 scRNA-Seq Recurrent oligodendroglioma

  Single Cell RNA seq from Recurrent oligodendroglioma sample. Gender Male Age 67. NovaSeq6000 was used for RNA seq. The files uploaded are bam files created with grch38 reference through Cellranger count (10xGenomics.)

  Dataset EGAD00001005411

• RNAseq following LDC67 or JQ1 treatment

  For RNA-Seq total RNA was isolated following LDC67 or JQ1 treatment. 3’RNAseq libraries were prepared with QUANT SEQ FWD 3´mRNA-Seq Kit (Lexogen, Austria), sequenced on an Illumina HiSeq 4000

  Dataset EGAD00001003464

• SF9259S snRNA-Seq Primary GBM

  Single Nuceli Primary GBM 73 Male. Single Nuclei RNA seq from high grade primary glioma sample. NovaSeq6000 was used for RNA seq. The files uploaded are bam files created with grch38 reference.

  Dataset EGAD00001005428

• Brain Cell Type-Specific Enhancer-Promoter Connectivity Maps and Disease Risk Association

  These studies map the epigenetic landscape of microglia, neurons, astrocytes and oligodendrocytes. The initial study compared acutely isolated microglia to cultured microglia from the same donors, to identify changes that occur when microglia are removed from the brain microenvironment. In collaboration with the Rady Children's Hospital, we obtained brain tissue samples from pediatric patients representing putative normal tissue resected to gain access to brain tumors as well as tissue resected to remove epileptic foci. We assayed microglia isolated from these samples using RNA-seq, ATAC-seq and ChIP-seq for PU.1, H3K4me2, and H3K27ac. For patients from whom a sufficient cell number was obtained, we additionally cultured their microglia for up to 7 days and subjected these to the same suite of assays. In a follow-up study, we obtained brain tissue from a comparable cohort of pediatric epilepsy patients from Rady Children's Hospital. We isolated nuclei from microglia, neurons, astrocytes and oligodendrocytes, and assayed for ATAC-seq, ChIP-seq for H3K4me3, and H3K27ac, and PLAC-seq (Proximity Ligated ChIP-seq). We isolated microglia from human fetal and postnatal sample and assayed for bulk RNA-seq, bulk ATAC-seq, bulk H3K27ac ChIP-seq, single-cell RNA-seq (scRNA-seq) and single-cell ATAC-seq. For these studies, human microglia are defined by signature genes/proteins, such as P2RY12, SALL1, and CX3CR1.

  Study phs001373

• scRNA-seq using 10X Genomics platform

  Matrices of counts from single-cell RNA-seq data for 15 samples from patients with ovarian cancer, 5 samples for each of the 3 tumor immune phenotypes (Infiltrated, Excluded and Desert). Dissociated cells from each tumor sample have been sorted to isolate live cells from the 3 compartments: tumor, immune and stromal. Each of the compartments has been analyzed separately by scRNAseq, excluding some desert tumors for which cells from the stromal and immune compartments have been pooled. Sequencing was performed using 10X Genomics Chromium Single Cell platform (v2 Chemistry).

  Dataset EGAD00001006974

• H3K27ac/H3K27me3 landscape of medulloblastoma

  We profiled 111 patient medulloblastoma primary tumor samples by bulk RNA-seq (19 samples), 27ac (98 samples) / 27me3 (61 samples) ChIP-Seq, WGS (4 samples) and 27ac hichip (8 samples). Submitted data consists of data generated from previously unpublished tumors as well as complementary data for data sets already published for identical medulloblastoma tumors (ex: 27me3 ChIP-Seq and RNA-Seq data submitted for a tumor with publicly available WGS data). The raw fastqs and hg19 aligned RNA-Seq bams are provided.

  Dataset EGAD00001009709

• Complex-I stratification of Parkinson's disease - single nucleus RNAseq

  Single nucleus RNA seq dataset for CI stratification of Parkinson's disease Single nucleus RNA seq dataset of prefrontal cortex tissue from a total of N=18 samples. Tissue samples correspond to patients with idiopathic Parkison's disease (PD) with varying levels of Complex-I activity (N=12) and neurologically healthy controls (N=6). Single nucleus RNA seq was carried out using 10X Genomics Chromium. This dataset corresponds to a subset of the samples sequenced using conventional bulk tissue RNA seq and available within this Study as a separate Dataset.

  Dataset EGAD50000000432

• Single-cell profiling reveals monocyte mitochondrial dysfunction in patients with cirrhosis progressing to acute-on-chronic liver failure

  We performed single-cell RNA-seq and CITE-seq in PBMC samples derived from patients with acutely decompensated cirrhosis at risk of developing ACLF, as well as from Healthy Donors. We identified a population of monocytes associated with disease progression. We also explored the presence of this cell population in blood samples by bulk RNA-sequencing.

  Study EGAS50000001127

• Deciphering RBP - alternative splicing networks in ALS iPSC-MN: NOVA1 gain and loss of function systems

  We produced NOVA1 and EGFP over-expression RNA-seq datasets in three healthy control individuals using lentivirus-mediated gene transfer to model NOVA1 gain of function. Additionally, we produced a NOVA1 loss of function RNA-seq dataset by generating iPSC-MN with NOVA1 knock out produced by CRISPR/Cas9 in the CV-B iPSC line.

  Study EGAS00001005881

• HTAN MCL Pre-Cancer Atlas Pilot Project - Targeted Sequencing Development Study

  The HTAN-MCL Pre-Cancer Atlas Pilot Project (PCAPP) is the result of a collaboration between the seven members of the MCL consortium. Across four organ types, PCAPP's goal is to collect and profile pre-malignant lesions for gene expression, DNA mutations, single-cell gene expression and immune-environment. Most PML are small in size and only available come from formalin fixed paraffin embedded archived tissue. The primary goal of PCAPP is to 1) understand the logistical challenges of PML specimen collection, 2) document technical limitations of the assays that are specific to the PML and 3) overcome them to support the generation of a more comprehensive Pre-Cancer Atlas in the future. The current upload provides RNA and DNA sequencing from participants with DCIS who were studied at the University of San Diego and the University of Vermont. Description of the overall study: A. Background/Significance One of the critical barriers to developing new approaches for cancer detection and prevention is the lack of understanding of the key molecular and cellular changes that cause cancer initiation and progression. Unlike the extensive work that has been done profiling advanced stage tumors, few studies have comprehensively profiled the molecular alterations found in precancerous tissues. Premalignant lesions are currently characterized by histologic changes that precede the development of invasive carcinoma1,2.These lesions can often be identified in regions surrounding an invasive tumor or in biopsies taken from patients undergoing diagnostic evaluation for suspicion of cancer. Currently, limited metrics exist to identify lesions that will likely progress to carcinoma and require intervention from those that will naturally regress or remain stable3,4. Characterization of the molecular alterations in premalignant lesions and the corresponding changes in the microenvironment would hasten the development of biomarkers for early detection and risk stratification as well as suggest preventive interventions to reverse or delay the development of cancer. Our pilot study will establish the feasibility of transcriptomic, genomic and immune profiling of FFPE premalignant lesions from multiple organ sites, collected and profiled with uniform SOPs across multiple institutions within the MCL consortium. We will characterize the molecular alterations in precancerous lesions and the corresponding microenvironment in four major organ sites, in order to uncover the molecular and cellular determinants of premalignancy, and establish standardized sequencing and immunohistochemistry protocols on FFPE precancerous tissue. We will also evaluate the technical feasibility of single nuclei sequencing of small FFPE pre-cancer lesions. Successful completion of the proposed pilot study will set the stage for expansion and development of a comprehensive Pre-Cancer Atlas (PCA) as part of the NCI's moonshot.B. Specific Aims Aim 1: Collect premalignant lesions (PML) and their associated microenvironment via LCM from FFPE tissue across four organ sites (breast, lung, pancreas & prostate). Aim 2: Perform bulk RNA and DNA seq on premalignant FFPE samples (and flash frozen tissue where available) and compare the genomic/transcriptomic alterations within and across organ sites. C. ApproachAim 1: Collect premalignant lesions (PML) and their associated microenvironment via LCM from FFPE tissue across four organ sites (breast, lung, pancreas & prostate). MethodsI. Patient Population/Sample Collection: Overview of the sites collecting PML tissue from the respective organs is provided in Table 1 and a full description of the biospecimens to be obtained is described in detail for each organ type below. Table 1. Breakdown of cohort by tissue type and collection site.Organ siteBreastLungPancreasProstateType of PMLDCISAAH, Squamous Dysplasia/CISIPMNsPINCollection of PatientsUCSF/UCSDUVMBU*/UCLAVanderbilt/MoffittMDACC*JHUStanford*# of Patients201920 (10 of each type)20 (10 of each type)242020Total patients per Organ39402440Note: single nuclei/cell RNA-Seq will be performed on 4-5 FFPE samples from each of the organ types 1. DCIS lesions from breast tissue: DCIS lesions will be collected from 39 patients (20 from UCSF/UCSD & 19 from UVM) with primary low or high-grade DCIS diagnosed from a breast core biopsy. Subsequent resected lumpectomy or mastectomy tissues will be prospectively sampled in the vicinity of the prior biopsy site using multiple approaches: 1) Live cells (heterogeneous mix) will be obtained as a cell scrape slurry from the lesion surface or by fine needle aspirate (FNA); 2) For a subset of specimens where size is sufficient, a block of breast tissue with DCIS will be fresh-frozen; 3) The remainder of the specimen will be taken for routine formalin-fixation and paraffin-embedding (FFPE). The FFPE sample will be annotated to identify the matched FFPE tissue block adjacent to the fresh-frozen sample and will be sectioned for use in bulk and single nuclei sequencing . We will dissect DCIS, adjacent normal and when available, associated carcinoma. In addition, when possible, normal tissue will be collected from a tissue block lacking lesions as well as collection of blood. A subset of patients (n = 5 | FFPE, flash frozen and fresh) will be sent to the Broad Institute for single nuclei/cell sequencing.2. AAH and squamous dysplastic/CIS lesions from airway and lung tissue: For squamous cell lung cancer, we will collect endobronchial biopsies from abnormal airway regions identified on autofluroscence bronchoscopy or identify PMLs in the margins of resected lung tissue. We will study 20 patients (5 each from BU/UCLA/Vanderbilt/Moffitt) with pre-invasive squamous lesions (moderate-severe dysplasia or carcinoma in situ (CIS)) identified on pathologic examination. LCM of the premalignant region and adjacent normal epithelium will be performed as well as the invasive tumor for those collected from the resection margin (n=5 from UCLA). On a subset of lesions collected at bronchoscopy (n=5), we will collect additional biopsies that will be flash frozen and fresh for single nuclei and cell sequencing, respectively, performed at the Broad Institute. In parallel to the work at the Broad, BU will perform single cell RNA-seq on these freshly cell sorted tissues (n = 5). Blood will be collected on all patients for genomic studies. For lung adenocarcinoma, we will collect resected FFPE lung tissues from 20 patients (10 from UCLA and 10 from Vanderbilt/Moffitt) with early stage lung adenocarcinoma that harbor atypical adenomatous (AAH) premalignant lesions in the resection margin. We will LCM multiple AAH regions (3-5 per patient) as well as adjacent regions of normal epithelium and invasive adenocarcinoma. In addition, blood will be collected on all patients for genomic studies. 3. IPMNs from pancreatic tissue: For pancreatic cancer PML, we will collect low and high grade lesions from 24 patients representing macroscopic Intraductal Papillary Mucinous Neoplasms (IPMN) (n=24) from surgically resected specimens along with blood samples. Archival FFPE specimens of microscopic PanIN lesions, occurring multi-focally adjacent to invasive PDAC, and archival IPMN lesions (with or without associated invasive cancer), along with the adjacent normal tissue, will undergo LCM and utilized for bulk DNA and RNA sequencing. If matched frozen tissues are available for a subset of these FFPE samples, we will bank for comparison of profiles. Because IPMNs are macroscopic lesions, they provide an opportunity for obtaining the samples fresh and therefore can be used for single cell sequencing (in contrast to PanINs). Therefore, 5 freshly obtained IPMNs will be used for the single cell RNA sequencing studies performed at both the Broad Institute and MDACC, and the matched FFPE and/or frozen sections from these lesions (obtained from the adjacent PML) will be sent to Broad Institute as a pilot to assess "single nuclei" RNA sequencing.4. PINs from prostate tissue: For prostate cancer PML, there will be 40 samples of Prostatic Intraepithelial Neoplasia (PIN) collected between the Stanford and JHU sites (20 cases per site). At the Stanford site, 20 prostate specimens detected by PSA screening who have/will undergo surgery (radical prostatectomy) for clinically localized disease will make up the final cohort. The age range of the participants would be 40-75, and we anticipate that 18 will be Caucasian, 1 Asian and 1 Latino or African American based on the practice demographics practice at Stanford. Clinical and MRI data will also be collected for these samples. We will collect low grade (e.g. Gleason score of 6/Grade group 1; n=10) and high grade (Gleason score 4+3=7 or higher/Grade group 3 or higher; n=10) PINs from FFPE samples that have prostate carcinoma. In addition to obtaining LCM archival samples of low and high grade PIN, we will also obtain normal prostatic epithelial from the peripheral, central and transition zones as well as multiple samples of prostate carcinoma in order to obtain the spectrum of Gleason grades in the carcinoma as needed. LCM samples will be used for bulk DNA and RNA sequencing. In addition, single cells will be dissected from FFPE samples to prepare single cell RNA seq libraries using techniques developed at Stanford, and FFPE tissue will be sent to the Broad for single nuclei sequencing. When available, flash frozen and fresh samples from these prostates will be archived and prepared for single nuclei and cell sequencing, respectively, at the Broad Institute and at Stanford (single cell only). JHU will also capture 10 cases (5 grade group 1 and 5 grade group 2) of high grade PIN, normal and invasive adenocarcinoma using frozen sections from fresh frozen tissues. When possible these will be from the same patients as the FFPE samples. Since frozen sections can be quite challenging to morphologically determine high grade PIN from normal epithelium, for these samples we will perform a number of additional tissue-based characterizations. These will include a multicolor combined basal cells (p63 and CK903) and PIN/carcinoma markers (AMACR) referred to in the cocktail as "PIN4", c-MYC (referred to as MYC) protein5, by IHC and mRNA by in situ hybridization (AM De Marzo, Q Zheng unpublished observations), telomere length by in situ hybridization6 and the 5'ETS/45S rRNA7. For these slides, the whole slides will be scanned with a Hammamatsu Nanozoomer with a 40x objective and regions of interest will be annotated as a guide for LCM.II. Laser-capture microdissection (LCM): FFPE tissue blocks will be sectioned at 7μm thickness and serial sections will be stained with H&E. LCM will be performed utilizing standard LCM systems, such as Leica LMD7000 and ArcturusXT at each site. Regions of premalignancy will be dissected and RNA/DNA will be extracted from microdissected cells using the Qiagen All Prep DNA/RNA FFPE Kit. Aim 2: Perform bulk RNA and DNA seq on premalignant FFPE samples and compare the genomic/transcriptomic alterations within and across organ sites. Rationale: There have been limited studies characterizing the genomic and transcriptomic landscape of premalignant lesions associated with breast, pancreatic, lung or prostate cancers. Characterizing the molecular determinants of premalignant disease that are unique and shared across multiple organs will enable new candidate biomarkers for early detection and novel therapeutic strategies for early intervention. MethodsBulk RNA-seq of LCM FFPE tissue: All participating sites will perform bulk RNA-seq in accordance with SOPs developed at BU. In brief, total RNA will be isolated from LCM'd lesion and associated microenvironment tissue using the Qiagen All Prep DNA/RNA FFPE Kit and quality will be assessed with the Agilent Bioanalyzer. Libraries will be generated with the Illumina TruSeq Access kit (for FFPE samples). They will be sequenced on the Illumina HiSeq2500 with 75base-pair paired-end reads. Quality of FASTQ files will be assessed with FastQC. Reads will be aligned to the human genome with STAR and gene-level and isoform-level expression will be quantified with RSEM. Splice junction saturation, transcript integrity, and biotype distributions will be calculated for each sample with RSeQC. DESeq2 and EdgeR will be used to identify associations between gene expression profiles and clinical variables while controlling for confounding covariates. BU will serve as an RNA-seq Core to assess reproducibility of FFPE RNA-seq methods across sites. We will perform RNA-seq according to the SOP listed above on a subset of samples for each organ type (total n ~ 20). Bulk seq of DNA from FFPE tissue: All participating sites will perform targeted or whole exome-seq (WES) in accordance with SOPs. In brief, DNA from laser captured material will be isolated using the Qiagen All Prep DNA/RNA FFPE Kit and undergo stringent quality control to ensure high quality input material for genomic profiling. Purified DNA (ideally 100-200 ng) will be used for library preparation and amplification, followed by next generation sequencing using standard protocols distributed by CDMG. Exome-seq methods are considered standardized, thus we will not need a DNA-seq Core to assess reproducibility across sites. We anticipate local centers will use Illumina paired end reads, following the following general approach. 1) DNA library preparation: Paired-end libraries will be prepared following the manufacturer's protocols (Illumina and Agilent), fragmented to 150-200 bp 2) Capture of targeted exome: Whole exome capture will be carried out using the protocol for Agilent's SureSelect Human All Exon kit. Purified capture products will be amplified using the SureSelect GA PCR primers (Agilent) for 12 cycles. 3) Sequencing will be carried out for the captured libraries using at least 100 bp paired-end reads. To achieve high level sensitivity and accuracy for detecting all the mutations in the whole exome, each sample will be sequenced at 200X mean depth. 4) Read mapping and alignment and variant analysis: Sequence short reads will be aligned to a reference genome (NCBI human genome assembly build 38) using BWA-MEM. Local realignment of aligned reads will be performed using Genome Analysis Toolkit (GATK).Data QC: To ensure scientific rigor and consistency among sites in RNA and DNA processing we will include a preliminary analysis of steps in processing and analysis. Protocols for extraction of high quality RNA and DNA from formalin fixed paraffin embedded (FFPE) tissues, which will be used extensively in these studies continue to improve and may have variable implementation among the sites participating in this study. To evaluate consistency of preliminary steps in processing and downstream analyses, we will initially distribute slides from one large FFPE fixed cancer of origin from prostate, breast, lung and pancreatic cancer. Analysis of these samples will allow us to review the DNA and RNA characteristics (yield, purity and strand length) among sites. Downstream analysis of these same samples will also allow us to compare among sites the consistency of variant calls among centers. We will be able to identify if there are some times of calls (such as small insertion deletions) that are more variable among centers versus other types of calls (such as relative gene expression or single base pair substitutions) that we expect to be less variable and to characterize the reliability of findings across sites. We are also including a 5% blind duplicate analysis of RNA sequencing. Samples will be analysed by the participating genomics cores without knowledge of the phenotype. RNA seq and CNA analyses are normalized for batch effects. We will also compare the observed sex to the self-reported sex as based on RNA profiles and exome sequencing of X chromosome genes as another check for processing accuracy and sample management. D. References 1. Wacholder, S. Precursors in Cancer Epidemiology: Aligning Definition and Function. Cancer Epidemiol. Prev. Biomark. 22, 521-527 (2013). PMID: 23549395.2. Berman, J. J. Precancer: The Beginning and the End of Cancer. (Jones & Bartlett Learning, 2011).3. Nasiell, K., Nasiell, M. & Vaćlavinková, V. Behavior of moderate cervical dysplasia during long-term follow-up. Obstet. Gynecol. 61, 609-614 (1983). PMID: 6835614.4. Merrick, D. T. et al. Persistence of Bronchial Dysplasia Is Associated with Development of Invasive Squamous Cell Carcinoma. Cancer Prev. Res. (Phila. Pa.) 9, 96-104 (2016). PMID: 26542061.5. Gurel, B. et al. Nuclear MYC protein overexpression is an early alteration in human prostate carcinogenesis. Mod. Pathol. Off. J. U. S. Can. Acad. Pathol. Inc 21, 1156-1167 (2008). PMID: 18567993.6. Meeker, A. K. et al. Telomere shortening is an early somatic DNA alteration in human prostate tumorigenesis. Cancer Res. 62, 6405-6409 (2002). PMID: 12438224.7. Guner, G. et al. Novel Assay to Detect RNA Polymerase I Activity In Vivo. Mol. Cancer Res. MCR 15, 577-584 (2017). PMID: 28119429.

  Study phs002225

• Human data for chromatin accessibility (ATAC-Seq and scATAC-Seq) and transcriptome (RNA-Seq and scRNA-Seq) in eight B-cell precursors, from HSC to Naive B cells

  We generated paired transcriptomic (RNA-Seq) and chromatin accessibility (ATAC-Seq) profiles from 8 immune cell populations representing the early lymphoid hematopoietic differentiation stages along the B cell lineage: HSC, CLP, pro-B, pre-B, Immature B, Transitional B, Naive B CD5-, Naive B CD5+. Cell populations were isolated from healthy adult human bone marrow precursors and peripheral blood (n=13) following a predefined sorting strategy. For each donor, a subset of different cell type populations was obtained. Additionally, to explore the continuous dynamic of RNA and ATAC, we generated a single-cell sample of human CD34+ B cell precursors.

  Study EGAS00001007296

• Single-cell RNA-seq cases

  Single-cell RNA-seq cases (Tumor and adjacent tissue)

  Dataset EGAD00001008835

• Dissecting Cell Composition and Drug Sensitivity in Human Adenoid Cystic Carcinomas (ACCs)

  The study reports the results of single-cell RNA sequencing (scRNA-seq), RNA sequencing (RNA-seq), and whole-exome sequencing (WES) experiments performed on malignant cells isolated by flow cytometry from seven patient-derived xenograft (PDX) lines established from human Adenoid Cystic Carcinomas (ACCs). Of these seven lines, five are characterized by well differentiated histology (ACCX5M1, ACCX6, ACCX14, ACCX22, SGTX6) and two by solid histology (ACCX9, ACCX11). Analysis by scRNA-seq of the well-differentiated PDX line ACCX22 led to identification of cell-surface markers (CD49f, KIT) that enabled the differential purification of the two major cellular components contained in well-differentiated ACCs: myoepithelial-like (CD49f-high/KIT-negative) and ductal-like (CD49f-low/KIT-positive) cells. The study reports the analysis by RNA-seq and WES of purified preparations of myoepithelial-like and ductal-like cells from well-differentiated PDX lines (ACCX5M1, ACCX6, ACCX14, ACCX22, SGTX6) and of ductal-like (CD49f-low/KIT-positive) cells from solid PDX lines (ACCX9, ACCX11).

  Study phs002764

• Single-cell RNA-seq of immune cells from Melanoma tumors (Li et al, 2018)

  Single-cell RNA-seq profiling of immune cells sorted from human Melanoma tumors (and several matching PBMC samples). Contains de-multiplexed FASTQ files per plate (MARS-seq amplification batch, total 204 samples) and also de-multiplexed FASTQ files of single-cell TCRb-seq.

  Dataset EGAD00001004497

• NKI-AvL OpACIN RNA-seq of stage III melanoma patients

  The dataset “NKI-AvL OpACIN RNA-seq of stage III melanoma patients" includes 18 FASTQ files from single-end total RNA sequencing on Illumina HiSeq2500 for 18 stage III melanoma patients.

  Dataset EGAD00001004216

• DDX50 Cooperates With STAU1 to Effect Stabilization of Pro-Differentiation RNAs

  This study was undertaken to determine the role of DDX50 in epidermal homeostasis. Primary neonatal foreskin keratinocytes were obtained from discarded surgical samples at a California hospital. Samples were anonymous and not selected. DDX50, which was associated with STAU1, was found to promote epidermal differentiation and transcript stability using the methods of RNA-seq, CLIP-seq and DMS-MaP-seq.

  Study phs003766

• RNA-Seq files for St. Jude Clinical Pilot

  This dataset contains whole genome sequencing BAM files for 78 tumor-normal pairs (a total of 156 samples) used in the St. Jude Clinical Pilot. Mapping was performed using BWA. This dataset accompanies the paper "Clinical Cancer Genomic Profiling by Three-Platform Sequencing of Whole Genome, Whole Exome and Transcriptome"

  Dataset EGAD00001004280

• Colon Cancer Organoid Cultures and Tumors RNASeq Data

  Four micrograms of total RNA was used for cDNA library construction using the KAPA Stranded mRNA-Seq Kit (KR0960-v3.15), following manufacturer's protocol. The adaptor-ligated libraries were enriched by 6 cycles of polymerase chain reaction (PCR). Libraries were sequenced using the Novaseq 6000 with paired end 151bp reads.

  Dataset EGAD00001005753

• Transcriptome profiling (RNA-seq) of human acute leukemias

  Total RNA-seq of blasts derived from 131 adult T-ALL cases, 7 AML cases and 1 mixed myeloid/lymphoid leukemia with CpG Island Methylator Phenotype (CIMP). The other RNA-seq data used in this study has been previously published and is available at EGAD00001007581 (AML) and EGAD00001007646 (CD34+ cells).

  Dataset EGAD00001011054

• An Advanced Molecular Medicine Case Report of a Rare Human Tumor Using Genomics, Pathomics, and Radiomics

  The associated data of this study is derived from a single female patient with pulmonary sclerosing pneumocytoma (PSP). The aim of this study is to provide the most comprehensive, multi-modality sequencing study of a single case of PSP to-date. To this end, the following sequencing experiments were performed: i) RNA-Seq of the primary tumor and adjacent normal tissues (6 replicates for each tissue type), which was intended to analyze RNA-Seq fusions, gene expression, expression mutations, ii) low-pass DNA whole genome sequencing (WGS) from primary tumor tissue and germline, which was intended to analyze copy number aberrations, and iii) DNA targeted panel sequencing of lung cancer associated genes, using primary tumor tissue and germline from white blood cells, which was intended to analyze somatic mutations. Principal findings: i) the PSP hallmark mutation AKT1 (p.E17K) was detected within both the DNA and RNA, and ii) the TP53 signaling pathway was found to be statistically significant by three different pathway analysis tools of analyzing gene expression and pathway ramifications. Among proteins within the TP53 signaling pathway, the p53 inhibitor encoded by MDM2 was found to be overexpressed (by differential gene expression analysis). The original sequencing data (i.e., FastQ files) from each of the aforementioned samples will be accessible through dbGaP.

  Study phs003154

• Transcriptomic Profiling of an Anti-PD-L1 Treated Cohort of Newly Diagnosed GBM Patients

  Checkpoint inhibitor therapy has limited efficacy in glioblastoma patient outcomes. This study involves RNA sequencing (RNA-seq) of newly diagnosed GBM patients treated with concurrent atezolizumab, radiation therapy, and temozolomide. By analyzing RNA-seq data from 39 tumors, the study aims to explore tumor transcriptomic features, including immune-based transcriptomic programs and immune infiltration, to understand their influence on PD-L1 blockade treatment efficacy and to identify which patients may benefit more from such therapy.

  Study EGAS50000000784

• Mutational landscapes of primary triple negative breast cancers - RNA seq

  Mutational landscapes of primary triple negative breast cancers - RNA seq

  Dataset EGAD00001000132

• Whole Transcriptome Profiling of prDLBCL

  Gene expression profiling using RNA-seq was performed using material from 30 patients.

  Study EGAS50000000402

• Mapped RNA-Seq data

  Mapped whole transcriptome RNA-Seq data from 476 human samples of early stage urothelial carcinoma.

  Dataset EGAD00001001939

• RNA-Seq Medulloblastoma data

  RNA-Seq data from 12 medulloblastoma samples, all group 4 medulloblastomas. The data consists of BAM files aligned using STAR

  Dataset EGAD00001010850

• Transcriptomics of Liver and PBMCs in Alcohol-Associated Liver Disease

  This study was designed to characterize the transcriptome of alcohol-associated hepatitis using RNA sequencing (RNA-seq) technology for both liver tissue biopsies and peripheral blood mononuclear cells (PBMCs) extracted from blood samples, in order to identify expressed genes that are markers of the disease. To distinguish genes that differentiate alcohol-associated hepatitis from other inflammatory liver diseases, we compared the RNA-seq data generated from participants with alcohol-associated hepatitis, alcohol-associated cirrhosis, chronic hepatitis C infection, non-alcohol-associated fatty liver disease, and healthy controls. We utilized computational approaches to demonstrate that gene expression data generated from either liver tissue or blood samples can be used to identify a small set of gene biomarkers to distinguish between these liver diseases. The raw RNA-seq data will be available through dbGaP.

  Study phs003112

• RNA-seq of Shprintzen-Goldberg syndrome dermal fibroblasts

  RNA-seq of dermal fibroblasts treated ± TGF-β from control and Shprintzen-Goldberg syndrome patients.

  Dataset EGAD00001006801

• JAGuaR outputs from RNA-seq

  JAGuaR outputs from RNA-seq of 35 pancreatic neuroendocrine neoplasms.

  Dataset EGAD00001006990

• Paired-end RNA-seq analysis of the 3D spatially mapped GBM samples.

  Dataset contains 46 paired-end RNA-seq samples from 3 patients.

  Dataset EGAD00001010290

• McGill EMC Release 4 for assay "RNA-seq"

  McGill EMC Release 4 for assay "RNA-seq": Transcriptome profiling by high-throughput sequencing

  Dataset EGAD00001001290

• RNA sequencing of tumor samples from patients with Waldenstrom macroglobulinemia

  RNA was extracted from flow-sorted CD19+. RNA-Seq was performed on 32 samples of 30 patients (2 replicates per samples). RNA-Seq libraries were subjected to non-stranded paired-end (2 x 75 bp) sequencing on HiSeq 2500 (Illumina). The files are in FASTQ format.

  Dataset EGAD00001005324

• MAITS in HCC

  Hepatocellular Carcinoma (HCC) is a leading cause of cancer-related death and can be considered a prototype of inflammation-derived cancer arising from chronic liver injury. The cell composition of the HCC tumor immune microenvironment (TiME) has a major impact on cancer biology as the TiME can have divergent capacities on tumor initiation, progress, and response to therapy. Recent development of multi-omics and single-cell technologies help us to comprehensively quantify the cellular heterogeneity and spatial organization of the TiME and to further our understanding of antitumor immunity. We investigated the cellular composition of liver cancer patient samples (n=8) using single-cell RNA-seq. For this purpose, mononuclear cells were isolated from human liver cancer samples. Three locations within the tumor-bearing liver were used: adjacent liver, rim and tumor core. Then, cells were FACS sorted (either CD45+ cells or MAIT cells) and subjected to 10X Chromium-based scRNA-seq. We investigated immune cell changes and the cellular heterogeneity of the HCC TiME with a focus on MAIT cells. Annotated H5 files are provided. Clinical metadata including TMN stage, sex, gender, ethnicity, pretreatment, and histopathological reports are available for all patient samples. Further details on the study can be obtained in our paper once it’s published.A complementary dataset derived from ultrahighplex CO-Detection by indEXing (CODEX) from paired patient samples can be accessed through The Cancer Imaging Archives (TCIA) under DOI: https://doi.org/10.7937/bh0r-y074.

  Study phs003279

• Prediction and quantification of splice events from RNA-seq data

  Analysis of splice variants from short read RNA-seq data remains a challenging problem. Here we present a novel method for the genome-guided prediction and quantification of splice events from RNA-seq data, which enables the analysis of unannotated and complex splice events. Splice junctions and exons are predicted from reads mapped to a reference genome and are assembled into a genome-wide splice graph. Splice events are identified recursively from the graph and are quantified locally based on reads extending across the start or end of each splice variant. We assess prediction accuracy based on simulated and real RNA-seq data, and illustrate how different read aligners (GSNAP, HISAT2, STAR, TopHat2) affect prediction results. We validate our approach for quantification based on simulated data, and compare local estimates of relative splice variant usage with those from other methods (MISO, Cufflinks) based on simulated and real RNA-seq data. In a proof-of-concept study of splice variants in 16 normal human tissues (Illumina Body Map 2.0) we identify 249 internal exons that belong to known genes but are not related to annotated exons. Using independent RNA samples from 14 matched normal human tissues, we validate 9/9 of these exons by RT-PCR and 216/249 by paired-end RNA-seq (2 x 250 bp). These results indicate that de novo prediction of splice variants remains beneficial even in well-studied systems. An implementation of our method is freely available as an R/Bioconductor package SGSeq.

  Study EGAS00001001026

• Single cell RNA-seq and ATAC-seq of human fetal forebrain tissue, weeks 8 to 11.

  Single cell RNA-seq from 6 and single nuclei ATAC-seq from 3 human fetal tissue samples. Samples from 8 to 11 weeks. Includes a 8.5 weeks samples with matching both ATAC-seq and RNA-seq runs. Data was sequenced using 10X Genomics chromium technology, for scRNA-seq samples belong to v2 and v3.

  Dataset EGAD00001008653

• SF11644 scRNA-Seq primary GBM

  SF11644 Primary GBM Gender Male age 57. Single Cell RNA seq from high grade primary glioma sample. NovaSeq6000 was used for RNA seq. The files uploaded are bam files created with grch38 reference through Cellranger count (10xGenomics.)

  Dataset EGAD00001005393

• SF11956 scRNA-Seq GBM IDHR132H Wildtype Male

  SF11956 IDHR132H WT GBM. Male, 63. Single Cell RNA seq from high grade glioma sample. NovaSeq6000 was used for RNA seq. The files uploaded are bam files created with grch38 reference through Cellranger count (10xGenomics.)

  Dataset EGAD00001005392

• SF11979 snRNA-Seq Primary GBM IDHR132H Wildtype Female

  Single Nuclei Primary GBM IDHR132H Wildtype. Female 76. Single Nuclei RNA seq from high grade primary glioma sample. NovaSeq6000 was used for RNA seq. The files uploaded are bam files created with grch38 reference.

  Dataset EGAD00001005430

• RNA-seq

  RNA-sequencing data

  Dataset EGAD00010001719

• Transcriptome of Ewing sarcoma tumors - RNA-seq (ICGC)

  Transcriptome of Ewing sarcoma tumors (ICGC project). Fastq files of 57 RNA-seq are available (2x101bp).

  Dataset EGAD00001004493

• Single Cell RNA-Seq

  Examination of Sample Multiplexing Reagents for Single Cell RNA-Seq. Nine techniques applied to samples from four PDX models: #105, #177, #233, #264

  Dataset EGAD50000000216

• Single nucleus RNA-seq of human epiglottis and subglottis

  This dataset contains single nucleus RNA-seq of human epiglottis and subglottis tissue performed using the 10X platform

  Dataset EGAD50000001279

• Bulk RNA-seq of Human Epiglottis and Subglottis

  This study contains bulk and single nucleus RNA-seq of human epiglottis and subglottis tissue.

  Dataset EGAD50000001278

• Glioblastoma stem cell lines RNA-seq

  RNA-seq data for three Glioblastoma stem cell (GSC) lines exposed to PRMT5 inhibitor and control samples.

  Dataset EGAD00001006095