12213 results for "cancer rna-seq"

in 27.50 milliseconds.

• UROMOL 2020 - RNA-seq data for validation

  Total RNAseq data from 47 patients with NMIBC.

  Study EGAS00001005050

• sc-RNA-Seq soft tissue sarcoma tumoroid biobank

  Dataset EGAD00001009002

• RNA-seq data from diagnosis and relapse samples of Down Syndrome acute lymphoblastic leukemia.

  Dataset EGAD00001003280

• IMpower133 subtype assignments

  This dataset contains subtype assignments for 271 tumor samples profiled by RNA-seq.

  Dataset EGAD00001006926

• TenK10K Phase 1:  Single Cell

  Understanding the genetic basis of gene expression can shed light on the regulatory mechanisms underlying complex traits and diseases. Single-cell resolved measures of RNA levels and single-cell expression quantitative trait loci (sc-eQTLs) have revealed genetic regulation that drives sub-tissue cell states and types across diverse human tissues. Here, we describe the first phase of TenK10K, the largest- to-date dataset of matched whole-genome sequencing (WGS) and single-cell RNA-sequencing (scRNA-seq). We leverage scRNA-seq data from over 5 million cells across 28 immune cell types and matched WGS from 1,925 individuals. This provides power to detect associations between rare and low-frequency genetic variants that have largely been uncharacterised in their impact on cell-specific gene expression. We map the effects of both common and rare variants in a cell type specific manner using SAIGE-QTL.

  Study EGAS50000001653

• Newcastle COVID-19 Single-cell PBMC

  Single-cell multi-omic profiling of healthy controls, asymptomatic and hosptial-admitted COVID19 patients recruited from Newcastle University hospitals. These data also include healthy control volunteers treated with IV-LPS as inflammatory controls. Data represent RNA-seq, surface protein measurements (CITE-seq) of 192 antibody targets, along with VDJ-seq profiling of single T cell and B cell receptors.

  Dataset EGAD00001007866

• Exploring Genomic Mutations in Gastric Cancer among Japanese Populations

  We conducted a comprehensive analysis of whole-genome and RNA sequences obtained from biopsy samples of 65 patients with advanced gastric cancer prior to chemotherapy treatment.

  Study JGAS000754

• Non-coding RNAs in breast cancer

  Using RNA CaptureSeq we annotated non-coding RNAs transcribed from genome intervals surrounding breast cancer risk signals in a range of mammary-derived tissue and cell lines.

  Study EGAS00001003353

• Sequencing of patient tissue and patient derived organoids in colon cancer

  Multiome (ATAC + RNA) sequencing of cancerous primary tissue and cancer organoids derived from the same patient. Nanopore and whole exome sequencing of selected samples.

  Study EGAS00001008067

• Contribution of systemic and somatic factors to clinical response and resistance in urothelial cancer: an exploratory multi-omic analysis

  Patients with metastatic urothelial cancer (n=29) were treated with atezolizumab (anti-PD-L1). All 29 patients had T-cell receptor sequencing (TCR-seq) of the pre-treatment blood; 24 also had TCR-seq on at least one post-treatment blood sample. 24 patients had TCR-seq of their pre-treatment tumors. TCR-seq data can be found at http://doi.org/10.5281/zenodo.546110. 26 patients also had RNAseq of their tumors and whole exome sequencing (WES) of their tumors and matched normal blood. WES results for one sample were excluded from our analysis after failing to meet coverage requirements.

  Study phs001743

• p200503_fn2

  RNA-Sequencing data of Fusobacterium nucleatum (Fna 71) treated CRC patient derived (CAF180, CAF181) and CAF05 cancer associated fibroblasts (CAFs).

  Study EGAS50000001121

• Baulac Lab - Paris Brain Institute

  Raw 10X single-nucleus RNA-seq data from human postoperative brain samples of patients with epilepsy and cortical malformations.

  Dac EGAC50000000546

• RNA-Seq data for AEL paper

  Dataset EGAD00001003412

• Transcriptomic profiling of lymphedema

  RNA-seq of skin from human subjects with and without lymphedema

  Dataset EGAD00001006246

• scEC&T-seq manuscript data

  This dataset includes all data produced in the study describing "scEC&T-seq", a method for parallel sequencing of extrachromosomal circular DNA and transcriptome in single cells. This dataset includes: - Illumina scEC&T-seq Circle-seq data (scCircle-seq) for a total of 626 single cells / nuclei - bam files - Illumina scEC&T-seq RNA-seq data (scRNA-seq-Illumina) for the same single cells / nuclei - bam files - Nanopore scCircle-seq data for 18 single cells - bam files - Nanopore bulk WGS for 2 cell lines and 2 primary tumor samples - bam files - Illumina bulk WGS for 2 cell lines - bam files - Illumina bulk Circle-seq data from 1 cell line - bam file - Illumina ChIP-seq H3K27me3 data from 1 cell line - fasta files + peaks bed file + coverage bw file

  Dataset EGAD00001010071

• Pulmonary atypical carcinoid multi-omic dataset on up to 42 tumours from same patient

  Pulmonary atypical carcinoid. DNA WES and RNA-Seq on 42 tumour samples collected at autopsy, 10x Chromium linked read whole genome sequencing on four tumours plus one normal sample, and targeted DNA sequencing on two clinical biopsies and one blood plasma sample. Note – RNA-Seq dataset features complex batch effect attributable to tissue processing artefacts, as described in "Complex patterns of genomic heterogeneity identified in 42 tumor samples and ctDNA of a pulmonary atypical carcinoid patient" - Robb et al., 2022 and detailed in Supplementary Table S3. Note – RNA-Seq dataset features complex batch effect attributable to tissue processing artefacts, as described in "Complex patterns of genomic heterogeneity identified in 42 tumor samples and ctDNA of a pulmonary atypical carcinoid patient" - Robb et al., 2022 and detailed in Supplementary Table S3.

  Dataset EGAD00001009296

• Single cell RNA sequencing of colorectal cancer

  To understand intrinsic cancer cell signatures and the surrounding microenvironemt and their interactions, we performed single-cell RNA sequencing on 63,689 cells from 23 patients with 23 primary colorectal cancer and 10 matched normal mucosa samples. Analyzing of primary colorectal cancer and normal mucosa samples show a comprehensive cellular landscape of colon cancer, which is a valuable resource for the development of therapeutic strategies.

  Dataset EGAD00001005198

• Transcriptome sequencing of gingivo-buccal oral squamous cell carcinoma for integrative analysis: alterations in expression of genes attributable to methylation changes

  Gingivo-buccal oral squamous cell carcinoma (OSCC-GB) is the most common cancer among men in India and is associated with high mortality. Although OSCC-GB is known to be quite different from tongue cancer in its genomic presentation and its clinical behavior, it is treated identically as tongue cancer. Predictive markers of prognosis and therapy that are specific to OSCC-GB are, therefore, required. To achieve this, we have carried out transcriptome (n=72) profiling of paired tumor-normal tissues collected from OSCC-GB patients from India. RNA sequencing was performed on tumor and paired adjacent normal samples from 72 OSCC-GB patients. From each patient, total RNA was extracted from tumor and normal tissue samples using AllPrep DNA/RNA Mini Kit (QIAGEN). The quality and concentration of isolated total RNA were checked using Agilent 2100 Bioanalyzer and NanoDrop 2000 (Thermo Fisher Scientific). The OD260/OD280 ratio was ≥ 2 and RNA Integrity Number (RIN) was ≥ 7.0 for all sequenced samples. Ribosomal RNA (rRNA) was removed from the RNA samples, using Ribo-Zero Magnetic Kit (Epicentre). Sequencing libraries were prepared from rRNA depleted samples using TrueSeq RNA Sample Preparation Kit (Illumina). Each triplex cDNA library pool was sequenced as 100bp paired-end on HiSeq-2000 or HiSeq-2500 (Illumina). Protocols suggested by the manufacturers were used for all assays.

  Study EGAS00001003893

• NKI-AvL CRC-OVC DNA-seq

  The dataset “NKI-AvL CRC-OVC DNA-seq" includes 4 normal and 4 tumor BAM files from paired-end whole exome sequencing on Illumina HiSeq2500 and Illumina NovaSeq6000 for 2 colorectal cancer and 2 ovarian cancer patients.

  Dataset EGAD00001004340

• Patient-derived lung cancer organoids

  Patient-derived lung cancer organoids cram files : targeted seq 13 samples, whole exome seq 12 samples mutation profiles of PDO and matched tissue : aggregated vcf 1 file details : https://www.nature.com/articles/s41467-019-11867-6

  Dataset EGAD00001005317

• Pancreatic cancer RNA sequencing

  Study EGAS00001004706

• Effect of the Placental Transcriptome on Stunting in a Longitudinal African Cohort

  Mammals with placentas and flowering plants evolved genomic imprinting in nutritive tissues, the placenta and endosperm, respectively. In these tissues, the genomes inherited from the mother and father are in conflict over resource allocation to offspring. In imprinted genes, transcription is repressed on the paternal or the maternal allele, resulting in allele specific expression (ASE). We investigated the variation in ASE of imprinted genes in term human placentas in order to detect loss of imprinting (LOI), which leads to partial expression of the repressed allele. We collected the placental tissue from birth to women in our multigenerational, prospective cohort study in Mali, West Africa. We followed the women from their own infancies, early childhood to adulthood (age 18+ years). Contingent on prior informed consent, we collected placental tissue and umbilical cord tissue. We conducted RNA-Seq of the fetal compartment of the placentas and targeted DNA-Seq of the umbilical cord tissue, which is fetal, and of saliva samples from parents. Our sequencing effort produced two datasets. The first dataset is the whole transcriptome RNA-Seq dataset of 111 biopsies (59 placentas) and genotyping of 96 genes. Birth length, weight, and placental weight for the children associated with this dataset are listed in the phenotype file (DOI: 10.1093/molbev/msz226). The second dataset includes targeted RNA-Seq of 262 samples (251 placentas) and targeted DNA-Seq for 766 genes. The genes were selected because they met any one of the following criteria: (1) imprinted and reported in some studies, (2) highly expressed in placenta, or (3) relevant to diseases of interest in this cohort (https://doi.org/10.1093/g3journal/jkab176). This study provides the first systematic analysis of variation in LOI across genes and individuals in a human population, and should shed light on the hypothesis that the modulation of imprinting is an epigenetic mechanism and plays a role in the regulation of offspring growth.

  Study phs001782

• Human embryonic stem cells dopaminergic neurons scRNA-seq

  The dataset includes 2 samples of Single cell RNA-seq (10X Genomics) from Human Embryonic stem cells. 2 samples with 2 replicates of timepoints 17 and 28 of dopaminergic neuron differentiation.

  Dataset EGAD50000001592

• Extracellular transcriptome in seminal plasma for non-invasive prostate cancer diagnosis

  A diagnostic non-invasive biomarker test for prostate cancer at an early stage, with high sensitivity and specificity, would improve diagnostic decision making. Extracellular RNAs present in seminal plasma might contain biomarker potential for the accurate detection of clinically significant prostate cancer. So far, the extracellular messenger RNA (mRNA) profile of seminal plasma has not been interrogated for its biomarker potential in the context of prostate cancer. Here, we investigate the mRNA transcriptome in seminal plasma samples obtained from prostate cancer patients (n=25), patients with benign prostate hyperplasia (n=26) and individuals without prostatic disease (n=6). Seminal plasma harbors a complex mRNA repertoire that reflects prostate as its tissue of origin. The endogenous RNA content is higher in the prostate cancer samples compared to the control samples. Prostate cancer antigen 3 (PCA3), a long non-coding RNA with prostate cancer-specific overexpression, and ATP-binding cassette transporter 1 (ABCA1), known to be involved in the prostate cancer pathogenesis, were more abundant in the prostate cancer group. In addition, twelve high confidence fusion transcripts could be detected in prostate cancer samples, including the bona-fide prostate cancer fusion transcript TMPRSS2-ERG. Our findings provide proof-of-principle that the extracellular transcriptome of seminal plasma can reveal information of an underlying prostate cancer.

  Study EGAS00001005277

• Oncogenic FOXL2C134W has gain-of-function chromatin remodeling activity that reprograms glucocorticoid receptor occupancy to promote ovarian granulosa cell tumor growth

  This dataset contains bam files from the whole exome sequencing performed on granulosa cell tumors and 7 sequencing experiments performed on human-derived KGN granulosa cell tumor cells and isogenic cell lines. The latter include transcription factor ChIP-seq for Foxl2 as well as transcription factor ChIP-seq for Foxl2 and GR under dexamethasone treatment and untreated conditions, two ATAC-seq data sets, two RNA-seq data sets, and Histone marks ChIP-seq.

  Dataset EGAD50000000880

• ResolveCRPS study - RNA-fragment sequencing from snap-frozen skin biopsies

  Complex Regional Pain Syndrome (CRPS) is a disabling pain disorder with unclear cutaneous molecular mechanisms. To identify gene-expression signatures linked to pain intensity and disease progression, we profiled the local transcriptome of CRPS-affected skin. 42 patients agreed to skin biopsies from the CRPS-affected area, which underwent low-input total RNA-seq. Full experimental and bioinformatics protocols are provided in the associated publication and its supplementary material.

  Study EGAS50000001061

• RNA-sequencing of mechanical stress induced osteoarthritis-like damage in aged human cartilage explants treated with the anti-deiodinase iopanoic acid

  The current study aimed to confirm chondroprotective effects of the D2 inhibitor iopanoic acid (IOP) in an ex vivo aged human osteochondral explant model in which injurious mechanical stress is applied to inflict OA-like damage. This dataset contains RNA-seq (Illumina HiSeq X ten) of osteochondral explant cartilage from aged human patients that underwent a joint replacement surgery due to OA (N=10 donors; n=21 explants).

  Study EGAS00001006242

• Genetic regulation of RNA splicing in human pancreatic islets

  Genetic variants that influence transcriptional regulation in pancreatic islets play a major role in the susceptibility to type 2 diabetes (T2D). For many susceptibility loci, however, the mechanisms are unknown. To this end, we examined splicing and gene expression QTLs (sQTLs and eQTLs) in pancreatic islets from 399 human donors from publicly available cohorts and in-house data. Here we provide newly generated RNA-seq data and genotyping array data from 101 donors.

  Study EGAS00001006440

• RNA-seq of cultured human hematopoietic stem and progenitor cells from umblical cord blood in cytokine-rich ex vivo culture conditions following sphingolipid modulation

  In order to elucidate the biological pathways altered by sphingolipid modulation with N-(4-hydroxyphenyl) retinamide (4HPR) treatment in human HSPC that may contribute to the restraint in proliferation while promoting persistence of HSC self-renewal, as well as determine the mechanism of synergy in enhancement of HSC self-renewal with CB CD34+ agonists UM171 and StemRegenin 1 (SR1), we performed RNA-sequencing (RNA-Seq) of 3 pools of lin-CB cells following 2 or 4 days with DMSO, 4HPR, UM171+SR1 or 3-Factor (4HPR+UM171+SR1). We identified modulation of sphingolipid metabolism regulates self-renewal through activating coordinated stress pathways that coalesce on endoplasmic reticulum stress and autophagy programs.

  Dataset EGAD00001005140

• single cell RNA-seq of bone marrow from infants with MLL-rearranged Acute Lymphoblastic Leukemia by single cell RNA-sequencing

  This dataset contains two experiments. 1) Single cell RNA-seq of diagnostic samples from patients with MLL-rearranged infant ALL that underwent relapse or not (samples ending in R relapsed, samples ending in N did not). For some of the patients, multiple indipendent plates were produced (each plate is a sample). 2) in vitro prednisolone exposure experiment. diagnostic bone marrow samples from patient 4662R were cultured for three days with and without prednisolone. Single cell experiments were conducted according to Muraro et al (cell systems, 2016, doi:10.1016/j.cels.2016.09.002). Cell barcodes and UMI sequences are embedded in the header of each fastq entry. Cell barcodes irrelevant to this experiment were removed before submission.

  Dataset EGAD00001005461

• RNA-Seq Profiles from the CheckMate-577 Clinical Trial

  The dataset contains RNAseq profiles of 386 patients from the CheckMate-577 (CA209-577) clinical trial whose ICF allows data deposition into a public repository. The Allprep DNA/RNA FFPE kit was used to simultaneously purify genomic DNA and total RNA from formalin-fixed, paraffin embedded (FFPE) tissue sections. RNA was analyzed using the Illumina TruSeq RNA Access method for library preparation, followed by sequencing on the Illumina NovaSeq platform with a 50bp paired-end strategy and a target read depth of 50M per sample. Fastq files are included.

  Dataset EGAD50000002394

• 10X single-cell Multiome (RNA+ATAC) of cord blood-derived HSPC

  10X genomics chromium single-cell ATAC+RNA (Multiome) was use to prepare single-nucleus RNA- and ATAC-seq libraries, sequenced on an Illumina NovaSeq 6000 platform. BAM files are provided. Samples from 4 donors were sequenced after sorting for primitive HSPCs in 2 libraries, each with cells from 1 male and 1 female CB.

  Dataset EGAD50000002327

• Microvascular Permeability, Inflammation, and Lesion Physiology in Endometriosis: A Microphysiological Systems Approach

  The goal of the study is to identify novel molecular pathways involved in the etiology of endometriosis and to develop in vitro model systems to enable the study of the mechanisms regulating reproductive function. Using a physiomimetic modeling approach, primary tissue samples of the human endometrium derived from patients diagnosed with and without endometriosis and/or endometriotic diseases (e.g. adenomyosis) were used for secondary analysis using multi-omic systems biology analysis and to build tissue engineered models. We performed single cell RNA sequencing (scRNA-Seq, N=6)) to study the cellular and transcriptomic composition of the endometrial tissue microenvironment and proteomics (LC-MS/MS) to study protein-level signaling pathways and the matrisome of the native endometrium. Systems biology analysis of these datasets helped identified a putative dysregulated inflammatory signaling pathway in endometriosis patients compared to controls. Moreover, subsets from these scRNA-Seq and proteomic datasets were used to aid the design of a synthetic extracellular matrix hydrogel as an in vitro platform for the culture of endometrial cells. Tissue-engineered synthetic PEG hydrogel enables the simultaneous co-culture of endometrial epithelial organoids and stromal fibroblasts in a fully defined, 3D, and tunable environment. We demonstrated that the in vitro co-culture model recapitulates key phenotypic processes observed across the human menstrual cycle in response to sex hormone stimulation through both biochemical and transcriptomic (bulk RNA-sequencing) analysis. This model will enable future mechanistic studies that examine origins of endometriotic disease phenotypes. The transcriptomic data generated for this study including the scRNA-Seq data from primary human endometrial tissues and the bulk RNA-Seq datasets from the experimental co-culture models is available through dbGAP.

  Study phs003326

• CITE-seq for peripheral blood samples of 5 breast cancer patients

  Study EGAS00001006241

• Normal CD19

  Bulk RNA-seq on normal human CD19+ cells

  Dataset EGAD00001011105

• SPECTA Lung cancer RNA FASTQ files

  SPECTA Lung cancer RNA FASTQ files (Illumina TST170 targeted analysis)

  Dataset EGAD00001006222

• IgM heavy chain V(D)J library sequencing using varied PCR parameters

  When amplifying the rearranged V(D)J genes encoding antigen receptors during adaptive immune receptor repertoire sequencing (AIRR-seq), each cycle of the PCR can produce spurious “chimeric” hybrids of two or more different template sequences. To test the effects of PCR protocol variation on chimera formation in AIRR-seq, we produced IgM heavy chain V(D)J repertoire libraries under 8 conditions, varying the number of PCR cycles, indexing PCR template, input RNA, and extension time. Three donors were sequenced using all 8 conditions.

  Study EGAS50000001037

• Whole-transcriptome characterization of cell-free RNA (cfRNA) in cancer and non-cancer patients

  Whole-transcriptome characterization of cfRNA in cancer (stage III breast [n=46], lung [n=30]) and non-cancer (n=89) participants from the Circulating Cell-free Genome Atlas (NCT02889978). Dataset includes collapsed BAM files for plasma cfRNA from each patient, as well as collapsed BAM files for RNA from matched tumor tissue (when available).

  Dataset EGAD00001006484

• Multi-omic analysis of Down Syndrome in thyroid

  Background: Down syndrome (DS) is a chromosomal disorder characterized by trisomy of chromosome 21 and is associated with various health problems. Congenital thyroid dysfunction is a common endocrine abnormality in DS, but its exact cause remains unknown. This work aimed at studying the etiology of congenital thyroid dysfunction in DS. Methods: Thyroid tissue of fetuses with DS (n=4) and fetuses without a genetic/developmental abnormality (n=5) were analyzed using histopathological evaluation, RNA sequencing (RNA-seq), and DNA methylation (DNAm) profiling. Results: Histological examination showed abnormally developed DS thyroid tissue compared to non-DS/healthy tissue. RNA-seq analysis showed numerous differences in gene expression between DS and healthy thyroid tissue, including significant downregulation of thyroid genes FOXE1, IYD and DIO2. Gene set enrichment analysis showed disruption of vital cellular processes and functions in DS fetal thyroid tissue. Analysis of DNAm and expression quantitative trait methylation (eQTM) analysis identified functional and biologically relevant associations, that can be linked to plausible disrupted pathways in DS. Conclusions: Based on these observations, we conclude that congenital non-autoimmune thyroid dysfunction in DS is probably a DS-specific form of thyroid dysfunction, characterized by abnormal thyroid development, altered expression of genes that regulate thyroid development and hormone production, and altered expression and DNAm of genes pertaining to numerous vital pathways leading to significant metabolic disturbances. The disturbances as investigated by multi-omics analyses (RNA-seq, DNAm and eQTM analyses) are widely spread over the genome, and presumably caused by the direct and downstream effects of overexpressed chromosome 21 genes (dosage effect), and (epi)genomic disturbances.

  Study EGAS00001007677

• Direct Comparative Analysis of 10X Genomics Chromium and Smart-seq2

  Single cell RNA sequencing (scRNA-seq) is widely used for profiling transcriptomes of individual cells. The droplet-based 10X Genomics Chromium (10X) approach and the plate-based Smart-seq2 full-length method are two frequently-used scRNA-seq platforms, yet there are only a few thorough and systematic comparisons of their advantages and limitations. Here, by directly comparing the scRNA-seq data by the two platforms from the same samples of CD45- cells, we systematically evaluated their features using a wide spectrum of analysis. Smart-seq2 detected more genes in a cell, especially low abundance transcripts as well as alternatively spliced transcripts, but captured higher proportion of mitochondrial genes. The composite of Smart-seq2 data also resembled bulk RNA-seq data better. For 10X-based data, we observed higher noise for mRNA in the low expression level. Despite the poly(A) enrichment, approximately 10-30% of all detected transcripts by both platforms were from non-coding genes, with lncRNA accounting for a higher proportion in 10X. 10X-based data displayed more severe dropout problem, especially for genes with lower expression levels. However, 10X-data can better detect rare cell types given its ability to cover a large number of cells. In addition, each platform detected different sets of differentially expressed genes between cell clusters, indicating the complementary nature of these technologies. Our comprehensive benchmark analysis offers the basis for selecting the optimal scRNA-seq strategy based on the objectives of each study.

  Study EGAS00001003973

• Single-Cell Sequencing Reveals Distinct Microenvironment Cell Types Associated with Response to High Dose Melphalan and Autologous Stem Cell Transplant in Multiple Myeloma

  The aim of this study is to understand the transcriptome of microenvironment cells in pre- and post- autologous stem cell transplantation (ASCT) samples between patients with complete response and partial response to transplant. Here, we studied immune cells from 40 multiple myeloma patients who have undergone ASCT. Samples were collected from 40 multiple myeloma patients before and after ASCT. Samples were sorted using magnetic activated cell sorting (MACS) and the non-tumor CD138- fraction underwent 3' single cell RNA-sequencing (scRNA-seq) or 5' scRNA-seq + 5' T cell receptor sequencing (TCRseq), to determine the scRNA transcriptome and TCR repertoire. This study includes 24 samples with 3' scRNA-seq, 56 samples with 5' scRNA-seq, and 56 samples with 5' TCRseq data.

  Study phs003219

• Tumor detection by analysis of both symmetric- and hemi-methylation of plasma cell-free DNA

  Aberrant DNA methylation plays a critical role in tumorigenesis. While DNA methylation has been used for cancer detection and classification, DNA hemi-methylation, a novel epigenetic mark has not been analyzed extensively in cancer epigenomes. Here we report a strand-specific (ss) sequencing method (MeDIP-Seq) for plasma cell free (cf) DNA (sscf-MeDIP-Seq), which can analyze both symmetrically methylated DNA regions (DMRs) as well as hemi-methylated regions (DHMRs). Using the sscf-MeDIP-Seq method, we analyzed plasma cfDNA methylomes of 271 samples from subjects with liver cancer and brain cancer and from individuals without cancer (controls). Among them, 215 samples were chosen as the discovery cohort for the identification of DMRs and DHMRs specific in each subject group and for the training of machine learning models of multi-cancer detection (MCD) using DMRs, DHMRs and DMRs+DHMRs as inputs. These models were then used to predict the 56 samples in the validation cohort. We found that models trained with DMRs+DHMRs as inputs in general outperformed models trained with DMRs or DHMRs alone, with AUROC being 0.971, 0.981, and 0.99 in predicting control, liver and brain cancer samples in the validation cohort, respectively, by the DMR+DHMR-trained models.

  Study phs003462

• FBSeq: RNA sequencing of human fetal brain.

  RNA sequencing of 120 human fetal brains (aged 12-19 post-conception weeks) was performed as part of a Medical Research Council (U.K.) funded project (MR/L010674/2) to investigate genetic effects on gene expression in the developing human brain and their role in neuropsychiatric disorders. Human fetal brain tissue from elective terminations of pregnancy was provided by the Human Developmental Biology Resource (HDBR) (http://www.hdbr.org), with ethical approval (#08/H0712/34), and with consent for use of fetal material for medical research provided by female donors. Total RNA was extracted from half of the available brain tissue from each fetus using Tri-Reagent and treated with TURBO DNase. RNA-Seq libraries were prepared using 1µg of purified total RNA and the Illumina TruSeq Stranded Total RNA Library Prep kit, following ribosomal RNA depletion. Libraries were sequenced on the Illumina HiSeq 2500 or HiSeq 4000 systems, generating at least 50 million read pairs per sample.

  Study EGAS00001003214

• scRNA-seq data set for 13 AML patients

  This dataset includes single cell (sc)RNA-seq of N=13 AML patients. The dataset includes bam files that have been purged of reads mapping to cells where a majority of the reads from that cell mapped to the human genome. All scRNA-seq data were generated using the Chromium Next GEM Single Cell 5' Reagent Kits v2 (Dual Index) from 10X Genomics.

  Dataset EGAD50000001346

• RNA-Seq dataset for malignant pleural and peritoneal mesothelioma

  This dataset contains RNA sequencing data of pleural and peritoneal mesothelioma. The number of samples is 18. Sequencing was performed on Illumina NovaSeq 6000 and Illumina NovaSeqXPlus. The sequencing was always paired.

  Dataset EGAD50000001342

• ImmuNEO CD8 cell lines

  Paired RNA-Seq data from 16 samples of different tumors CD8+ T cells added to the study "Proteogenomic analysis reveals RNA as a source for tumor-agnostic neoantigen identification (H021)". Sequencing was performed on Illumina NextSeq 500. The sequencing was always paired

  Dataset EGAD50000000193

• Sequencing data for oesophageal and related samples - Rogerson et al (RNA)

  Data supporting: "Repurposing of KLF5 activates a cell cycle signature during the progression from Barrett’s Oesophagus to Oesophageal Adenocarcinoma." Rogerson et al. RNA-seq data 2 samples

  Dataset EGAD00001005915

• RNA-seq of iPSC-derived neurons treated with miRNA mimics and inhibitors

  iPSC-derived neurons were treated with mimics and inhibitors of the miRNAs miR-150-5p, hsa-mir-193a-3p and hsa-miR-19b-3p. RNA-sequencing was then performed to examine the effects of miRNA up-regulation and inhibition.

  Dataset EGAD00001006844

• Stereotyped subset CLL RNA-seq from 100 patient with CLL

  RNA-sequencing data for 100 stereotypes CLL subset cases. Illumina based short-read sequencing data from a 100 samples from patients with CLL. Oxford Nanopore Technologies long-read sequencing data from 5 samples from patients with CLL.

  Dataset EGAD00001009729

• DNA methylation database for gynecological cancer detection, classification and assay development

  DNA methylation changes are associated with cancer the best predictor of the presence of cancer. In order to detect and classify gynecological cancer, we performed MeD-seq to generate DNA methylation profiles of vulvar, cervical, endometrial, fallopian tube and ovarian cancers and their associated healthy tissues. DNA methylation profiles were used to find general cancer, regional cancer and specific cancer biomarkers that can be used for cancer detection, classification and treatment monitoring.

  Study EGAS50000000417