14980 results for "cancer studies using rna-seq"

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• sc-RNA-Seq soft tissue sarcoma tumoroid biobank

  Dataset EGAD00001009002

• IMpower133 subtype assignments

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

  Dataset EGAD00001006926

• UROMOL 2020 - RNA-seq data for validation

  Total RNAseq data from 47 patients with NMIBC.

  Study EGAS00001005050

• Single-nuclei ATAC sequencing of pheochromocytoma and paraganglioma arising from germline SDHB mutations

  10x genomics single-nuclei ATAC sequencing of 7 SDHB-deficient non-metastatic and metastatic pheochromocytoma and paraganglioma, and 1 normal adrenal medulla sample. snATAC-seq was conducted using the "Van Helsing" protocol (dx.doi.org/10.17504/protocols.io.bw52pg8e). Once processed, snATAC-seq libraries were sequenced on the Illumina NextSeq 500 (Illumina, USA) using 50bp paired-end sequencing. Raw sequencing data in BCL format was demultiplexed using cellranger-atac mkfastq (V2.0.0). This dataset contains sequencing reads in FASTQ format. R1/R3 files contain the forward and reverse sequencing reads, respectively, R2 contains the 10x Barcode.

  Dataset EGAD50000000503

• Sequence Data for DEEP Release August 2016

  DEEP (German Epigenome Project) sequence data of following samples (Sequencing Types: Chip-Seq, WGBS-Seq, RNA-Seq, sncRNA-Seq, NOMe-Se, DNase-Seq): 41_Hf01_LiHe_Ct, 41_Hf02_LiHe_Ct, 41_Hf03_LiHe_Ct, 01_HepG2_LiHG_Ct1, 01_HepG2_LiHG_Ct2, 01_HepaRG_LiHR_D31, 01_HepaRG_LiHR_D32, 01_HepaRG_LiHR_D33, 43_Hm01_BlMo_Ct, 43_Hm03_BlMo_Ct, 43_Hm05_BlMo_Ct, 43_Hm03_BlMa_Ct, 43_Hm05_BlMa_Ct, 43_Hm03_BlMa_TO, 43_Hm05_BlMa_TO, 43_Hm03_BlMa_TE, 43_Hm05_BlMa_TE, 51_Hf01_BlCM_Ct, 51_Hf03_BlCM_Ct, 51_Hf04_BlCM_Ct, 51_Hf02_BlCM_Ct, 51_Hf05_BlCM_Ct, 51_Hf06_BlCM_Ct, 51_Hf06_BlCM_T1, 51_Hf06_BlCM_T2, 51_Hf03_BlEM_Ct, 51_Hf04_BlEM_Ct, 51_Hf02_BlEM_Ct, 51_Hf05_BlEM_Ct, 51_Hf06_BlEM_Ct, 51_Hf06_BlEM_T1, 51_Hf06_BlEM_T2, 51_Hf03_BlTN_Ct, 51_Hf04_BlTN_Ct, 51_Hf02_BlTN_Ct, 51_Hf05_BlTN_Ct, 51_Hf06_BlTN_Ct, 51_Hf06_BlTN_T1, 51_Hf06_BlTN_T2, 51_Hf07_BmTM4_Ct, 51_Hf08_BlTM4_Ct, 51_Hf08_BmTM4_SP1, 51_Hf08_BmTM4_SP2, 51_Hf05_BlTA_Ct, 44_Mm01_WEAd_C2, 44_Mm03_WEAd_C2, 44_Mm02_WEAd_C2, 44_Mm07_WEAd_C2, 44_Mm04_WEAd_C1, 44_Mm05_WEAd_C1

  Dataset EGAD00001002527

• RNA-sequencing of Human Thyroid cancer cell lines

  RNA-sequencing data of 5 human thyroid cancer cell lines cultured in control conditions

  Dataset EGAD00001011677

• SPECTA Lung cancer RNA FASTQ files

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

  Dataset EGAD00001006222

• Evaluation of triple negative breast cancer with heterogeneous immune infiltration

  Identification of potential RNA markers fir TILs infiltration in patients with triple negative breast cancer with heterogeneous immune infiltration.

  Study EGAS00001007159

• Whole genome and transcriptome analysis of a sporadic and recurring parathyroid carcinoma

  Parathyroid carcinoma is an extremely rare endocrine malignancy with an estimated incidence of less than 1 per million population. Excessive secretion of parathyroid hormone, extremely high serum calcium level and the deleterious effects of hypercalcemia are the clinical manifestations of the disease. Up to 60% of patients develop multiple disease recurrences and although long-term survival is possible with palliative surgery, permanent remission is rarely achieved. Molecular drivers of sporadic parathyroid carcinoma have remained largely unknown. Previous studies, mostly based on familial cases of the disease, suggested a potential role for the tumor suppressor MEN1 and proto-oncogene RET in benign parathyroid tumorigenesis while the tumor suppressor HRPT2 and proto-oncogene CCND1 may also act as drivers in parathyroid cancer. Here, we report the complete genomic analysis of a sporadic and recurring parathyroid carcinoma. Mutational landscapes of the primary and recurrent tumor specimens were analyzed using high throughput sequencing technologies. Such molecular profiling allowed for identification of somatic mutations never previously identified in this malignancy. These included single nucleotide point mutations in well characterized cancer genes such as mTOR, MLL2, CDKN2C and PIK3CA. Comparison of acquired mutations in patient-matched primary and recurrent tumors revealed loss of PIK3CA activating mutation during the evolution of the tumor from the primary to the recurrence. Structural variations leading to gene fusions and regions of copy loss and gain were identified at a single-base resolution. Loss of short arm of chromosome 1 along with somatic mis-sense and truncating mutations in CDKN2C and THRAP3, respectively, provide new evidence for the potential role of these genes as tumor suppressors in parathyroid cancer. The key somatic mutations indentified in this study can serve as novel diagnostic markers as well as therapeutic targets.

  Study EGAS00001000484

• 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

• 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

• Transcriptomic profiling of lymphedema

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

  Dataset EGAD00001006246

• 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

• 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

• Dual RNA-seq data of chlamydia infected M2 macrophages

  This dataset contains 8 unpaired fastq files sequenced with Ilumina NextSeq 2000. The files contain transcriptome data of host and pathogen from infected and uninfected samples from extracted total RNA at 30 hours post infection.

  Dataset EGAD50000001398

• Cardiac Translatomes of 80 Human Samples

  Dataset consists of fastq files of Ribo-seq, polyA-RNA and total RNA sequencing of 80 samples (65 DCM cases and 15 controls)

  Dataset EGAD00001004394

• RNA-seq explants chondrocytes

  Osteochondral explants were obtained from knee joints (n=17 explants) from the RAAK study. Paired-end 2x150 bp RNA sequencing (Illumina TruSeq mRNA Library Prep Kit, Illumina HiSeq X) was performed.

  Dataset EGAD00001008752

• ChIP-seq in colorectal cancer and paired adjacent normal mucosa

  This dataset includes ChIP-seq data for H3K27ac and H3K4me1 on 20 paired samples of colorectal cancer and adjacent normal mucosa. One tumor sample that failed QC is not available.

  Dataset EGAD00001007707

• Detection of clinically relevant genetic and transcriptomic landscape in DLBCL uniformly treated by R-CHOP

  Recent studies using next-generation sequencing strategies have described the landscape of genetic alterations in diffuse large B-cell lymphoma (DLBCL). However, little is known about the clinical relevance of recurrent mutations and copy number alterations and their transcriptional footprints. This study examines the frequency, interaction and clinical impact of recurrent genetic aberrations in DLBCL using high-resolution technologies in a large population-based cohort.

  Dataset EGAD00001003783

• 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

• RNA seq of MPNST tumour samples

  RNA sequencing of 10 MPNST tumour samples as part of a larger study of MPNST. Article: Lyskjær et al, 2020, J Pathol, "H3K27me3 expression and methylation status in histological variants of malignant peripheral nerve sheath tumours".

  Study EGAS00001004528

• Dataset for urologic_cancer-RNA

  Rare cancer sequencing data of 75 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008851

• Whole Exome sequencing of Gingivo-buccal Cancer : ICGC-India Project_Batch04

  Whole Exome Sequencing of Gingivo-buccal Cancer: ICGC-India ProjectAs a part of the ICGC, India has undertaken genomic studies on gingivobuccal cancer of the oral cavity, which is the most prevalent form of cancer among men in India. There are various known environmental (life-style) correlates of this cancer, the most important of which are tobacco chewing and HPV infection. Exome of paired DNA samples – isolated from the tumor tissue and from the blood of 47 patients – have been performed to catalog germline and somatic mutations. Association between each observed genomic alteration and exposure to environmental risk factors is being explored. Detailed clinical characterization of the patients, collection of data on demographic and environmental exposures, and isolation of DNA samples from blood and tumor tissues collected from each patient are being done at the Advanced Centre for Research, Treatment and Education on Cancer, Mumbai. Whole exome sequencing is being performed at the National Institute of Biomedical Genomics, Kalyani.

  Study EGAS00001002852

• Aurora US Metastatic Breast Cancer Retrospective Project

  The AURORA US Metastatic Breast Cancer project is funded by the Breast Cancer Research Foundation (BCRF) Evelyn H. Lauder Founder's Fund for Metastatic Breast Cancer Research. This multi-center effort conducted within the Translational Breast Cancer Research Consortium (TBCRC) and cancer researchers to better understand the metastatic process through the study of both the primary and metastatic tissue. In the retrospective phase, TBCRC sites submitted matched primary and metastatic tissues and blood from previous collections for piloting the process. Samples were profiled using whole genome DNA sequencing, whole exome DNA sequencing, DNA methylation arrays, and RNA sequencing. The final freeze set for samples with successful nucleic acid extraction and molecular assays included 55 patients with 31 primary tissues and 102 metastases. Twenty patients had tissue collected at autopsy and included 19 with more than one metastasis. Metastases were from 20 different tissue locations with the most common sites being liver, lung, lymph node, and brain. The median age at primary diagnosis was 49 years. The clinical subtypes of the primary tumors were 34% Triple Negative, 30% ER+HER2-, 11% ER+HER2+, and 7% ER-HER2+. Patients received an average of 3 lines of therapy in the metastatic setting. The median overall survival from primary diagnosis was 4.5 years and overall survival from metastatic diagnosis was 2 years.

  Study phs002622

• Melanoma_multi_site_metastases

  Despite multiple large-scale sequencing studies offering substantial insight into the genomic landscape of cutaneous melanoma, the molecular events surrounding disease progression and the resulting molecular heterogeneity between metastases have not been fully elucidated. We have been applying whole genome and transcriptome sequencing across metastases collected during post mortem as well as to multi-site metastases during treatment

  Study EGAS00001001348