14982 results for "cancer studies using rna-seq"

in 19.65 milliseconds.

• Colorectal organoids and tumoroids - pulldown (2018-08-13)

  Targeted capture of cancer gene panel bait set in single cell derived organoids from colon tissue and colorectal cancer from 1 patient. . This dataset contains all the data available for this study on 2018-08-13.

  Dataset EGAD00001004292

• Multi-sample whole exome sequencing from 4 cases of advanced breast cancer.

  Metastatic and primary tumour samples were collected from 4 patients with advanced breast cancer. Samples were collected at autopsy and also from biopsies taken during life. Tumour and germline samples are available. Whole exome sequencing was performed on all samples.

  Dataset EGAD00001003137

• Evolution of neoantigen landscape during immune checkpoint blockade in non-small cell lung cancer

  The dataset for Evolution of neoantigen landscape during immune checkpoint blockade in non-small cell lung cancer includes 17 bam files from next-generation sequencing on the Illumina HiSeq2500. The biospecimens analyzed include matched tumor pre-treatment, post-progression and normal samples.

  Dataset EGAD00001004336

• Harnessing transposons for drug resistance gene discovery in cancer

  This study involves a forward genetic screen to identify common insertion sites in drug resistant clones. We will be utilising piggybac transposon systems in order to generate multiple drug resistant clones in a range of human cancer cell lines.

  Dataset EGAD00001000980

• Sequencing files for "The Evolutionary Origins of Recurrent Pancreatic Cancer."

  Files from DNA sequencing from primary tumors and metastases from pancreatic cancer patients along with matched normal tissues. Sequencing files include those derived from whole exome sequencing as well as MSK-IMPACT sequencing.

  Dataset EGAD00001005779

• MicroRNA profiling by next-generation sequencing for colorectal cancer screening

  The dataset contains raw miRNA sequencing data of plasma samples from 20 newly diagnosed colorectal cancer cases and 20 controls free of colorectal neoplasms matched by age and sex. It includes files in the FASTQ compressed (.gz) format.

  Dataset EGAD00001006966

• WGS and WTS data for manuscript titled: Pathologist-initiated whole genome and transcriptome sequencing demonstrates diagnostic utility in resolving difficult-to-diagnose tumors

  Illumina platform sequencing of whole genome libraries prepared from normal and cancer samples and whole transcriptome libraries from cancer samples from 45 donors

  Dataset EGAD00001015615

• Data from Representation of genomic intratumor heterogeneity in multi-region non-small cell lung cancer patient-derived xenograft models

  Data from Representation of genomic intratumor heterogeneity in multi-region non-small cell lung cancer patient-derived xenograft models

  Dataset EGAD00001012228

• CRU-Ukrainian National Research Center for Radiation Medicine Trio Study

  Data on transgenerational effects following nuclear accidents are important for understanding fully the consequences of parental exposure to ionizing radiation. Few studies to date have had adequate statistical power to detect effects of the magnitude expected based on animal data, and most have not been of low-dose, protracted exposures associated with nuclear accidents and their aftermath. Although, to date, scant use has been made of the new genomic technologies, in Chernobyl-exposed areas of Ukraine and Belarus, excess minisatellite mutations have been seen in children born after the accident. We propose a study of parent-child trios in which at least one parent was exposed to Chernobyl radiation as a clean-up worker (mean dose>=100 mGy) and/or evacuee from a contaminated area (mean >=50 mGy). The specific aims are to investigate the transgenerational and de novo mutation rates of the spectrum of genetic variants in trios, in particular looking at effects in children and mapping them to possible parental origin of the chromsoome. Together with long-term collaborators at the Research Center for Radiation Medicine (RCRM) in Kiev, epidemiologic data will be collected for up to 450 trios of parents with preconceptional doses and their unexposed offspring. We will use state-of-the-art genomic technologies to characterize the landscape of the genomes of the trios to determine whether parental radiation exposure is associated with genetic mutations transmitted to the offspring, by examining de novo mutation rates, minisatellite mutations, copy number alterations, and variations in telomere length. The analysis will be conducted in peripheral blood and/or buccal samples (when blood is not available) from complete father-mother-child trios. Doses to the gonads from the time of the accident to the time of conception will be reconstructed for all parents using existing records supplemented by interview data. Trio subjects will be selected from representative populations exposed to radiation from Chernobyl who are under active follow-up in the Clinico-Epidemiologic Registry at RCRM. To help identify specific effects of paternal and maternal radiation exposure, we will initially select sets of trio subjects in five categories: (1) exposed father, unexposed mother; (2) unexposed father, exposed mother; (3) both parents exposed; (4) both parents unexposed; and (5) a group of high dose "emergency workers" with acute radiation syndrome. All trio members will be invited to the RCRM outpatient clinic for collection of a 20 ml blood sample (or buccal cells for those who refuse phlebotomy). Both parents will be asked to complete a general questionnaire to obtain demographic and lifestyle data. Then one or both will complete detailed dosimetry questionnaires, based on forms used in previous collaborations with RCRM and administered by specially trained interviewers. Once 50 trios have been recruited (10 from each of the 5 exposure categories), we will conduct an interim evaluation of participation rates, sample collection and quality, and dose reconstruction in order to modify the protocol as needed. The analytical approach will be to correlate the extent, especially for de novo events of genetic alterations in the offspring with parental pre-conceptional radiation dose overall and by parental origin. The statistical power in relation to de novo mutations is very high, in excess of 90%, but somewhat lower for trends in minisatellite mutations. Study findings will contribute importantly to knowledge of the heritable effects of moderate- and low-dose radiation exposure in humans and to radiation risk projection. Eventually data from the Trio Study may be shared with the international community through dbGap.

  Study phs001163

• NHLBI TOPMed - NHGRI CCDG: The BioMe Biobank at Mount Sinai

  The IPM BioMe Biobank, founded in September 2007, is an ongoing, broadly-consented electronic health record (EHR)-linked clinical care biobank that enrolls participants non-selectively from the Mount Sinai Medical Center patient population. BioMe currently comprises >42,000 participants from diverse ancestries, characterized by a broad spectrum of longitudinal biomedical traits. Participants are enrolled through an opt-in process and consent to be followed throughout their clinical care (past, present, and future) in real-time, allowing us to integrate their genomic information with their EHRs for discovery research and clinical care implementation. BioMe participants consent for recall, based on their genotype and/or phenotype, permitting in-depth follow-up and functional studies for selected participants at any time. Phenotypic and genomic data are stored in a secure database and made available to investigators, contingent on approval by the BioMe Governing Board. BioMe uses a "data-broker" system to protect confidentiality. Ancestral diversity - BioMe participants represent a broad racial, ethnic and socioeconomic diversity with a distinct and population-specific disease burden. Specifically, BioMe participants are of African (AA), Hispanic/Latino (HL), European (EA) and other/mixed ancestry. BioMe participants are predominantly of African (AA, 24%), Hispanic/Latino (HL, 35%), European (EA, 32%), and other ancestry (OA, 10%). Participants who self-identify as Hispanic/Latino further report to be of Puerto Rican (39%), Dominican (23%), Central/South American (17%), Mexican (5%) or other Hispanic (16%) ancestry. More than 40% of European ancestry participants are genetically determined to be of Ashkenazi Jewish ancestry. With this broad ancestral diversity, BioMe is uniquely positioned to examine the impact of demographic and evolutionary forces that have shaped common disease risk. Phenotypes available in BioMe - BioMe has a high-quality and validated set of fully implemented clinical phenotype data that has been culled by a multi-disciplinary team of experienced investigators, clinicians, information technologists, data-managers, and programmers who apply advanced medical informatics and data mining tools to extract and harmonize EHRs. BioMe, as a cohort, offers a great versatility for designing nested case-control sample-sets, particularly for studying longitudinal traits and co-morbidity in disease burden. Biomedical and clinical outcomes: The BioMe Biobank is linked to Mount Sinai's system-wide Epic EHR, which captures a full spectrum of biomedical phenotypes, including clinical outcomes, covariate and exposure data from past, present and future health care encounters. As such, the BioMe Biobank has a longitudinal design as participants consent to make all of their EHR data from past (dating back as far as 2003), present and future inpatient or outpatient encounters available for research, without restriction. The median number of outpatient encounters is 21 per participant, reflecting predominant enrollment of participants with common chronic conditions from primary care facilities. Environmental data: The clinical and EHR information is complemented by detailed demographic and lifestyle information, including ancestry, residence history, country of origin, personal and familial medical history, education, socio-economic status, physical activity, smoking, dietary habits, alcohol intake, and body weight history, which is collected in a systematic manner by interview-based questionnaire at time of enrollment. The IPM BioMe Biobank contributed ~10,600 DNA samples for whole genome sequencing to the TOPMed program. Samples were selected for the Coronary Artery Disease (CAD) and the Chronic Obstructive Pulmonary Disease (COPD) working groups. Using a Case-Definition-Algorithm (CDA), we identified ~4,100 individuals with CAD (~50% women) and ~3,000 individuals as controls (65% women). In addition, we identified ~800 individuals with COPD (62% women) and 1800 individuals as controls (72% women). Another 600 BioMe participants with Atrial Fibrillation, all of African ancestry, were included.

  Study phs001644

• Whole-exome sequencing data of patients with T follicular helper cell lymphomas

  This dataset contains WES data analyzed using the Genomon2 pipeline (v.2.6.2, https://github.com/Genomon-Project) from 14 patients with T follicular helper cell lymphomas (TFHLs). A list of somatic mutations called by the Genomon2 pipeline for each sample is provided as a txt file.

  Dataset EGAD50000000401

• Immuno-nephrology Data Access Committee Amsterdam UMC

  In this experiment we investigated kidney immunology in two autoimmune kidney diseases (ANCA-associated glomerulonephritis and Lupus Nephritis). Single cell transcriptome analysis of CD45+ immune cells from freshly retrieved kidney biopsies of 5 AGN patiënts, 1 disease-control with lupus nephritis (class II), and 1 "healthy" nephrectomy control was performed using 10X Genomics technology.

  Dac EGAC50000000024

• Exome - Uveal Melanomas

  Whole exome sequencing on 11 uveal melanoma samples and corresponding germline samples. Libraries were prepared using the SureSelectXT2 Clinical Research Exome V2 kit (Agilent, 5190-9500 and G9621B). Paired-end libraries (2 x 100 bp) were sequenced on HiSeq 2000/2500 or NovaSeq 6000 instruments (Illumina).

  Dataset EGAD50000000767

• Spatial transcriptomics of hepatoblastoma

  10X Genomics Visium Spatial transcriptomics analysis of hepatoblastoma and adjacent normal liver. Spatial data of normal liver and PT2 (fastq files), and and PT13, PT14 and PT16 (bam files) using the Visium Spatial Gene Expression Solution. All samples, except PT13, were collected post-chemotherapy.

  Dataset EGAD50000000797

• High-grade serous ovarian carcinoma tumour whole genome sequencing variants

  Tumour somatic variants (in VCF format) called from paired tumour-germline whole genome sequencing (using IDT library preparation system followed by sequencing on Illumina NovaSeq platform) of three high-grade serous ovarian tumour samples. The dataset comprises three VCF variant files.

  Dataset EGAD50000001131

• Whole genome sequencing raw data for fragile X associated unmethylated expansion carrier 1

  Whole genome sequencing raw data for fragile X associated unmethylated expansion carrier 1. DNA was sequenced using the illumina NovaSeq6000 system. 8x paired end FASTQ files from one DNA sample (UFM 1), 4x R1 files and 4x R2 files.

  Dataset EGAD50000000917

• Whole genome sequencing raw data for fragile X associated unmethylated expansion carrier 2

  Whole genome sequencing raw data for fragile X associated unmethylated expansion carrier 2. DNA was sequenced using the illumina NovaSeq6000 system. 8x paired end FASTQ files from one DNA sample (UFM 2), 4x R1 files and 4x R2 files.

  Dataset EGAD50000000921

• Paired whole genome sequencing of tumor-control pairs of Thymic epithelial tumors -Additional Dataset

  Paired whole genome and exome sequencing of tumor-control pairs of Thymic epithelial tumors, additional samples with paired fastq files, sequenced on Illumina-HiSeq_X_Ten and using the Illumina_TruSeq_Nano_DNA kit.

  Dataset EGAD50000000325

• APS-1 Global Gene Expression

  Single cell sequencing of expanded regulatory T cells (Tregs) in 9 APS-1 patients and 9 age and gender matched controls. Gene expression (GEX) libraries were generated by using the Library Construction Kit from 10x genomics.

  Dataset EGAD50000000260