15147 results for "cancer studies using rna-seq"

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• 20200819_EGA_Qld_Melanoma.biomarkers

  A study looking at Germline and Somatic biomarkers using WES data only

  Dataset EGAD00001006374

• INVADE cohort

  Cohort of bulk RNAseq data from DCIS, invasive and microinvasive breast cancer at diagnosis. This dataset covers 18 DCIS cases, 17 microinvasive and 20 primary invasive breast cancer.

  Study EGAS50000000219

• DAC for "Cancer-associated fibroblasts promote drug resistance in ALK-driven lung adenocarcinoma cells by upregulating lipid biosynthesis"

  DAC for the data of "Cancer-associated fibroblasts promote drug resistance in ALK-driven lung adenocarcinoma cells by upregulating lipid biosynthesis" with: • Holger Sültmann • h.sueltmann@dkfz.de • German Cancer Research Center (DKFZ) • Ann-Kathrin Daum • a.daum@dkfz.de • German Cancer Research Center (DKFZ)

  Dac EGAC50000000076

• Organoid_Derivation_Project___GRCh38___RNAseq

  Generation of normal and cancer organoids for sequencing as part of the WTSI-CRUK international collaboration with NCI (in HCMI) to generate the next generation of cancer cell lines.

  Study EGAS00001004677

• Organoid_Derivation_Project___GRCh38___WGS

  Generation of normal and cancer organoids for sequencing as part of the WTSI-CRUK international collaboration with NCI (in HCMI) to generate the next generation of cancer cell lines

  Study EGAS00001004712

• Organoid_Derivation_Project___GRCh38___TGS

  Generation of normal and cancer organoids for sequencing as part of the WTSI-CRUK international collaboration with NCI (in HCMI) to generate the next generation of cancer cell lines.

  Study EGAS00001007349

• Repeated Sampling experiment

  Repeated Sampling Experiment containing 135 fastq files of RNA sequencing. These represent time series of different organ areas sampled during autopsies.

  Dataset EGAD50000000330

• RCRF release 2

  RNA sequencing of a Chromophobe renal cell carcinoma, Non-clear cell chromophobe renal cell carcinoma, Neuroendocrine PDAC and clear cell sarcoma

  Dataset EGAD50000000872

• Single cell sequencing of mild and critical COVID19 PBMC

  Single-cell RNA sequencing of 13 ‘mild-moderate’ and 10 ‘critical’ COVID19 PBMC samples

  Dataset EGAD00001006997

• Single Nuclei RNA sequencing batch 1

  This dataset includes the first 9 PTC samples and 6 ATC samples profiled on the same sequencing flowcell.

  Dataset EGAD00001011366

• Dataset developed for use with EOSC4Cancer of synthetic colorectal cancer tumor/normal pairs.

  This dataset contains 10 tumor and normal pairs synthetic WGS data of colorectal cancer that were simulated in a standard format of Illumina paired-end reads. The NEAT read simulator (version 3.0, https://github.com/zstephens/neat-genreads) was utilized to synthetize these 10 pairs of tumor and normal WGS data. In the procedure of data generation, simulated parameters (i.e., sequencing error statistics, read fragment length distribution and GC% coverage bias) were learned from data models provided by NEAT. The average sequencing depth for tumor and normal samples aimed to reach around 110X and 60X, respectively. For generation of synthetic normal WGS data per each sample, a germline variant profile from a real patient was down-sampled randomly, representing 50% germline variants of a given patient. These were mixed with the other 50% in silico germline variants that were modelled randomly using an average mutation rate (0.001), finally constituting a full germline profile for normal synthetic WGS data. For generation of synthetic tumor WGS data per each sample, a pre-defined somatic short variant profile (SNVs+Indels) learnt from a real CRC patient was added to the germline variant profile used for creating the normal synthetic WGS data of the same patient, consisting of the variants for tumor sample. Neither copy number profile nor structural variation profile was introduced into the tumor synthetic WGS data. Tumor content and ploidy were assumed to be 100% and 2, respectively. For mapping/variant detection, the Sarek pipeline v3.1.2 (https://nf-co.re/sarek/3.1.2) was used, specifically: 1. BWA v0.7.17-r1188 for read mapping 2. GATK v4.3.0.0 for pre-processing BAM file (including markduplicates and recalibration). 2. Mutect2 (GATK v4.3.0.0) for somatic variant calling 3. Strelka2 v2.9.10 for germline and somatic variant calling Metadata information of 10 CRC patients used for the generation of synthetic normal and tumor WGS data: Patient_id Tumor_barcode Normal_barcode Age Sex Tissue Cancer SIM007 SIM007_T SIM007_N 71 F Rectal Primary CRC SIM008 SIM008_T SIM008_N 45 F Colon Neuroendocrine Metastasis CRC SIM010 SIM010_T SIM010_N 62 M Colon Metastasis CRC SIM011 SIM011_T SIM011_N 55 M Colon Neuroendocrine Metastasis CRC SIM012 SIM012_T SIM012_N 57 M Rectal Metastasis CRC SIM013 SIM013_T SIM013_N 69 M Colon Metastasis CRC SIM014 SIM014_T SIM014_N 68 M Colon Neuroendocrine primary CRC SIM015 SIM015_T SIM015_N 58 F Colon Primary CRC SIM016 SIM016_T SIM016_N 49 M Colon/Rectal Primary CRC SIM017 SIM017_T SIM017_N 78 M Colon Neuroendocrine primary CRC

  Dataset EGAD50000000564

• Cancer Genomics, ICR, cell line data

  DAC for cell line (non patient) data from the Cancer Genomics group, Division of Genetics and Epidemiology at the Institute of Cancer Research, Sutton, UK.

  Dac EGAC50000000023

• Validation_for_human_early_embryonic_substitutions_

  PCR and MiSeq validation for early embryonic substitution candidates from 400 Breast cancer patients

  Study EGAS00001001218

• Genomic profiling identified ERCC2 helicase domain mutations respond to platinum-based neoadjuvant therapy in urothelial bladder cancer

  Genomic profiling of tumours enables therapeutic decisions, and identifying drug-matched mutations will prolong survival and prognosis. Here, we generated a custom panel for detecting genetic alterations in 19 patients with urothelial bladder cancer. This panel targeted 71 genes associated with urological cancer. Targeted sequencing was performed on formalin-fixed paraffin-embedded tumour tissues. In this study, we revealed genome profiles and actionable mutations in patients with urothelial bladder cancer.

  Study JGAS000241

• Broad utility of ultrasensitive analysis of ctDNA dynamics across solid tumors treated with immunotherapy

  Plasma ctDNA sequencing data from samples collected from a pan-cancer cohort of patients with advanced-stage disease treated with IO therapies. This study highlights the broader applicability of ultrasensitive ctDNA as a biomarker across cancer types and immunotherapy modalities. Here, we applied a whole-genome, tumor-informed approach to study ctDNA in 202 advanced-stage cancer patients treated at VHIO. This study captures the plasma sequencing data from the project.

  Study EGAS50000001271

• Comparative genomics research for Chinese colorectal cancer

  In order to clarify the mechanisms of genesis and development of colorectal carcinoma, in this study, we performed comparative genomics research in Chinese colorectal cancer at various clinical stage via whole exon sequencing or whole genome sequencing. Comparative bioinformatical analysis was perform to find the difference on somatic single-nucleotide variants, short insert/deletion variants, genomic structural variants, and copy number variants between cancer tissue and normal tissue, or among cancer tissues at various clinical stages.

  Study EGAS00001002576

• Comparative genomics research for Chinese colorectal cancer

  In order to clarify the mechanisms of genesis and development of colorectal carcinoma, in this study, we performed comparative genomics research in Chinese colorectal cancer at various clinical stage via whole exon sequencing or whole genome sequencing. Comparative bioinformatical analysis was perform to find the difference on somatic single-nucleotide variants, short insert/deletion variants, genomic structural variants, and copy number variants between cancer tissue and normal tissue, or among cancer tissues at various clinical stages.

  Study EGAS00001002607

• Using de novo assembly to identify structural variation of complex immune system gene regions

  Driven by the necessity to survive environmental pathogens, the human immune system has evolved exceptional diversity and plasticity. Several factors contribute to this including inheritable structural polymorphism of the underlying genes. To investigate this we generated data for a single healthy European individual (identified as HV31) using multiple long-read (PacBio Sequel II and Oxford Nanopore), short-read (Illumina HiSeq and MGI G400) and linked-read (10X Chromium and stLFR) sequencing platforms, and optical mapping using the Bionano Saphyr platform. DNA was extracted from monocytes or from peripheral blood mononuclear cells. We used these data to assemble a set of regions encoding components of the immune system and to investigate structural variation. The raw data and analysis results are deposited here for generate research use. A complete description of these data and results can be found in the accompanying article at https://doi.org/10.1101/2021.02.03.429586.

  Study EGAS00001005046

• Colorectal_organoids_and_tumour_tissue___Whole_Genome_X10

  Whole genome sequencing of single cell derived organoids from normal colon tissue and colorectal cancer.

  Study EGAS00001001100

• Whole Genome Sequence and RNASeq Samples for Lung Cancer

  Short Read Whole Genome Sequence and RNASeq Samples for Lung Cancer

  Study EGAS00001007832