11979 results for "cancer rna-seq"

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• 4C Ovarian Cancer Data Access Committee

  Dac EGAC00001001953

• Breast Cancer Single-Cell Data Access Committee

  Dac EGAC00001002160

• AA/UTUC Cancer Data Access Commitee

  Dac EGAC00001002162

• Breast Cancer Single-Cell Data Access Committee

  Dac EGAC00001002291

• MDACC Breast Cancer MoonShot Data Access Committee

  Dac EGAC00001002362

• Laboratory of Applied Cancer Genomics team

  Dac EGAC00001002546

• Netherlands Cancer Institute (NKI) - Wilbert Zwart Group

  Dac EGAC00001002618

• Breast Cancer Single-Cell Data Access Committee

  Dac EGAC00001002691

• Computational Cancer Genomics and Tumor Evolution Group

  Dac EGAC00001002722

• Netherlands Cancer Institute (NKI) - Jos Jonkers Group

  Dac EGAC00001002749

• Netherlands Cancer Institute (NKI) - Wilbert Zwart Group

  Dac EGAC00001002782

• Netherlands Cancer Institute (NKI) - Jos Jonkers Group

  Dac EGAC00001002848

• Cancer Genomics of the Kidney - Targeted Sequencing

  Dac EGAC00001003091

• Cancer Genome Database Data Access Committee (CGDB_DAC)

  Dac EGAC01000000002

• Netherlands Cancer Institute (NKI) - Beatriz Carvalho group

  Dac EGAC00001003134

• DAC for Castleman. National Cancer Centre Singapore.

  Dac EGAC00001003328

• UCSF prostate cancer genomics data access committee

  Dac EGAC00001002685

• Genomic landscape of DS-ALL

  Study EGAS00001006600

• Genetic landscape of ENKTCL

  Study EGAS00001006906

• Breast cancer sequencing data

  Study EGAS00001007336

• Non-invasive bladder cancer cfDNA dataset DAC

  Dac EGAC50000000790

• Molecular Determinants of Tumor Behavior in Early Lung Adenocarcinoma

  Lung adenocarcinoma is the leading cause of cancer death worldwide and a heterogeneous disease with poor survival in the advanced stage. Given that the frequency of early diagnosis is increasing, the investigation of the molecular determinants of disease progression is paramount. Prediction of whether the tumor is indolent or aggressive has been the subject of recent investigations. Yet, the molecular underpinnings explaining the difference in tumor behavior remains largely unknown and is directly relevant to the efficacy and cost-effectiveness of lung cancer screening, including the risks of over-diagnosis and overtreatment. To gain insights into the molecular pathogenesis and the underlying molecular determinants of clinical behavior of early adenocarcinoma, we investigated genetic alterations in 102 surgically resected early adenocarcinomas. We hypothesized that the behavior of early adenocarcinoma can be predicted based on genomic underpinnings. To identify the type of genomic alterations associated with resected indolent and aggressive early lung ADC, the DNA samples from 21 adenocarcinoma in situ (AIS), 27 minimally invasive adenocarcinoma (MIA) and 54 fully invasive adenocarcinoma were collected and subjected to a deep next generation sequencing, targeting a custom 347 cancer gene panel. Our study provides new insights into cancer genomic evolution, helps to elucidate the interplay of somatic mutation and the adaptation of clones, and brings new insights into the key distinction between indolent and aggressive tumor behavior.

  Study phs001811

• This study generated Oxford Nanopore long read sequencing data of cancer cell line mixtures for validating long-read variant calls in cancer genomics

  Long-read sequencing (LRS) improves genome alignment and facilitates resolving variants in complex genomic regions, making it a promising approach for cancer variant detection and biomarker discovery. Here, we evaluate the performance of LRS to detect somatic variants across different tumour purities and sequencing depths by comparing to somatic variants from short-read sequencing. We generated experimental mixtures of cancer cell lines and matched normal cell lines to simulate 10 tumour purities (ranging from 0 to 100% tumour content). This resulted in 22 samples which were sequenced using whole genome LRS to a targeted depth of 60x and somatic variants were identified. We downsampled sequencing data to explore the optimal read depth for somatic variant detection. Our results show that long-read variant calling tools achieve recall rates comparable to short-read gold standards. While a tumour sequencing depth of 30x to 60× is generally sufficient for detecting common variants, particularly structural variants (SVs), sequencing the matched normal sample at adequate depth is crucial for accuracy. Notably, we found variants unique to LRS that may represent real and previously undetected events. This study highlights key factors for optimising cancer genome sequencing with LRS and provides a comprehensive dataset of cell line mixtures for future research and tool development.

  Study EGAS00001008107

• Reconstructed VDJ sequences from Smart-seq2 data

  The dataset contains reconstructed VDJ sequences (fasta files) and accompanying metadata for each cell (csv file) from scRNA-seq data generated with the Smart-seq2 protocol. The VDJ sequences were reconstructed with the computational tool BraCeR using raw fastq files as input. The dataset contains sequences from 355 IgA+ peripheral blood B-lineage cells of two untreated celiac disease patients. The sequences comprise both IgA+ transglutaminase 2-specific and other IgA+ B cells.

  Dataset EGAD50000000341

• Human brain development single cell sequencing additional samples

  69 tissue samples from various parts of the developing human embryo brain were dissociated and single cells were collected and processed without bias for mRNA-seq using 10X chromium 3' protocol. Libraries were sequenced on Illumina NovaSeq and reads aligned against the human GRCh38 genome. This is an addition to EGAD00001006049 Human development single cell sequencing, consisting of samples added after the original submission to EGA, but included in final paper.

  Dataset EGAD50000001295