11769 results for "cancer rna-seq"

in 19.40 milliseconds.

• How to use the EGA search box

  Tips on how to search Tip 1: Use simple and clear keywords No matter what you are searching: a study, a dataset or a committee; the search will look recursively in all the fields of all the entities in the database. Thus, there is no need to specify which type of data you are looking for. Try to use single and specific words and avoid long and complex expressions. Try it out: cancer rna-seq instead of cancer studies using RNA-seq. Tip 2: Narrow your results using search operators Search operators give you more control over the results. They manage the way keywords are used to restrict the search. By default, the search engine applies an OR operator when a space is inserted between two words. Include AND (capitalised) between the keywords in your search in order to show only pages containing both terms. Try it out: Search all the studies based on transcriptomic data, including those ones that use RNA sequencing to obtain this data. Tip 3: Do not worry about details The search engine automatically checks for the most common spelling mistakes of given words and suggests the use of the correct version. It also ignores capital letters and most punctuation so none of these mistakes will hamper the accuracy of your search. Besides, when using a combination of words, the search engine will suggest similar combinations with a higher number of results. Tip 4: Use words that are likely to appear within the text Unfortunately, our search engine is not able to interpret words yet. Therefore, words which do not appear in the body text will not return any result despite them denoting a group of present terms. For instance, if we are looking for studies focused on neurological diseases, using neurological as a keyword would not be the best option. Instead, you should use the name of each neurological disease such as bipolar disorder, schizophrenia or depression. In the same fashion, incomplete words do not return any result. Please include an asterisk (*) before and/or after the incomplete word in order to allow for partial matching. Try it out: canc* instead of canc(without wildcard). Also you can try out *oma, or even *geneti* Tip 5: Narrow down your search by filtering the results You can filter the results by the following options: general, study, and dataset. Under these categories, you will find more sub-filters to refine your search. Make sure that you click on Apply in order to filter the results. You can clear the filtering anytime. Advanced Searches Additional example queries: Search for DUO:0000005 and word lung example query Or write: DUO:0000005 AND lung Search for an exome sequencing study in dbGaP example query Or write: study_type: "exome sequencing" AND repository:dbGaP Search for an exome sequencing study in EGA example query Or write: study_type: "exome sequencing" AND repository:ega Search for a dataset that is at the EGA beacon and with type "Whole genome sequencing" and with number of samples between 100 and 2000 and with technology "Illumina HiSeq 2000" example query Or write: is_in_beacon:true AND dataset_types:"Whole genome sequencing" AND samples:[100 TO 2000] AND dataset_technologies:"Illumina HiSeq 2000" Search for PMIDS example query Or write: pmids:32025007

  Documentation discovery/metadata/search-box

• Single-cell transcriptomic analyses of peripheral blood mononuclear cells, peritoneal fluid, and peritoneal metastases from patients with colorectal cancer

  The data published here contains single-cell RNA- sequencing (scRNAseq) on peripheral blood mononuclear cells (PBMC), peritoneal fluid (PF), and peritoneal metastases (PM). PBMC, PF and, where possible, PM was acquired from patients with colorectal cancer (CRC) and subjected to 3' scRNAseq using the 10X Genomics (Chromium Single Cell 3’ Reagent). Sequencing was performed in a paired-ended fashion on either the HiSeq4000 or the NovaSeq6000.

  Study EGAS50000000173

• Transcriptome Sequencing PPGL

  RNA library preparation of samples with RNA Integrity Number (RIN) > 5.5 (median 7.1; range 5.6-8.9) was performed as described in the TruSeq Stranded mRNA Library Prep Kit (Illumina, RS-122-2101). Briefly, 0.7-1 µg total RNA PolyA+ fraction was purified and randomly fragmented, converted to double stranded cDNA and processed through subsequent enzymatic treatments of end-repair, dA-tailing, and ligation to adapters. Adapter-ligated library was completed by PCR with Illumina PE primers. The resulting purified cDNA library was applied to an Illumina flow cell for cluster generation and sequenced on an Illumina HiSeq2500 on a 51bp single-read format following manufacturer's protocols. cDNA libraries from FFPE tumors and low integrity RNA’s (RIN<5.5) from FF tumors (median 2.4; range 1-5.5) but % of fragments > 200 nucleotides higher than 20% (median 57.5; range 20-96) were prepared using QuantSeq 3’ mRNA-Seq Library Prep Kit FWD for Illumina (Lexogen, 015) with a UMI Second Strand Synthesis Module for QuantSeq FWD (Lexogen, 081), vendor’s protocol for low input/ low quality/ FFPE RNA using 200-500 ng of total RNA, according to availability, was used as starting material. PCR Add-on Kit for Illumina (Lexogen, 020) was used to adjust the library amplification number of cycles. Libraries were applied to an Illumina flow cell for cluster generation and sequenced on NovaSeq6000. Image analysis, per-cycle basecalling and quality score assignment was performed with Illumina Real Time Analysis software. Demultiplexing of BCL files to FASTQ format was performed with the bcl2fastq Software (Illumina).

  Study EGAS00001006044

• Healthy control cfMeDIP-seq

  This dataset comprises 46 raw fastq files derived from 23 plasma cfDNA samples from 23 healthy (CTL) cancer-free donors. For each sample, 10ng of cfDNA was used for library preparation utilizing the cfMeDIP-seq technology based on the 5mC antibody-immunoprecipitation strategy. Libraries were validated via Bioanalyzer trace analysis and sequenced on the Illumina NovaSeq 6000 platform with paired-end 150-bp model for ~100 million reads per sample.

  Dataset EGAD50000000652

• Chromatin accessibility of clear cell renal cell carcinoma

  To investigate chromatin accessibility in ccRCC, we performed ATAC-seq in 3 ccRCC cell lines (786-O, A498, UMRC-2), and 1 normal kidney epithelium cell line (HK-2), cultured under normoxic (20% O2) and hypoxic (0.05% O2) conditions

  Study EGAS50000001325

• TransNEO neoadjuvant breast cancer study

  Whole exome, shallow whole genome, and RNA-sequencing data from a cohort of 168 women with breast cancer receiving neoadjuvant chemotherapy.

  Dataset EGAD00001008269

• Derivation and Investigation of The First Human Cell-Based Model of Beckwith-Wiedemann Syndrome

  Beckwith-Wiedemann Syndrome (BWS) is the most common human imprinting disorder, and phenotypes include hepatomegaly and hepatoblastoma development. In approximately 20% of patients, this syndrome is caused by uniparental isodisomy of the paternal allele across chromosome 11p15 (pUPD11). From fibroblasts cultured from 2 BWS patients with pUPD11, we derived induced pluripotent stem cells (iPSC) and differentiated to hepatocytes to study a model of the BWS liver transcriptome through RNA-Seq. Compared to 2 similarly derived controls, we observed differential expression in insulin signaling, cell proliferation, and cell cycle pathways, indicating their potential importance in the liver overgrowth and oncogenesis associated with BWS.

  Study phs002365

• Single-cell characterization of human GBM reveals regional differences in tumor-infiltrating leukocyte activation

  Background: Glioblastoma (GBM) harbors a highly immunosuppressive tumor microenvironment (TME) which influences glioma growth. Major efforts have been undertaken to describe the TME on a single-cell level. However, human data on regional differences within the TME remain scarce. Goals: we performed single-cell RNA sequencing (scRNAseq) on paired biopsies from the tumor center, peripheral infiltration zone and blood of five patients with primary GBM Details: This EGA study includes raw data sequencing files for the scRNA-seq samples. The processed data (output from Alevin, UMI count matrix and cell metadata) are available from GEO https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE197543

  Study EGAS50000000302

• Bulk RNAseq - Calprotectin in vitro effects on human early hematopoiesis

  To explore the effects of calprotectin (CAL) on early hematopoiesis, we incubated CD34+ cells from four healthy donors for 7 days in the presence of stem cell factor (SCF), FLT3-ligand and thrombopoietin (TPO) without and with CAL, interleukin-6 (IL6) and the IL6_CAL combination before collecting cells. We performed the same experiment with CD34+ cells collected from three patients with JAK2-V617F-mediated myelofibrosis (MF). A total of 135,545 cells from healthy donors and 111,725 cells from myelofibrosis patients were analyzed by single-cell RNA sequencing (scRNA-seq) using the 10X Chromium droplet-based platform. Bulk RNAseq 4 conditons : -Control - Calprotectin - IL-6 - Calprotectin+IL-6

  Study EGAS50000000454

• Investigating immunopathological signatures associated with COVID-19 severity post Omicron

  The aim of this study was to investigate the type and duration of protective immunity alongside immunopathological signatures associated with COVID-19 severity. RNA sequencing was performed using the Illumina Stranded mRNA Prep Ligation kit to investigate the protein coding transcriptome of PBMC cells. T cell receptor (TCR) repertoire profiling was performed with the TakaraBio SMART-Seq Human TCR (with UMIs) on PBMC cells to investigate the depth and persistence of SARS-CoV-2 targeting TCRs. The cohort (n=20) consisted of 10 non-hospitalised and 10 Hospitalised individuals with samples analysed 3 months post positive SARS-CoV-2 PCR.

  Study EGAS50000000926