5032 results for "*oma"

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• Oliocapture sequencing libraries from the study "Histological Transformation and Progression in Follicular Lymphoma: a Clonal Evolution Study".

  Oliocapture sequencing libraries from the study "Histological Transformation and Progression in Follicular Lymphoma: a Clonal Evolution Study". These are sequencing libraries from the extension cohort of 277 patients. Specifically, there are 402 tumor libraries and 82 normal libraries.

  Dataset EGAD00001002898

• BLUEPRINT September 2016, ATAC-seq Multiple Myeloma for plasma cell from bone marrow, on Genome GRCh38

  ATAC-seq data for 4 sample(s) Multiple Myeloma for plasma cell from bone marrow, on Genome GRCh38. 4 run(s), 4 experiment(s), 4 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002920

• BLUEPRINT September 2016, ATAC-seq Mantle Cell Lymphoma from venous blood, on Genome GRCh38

  ATAC-seq data for 5 sample(s) Mantle Cell Lymphoma from venous blood, on Genome GRCh38. 5 run(s), 5 experiment(s), 5 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002918

• BLUEPRINT September 2016, ChIPmentation Activated B-Cell-Like Diffuse Large B-Cell Lymphoma from lymph node, on Genome GRCh38

  ChIPmentation data for 2 sample(s) Activated B-Cell-Like Diffuse Large B-Cell Lymphoma from lymph node, on Genome GRCh38. 3 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002944

• BLUEPRINT September 2016, ChIPmentation Burkitt Lymphoma from lymph node, on Genome GRCh38

  ChIPmentation data for 3 sample(s) Burkitt Lymphoma from lymph node, on Genome GRCh38. 10 run(s), 8 experiment(s), 8 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002939

• 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 AND operator when a space is inserted between two words. Include OR (capitalised) between the keywords in your search in order to show pages containing either term or 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

• DAC policy The Kids Research Institute Australia

  DAC policy related to the study: Visium CytAssist Spatial Gene Expression analysis for FFPE glioblastoma samples. Committee: Raelene Endersby raelene.endersby@thekids.org.au Anya Jones anya.jones@thekids.org.au Emma Stone emma.stone@thekids.org.au

  Dac EGAC50000000734

• Multi-omic signatures in multiple myeloma

  Multiple myeloma (MM) is a genetically and clinically heterogeneous plasma-cell malignancy. Quadruplet therapy, combining anti-CD38 antibodies, immunomodulatory drugs, proteasome inhibitors, and dexamethasone, is now the standard induction treatment for newly diagnosed multiple myeloma (NDMM). To gain a comprehensive view of molecular changes in MM and their association with treatment response in this new standard care, we performed a multi-omic study of 423 NDMM from the MIDAS trial.

  Study EGAS50000001573

• Comprehensive gene expression analysis in renal cell carcinoma

  The goal is to elucidate the mechanisms of tumor initiation and progression, identify gene signatures associated with drug efficacy and adverse effects, and develop more accurate and reliable diagnostic methods and therapeutic targets.

  Study JGAS000913

• RNA-seq of Liver Cancer

  The present series corresponds to 161 RNA-seq samples from tumors with matched WES or WGS. Hepatocellular carcinoma (HCC) accounts for more than 90% of liver cancers, and is a major health problem. It is the 3rd cause of cancer-related mortality. Advances in genomic analyses have formed a comprehensive understanding of different underlying pathobiological layers resulting in hepatocarcinogenesis. Thus, the development of next-generation sequencing technologies has made it possible to generate more comprehensive catalogues of somatic alteration events (single nucleotide substitutions, structural variations, and epigenetic changes) in liver cancer genome than ever before.

  Dataset EGAD00001003993