15152 results for "cancer studies using rna-seq"

in 29.52 milliseconds.

• Exome sequencing data

  Exome sequencing fastq files from 6 mutation carriers and 5 non-carriers from 2 families. One µg DNA was used for library preparation using the TruSeq DNA LT Sample Prep Kit v2 according to the manufacturer’s instructions (Illumina). Hybridization was performed using Nimblegen SeqCap EZ Exome v3 (Roche) and Paired-end Sequencing (2x100 bp) on the Illumina HiSeq 2000 with TruSeq v3 chemistry (Illumina).

  Dataset EGAD00001003745

• Whole genome sequencing of metastatic cutaneous squamous cell carcinoma

  This dataset includes somatic small variant calling files derived from fifteen metastatic samples from cutaneous squamous cell carcinoma matched to normal blood samples. These samples were whole-genome sequenced by HiSeq X Ten and the resulting reads were mapped against the human genome (hg37) using BWA-MEM 0.7.10-r789. Somatic variant calling was then performed using strelka 1 (version 2.0.17).

  Dataset EGAD00001004530

• Targeted_analysis_of_chondrosarcoma_cancer_genes

  Targeted analysis of chondrosarcoma cancer genes

  Study EGAS00001001765

• Perceptions, Prevalence, and Patterns of Cannabis Use among Cancer Patients in NCI-Designated Cancer Centers

  This study aimed to better understand the evolving landscape of cannabis use among patients undergoing cancer treatment across diverse geographic regions. It sought to characterize patterns of use, patient perceptions, and contextual influences such as legal status and provider engagement that affect cannabis use. Given the shifting legal environment, this study addresses a critical gap in the literature by providing data on how cancer patients integrate cannabis into their care. The available delivery methods of cannabis have also undergone dramatic changes and now include edibles, oils, tinctures, topicals, and inhaled forms. State-based policy changes are rapidly changing. Yet research on cannabis use by cancer patients across a variety of geographic settings remains limited. The extent of cannabis use, the perceived and real benefits and risks of use, potential interactions with cancer treatment and other medications, and impact on comorbid conditions are uncertain. Clinicians should be aware of the extent of use in order to assess potential drug-drug interactions, side effects, and contraindications; hence, an understanding of how cancer patients and clinicians engage in discussions about cannabis use is essential. Twelve NCI-designated cancer centers were awarded supplemental funding to conduct surveys assessing cannabis use among recently diagnosed cancer patients. Selected sites were in 8 states with varied legal status for medical and recreational cannabis use at the time of the survey. The 12 cancer centers independently received approval from their institutional review boards (IRB) and collected data from September 2021 to August 2023. Eligible participants were cancer patients or survivors who resided in their respective cancer center's catchment area. Cancer centers were responsible for sampling patients within their catchment area with the goal of 1,000 completed surveys. Ten out of 12 cancer centers drew probability samples of patients from patient lists defining their catchment area with some stratifying the sample in various ways including by cancer type, by sex, by race/ethnicity or by a combination of demographic variables.

  Study phs004046

• ATAC Dataset for 19 T-ALL patient samples and 1 normal control sample

  19 ATAC-seq from T-ALL patient samples and one healthy normal control sample. Reads were aligned to HG38 reference

  Dataset EGAD50000000024

• ChIPseq data for Glioblastoma (EGAS00001003953, H016)

  ChIPseq data for human glioblastoma patients, EGAS00001003953. Mix of input, H3K27ac, H3K27me1, H3K27me3, H3K36me3, H3K4me1, H3K4me3, H3K9me3 and BRD, 20 human samples, 2 cell lines (LN229, ZH487).

  Dataset EGAD00001006548

• ATAC Analysis of Treg and Tfh cells

  Treg and Tfh cells (8 Samples)from the same donors was subject to ATAC-seq processing. Paired end fastq-files are supplied.

  Dataset EGAD00001007660

• G&T-seq: Parallel sequencing of single-cell genomes and transcriptomes

  Development of a method for separation and parallel sequencing of the genomes and transcriptomes of single cells.

  Dataset EGAD00001001332

• Rapid detection of expanded short tandem repeats in personal genomics using hybrid sequencing.

  Long expansions of short tandem repeats (STRs); i.e., DNA repeats of 2-6 nucleotides, are associated with some genetic diseases. Cost-efficient high throughput sequencing can quickly produce billions of short reads that would be useful for uncovering disease-associated STRs. However, enumerating STRs in short reads remains largely unexplored because of the difficulty in elucidating STRs much longer than 100 bp, the typical length of short reads. We propose ab initio procedures for sensing and locating long STRs promptly by utilizing the frequency distribution of all STRs and paired-end read information. We validated the reproducibility of this method using biological replicates and used it to locate an STR associated with a brain disease (SCA31). Subsequently, we sequenced this STR site in eleven SCA31 samples using SMRT? sequencing (Pacific Biosciences), determined 2.3-3.1 kb sequences at nucleotide resolution, and revealed that (TGGAA)- and (TAAAATAGAA)-repeat expansions determined the instability of the repeat expansions associated with SCA31. Our method could also identify common STRs, (AAAG)- and (AAAAG)-repeat expansions, which are remarkably expanded at four positions in an SCA31 sample. This is the first proposed method for rapidly finding disease-associated long STRs in personal genomes using hybrid sequencing of short and long reads.

  Study JGAS000002

• A proteogenomic atlas of the human neural retina

  The human neural retina is enriched for alternative splicing, and it is estimated that more than 10% of variants associated with inherited retinal diseases (IRDs) alter splicing. Previous research mainly used short-read RNA-sequencing techniques to investigate retina-specific splicing and splicing factors. However, this technique provides limited information about transcript isoforms. To gain a deeper understanding of the human neural retina and its isoforms, we generated a proteogenomic atlas that combines PacBio long-read RNA-sequencing data with mass-spectrometry and whole-genome sequencing data from three healthy human neural retina samples. About one third of the nearly 60,000 transcript isoforms were novel, and ten novel peptides that confirmed novel isoforms were detected. This study highlights the importance of studying tissue-specific transcriptomes in greater detail to better understand tissue-specific regulation and to identify disease-causing variants.

  Study EGAS50000000070

• Controlling cell differentiation with precision through understanding the structure and dynamics of gene regulatory networks

  We established a set of transgenic human ES cell lines that carry inducible expression gene cassette for singe transcriptional factor induction with Tet-ON system. Transcriptome data was obtained 48h after transgene induction by Microarray. We established inducible human ES lines about more than 700 genes, and achieved microarray analysis on 115 genes. Our data set has multiple characteristic properties; 1) the same platform across data set, 2) analyzed with dynamic cell status, 3) pre-determined initiation point on gene perturbation, 4)inducible in different cell status. In parallele, we also performed RNA sequenncig analysis on 435 transgene induced ES lines as well. 32 transgene induced transcriptome data were obtained from both Microarray and RNA sequencing, normalizing technical gaps between them. We hope those systematic data set to reveal transcriptional network structure and molecular mechanisms of cell differentiation.

  Study JGAS000121

• RA-Map, A multi-omic survey of whole blood and subsets in early rheumatoid arthritis and vaccine study controls

  Rheumatoid arthritis (RA) is a chronic inflammatory disorder of unknown aetiology characterised by synovial inflammation with variable disease severity and drug responsiveness. To investigate the molecular heterogeneity and pathogenesis of RA, we performed a comprehensive clinical and molecular profile of 267 RA patients for up to 18 months to establish a high quality biobank of barcoded samples that included plasma, serum, peripheral blood cells, whole blood RNA, RNA from lymphocyte and monocyte subsets, genomic DNA and urine. We have performed extensive multi-omic immune phenotyping, including genomic, metabolomic, proteomic, transcriptomic and auto-antibody profiling. We anticipate that these detailed clinical and molecular data will serve as a fundamental resource for the development and application of targeted therapies for RA and reveal further insights into disease pathogenesis and therapeutic response.

  Study EGAS00001004424

• RBSC11

  One retinoblastoma sample was studied by single-cell RNA-sequencing (10X genomics Chromium).

  Dataset EGAD00001007497

• Genomic Analysis of Mucinous Tumours (GAMuT) - RNA

  RNASeq for Genomic Analysis of Mucinous Tumours (GAMuT)

  Dataset EGAD00001005190

• AI_NF1glioma_RNAseq

  The dataset includes 29 tumor samples from NF1-glioma patients profiled by RNA sequencing.

  Dataset EGAD00001004376

• Bulk-RNA Sequencing of high-grade pancreatic and non-pancreatic Neuroendocrine Neoplasms

  Therapeutic decisions in oncology depend on a precise pathological classification of individual neoplasms. Recent years have seen an intensification of research activities aimed at the extraction of clinically relevant information from patient-derived 'omics' data based on Machine-Learning models. However, a comprehensive training of Machine-Learning models requires sufficiently large numbers of training samples, which are usually not available for rare cancer types. The problem is worsened when individual tissues segregate into different cancer subtypes, as their discrimination would require even more training samples. Methods: Here, we report on a new data-augmentation technique to support the training of Machine-Learning models on ‘omics’ data from pancreatic neuroendocrine neoplasms (panNEN). PanNENs display all properties described above: Only about 2-3% of all pancreatic neoplasms are neuroendocrine and they fall into different subtypes with distinctly different prognosis, which makes the precise classification of such samples both difficult and important for therapy decisions. The approach reconstructs a given transcriptome based on healthy pancreatic cell type signatures and creates a Machine-Learning model that integrates the observed reconstruction error and predicted cell type proportions as features. Results: A benchmark of the deconvolution model predictions with the ground-truth found that the model could efficiently predict the sample grading, disease-related patient survival time, and differentiate between different subtypes in four panNEN and one mixed panNEN and non-pancreatic NENs dataset. We compared the predictive performance of the deconvolution-trained model to that of a model trained directly on the transcriptomic data, under inclusion of the Ki-67 classification gold standard biomarker, and found the performances to be comparable. Conclusion: Our approach serves as a data-augmentation technique to facilitate the training of Machine Learning models for rare cancer types via utilization of data from healthy origin. Additionally, the deconvolution results were clinically interpretable and further research can render the deconvolution approach an effective complementary asset for the clinical classification of neoplasms.

  Study EGAS00001004861

• EGAD00010000652

  Genotyped samples using Illumina HumanOmni2.5

  Dataset EGAD00010000652

• EGAD00010000096

  DBA case samples using 250K Nsp

  Dataset EGAD00010000096

• EGAD00010000381

  MRCE sample using 300K

  Dataset EGAD00010000381

• EGAD00010000383

  MRCA sample using 100K

  Dataset EGAD00010000383