15020 results for "cancer studies using rna-seq"

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• Rheumatoid Arthritis Finger Stick RNA Sequence Data

  This is a longitudinal study of 355 blood samples collected weekly from 5 patients with Rheumatoid Arthritis with varying disease activity over the course of one to five years. Blood samples were self collected from finger sticks and mailed from home. RNA was extracted, purified and sequenced.

  Study phs003179

• Single-cell bam files and RNA sequencing of viral RNA stocks

  244 infected single-cell alveolar bam files, 48 empty well bam files, and 52 RNA sequencing of amplicons (4 SARS-CoV-2 variants with 12 batches and 4 viral variants pool samples). 244 alveolar single cells were captured over 12 experimental batches and experimental condition is written in metadata uploaded as "infected_cells_final_revision.csv". on github (https://github.com/twkim-0510/SARS-CoV-2_viral_competition). Each bam file name corresponds to the sample_name column of the metadata.

  Dataset EGAD00001009711

• Effective screening strategy for deafness using a new-genetation sequencing platform: a consecutive analysis

  The diagnosis of the genetic etiology of deafness contributes to the clinical management of patients. We performed the following four genetic tests in three stages for 52 consecutive deafness subjects in one facility. We used the Invade assay and Sanger sequencing for the GJB2 gene or SLC26A4 gene in the first stage test, TaqMan genotyping assay in second stage test, and targeted exon sequencing using the massively parallel DNA sequencing in third stage test. Overall, we identified the genetic cause in 40% (21/52) of patients. The diagnostic rates of the first-, second- and third-stage genetic testing were 17%(9/52), 9%(4/43) and 21% (8/39), respectively. The combination approach using these genetic tests appears to be useful as a diagnostic tool for deafness patients. We recommended that genetic testing for the screening of common mutations in deafness genes using the Invader assay or TaqMan genotyping assay be performed as the initial evaluation. For the remaining undiagnosed cases, targeted exon sequencing using MPS is clinically and economically beneficial. This data set was MPS reads data (fastq) for 39 sensorineural hearing loss patients.

  Study JGAS000032

• Detection of MEIS2 inversion using ONT adaptive sequencing

  DNA was extracted from whole blood, and sequenced on a Promethion flow cell. The target region covered chr15:22874354-55370932 (Grch38). The resulting data was aligned to Grch38 using minimap2.

  Dataset EGAD50000000893

• Detection of human brain cancers using genomic and immune cell characterization of cerebrospinal fluid through CSF-BAM

  Detection of human brain cancers using genomic and immune cell characterization of cerebrospinal fluid through CSF-BAM

  Dataset EGAD00001015645

• ESGI___Whole_Genome_Sequencing_of_samples_from_the_Cilentogen_isolates

  Deep whole genome sequencing of sampels from the Cilento isolates. The samples are sequenced using the Illumina HiSeq X Ten system.

  Study EGAS00001001116

• ESGI___Whole_Genome_Sequencing_of_samples_from_the_Croatian_isolated_populations_

  Whole genome sequencing of sampels from isolated populations from Croatia. The samples are sequenced using the Illumina HiSeq X Ten system.

  Study EGAS00001001119

• Validation of a genome-wide polygenic score for body mass index in South Asians

  Genotyping data of BMI model markers generated using Global Screening Array (Illumina Inc.)

  Study EGAS00001008309

• Mayo Clinic and Illumina Collaborative Early Stage Ovarian Cancer (ESOC) Study

  Twenty five patients with early stage carcinoma (Stage I and II) of the ovary were consented for this study. As part of a collaborative agreement with Illumina Inc, Tumor-DNA and patient matched normal DNA from blood underwent whole genome sequencing and microarray genotyping and RNA underwent RNASeq. Tissue also underwent IHC staining for TP53 mutations. The aim of the study was to profile the genomic landscape of these early tumors to discover biomarkers for early detection or subgrouping into different genomic or outcome subgroups. Analyses performed include somatic mutation analysis, RNASeq mutation analysis, LOH analysis, structural variant detection. The current release of this study focusses on 17 patients with serous carcinoma and stage 1 or 2 and with high grade (3 or 4).

  Study phs000897

• CTCF scD&D-seq of IDH2 CHIP

  CTCF single-cell D&D-seq with GoT-ChA targeted genotyping of IDH2 R140Q CHIP variant from primary blood sample

  Dataset EGAD50000002273

• Single-cell CITE-seq from MDS patients with SF3B1 mutations

  Single-cell CITE(cellular indexing of transcriptomes and epitopes)-seq from MDS (n = 2, MDS02 in 2 replicates). cDNA from 10x Genomics 3' V3.

  Dataset EGAD00001011281

• The genomic landscape of acute lymphoblastic leukemia with intrachromosomal amplification of chromosome 21

  Use scRNA-seq data from 4 iAMP21 patients to study the clonal evolution of iAMP21.

  Study EGAS00001006577

• sn-RNAseq profiling of the impact of a cytokine storm model in human cardiac organoids

  We developed conditions to model conditions of diastolic dysfunction caused by inflammatory factors in human cardiac organoids. Our organoid model is based on conditions promoting maturation (Mills et al., PNAS, 2017 and Voges et al., In Revision) and incorporating human pluripotent stem cell derived cardiomyocytes, epicardial cells, fibroblasts, endothelial cells and pericytes. Following 15 days of differentiation (Voges et al., Development, 2017) we formed human cardiac organoids incorporating both our multicellular differentiation (Mills et al., PNAS, 2017) and additional endothelial cells (Voges et al., In Revision). After another 12 days of maturation in 5 days maturation medium and 5 days weaning medium (Voges et al., In Revision) human cardiac organoids were either treated with weaning medium or “cardiac cytokine storm” comprised of 100 ng/ml IFNg, 10 ng/ml IL-1 β and 10 µg/ml poly(I:C). After 48 hours human cardiac organoids were harvested and snap frozen in -80C before nuclei isolation and single nuclei RNA sequencing.Pooled hCO (~40) were homogenized in 4 mL lysis buffer (300 mM sucrose, 10 mM Tris-HCl (pH = 8), 5 mM CaCl2, 5 mM magnesium acetate, 2 mM EDTA, 0.5 mM EGTA, 1 mM DTT)(all Sigma-Aldrich) with 30 strokes of a dounce tissue grinder (Wheaton). Large pieces of hCO were allowed to settle and homogenate was passed through pre-wetted 40 µm cell strainers (Becton Dickinson). Remaining hCO material in the douncer was resuspended in 4 mL and the douncing and filtering steps were repeated twice. All steps of the homogenization were performed on ice. The filtered homogenate was centrifuged at 1500 x g for 5 min at 4oC. Nuclei pellets were then re-suspended in PBS. A fraction of resuspended nuclei were then stained with hoechst nuclear stain (1:500 dilution) and counted on a haemocytometer under fluorescent microscope.The nuclei were then re-centrifuged (1500 x g for 5 min at 4°C) and resuspended at a density to load ~5,000 nuclei per sample. Cell were loaded into the Chromium Controller (10X Genomics) for gel bead emulsion (GEM) formation. Library preparation was conducted according to the manufacturer’s recommended protocol using the Chromium Next GEM Single Cell 3’ GEM, Library & Gel Bead Kit v3.1. Libraries were sequenced on the NextSeq 500/550 v2 (Illumina) with 150 bp reads.In the snRNA-seq we note the lower than expected percentage of non-myocytes and the loss of endothelial cells (Mills et al., PNAS, 2017 and Voges et al., In Revision), indicating the protocol requires further optimization for hCO samples.

  Study EGAS00001005174

• Whole genome sequencing of breast cancer and related clones

  Paired WGA (whole-genome amplificated) samples from 65 single cell-derived organoids and six fresh single cell-derived organoid samples both of which were established from normal mammary epithelial cells, 84 FFPE LCM (laser-capture microdissection) samples of breast cancer and related clones, 79 fresh-frozen LCM samples of non-cancer lobules of breast cancer patients, and 36 matched germline controls were subjected to whole genome sequencing using NovaSeq 6000 system (Illumina) or DNBSEQ-G400RS (MGI Tech).

  Dataset EGAD00001010833

• Genotypic data of the individuals in HPP project

  Dataset contains imputed SNP genetics data for human phenotype project https://humanphenotypeproject.org/. The samples are genotypes of 8958 individuals that underwent genetic sequencing based on buccal swab.Sequencing is performed at Neogen GeneSeek Laboratories using Illumina Novaseq at low-pass, targeting coverage levels of 0.5× and 1×. Imputation was done using loimpute-v0.1.0 over the aligned reads. Imputation is done for the autosomal chromosomes (1-22) using The 1000 Genomes phase 3 haplotypes (1KGP3) as a reference panel. The samples are provided in merged PLINK 1 binaries (.bed/.bim/.fam)

  Dataset EGAD00010002714

• stem cell-derived beta cells from cell lines RC9 and HUES8

  stem cell-derived beta cells differentiated using two different cell lines: HUES8 (non-clinical grade) and RC9 (clinical grade). iPSC stem cell lines were differentiated to pancreatic islets using differentiation protocol as detailed by Rajaei et al. (2025, Science Translational Medicine, accepted for publication). scRNAseq was done at stage 7 of differentiation (day 30) using 10X genomics 3' sequencing. Cells were annotated then subsetted for beta cells for downstream analysis

  Dataset EGAD50000001322

• RRBS of 25 pleural mesothelioma samples (Single-end)

  The dataset contains RRBS data (bam files) for 25 pleural mesothelioma samples.The samples were sequenced on NovaSeq6000 (Illumina, San Diego, CA) in single-end 150bp mode. RRBS raw reads were trimmed for adaptor sequences using trim galore (v. 0.6.5) (http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) and filtered for low-quality sequences using fastQC (v. 0.11.8) (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). High quality trimmed reads were mapped to the Human reference genome (UCSC genome assembly GRCh38/hg38) using Bismark (v. 0.22.3) with default parameters.

  Dataset EGAD50000002130

• WGS data of paediatric ETV6::RUNX1 B cell acute lymphoblastic leukemia (set1)

  The dataset includes FASTQ and BAM/CRAM files from diagnostic and matched remission (control) samples of five ETV6::RUNX1 ALL patients (ALLT-380, ALLK-114, ALLT-388, ALLT-392, ALLT-394). Sequencing libraries were made using PCR free-based WGS library preparation (PCR-), using NEBNext® Ultra™ II DNA Library Prep Kit (Cat No. E7645). Both diagnostic (90x) and remission (30x) samples were sequenced using Illumina NovaSeq 6000.

  Dataset EGAD50000002285

• Hyperhaploid multiple myeloma

  DNA was obtained from either CD138+ cells from the bone marrow of multiple myeloma patients (tumor) or from stem cell harvests from the same patient (control). 100 ng of DNA was fragmented, end-repaired, and adapters ligated using the HyperPlus kit (KAPA Biosystems). After PCR amplification the libraries were hybridized with probes against either the entire exome (MedExome, Nimblegen) or a targeted panel of 140 genes using SeqCap reagents (Nimblegen). Hybridized libraries underwent further amplification before being sequenced on a NextSeq500 (Illumina) using 75 bp paired end reads.

  Dataset EGAD00001004328

• RNA-Seq of vocal fold fibroblasts in Reinke’s edema and control subjects

  The voice disorder Reinke’s edema (RE) is a smoking- and voice-abuse associated benign lesion of the vocal folds, defined by an edema of the Reinke space, accompanied with pathological microvasculature changes and immune cell infiltration. Vocal fold fibroblasts (VFF) are the main cell type of the lamina propria and play a key role in the disease progression. Current therapy is restricted to symptomatic treatment. Hence, there is an urgent need for a better understanding of the molecular causes of the disease. In the present study, we investigated differential expression profiles of RE and control VFF by means of RNA sequencing. In addition, fast gene set enrichment analysis (FGSEA) was performed in order to obtain involved biological processes, mRNA and protein levels of targets of interest were further evaluated. We identified 74 differentially regulated genes in total, 19 of which were upregulated and 55 downregulated. Differential expression analysis and FGSEA revealed upregulated genes and pathways involved in extracellular matrix (ECM) remodeling, inflammation and fibrosis. Downregulated genes and pathways were involved in ECM degradation, cell cycle control and proliferation. The current study addressed for the first time a direct comparison of VFF from RE to control and evaluated immediate functional consequences.

  Study EGAS00001005130

• We evaluate the PGD/PGS including 129 couples with NGS test and 266 couples with SNP-array test for the detection of embryonic chromosomal abnormalities.

  A total of 395 couples were subjected to IVF-PGD treatment, including 129 couples with NGS test and 266 couples with SNP-array test for the detection of embryonic chromosomal abnormalities. The NGS test was performed using low coverage whole genome sequencing with HiSeq 2000 platform. And the SNP array test was using Affymetrix Gene Chip Mapping Nsp I 262K.

  Study EGAS00001000981

• Gut metagenome/FINRISK 2002 (Salosensaari et al. Nature Comms 2021)

  The main goal of the project is the study the associations between the gut metagenome and human health. The dataset contains data for n=7211 FINRISK 2002 participants who underwent fecal sampling. Demultiplexed shallow shotgun metagenomic sequences were quality filtered and adapter trimmed using Atropos (Didion et al., 2017), and human filtered using Bowtie2 (Langmead and Salzberg, 2012).

  Study EGAS00001005020

• How upcycled prostate cancer sequences enabled key findings on telomeres length

  The length of telomeres, the chromosomes’ end tails, as depicted in the figure below, physiologically shorten with aging. This means that younger cells have longer telomeres compared to their aged counterparts; one could say that the length of cells’ telomeres is a measure of their replicative power. Cancer cells are known to be able to turn this into their favour, and expand their replicative immortality through telomere lengthening and maintenance. Despite being a well-studied pan-cancer hallmark, the relation of the telomeres length to specific cancer genomic features is poorly understood. Julia Livingstone and her colleagues from P.C. Boutros' team in Los Angeles focused their work on prostate cancer. To investigate the relations between telomere length and the clinico-genomics of prostate tumours, they used whole genome sequencing (WGS) of 392 published tumour–reference pairs previously deposited at the EGA under accession numbers EGAS00001000400 and EGAS00001000900. Their work was published on Nature Communications in November 2021. They determined the telomere length of the sequencing sample, from both tumour and non-tumour adjacent tissues, and subsequently ran statistical analysis to investigate possible correlation between the telomeres length with several cancer features. The team discovered that telomeres length does not correlate with any specific driver gene mutation, but it does with the overall frequency of single nucleotides variants in a sample. This finding suggests that tumours with shorter telomeres accumulate more mutations without strong selective pressures for specific ones. While proliferation rate did not correlate directly with telomere length, methylation of almost half of all genes strongly did. The team’s results are massive and reveal a complicated and fascinating relation of different clinical and genomic features of prostate cancer with telomere length. In the paper discussion, they smartly consider different possibilities. As noted above, all these amazing results have been obtained analysing previously sequenced samples. Remarkably, the researchers who generated the samples did not focus at all on telomeres length. They indeed studied specific genes mutations and the features of early and late-onset prostate cancer. This proves, once again, the power of sequencing data re-use. This data is tremendously rich in information, and can hardly be exhausted with any number of analyses. Generating sequencing data, especially Whole Genome Sequencing, even better if tumor-reference paired, is like shopping every last article at the grocery store. You can then cook several recipes, or prepare a seven-course dinner, and still have ingredients left for someone else to borrow and cook something you have not thought about, a recipe that is just not in your cookbook. Noteworthy, the data retrieved from the EGA for this paper is associated with rich and complete metadata, meaning pheno-clinical information regarding the sample donors. As Julia Livingstone and her team wrote, using rich clinical annotations enabled them to assess the relationship between telomere length and patients outcome. This highlights once again the importance of reporting and annotating metadata correctly; It might feel like extra work for the researchers depositing their data, but it really has a huge value. Our team at the EGA is currently working to improve the metadata annotation process, with the aim to maximize the benefits for the scientific community.

  Blog prostate-cancer-sequences-enabled-key-findings-on-telomeres-length

• Youth-GEMs, the project using data to define the mental health trajectories of young people

  Mental health trajectories are shaped by genetic, cognitive, social, and environmental factors that are particularly influential during human development. How exactly the interplay between these factors is impacting mental health, however, is yet unknown. The project “Gene Environment interactions in Mental health trajectories of Youth (Youth-GEMs)”, funded by the European Commission under the Horizon Europe programme, aims to help reducing mental suffering among European youth. How do Youth-GEMs contribute to bettering mental health in young people? Youth-GEMs aims to identify specific neurological, (epi)genetic and environmental drivers on mental health trajectories of young people. To do so, the project is working on a public knowledge base on functional genomics of the developing human brain, between birth and 24 years old. Reliable predictive models for trajectories of onset and course of mental illness in young people of the general population in Europe will be developed. Additionally, a secure and FAIR data infrastructure in mental health is being produced. Engaging and empowering young people Youth-GEMs is also working on the engagement of young people in the scientific field of Psychiatry. For that, the project is developing initiatives such as Science Café, intended to create a learning community where ideas and stories are shared. The 10th Science Café will take place today, with the participation of Lauren Fromont from the EGA. How do the EGA contribute to Youth-GEMs? The contributions require careful data management and harmonisation: we are dealing with an extraordinary amount of data types, sources, and time points (since the studies are longitudinal). To analyse them together and to determine which factors and interactions are truly key to account for mental health trajectories, the variables need to be transformed in a common “language”, i.e., harmonised under a common data model. Further, to guarantee FAIRness of data storage and sharing for future projects that will further advance our knowledge in the mental health field, we are ensuring that data are processed securely but can also be discovered easily. Find out more information about the project in the Youth-GEMs website.

  Blog youth-gems-mental-health

• Preeclampsia InterPregGen Consortium: Whole Genome Sequencing of 100 unrelated Uzbeks (DNA samples from the Institute of Immunology, Uzbek Academy of Sciences, Tashkent, Uzbekistan; Republic Specialized Scientific Practical Medical Centre of Obstetrics and Gynecology, Tashkent, Uzbekistan)

  Whole genome sequencing of 100 unrelated Uzbeks in order to impute genotypes into PE cases and controls from Uzbekistan and to provide genetic data and infrastructure for future genetic studies in Uzbekistan and Central Asia more generally and to fill a gap in worldwide information as Central Asia is not adequately represented in available genomic data. This dataset is one component of the InterPregGen FP7 project. DNA samples for this component were collected by InterPregGen Consortium collaborators in Tashkent, Uzbekistan at the Institute of Immunology, Uzbek Academy of Sciences and at the Republic Specialized Scientific Practical Medical Centre of Obstetrics and Gynecology.

  Dataset EGAD00001005466