15120 results for "cancer studies using rna-seq"

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• smRNA-Seq assays for reference epigenomes generated by Centre for Epigenome Mapping Technologies at Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, as part of the International Human Epigenome Consortium.

  smRNA-Seq assays for reference epigenomes generated by Centre for Epigenome Mapping Technologies at Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Canada as part of the International Human Epigenome Consortium.

  Dataset EGAD00001001226

• Fastq data for smRNA-Seq assays for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency as part of the International Human Epigenome Consortium.

  Fastq data for smRNA-Seq assays assay for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency, Vancouver, Canada as part of the International Human Epigenome Consortium.

  Dataset EGAD00001001401

• Targeted sequencing of breast cancer susceptibility genes for 1,995 Japanese breast cancer patients

  In this study, we investigated pathogenic germline variants in 11 genes implicated in hereditary breast cancer, including BRCA1, BRCA2, TP53, PTEN, CDH1, STK11, NF1, PALB2, ATM, CHEK2, NBN1, in a total of 1,995 unselected Japanese women with breast cancer using targeted-capture sequencing of pooled DNA (n = 106).

  Study EGAS00001004630

• Dataset contains all Single cell RNAseq data of human neurons from the study Bouwen et al Nat Comm 2025

  These documents contain the patch seq data presented in the Bouwen et al Nat Comm 2025. Human brain samples were gathered during surgery, neurons were isolated using patch clamp glass pipette. Isolated cells were processed using Smartseq2 pipeline, and sequenced using an Illumina 2500. Files are formatted in fastq format. There are a total of 12 patient samples, each with 2 replication runs.

  Dataset EGAD50000001979

• Genome-Wide Association Studies of Prematurity and Its Complications (African American)

  Preterm delivery resulting in the birth of a premature infant is a complex problem with a devastating impact on individuals, families and society. The prevalence of preterm birth has increased steadily in developed countries over the last 20 years and more than three million children die of preterm birth worldwide each year. Despite the importance of the problem and its disproportionate occurrence in poor and minority populations, the underlying causes have been difficult to identify. Spontaneous preterm labor has as its suspected triggers infection, stress, poor nutrition and inherited factors. The single best predictor for preterm delivery is a previous preterm birth. Studies of twins and of recurrences within families provide evidence that genetic factors underlie a substantive component of the risk for prematurity. One major challenge in studying genetic factors in prematurity is that the risk case is not truly established. The genetic risk could reside either in the mother and her uterus or in the infant/placenta. Identification of genetic factors in the mother and/or infant could provide insights into identifying relevant environmental covariates that may be more amenable to rapid interventions but difficult to find using standard epidemiology alone. A comprehensive genome-wide association study (GWAS) is the ideal way to identify those genes that would not be suspected based on our current understanding of the biology of parturition. We are using 2200 African American samples with term or preterm labor. A subset of these (~800) are infant samples recruited by the Neonatal Research Network as part of a study on cytokines and infection in extremely low birth weight infants (Schelonka RL, et al., 2011. PMID: 21145756). Therefore, this group consists of infants <1,000 grams with clinical outcome data for the infant allowing study of the genetic contributors for not only preterm birth but also the complications that often accompany preterm birth. The result will enable a better understanding of the biology of parturition and suggest environmental modifications that can prolong gestations to improve neonatal and adult outcomes. This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org) funded by the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to preterm birth through large-scale genome-wide association studies of African-American cases and controls from multiple sites in the United States. Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR). Data cleaning and harmonization were done at the GEI-funded GENEVA Coordinating Center at the University of Washington.

  Study phs000353

• MATISSE bulk RNA-sequencing data

  Tumor availability: Bulk RNA sequencing was performed on tumor biopsies of 48 patients at baseline and 45 patients across time points (baseline n=48, week 1 n=38, week 2 n=34, week 4 n=37), including lymph node metastasis that were sequenced in addition to the primary tumor. Tumor RNA was extracted from formalin-fixed paraffin-embedded (FFPE) primary tumor and lymph node metastasis sections containing at least 10% viable tumor cells present in the sample, except for on-treatment samples with a complete pathological response in which we isolated samples with at least 10% immune-infiltration with no viable tumor cells. A pathologist (LS) scored the tumor percentage and indicated the most tumor-dense region on a hematoxylin and eosin (H&E) stain slide for subsequent RNA isolation. According to the manufacturer's protocol, five to 10 FFPE slides (10 µm) were used for RNA isolation using the AllPrep DNA/RNA FFPE isolation kit (Qiagen, 80234) and the QIAcube. Genetic Diagnostics and Sequencing Services (CeGaT) performed RNA sequencing in Germany. Data processing: Demultiplexing of the sequencing reads was performed with Illumina bcl2fastq (v2.20). Adapters of the reads (Pico v2 SMART adapter, first three nucleotides of the second sequencing read) were trimmed with Skewer (v0.2.2), without quality trimming. RNA-sequencing data was aligned to GRCh38 (Ensembl v109) using STAR (v2.7.9) in 2-pass mode with default settings. Gene counts were generated with the HTSeq (v2.0.2). FastQC (v0.12.1) and MultiQC (v1.14) were used for assessing data quality, on FASTQ files and intermediate processing steps.

  Dataset EGAD50000001470

• Dataset for leiomyosarcoma-RNA

  Rare cancer sequencing data of 87 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008849

• Dataset for head_and_neck_cancer-RNA

  Rare cancer sequencing data of 92 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008861

• TRACERx 100: metastatic samples

  Whole exome sequencing of 10 metastatic biopsies from four TRACERx100 patients (see EGA dataset EGAS00001002247), collected either after relapse or death. The data from these samples are initially published with Abbosh, C. et al. Phylogenetic ctDNA analysis depicts early stage lung cancer evolution. Nature http://dx.doi.org/10.1038/nature22364 (2017). Abstract: Earlier detection of relapse following primary surgery for non-small cell lung cancer and the characterization of emerging subclones seeding metastatic sites might offer new therapeutic approaches to limit tumor recurrence. The potential to non-invasively track tumor evolutionary dynamics in ctDNA of early-stage lung cancer is not established. Here we conduct a patient-specific approach to ctDNA profiling in the first 100 lung TRACERx (TRAcking Cancer Evolution through therapy (Rx)) study participants, including one patient co-recruited to the PEACE (Posthumous Evaluation of Advanced Cancer Environment) post-mortem study. We identify independent predictors of ctDNA release in early-stage non-small cell lung cancer and perform tumor volume limit of detection analyses. Through blinded profiling of post-operative plasma, we observe evidence of adjuvant chemotherapy resistance and identify patients destined to experience recurrence of their lung cancer. Finally, we show that phylogenetic ctDNA profiling tracks the subclonal nature of lung cancer relapse and metastases, providing a new approach for ctDNA driven therapeutic studies.

  Study EGAS00001002415

• MutWP1__CRUK_Grand_Challenge_Mutographs_of_Cancer__head___neck

  The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, the International Agency for Research on Cancer is coordinating the recruitment of 5000 individuals with cancer (colorectal, renal, pancreatic, oesophageal adenocarcinoma or oesophageal squamous cancers) across 5 continents to explore whether different mutational signatures explain marked variation in incidence. In brief, through an international network of collaborators around the world, biological materials are collected, along with demographic, histological, clinical and questionnaire data. Whole genome sequences of tumour-germline DNA pairs are generated at the Wellcome Trust Sanger Institute. Non-tumour DNA is also studied for some cancer types. Somatic mutational signatures are subsequently extracted by non-negative matrix factorisation methods and correlated with risk factors data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development.

  Study EGAS00001005450

• MutWP1__CRUK_Grand_Challenge__normal_kidney_Nanoseq

  The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, the International Agency for Research on Cancer is coordinating the recruitment of 5000 individuals with cancer (colorectal, renal, pancreatic, oesophageal adenocarcinoma or oesophageal squamous cancers) across 5 continents to explore whether different mutational signatures explain marked variation in incidence. In brief, through an international network of collaborators around the world, biological materials are collected, along with demographic, histological, clinical and questionnaire data. Whole genome sequences of tumour-germline DNA pairs are generated at the Wellcome Trust Sanger Institute. Non-tumour DNA is also studied for some cancer types. Somatic mutational signatures are subsequently extracted by non-negative matrix factorisation methods and correlated with risk factors data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development.

  Study EGAS00001005451

• MutWP1__CRUK_Grand_Challenge__matched_blood_Nanoseq

  The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, the International Agency for Research on Cancer is coordinating the recruitment of 5000 individuals with cancer (colorectal, renal, pancreatic, oesophageal adenocarcinoma or oesophageal squamous cancers) across 5 continents to explore whether different mutational signatures explain marked variation in incidence. In brief, through an international network of collaborators around the world, biological materials are collected, along with demographic, histological, clinical and questionnaire data. Whole genome sequences of tumour-germline DNA pairs are generated at the Wellcome Trust Sanger Institute. Non-tumour DNA is also studied for some cancer types. Somatic mutational signatures are subsequently extracted by non-negative matrix factorisation methods and correlated with risk factors data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development.

  Study EGAS00001006774

• Childhood Cancer Data Initiative (CCDI): Pediatric In Vivo Testing Program - Neuroblastoma

  The goal of NCI's Pediatric In Vivo Testing Consortium (PIVOT) is to advance the development of effective treatments for pediatric cancers through preclinical in vivo testing of novel therapeutic agents. To design impactful experiments that can be rapidly translated to the clinic, PIVOT investigators require comprehensive genomic characterization of the patient-derived xenograft (PDX) models used across the consortium. This robust experimental design enhances collaboration with industry partners by providing proof-of-concept data for drugs in development. As part of this effort, neuroblastoma PDX models are being molecularly characterized to identify promising therapeutic candidates. The molecular profiling includes whole exome sequencing (WES), RNA-seq, MethylEPIC array, CytoSNP array, and DNA fingerprinting for quality control. Additionally, demographic and clinical data (e.g., diagnosis, disease site, disease status) are shared. These characterized PDX models, matched with patient tumors, guide model selection for preclinical drug testing.

  Study phs003163

• GATA1 scD&D-seq integrated with Multiome

  Ex vivo differentiation experiments were performed using human CD34+ hematopoietic stem and progenitor cells along the erythropoietic trajectory at multiple timepoints (differentiation days 0, 5, 8, 18, and 24). Simultaneous profiling of transcriptome, chromatin accessibility, and GATA1 binding were performed by integrating D&D-seq with 10X Multiome.

  Dataset EGAD50000002299

• Human tumor scMultiome

  We profile 10 high-grade gliomas patient brain tumor samples by single-cell multiome ATAC + gene expression, using the 10X Chromium technology. 3 sets of fastq are provided for each samples: R1 and R2 for gene expression, R1 and R2 for ATAC-seq as well as index1 and index2 for ATAC-seq.

  Dataset EGAD00001008349

• The_Genomic_Advances_in_Sepsis__GAinS__RNA_seq

  Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. This cohort comprises a subset of patients enrolled in the Genomic Advances in Sepsis (GAinS) study, an established biobank of adult sepsis patients. Patients with sepsis due to community acquired pneumonia or faecal peritonitis were recruited from 34 hospitals across the UK from 2005-2018, with samples for functional genomics and detailed clinical information collected on the first, third and/or fifth day following ICU admission. RNA was extracted from leukocytes isolated at the bedside using LeukoLOCK kits. We have previously identified sepsis response signatures (SRSs), transcriptomic endotypes that are associated with differential early mortality (Davenport et al, Lancet Respir Med, 2016; Burnham et al, AJRCCM, 2017) and response to treatment in a clinical trial (Antcliffe et al, AJRCCM, 2018). We generated RNA sequencing data on 903 samples, including 134 samples repeated from our previously released microarray data. Libraries were prepared using NEB Ultra II Library Prep kits (Illumina) and sequenced on a NovaSeq 6000. Reads were aligned to the reference genome (GRCh38) using STAR and gene counts quantified using featureCounts (annotation Ensembl v99). Counts were TMM-normalised and log-transformed. Following QC, processed data were available on 864 samples from 667 unique patients.

  Study EGAS00001003772

• Whole exome sequencing of SarBC-01- and UroBC-01-related samples.

  This dataset contains raw FASTQ files from DNA of SarBC-01-related samples (SarBC-01 patient Germline, SarBC-01 patient Urothelial tumor, SarBC-01 patient Sarcomatoid tumor, SarBC-01 Passage 6 organoids, SarBC-01 Passage 20 organoids) and UroBC-01 related samples (UroBC-01 patient Germline, UroBC-01 patient Urothelial tumor, UroBC-01 Passage 19 organoids, UroBC-01 Passage 6 organoids). DNA was extracted from FFPE tissue, using RecoverAll RNA/DNA extraction kit (Invitrogen, Carlsbad, CA, USA, AM1975) followed by incubation with Uracil-DNA Glycosylase, and fresh or flash frozen tissue, using Quick-DNA/RNA Miniprep kit (Zymo Research, Irvine, CA, USA, D7001). Twist Human Core Exome + RefSeq + Mito-Panel kit (Twist Bioscience, 102031) was used for the whole exome capturing. Sequencing was performed on Illumina NovaSeq 6000 using paired-end 100-bp reads.

  Dataset EGAD00001011157

• Subtype specific studies of breast cancer progression. Milan cohort.

  The aim of this study is to compare DCIS and IBC in a subtype stratified manner in several genomic levels (gene expression, copy number, methylation).The data in this archive is supplementary to previously published data.

  Study EGAS00001004390

• Dataset for leiomyosarcoma-EXON

  Rare cancer sequencing data of 96 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008885

• Dataset for bone_cancer-EXON

  Rare cancer sequencing data of 87 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008865