15130 results for "cancer studies using rna-seq"

in 18.85 milliseconds.

• Abnormal_foetal_development_exome_trios

  This project is to explore the contribution of de novo mutations to severe structural malformations diagnosed prenatally using ultrasound. These malformations include heart, CNS, renal and GI abnormalities. In this pilot project we aim to exome sequence 30 parent-foetus trios to ~50X mean coverage and identify de novo functional variants using an algorithm developed in the Hurles group

  Study EGAS00001000167

• Profiling of H3K27ac landscape in immune cells from rheumatoid arthritis patients and healthy controls

  We collected peripheral blood mononuclear cells (PBMC) from 6 RA patients and 4 healthy controls, as well as synovial fluid from the same RA patients. We then sorted B cells, CD4+ and CD8+ T cells, regulatory T cells and monocytes using flow cytometry and profiled regions marked with H3K27ac using CUT&Tag.

  Study EGAS00001005085

• Epi2Diag

  Genomic DNA was extracted from whole blood and bisulfite converted using the EZ DNA Methylation Lightning Kit (Zymo research). DNA methylation profile was then derived using Illumina’s Infinium EPIC array v2.0. in accordance with the manufacturer’s protocol at the ASGARD-Rouen genomic platform (Inserm U1245, University of Rouen and Rouen University Hospital, Rouen France) on an Illumina NextSeq550 scanner.

  Study EGAS00001008070

• Gut metagenome/FR 2002

  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 EGAS00001005038

• Whole exome sequencing of atypical 3q26 samples

  The Genomic DNA Clean & Concentrator kit (ZYMO Research) was used to remove EDTA from the DNA samples. Sample libraries were prepared using 100 ng of input according to the KAPA HyperPlus Kit (Roche) using Unique Dual Index adapters (Integrated DNA Technologies, Inc.). Exomes were captured using the SeqCap EZ MedExome (Roche Nimblegen) according to SeqCap EZ HyperCap Library v1.0 Guide (Roche) with the xGen Universal blockers – TS Mix (Integrated DNA Technologies, Inc.). The amplified captured sample libraries were paired-end sequenced (2x100 bp) on the Novaseq 6000 platform (Illumina) and aligned to the hg19 reference genome using the Burrows-Wheeler Aligner (BWA).

  Dataset EGAD00001006102

• Inferring tumor genomes from peripheral blood i.e. CTCs and plasma-DNA using deep sequencing and targeted enrichment

  In this study we tested whether complex tumor genomes can be inferred from the peripheral blood (plasma DNA /CTCs) of 32 patients with stage IV colorectal carcinoma (CRC). To establish the percentage of mutated plasma DNA fragments we used 8 patient samples with different plasma DNA size distributions and KRAS mutations in their corresponding primary tumors and employed ultra-deep pyrosequencing with 3 different sequencing reaction sizes (i.e., 119 bp, 168 bp, and 323 bp). Furthermore, we used massive parallel sequencing of a panel of 68 CRC-associated genes to establish the mutation spectrum of cancer driver genes in the primary tumors, metastases, and CTCs of two colorectal cancer patients. To investigate, whether mutations found in only one CTC (“private CTC mutations”) are true mutations occurring with allele frequencies below the resolution limits of current detection methods, or whether they are amplification or sequencing artifacts, we performed ultra-deep sequencing of the primary tumors, metastases, plasma DNAs and normal tissues.

  Study EGAS00001000337

• Whole-exome-sequencing and Whole-genome-sequencing and RNA-sequencing in Familial amyotrophic lateral sclerosis (ALS)

  To identify the genetic cause and modifiers of Familial amyotrophic lateral sclerosis (ALS), we performed Whole-exome-sequencing and Whole-genome-sequencing and RNA-sequencing of Familial amyotrophic lateral sclerosis (ALS) patients.

  Study JGAS000358

• Head and Neck Organoid Biobank cohort, Issing et. al., 2025, RNA + WES data

  This dataset contains the raw fastq files of RNA and whole exome sequencing of the head and neck organoid biobank. For RNA sequencing, 41 organoid samples collected at different timepoints as well as before and after genetic modification are included. For WES, 25 samples of early organoid cultures or matching tumor tissues are included.

  Dataset EGAD50000001733

• Altered Blood and Keratinocyte microRNA/transfer RNA Fragment Profiles Related to Fibromyalgia Syndrome and its Severity

  We provide small RNA transcriptome signatures of whole blood and cultured keratinocytes, which separate FMS patients from healthy controls. This opens new perspectives for FMS diagnostics, symptom monitoring, and clinical management.

  Study EGAS50000000638

• reChIP-seq reveals widespread bivalency of H3K4me3 and H3K27me3 in CD4+ memory T-Cells

  Identification of genomic loci exhibiting the physical co-localization of two distinct DNA-associated proteins remains a technical challenge. To tackle this challenge we have developed a novel reChIP-seq approach that enriches for two distinct DNA-associated proteins in an unbiased and genome-wide manner. We illustrate the utility of our approach by identifying nucleosomes bivalently modified by H3K4me3 and H3K27me3 in primary human CD4+ central memory T-cells (TCMs). We unravel widespread bivalency of many unmethylated CpG-rich regions in TCMs overlapping with transcriptional start sites of developmental regulators. Furthermore, reChIP-seq uncovers two yet unobserved classes of bivalency, where either H3K4me3 or H3K27me3 is mixed with low but detectable bivalency. We provide evidence that bivalency is established by an interplay between genome and epigenome and thus attains stability. Our described reChIP-seq approach augments conventional ChIP-seq and is broadly applicable in the study of co-localization of DNA-associated proteins to unravel the intricate possibilities of combinatorial action.

  Study EGAS00001001568

• Reference epigenome Islet-derived-MSC04 h3k36me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC04 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007431

• Reference epigenome Islet-derived-MSC02 input ChIP-Seq data generated from KEP study

  Islet-derived-MSC02 input ChIP-Seq paired end data

  Dataset EGAD00001007415

• Reference epigenome Islet-derived-MSC04 h3k4me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC04 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007433

• Reference epigenome Islet-derived-iPSC02 input ChIP-Seq data generated from KEP study

  Islet-derived-iPSC02 input ChIP-Seq paired end data

  Dataset EGAD00001007449

• Reference epigenome Islet-derived-MSC04 h3k9me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC04 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007434

• ATAC-seq

  ATAC-seq data for 26 CLL samples (7 controls, 19 tumor) of the CancerEpiSys-PRECiSe project.

  Dataset EGAD00001005967

• Reference epigenome Islet-derived-MSC03 h3k27ac ChIP-Seq data generated from KEP study

  Islet-derived-MSC03 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007416

• Reference epigenome Islet-derived-MSC04 h3k27me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC04 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007430

• Reference epigenome Islet-derived-MSC04 input ChIP-Seq data generated from KEP study

  Islet-derived-MSC04 input ChIP-Seq paired end data

  Dataset EGAD00001007435

• Reference epigenome Islet-derived-iPSC01 h3k4me1 ChIP-Seq data generated from KEP study

  Islet-derived-iPSC01 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007439

• Reference epigenome Islet-derived-iPSC02 h3k27me3 ChIP-Seq data generated from KEP study

  Islet-derived-iPSC02 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007444

• Reference epigenome Islet-derived-MSC01 h3k27ac ChIP-Seq data generated from KEP study

  Islet-derived-MSC01 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007402

• Reference epigenome Islet-derived-MSC01 h3k4me1 ChIP-Seq data generated from KEP study

  Islet-derived-MSC01 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007405

• Reference epigenome Islet-derived-MSC01 h3k27me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC01 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007403

• Reference epigenome Islet-derived-MSC01 h3k4me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC01 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007406

• Reference epigenome Islet-derived-MSC01 h3k9me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC01 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007407

• Reference epigenome Islet-derived-MSC01 input ChIP-Seq data generated from KEP study

  Islet-derived-MSC01 input ChIP-Seq paired end data

  Dataset EGAD00001007408

• Reference epigenome Islet-derived-MSC02 h3k27ac ChIP-Seq data generated from KEP study

  Islet-derived-MSC02 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007409

• Reference epigenome Islet-derived-MSC02 h3k27me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC02 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007410

• Reference epigenome Islet-derived-MSC02 h3k36me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC02 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007411

• Reference epigenome Islet-derived-MSC03 h3k27me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC03 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007417

• Reference epigenome Islet-derived-MSC02 h3k4me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC02 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007413