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• Drug-induced epigenomic plasticity reprograms circadian rhythm regulation to drive prostate cancer towards androgen-independence (RNA-seq)

  Understanding how prostate cancer cells adapt to AR-targeted interventions is critical for identifying novel drug targets to improve the clinical management of treatment-resistant disease. Our study revealed an enzalutamide-induced epigenetic plasticity towards pro-survival signaling, and uncovered circadian regulator ARNTL as an acquired vulnerability after AR inhibition, presenting a novel clinical lead for therapeutic development.

  Study EGAS00001006016

• Drug-induced epigenomic plasticity reprograms circadian rhythm regulation to drive prostate cancer towards androgen-independence (ChIP-seq)

  Understanding how prostate cancer cells adapt to AR-targeted interventions is critical for identifying novel drug targets to improve the clinical management of treatment-resistant disease. Our study revealed an enzalutamide-induced epigenetic plasticity towards pro-survival signaling, and uncovered circadian regulator ARNTL as an acquired vulnerability after AR inhibition, presenting a novel clinical lead for therapeutic development.

  Study EGAS00001006017

• Single Cell Multiome ATAC + Gene Expression sequencing

  The SHH medulloblastoma tumor 7316-178 was received through the Childhood Brain Tumor Network (CBTN). From the patient tumor, disassociated cryopreserved cells stored in 10% DMSO/FBS were used. At least 50 mg of tissue (1 M cells) was used for both samples. Disassociated cells were prepared for Single Cell Multiome ATAC + Gene Expression sequencing (10× Genomics) according to the manufacturer’s instructions. Sequencing was performed on an Illumina NovaSeq S4 200 to a depth of at least 250 M reads for snATAC-seq and 200 M reads for snRNA-seq.

  Dataset EGAD50000001515

• ICARUS-LUNG01 dataset

  A total of 46 frozen tumor biopsies underwent WES analysis, while 41 blood samples were used as germline controls.Tumor samples qualified for whole exome sequencing (WES) if they contained ≥10% tumor cells. For bulk RNA-seq, 20 paired baseline and on-treatment frozen tumor biopsies were analyzed. Tumor samples qualified if they contained ≥30% tumor cells. For GeoMx spatial transcriptomic study we used 7 patients from the ICARUS LUNG01 trial focusing on tumour and immune cell compartments. We selected 7 paired baseline and on-treatment patient samples for analysis. We selected up to 4 regions of interest (ROI) per sample tissue. We identified tumour and immune cell areas of illumination (AOI) using CK and CD45 antibodies respectively

  Dataset EGAD50000001014

• Targeted panel somatic variant sequencing dataset for "Molecular counting enables accurate and precise quantification of methylated ctDNA"

  Targeted panel somatic variant sequencing dataset for "Molecular counting enables accurate and precise quantification of methylated ctDNA." This dataset was created by running 60 cfDNA samples from patients with metastatic cancer to the lung through a research-use, tumor-naive, targeted-panel assay for measuring somatic mutations across a panel of 80 genes. All samples were sequenced on Illumina NextSeq 2000 with 150x2 paired end sequencing; files are in fastq.gz format. For the original study, the variant allele fractions of these somatic mutations were compared with the Tumor Methylation Scores calculated from Northstar Response in the same samples.

  Dataset EGAD50000001016

• March 2018 cumulative data release 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.

  March 2018 cumulative data release (bams,fastqs) 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

  Dataset EGAD00001003963

• November 2020 data update (fastq) 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.

  November 2020 data update (fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies (Canadian Epigenetics, Environment and Health Research Consortium), Genome Sciences Center, B.C. Cancer Agency, Vancouver, Canada as part of the International Human Epigenome Consortium.

  Dataset EGAD00001007529

• HV31 - Read identifier list for local CCS, CLR, ONT and MGI reads

  This file contains read identifiers for local CCS, CLR, ONT and MGI reads for each of the eight selected genomic regions (HLA, KIR, IGH, IGK, IGL, TRA, TRD, andTRG). We extracted these reads by aligning whole-genome sequencing data to a draft whole-genome de novo assembly, and selecting reads that map to contigs representing each region. These reads were involved in the polishing and validation of the HV31 assembly. Please refer to the relevant manuscript (https://doi.org/10.1101/2021.02.03.429586) for additional details. Read identifiers are stored in JSON format. Along with the full FASTQ files, this file enables convenient re-analysis of the HV31 sequencing data in the eight selected regions.

  Dataset EGAD00001007761

• A standardised framework for robust fragmentomic feature extraction from cell-free DNA sequencing data

  Fragmentomic features of cell-free DNA represent promising non-invasive biomarkers for cancer diagnosis. However, a lack of systematic evaluation of biases in feature quantification has hindered the adoption of such applications. We compared features derived from whole-genome sequencing of ten healthy donors using nine library kits and ten data-processing routes, and validated them in 1,182 plasma samples from published studies. Our results clarify the variations resulting from library preparation and feature quantification methods. We designed the Trim Align Pipeline and the cfDNAPro R package as unified interfaces for data pre-processing, feature extraction, and visualisation, aiming to standardise multimodal feature engineering and integration for machine learning.

  Dataset EGAD00001015535

• Chromatin accessibility (ATAC-seq) and transcriptome (RNA-seq) data from immune cells for healthy young and healthy old subjects

  Dataset consisting of: (1) N=234 genome-wide chromatin accessibility (ATAC-seq) profiles for distinct N=21 healthy old and N=28 healthy young subjects. ATAC-seq biological samples provided for the following tissues: PBMC (N=24), CD14+ monocytes (N=18), CD8+ memory T cells (N=7), CD8+ naive T cells (N=7), CD4+ memory T cells (N=7), CD4+ naive T cells (N=7), and naive B cells (N=7). (2) N=39 genome-wide transcription (RNA-seq) data for distinct N=15 healthy old and N=24 healthy young subjects' PBMCs.

  Dataset EGAD00001003602