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• ICGC PCAWG Dataset: OV-AU_PCAWG_RNA-Seq_Star

  ICGC PCAWG Dataset for RNA-Seq BAM aligned using Star. Project: OV-AU.

  Dataset EGAD00001003415

• ICGC PCAWG Dataset: OV-AU_PCAWG_RNA-Seq_TopHat2

  ICGC PCAWG Dataset for RNA-Seq BAM aligned using TopHat2. Project: OV-AU.

  Dataset EGAD00001003416

• ICGC PCAWG Dataset: LIRI-JP_PCAWG_RNA-Seq_TopHat2

  ICGC PCAWG Dataset for RNA-Seq BAM aligned using TopHat2. Project: LIRI-JP.

  Dataset EGAD00001003546

• Genomic Landscape of Chordoid Glioma

  This data set consist genomic information of 10 Chordoid Glioma samples: - Exome sequencing: 10 tumors and matched normal DNA for four of them (BAM files) - RNAseq : 10 tumors (fastq files) - CNV array: 9 tumors (IDAT files)

  Dataset EGAD00001004112

• SNV vcf files for CPCG Subclonal Heterogeneity in multiple and single regions

  SNV calls generated using the MuTect-TITAN-PhyloWGS from the CPC-GENE Subclonal Heterogeneity study using single and multiple regions

  Dataset EGAD00001004433

• ICGC PCAWG Dataset: LAML-KR_PCAWG_WGS_BWA

  ICGC PCAWG Dataset for WGS BAM aligned using BWA MEM. Project: LAML-KR.

  Dataset EGAD00001002119

• ICGC PCAWG Dataset: BTCA-SG_PCAWG_WGS_BWA

  ICGC PCAWG Dataset for WGS BAM aligned using BWA MEM. Project: BTCA-SG.

  Dataset EGAD00001002121

• ICGC PCAWG Dataset: BRCA-UK_PCAWG_WGS_BWA

  ICGC PCAWG Dataset for WGS BAM aligned using BWA MEM. Project: BRCA-UK.

  Dataset EGAD00001002122

• ICGC PCAWG Dataset: PAEN-IT_PCAWG_WGS_BWA

  ICGC PCAWG Dataset for WGS BAM aligned using BWA MEM. Project: PAEN-IT.

  Dataset EGAD00001002153

• Exploration of neuroblastoma xenograft models for tumor extracellular RNA profiling in murine blood plasma

  Total RNA sequencing (SMARTer Stranded Total RNA-Seq Kit v2) data of extracellular RNA (exRNA) from liquid biopsies of neuroblastoma xenograft models.

  Dataset EGAD00001010905

• Bulk RNA from sorted CD8+ T cells after 48h co-culture with human Mito-DsRed MSCs.

  For human samples, total cellular RNA was isolated from post-sorted Mito+ and Mito CD8+ cells using the RNeasy Mini Kit (Qiagen). Stranded RNA libraries were created using SMARTSeq Stranded.

  Dataset EGAD00001011082

• ICGC PCAWG Dataset: CMDI-UK_PCAWG_WGS_BWA

  ICGC PCAWG Dataset for WGS BAM aligned using BWA MEM. Project: CMDI-UK.

  Dataset EGAD00001002664

• BLUEPRINT DNA methylation profiles of human hematopoietic progenitors

  DNA methylation was analyzed for stem/progenitor cell types and terminally differentiated cell types of the human blood lineage (HSC, MPP, CMP, MEP, GMP, CLP, MLP0, MLP1, MLP2, MLP3, MK, CD4+ Tcell, CD8+ Tcell, Bcell, NK, Neut, Mono).

  Dataset EGAD00001002732

• SF11979 snATAC seq IDHR132H Wildtype GBM Female

  SF11979 snATAC, IDHR132H WT GBM Female, 76 Single Nuclei ATAC seq data from high grade human glioma samples. NovaSeq6000 was used for ATAC seq. The files uploaded are bam files created with grch38 reference.

  Dataset EGAD00001005418

• Multi-region sequencing of a RET fusion positive cancer patient

  Deep WGS sequencing (160x) of 2 different sites of disease of a patient with a RET fusion positive cancer. Amplicon sequencing of 19 other sites of the same patients for the RET fusion.

  Dataset EGAD00001005776

• Dataset for RNA and Exome sequencing of Glioblastoma samples

  This dataset contains RNA Seq, 10x scRNA Seq and Exome sequencing of glioblastoma samples. Sequencing was performed on a Illumina NovaSeq 6000 and Illumina HiSeq 4000. The sequencing was always paired.

  Dataset EGAD00001012235

• HV31 - PacBio long-read circular consensus (CCS) senquencing

  PacBio long-read circular consensus (CCS) sequencing data for individual HV31 generated on PacBio Sequel II instrument, using size-selected (10-15 kb) DNA from CD14+ monocytes, to a sequencing depth of ~12×. Sequencing was performed at the Wellcome Sanger Institute.

  Dataset EGAD00001006979

• Exome sequencing data for 40 cases of alopecia areata and vitiligo

  Sequencing of independent unrelated cases of alopecia areata (total/ universal) and vitiligo (>10%, affected family member). DNA obtained from blood

  Study EGAS00001003831

• Rapid Acceleration of Diagnostics - Digital Health Technologies (RADx-DHT): Covidseeker and COVID-19 Citizen Science: Leveraging Citizen Science and Real-Time Geospatial Temporal Mobile Data for Digital Contact Tracing and SARS-CoV-2 Hotspotting

  The Covidseeker and COVID-19 Citizen Science Study integrates a retrospectively-determined geolocation digital program into an established digital infrastructure housed within the NIH-funded Eureka platform to enroll SARS-CoV-2 positive and negative individuals. The solution also leverages the ongoing COVID-19 Citizen Science Study cohort both in-person and remotely to test and implement existing technology to enhance readily accessible contact tracing methods and identify “hot spots” of transmission of SARS-CoV-2. The tools are designed to alert users regarding overlap with SARS-CoV-2 infected individuals, identifying businesses that were visited by someone who later tested positive for COVID-19, and working with those businesses and public health departments on strategies to reduce the spread of the virus. DOI: https://rapids.ll.mit.edu/10.57895/me7r-vp06

  Study phs002519

• A benchmark of DNA methylation deconvolution methods for tumoral fraction estimation using DecoNFlow

  In cancer patients, circulating cell-free DNA (cfDNA) originates from both healthy and cancer cells, and the proportion of tumor-derived cfDNA serves as a surrogate marker of tumor burden. Tumoral cfDNA can be identified using patient-specific mutations or more general tumor-specific DNA methylation patterns that are preserved in cfDNA, enabling non-invasive cancer detection and monitoring. Accurately estimating tumor fractions remains challenging due to the heterogeneous mixture of cfDNA sources in body fluids. Computational DNA methylation deconvolution methods can address this by estimating tumoral fraction with or without reference methylomes. Here, we benchmarked 10 reference-based and 2 reference-free DNAm deconvolution tools and provide the data generated for this benchmarking, consisting of 10 neuroblastoma cell lines and one healthy cfDNA sample (pooled from 15 healthy cfDNA samples), profiled using cfRRBS.

  Study EGAS50000001529

• BLUEPRINT ChIP-seq data for cells in the haematopoietic lineages, from adult and cord blood samples.

  ChIP-seq data for cells in the haematopoietic lineages, from adult and cord blood samples.

  Study EGAS00001000326

• Pilot study for Illumina TST170 NGS panel on cutaneous T cell lymphoma samples

  A number of genomic studies using next generation sequencing (NGS) techniques have attempted to understand the underlying genetic basis of cutaneous T-cell lymphoma and found alterations in genes that are involved in T-cell activation, NF-kB and JAK-signal transducer and activator of transcription (STAT) pathways. 8 diagnostic skin biopsy samples (2xplaque, 6xtumour) on FFPE blocks were selected from 8 MF patients (stage IB:n=1, IIB:n=6, IVA2:n=1). An H&E was performed in order to assess percentage of tumor cells, viable cells, necrosis and immune infiltration, to determine the number of slides required for NGS. Freshly cut unstained slides were manually macrodissected prior to DNA and RNA extraction. Targeted deep sequencing was performed using panel of 170 genes associated with common solid tumours (Illumina TruSight Tumour 170 NGS panel). VCF files generated via the Illumina’s Basespace platform.

  Study EGAS00001002567

• Targeted sequencing data for various human bulk tissues

  To estimate the contribution of early embryogenic cell lineages in adult tissues, we performed deep targeted sequencing on 379 bulk tissues from various organs of the five individuals (DB3, DB6, DB8, DB9, DB10). Of the 441 early embryonic mutations targeted, 411 mutations could have high-quality baits designed for them. DNA libraries were prepared by SureSelectXT Library Prep Kit (Agilent), hybridized to the appropriate capture panel, multiplexed on flow cells, and subjected to paired-end sequencing (150-bp reads) on the NovaSeq 6000 platform (Illumina) with a mean ~2,900x depth of coverage for the early mutations. Sequence reads were trimmed and mapped to the human reference genome (GRCh37) using the BWA-MEM algorithm.

  Dataset EGAD00001007704

• Diverse mutational landscapes in human lymphocytes

  A lymphocyte suffers many threats to its genome, including programmed mutation during differentiation, antigen-driven proliferation and residency in diverse microenvironments. After developing protocols for single-cell lymphocyte expansions, we sequenced whole genomes from 717 normal naive and memory B and T lymphocytes and hematopoietic stem cells. All lymphocyte subsets carried more point mutations and structural variants than haematopoietic stem cells – the extra mutations were mostly acquired during differentiation, with burdens higher in memory than naive lymphocytes, although T cells also had a higher rate of mutation accumulation throughout life. Off-target effects of immunological diversification accounted for most of the additional differentiation-associated mutations in lymphocytes. Memory B cells acquired, on average, 18 off-target mutations genome-wide for every one on-target IGV mutation during the germinal centre reaction. Structural variation was 16-fold higher in lymphocytes than stem cells, with ~15% of deletions being attributable to off-target RAG activity. Mutational processes associated with ultraviolet light exposure and other sporadic mutational processes generated hundreds to thousands of mutations in some memory lymphocytes. The mutation burden and signatures of normal B lymphocytes were broadly comparable to those seen in many B-cell cancers, suggesting that malignant transformation of lymphocytes arises from the same mutational processes active across normal ontogeny. The mutational landscape of normal lymphocytes chronicles the off-target effects of programmed genome engineering during immunological diversification and the consequences of differentiation, proliferation and residency in diverse microenvironments.

  Dataset EGAD00001008107

• WGS and WTS data of patients diagnosed with MEITL.

  WGS from 4 patients, WTS from only 3 patients (insufficient tissue from 4th patient for WTS). Whole-genome sequencing (WGS) was performed for 60 pairs of tumor-normal samples from patients diagnosed with NKTL. Genomic DNA from tumor tissue was extracted with QIAamp DNA Mini Kit. The DNA for the matching normal was obtained from blood or buccal swabs and purified by Blood and Cell Culture DNA Mini kit or E.Z.N.A. Tissue DNA Kit (Omega Bio-tek) according to manufacturer’s instructions. The quantity and quality were assessed by Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen) and agarose gel electrophoresis. All sequencing libraries were prepared using TruSeq Nano DNA Library Prep Kit (Illumina). Paired-end sequencing was performed on Illumina HiSeq 2000. Whole-transcriptome sequencing (WTS): Total RNA from snap frozen EITL tumor samples was extracted using TRIzol (Invitrogen) and purified with RNeasy Mini Kit (Qiagen) according to manufacturer’s instructions. The integrity of RNA was determined by electrophoresis using 2100 Bioanalyzer (Agilent Technologies). 500 ng of total RNA was reverse transcribed with iScript cDNA Synthesis Kit (Bio-Rad, Hercules, CA, USA). Quantification was performed using SsoFast EvaGreen Supermix and CFX96 Real-Time PCR System (both Bio-Rad). Sequencing libraries were prepared using the TruSeq Stranded Total RNA Library Prep Kit with Ribo-Zero (Illumina) and WTS was performed on Illumina HiSeq 2500 with 2x101 bp read length. Description of prefix used in filenames: T: Tumor samples N: Normal samples (Blood) P: PDX samples

  Dataset EGAD00001005341