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• Profiling the Microbiome of Pediatric Gut with Metagenomic Short-Read Sequencing

  This dataset contains short-read metagenomic sequencing of stool, as well as intestinal tissue and tissue washes, from preterm infants. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Dataset EGAD00001016052

• Whole Exome Sequencing of Non-Hodgkin Lymphoma Patients

  This dataset contains whole exome sequencing data from 20 Non-Hodgkin Lymphoma patients sequenced on the Illumina NovaSeq 6000 platform. Data includes paired-end FASTQ files (2×150 bp) for all samples representing diverse histopathological subtypes including DLBCL, Follicular Lymphoma, and other NHL variants from a Saudi Arabian cohort.

  Dataset EGAD50000002551

• Tam-seq of tumor samples for HGSOC copy-number signatures study

  This dataset contains targeted amplicon sequencing of DNA extracted from 300 samples of 142 patients (158 methanol-fixed relapse biopsies and 142 FFPE archival diagnostic tissues). Samples were sequenced on Illumina HiSeq 2500 and were aligned to human genome assembly GRCh37 (hg19)to produce 600 bam files (2 technical replicates per sample).

  Dataset EGAD00001004173

• RNAseq profiling of pediatric osteosarcoma

  Primary pediatric osteosarcoma samples were collected and profiled using RNAseq. When possible, germline and tumor samples were collected. For some patients, multiple tumor tissues were collected and sequenced. Some of the samples were used to derive PDTX models, which were also profiled with RNAseq. Paired end sequencing was performed on Illumina HiSeq instruments and FASTQ files reported.

  Dataset EGAD00001004538

• 666PG Whole genome alignment

  Aligned whole genome sequences in this dataset are from CPCG-GENE Normal/Tumor pairs used in the 666PG study. Raw sequences data was aligned against the human reference (GRCh37 + decoy) using bwa-mem and GATK for indel-realignment and recalibration

  Dataset EGAD00001004957

• RNA sequencing and whole-genome mate-pair sequencing of osteosarcoma

  This data set includes 72 mate pair sequenced osteosarcomas (36 as part of a discovery cohort and 36 as part of a validation cohort). It also includes RNA-sequencing data on 67 osteosarcomas (mostly overlapping with the above mate pair sequenced cases) and 13 osteoblastomas used as controls for gene expression levels.

  Dataset EGAD00001005307

• Sequencing data for oesophageal and related samples - Mourikis et al (RNA)

  Data supporting: "Patient-specific detection of cancer genes reveals recurrently perturbed processes in esophageal adenocarcinoma." Mourikis et al. RNAseq (BAM files) 137 samples

  Dataset EGAD00001004776

• Phase I Study and Cell-Free DNA Analysis of T-DM1 and Metronomic Temozolomide for Secondary Prevention of HER2-Positive Breast Cancer Brain Metastases

  This study is a phase I trial combining ado-trastuzumab emtansine (T-DM1) with Temozolomide (TMZ) for the prevention of additional brain metastases after previous occurrence and local treatment in HER2+ breast cancer patients. Twelve patients were enrolled in this study. Data includes whole exome sequencing (WES) from patients' cerebrospinal fluid (CSF) and plasma samples. The CSF was collected from each patient at trial entry and after the initial two treatment cycles of TMZ and T-DM1 treatment. Blood was collected through a standard peripheral venipuncture at the following four time points: 1) upon trial entry, 2) after two treatment cycles, 3) the treatment cycle prior to the last follow-up, and 4) most recent follow-up. Whole exome libraries were captured by the SureSelect Human All Exon V8 Kit (Agilent, Santa Clara, CA, USA) and sequenced by NovaSeq 6000 S2 Sequencer in paired-end sequencing mode (Illumina, San Diego, CA, USA). Gene mutations in DNMT3A, ELP1, NOTCH4, KMT2B, CRLF2, and KCNJ18 were observed in patients' CSF.

  Study phs003165

• Epigenome Wide DNA Methylation Study for Osteoporosis Risk

  Osteoporosis is characterized by low bone mineral density (BMD). The advancement of high-throughput technologies and integrative approaches provided an opportunity for deciphering the mechanisms underlying osteoporosis. In this study, we generated genomic (from whole blood DNA), transcriptomic and methylomic (from peripheral blood monocytes, PBMs), and serum metabolomic datasets from 143 premenopausal Caucasian females aged 25-40 years, including 74 with relatively high hip BMD (Z scores ≥ 0.8) and 69 with relatively low hip BMD (Z scores ≤ -0.8). The BMD Z score was defined as the number of standard deviations a subject's BMD differed from the mean BMD of their age-, gender-, and ethnicity-matched population. We identified differentially methylated CpG sites (DMCs) in peripheral blood monocytes (PBMs) at the whole methylome level between premenopausal women with high BMD and those with low BMD and deciphered how the DMCs influence the BMD variation through affecting the expression of the relevant genes. We also performed differential gene expression and metabolic analysis to assess their biological interaction and causal mechanisms. Our study provided novel insights into the pathogenesis of osteoporosis.

  Study phs001960

• Advancing fast in the analysis of circulating tumour DNA

  In cancer patients, solid tumours also release DNA fragments into the blood, and they are called Circulating Tumours DNA (ctDNA). Nowadays, the standard procedure for cancer monitoring is analysis of surgically obtained biopsies. They generate a big amount of material, but the operation is highly invasive and provide medical doctors with a single-time snapshot of the tumours, which are often highly changeable. Analysis of ctDNA, on the other hand, can provide continuous information regarding the tumour itself without the need of invasive procedures. This is why interest in this method is growing, and with it, the need for its technological optimization. Guanhua Zhu and colleagues, at the Genome Institute of Singapore, developed a methodology to quantify ctDNA burden, which means to estimate the amount of tumours derived circulating DNA among all cell-free DNA in the blood of individuals. Their work, published in Nature Communications in april 2021 and summarized in a sketch below, uses state-of-the art methods to analyse cfDNA degradation patterns at promoters and exon regions, to discern it from the DNA coming from the tumours. The bright point of their approach is that simultaneously enable for profiling of actionable cancer mutations and quantitative estimation of ctDNA burden. Their sequencing data of ctDNA has been deposited in the EGA under the accession number EGAS00001004657. This study sums up to a total of 37 of cfDNA and 36 about ctDNA present up to date in the EGA archive. The existence of cfDNA was firstly described back in 2002, while the use of ctDNA analysis in the oncological field exploded only after 2016. The approach is rapidly evolving, moving in parallel with the ever-improving sequencing technologies. Especially, the possibility to obtain high quality sequences from minimal amount of material is a crucial factor. Being able to alleviate cancer patients of disturbing and often slow-recovery procedures, while at the same time improving the quality of diagnosis and disease monitoring seems like a lot to ask. Thankfully, we are blessed with brilliant and dedicated scientists from all around the world who collaborate to make this a reality, sharing their results, knowledge and data.

  Blog advancing-fast-in-the-analysis-of-circulating-tumour-dna