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• Next Generation Sequencing platform for targeted Personalized Therapy of Leukemia - Acute myeloid leukemia

  The advent of high-throughput next generation sequencing (NGS) technologies that are revolutionizing genomics and transcriptomics by providing a single base resolution tool for a unified deep analysis of diseases complexity allows a fast and cost-efficient fine-scale assessment of the genetic variability hidden within cohorts of patients affected by the same leukemia. That being so, by potentially highlighting inter-individual differences that may play a role in the differential success of diverse therapeutic interventions, they promise to be crucial for selecting the most appropriate medical treatments. The project aims at the identification of novel prognostic biomarkers for acute myeloid leukemia (AML) and studied the molecular differences between aneuploid and euploid AML.

  Study EGAS00001005464

• EPIC arrays data for chemotherapy response project

  In this study, a total of 300 patients with MIBC receiving chemotherapy were included; 62 received NAC before cystectomy and 245 received first-line chemotherapy upon detection of locally-advanced (T4b) or metastatic disease. Treatment response, defined as pathological downstaging (< pTa,CIS,N0) after NAC or complete or partial response after first-line treatment (RECIST criteria). DNA methylation analysis was performed using DNA from 72 patients. We used 500 ng genomic DNA for bisulfite conversion followed by whole-genome amplification prior to hybridization to EPIC BeadChip (Illumina, San Diego, CA) overnight as described by the manufacturer and then scanned with the Illumina iSCAN system. Data provided here consist of 144 idat files.

  Study EGAS00001004515

• MutWP1: CRUK Grand Challenge Mutographs of Cancer: Kidney (1)

  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. 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.

  Dataset EGAD00001006427

• MutWP4: CRUK Grand Challenge Mutographs of Cancer: Pancreatic Organoids (2021-02-02)

  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, Kings College London will characterise the mutational signatures induced by putative human carcinogens in order to identify the origins of mutational signatures found in human cancers. To achieve this human organoid cell cultures will be exposed to a representative catalogue of known or suspected human carcinogens and mutagens and, using whole genome sequencing, the patterns of mutations induced by them will be determined. Somatic mutational signatures will be subsequently extracted by non-negative matrix factorisation methods and correlated with exposure 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. . This dataset contains all the data available for this study on 2021-02-02.

  Dataset EGAD00001006933

• TB-DAR Whole Genome Sequencing Study

  116 Whole Genome Sequencing (WGS) samples from the TB-DAR study, based on a cohort of adult pulmonary tuberculosis patients recruited in Dar es Salaam, Tanzania. WGS was performed at the Health2030 Genome Center in Geneva on the Illumina NovaSeq 6000 instrument (Illumina Inc, San Diego CA, USA), starting from1μg of whole blood genomic DNA and using Illumina TruSeq DNA PCR-Free reagents for library preparation and the 150nt paired-end sequencing configuration. Average coverage was above 30X for 75 samples, between 10X and 30Xfor 40 samples, and approximately 8X for a single sample. Sequencing reads were aligned to the GRCh38 (GCA_000001405.15) reference genome using bwa (Version 0.7.17).

  Dataset EGAD00001008400

• Targeted de novo phasing and long-range assembly by template mutagenesis

  Long-range sequencing with low error rate has been challenging. Sequence assembly and phasing usually require a high-quality reference genome for mapping, so working on highly-variable genomic regions or regions with no reference genome information would be difficult. In this study, we describe novel bench protocols and algorithms to obtain ultra-low-error-rate haplotype-phased sequence assemblies of regions 10 KB in length using a short-read sequencing platform that simultaneously solves the above two problems. We accomplish this by imprinting each template strand from a target region with a dense and unique mutation pattern. The mutation process randomly and independently converts ~50% of cytosines to uracils. Short-read sequencing libraries are made from both mutated and unmutated templates. A conservative de Bruijn graph approach seeds an assembly of the mutated templates, which we then extend by mapping paired-end reads. We next partition the template assemblies into two or more haplotypes after using the unmutated sequence library to recover almost all of the mutated bases. The final haplotype is assembled and corrected for residual template mutations and PCR errors. We obtain per-base-error rates below 10 9. We apply this method to a human family, correctly assembling and phasing three genomic intervals, including the highly polymorphic HLA-B gene.

  Dataset EGAD00001008444

• The 5-hydroxymethylcytosine Landscape of Prostate Cancer

  This dataset consists of genome-wide 5hmC methylomes at various stages of prostate cancer, including not only 93 metastases from castration-resistant prostate cancer (mCRPC) patients, but also 5hmC patterns in cell-free DNA (cfDNA). There are 2000 runs in total as fastq files.

  Dataset EGAD00001008462

• MutWP4: CRUK Grand Challenge Mutographs of Cancer: Gastric Organoids (2019-08-07)

  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, Kings College London will characterise the mutational signatures induced by putative human carcinogens in order to identify the origins of mutational signatures found in human cancers. To achieve this human organoid cell cultures will be exposed to a representative catalogue of known or suspected human carcinogens and mutagens and, using whole genome sequencing, the patterns of mutations induced by them will be determined. Somatic mutational signatures will be subsequently extracted by non-negative matrix factorisation methods and correlated with exposure 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. . This dataset contains all the data available for this study on 2019-08-07.

  Dataset EGAD00001005234

• sWGS of HGSOC samples for fixative comparison study

  Shallow whole-genome sequencing of samples from the study "Methanol-based fixation is superior to buffered formalin for next-generation sequencing of DNA from clinical cancer samples". DNA from each sample (100ng) was sheared on Covaris S220 (Covaris): duty cycle - 10%, intensity -5.0, bursts per sec - 200, duration - 300 sec, mode - frequency sweeping, power - 23V, temperature -5:5 C to 6 C, water level - 13. Libraries were prepared with the TruSeq Nano DNA LT Sample Prep Kit (Illumina) using a modi?ed protocol - Sample Puri?cation Beads were replaced by Agencourt AMPure XP beads (Beckman Coultier) and size selection after the End Repair was done to remove only the short fragments. Quality and quantity for contructed libraries were assessed with DNA 7500 kit on Agilent 2100 Bioanalyzer and with Kapa Quanti?cation kit (KAPA Biosystems) on 7900HT Fast Real-Time PCR System (Applied Biosystems) according to the supplier's recommendations, respectively. Libraries from 18 barcoded samples were pooled together in equimolar amounts and each pool was loaded on a single lane of a HiSeq Single End Flowcell (Illumina), followed by cluster generation on a cBot (Illumina) and sequencing on a HiSeq 2500 (Illumina) in a single-read 50bp mode. Reads were aligned using bwa-mem v0.7.12-r1039 to the 1000 genomes version of human genome build GRCh37. Picard (http://picard.sourceforge.net) was used to remove duplicate reads.

  Dataset EGAD00001001938

• comparing the snRNA-seq from placentas of mothers with or without obesity

  Obesity poses risks to maternal health and increases the likelihood of short- and long-term adverse pregnancy outcomes in the offspring. The placenta, a key organ at the maternal-fetal interface, responds to maternal obesity and regulates fetal growth. To investigate the molecular features of physiological adaptation, we perform single-nuclei RNA-seq on human placentas and compared the transcriptomic profiles of women with obesity delivering appropriate- or large-for-gestational age (i.e., AGA and LGA) babies with those from normal-weight healthy controls with AGA babies. The snRNA-seq libraries were generated with Chromium Single Cell 3’ kit v3.1 (10X Genomics) and sequenced on Illumina NovaSeq 6000 at Novogene.

  Study EGAS50000000834