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• scRNAseq_of_newly_diagnosed_IBD

  We will perform scRNAseq of newly diagnosed IBD patient terminal ileum and blood, and patients will be longitudinally followed up 1) 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 please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001006219

• Correction of FFPE artefacts in WGS data

  The ability to precisely characterise mutational signatures from FFPE-derived DNA has tremendous translational potential. However, sequencing of DNA derived from FFPE material is known to be riddled with artefacts. To correct for this, we introduce FFPEsig, a computational algorithm to rectify the formalin-induced artefacts in the mutational profile.

  Study EGAS00001005331

• 99 Cases of Small Cell Lung Cancer Study

  Small cell lung cancer (SCLC) is an aggressive disease with poor survival. A few sequencing studies performed on limited number of samples have revealed potential disease-driving genes in SCLC, however, much still remains unknown, particularly in the Asian patient population. Here we conducted whole exome sequencing (WES) and transcriptomic sequencing of primary tumors from 99 Chinese SCLC patients. Dysregulation of tumor suppressor genes TP53 and RB1 was observed in 82% and 62% of SCLC patients, respectively, and more than half of the SCLC patients (62%) harbored TP53 and RB1 mutation and/or copy number loss. Additionally, Serine/Arginine Splicing Factor 1 (SRSF1) DNA copy number gain and mRNA over-expression was strongly associated with poor survival using both discovery and validation patient cohorts. Functional studies in vitro and in vivo demonstrate that SRSF1 is important for tumorigenicity of SCLC and may play a key role in DNA repair and chemo-sensitivity. These results strongly support SRSF1 as a prognostic biomarker in SCLC and provide a rationale for personalized therapy in SCLC. The manuscript has been accepted by Plos Genetics.

  Study phs001083

• Genomic data from analysis of the human placenta, part of the Pregnancy Outcome Prediction study (POPs)

  The Pregnancy Outcome Prediction (POP) study is a prospective cohort study of nulliparous women attending the Rosie Hospital (Cambridge, UK) for their dating ultrasound scan. The study included 4512 women with a viable singleton pregnancy; study participants provided written informed consent and were recruited between January 2008 and July 2012. The POP study aimed both at evaluating performance of known biomarkers and serial ultrasonography in assessing maternal and fetal well-being, as well as identifying novel biomarkers. The study is sufficiently large to be powered for relatively uncommon adverse pregnancy outcomes. Women attended four study visits scheduled every 8 weeks, starting with the first trimester. Participants had blood taken during the dating/recruitment visit (at approximately 12 weeks gestation (wkGA)), as well as at three subsequent visits (at ∼20wkGA, ∼28wkGA and ∼36wkGA). For more detail, please visit: https://www.obgyn.cam.ac.uk/research/pops-2/.

  Study EGAS00001002205

• Gastrointestinal Cancer Treatment Responders

  Comprehensive genomic profiling of colon adenocarcinomas has revealed multiple recurrent alterations that may inform new treatment strategies. Clinical trials of these agents are ongoing, although the mechanisms of response and resistance to these agents are not well characterized. The goal of this study is to perform comprehensive profiling of pre-treatment and post-resistance tumor and germline samples obtained from patients with colon cancer who receive these agents by carrying out whole exome sequencing and RNA sequencing, and to use these data to identify mechanisms of response and resistance.

  Study phs000803

• ATAC-SEQ MAIN - PHASE 1

  This is a study to test ATAC-seq protocols. CD4+ and CD8+ cells have been obtained from three different anatomical compartments. We aim to assay open-chromatin regions across these cells and perform comparative analyses.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 EGAD00001001320

• Functional_genomics_approaches_to_understand_osteoarthritis

  The aim of this work is to apply an integrated systems approach to understand the biological underpinnings of large joint (hip and knee) osteoarthritis which culminates in the need for total joint replacement (TJR). We will obtain diseased and non-diseased cartilage as well as other disease-relevant tissue following TJR, coupled with a blood sample. We will generate genotype data and will characterise the pairs of diseased and non-diseased tissue samples in terms of methylation, transcription (RNASeq) and expression (quantitative proteomics). We will apply integrative approaches to combine information across the –omics levels to characterise genes, pathways, and networks that underlie osteoarthritis progression. 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 please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001002322

• Germline variants in the SEMA4A gene predispose to familial colorectal cancer type X

  Familial colorectal cancer type X (FCCTX) is characterized by clinical features of hereditary non-polyposis colorectal cancer with a yet undefined genetic background. Here, using an integrative genomics strategy, we identify the SEMA4A p.Val78Met germline mutation in an Austrian kindred with FCCTX. Compared with wild-type protein, SEMA4AV78M demonstrates significantly increased MAPK/Erk and PI3K/Akt signaling as well as cell cycle progression of SEMA4A deficient HCT-116 colorectal cancer cells. In a cohort of 53 patients with FCCTX we depict two further SEMA4A mutations, p.Gly484Ala and p.Ser326Phe, and the single nucleotide polymorphism (SNP) p.Pro682Ser. This SNP is highly associated with the FCCTX phenotype exhibiting increased risk for colorectal cancer (OR 6.79, 95% CI 2.63 to 17.52). Our study shows previously unidentified germline variants in SEMA4A predisposing to FCCTX which has implications for surveillance strategies of patients and their families.

  Study EGAS00001000957

• Functional_genomics_approaches_to_understand_osteoarthritis

  The aim of this work is to apply an integrated systems approach to understand the biological underpinnings of large joint (hip and knee) osteoarthritis which culminates in the need for total joint replacement (TJR). We will obtain diseased and non-diseased cartilage as well as other disease-relevant tissue following TJR, coupled with a blood sample. We will generate genotype data and will characterise the pairs of diseased and non-diseased tissue samples in terms of methylation, transcription (RNASeq) and expression (quantitative proteomics). We will apply integrative approaches to combine information across the –omics levels to characterise genes, pathways, and networks that underlie osteoarthritis progression. 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/

  Study EGAS00001002255

• Genome_Diversity_in_Africa_Project___GemCode_libraries

  Globally, human populations show structured genetic diversity as a result of geographical dispersion, selection and drift. Understanding this genetic variation can provide insights into the evolutionary processes that shape both human adaptation and variation in disease. Populations from Africa have the highest levels of genetic diversity. This characteristic, in addition to historical genetic admixture, can lead to complexities in the design of studies assessing the genetic determinants of disease and human variation. However, such studies of African populations are also likely to provide new opportunities to discover novel disease susceptibility loci and variants and refine gene–disease association signals. A systematic assessment of genetic diversity within Africa would facilitate genomic epidemiological studies in the region.The Genome Diversity in Africa Project (GDAP) will extend and expand the African Genome Variation (AGV) project, an international collaboration aimed to produce a comprehensive catalogue of human genetic variation in Sub-Saharan Africa (SSA). Using a sequencing-based approach, GDA project aims to capture human genetic variation in Africa, including single nucleotide polymorphisms (SNPs), structural variants, and haplotypes. This resource will make a substantial contribution to understanding patterns of genetic diversity within and among populations in Africa, as well as providing a global resource to help design, implement and interpret genomic studies in African populations and studies comprising globally diverse populations, complementing existing genomic resources. Specifically, we plan to carry out whole genome sequencing at high depth (30x) of up to 1000 individuals across Africa (25 individuals from each ethnolinguistic group). We have already completed sequencing from 5 ethno-linguistic groups and we are now adding others.Our scientific objectives are to: 1) develop a resource that provides a comprehensive catalogue of genetic variation in populations from Africa accessible to the global scientific community; 2) characterise population genetic diversity, structure, gene flow and admixture across Africa; 3) develop a cost-efficient, next-generation genotype array for diverse populations across Africa; and 4) facilitate whole genome-sequencing association studies of complex traits and diseases by developing a reference panel for imputation and resource for enhancing fine-mapping disease susceptibility loci. These scientific objectives will be supported by cross-cutting operational activities, including network and management of the consortium, research ethics, and research capacity building in statistical genetics and bioinformatics.

  Study EGAS00001001589