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• An exome sequencing study of the HIV elite-long term non progressors and rapid progressors (CASCADE cohorts)

  Background: A rare subgroup of HIV infected individuals naturally controls infection without treatment. These ?elite controllers? constitute an important model for the natural control of HIV infection. Indeed, the study of these individuals may provide insights into strategies for the development of HIV vaccines. Although several HLA and chemokine alleles are known to be over-represented in elite controllers, only a small portion of HIV phenotypic variation is explained by known genetic variants. The elite controller phenotype is rare and distinct, representing the extreme of an infectious disease trait. As such, this phenotype may be partly explained by variation in host immune control, which may be characterized by differences in rare functional genetic variants. Genomic regions underlying elite control can be potentially identified by comparing the presence or frequency of variants in this group to that representing the opposite extreme. In this context, ?rapid progressors? is a group defined by its rapid immunological and clinical disease progression. Aim: To extend an existing study, in order to identify DNA sequence variants involved in the control of HIV infection with greater statistical resolution. Specifically, we aim to sequence up to 200 exomes from multiple cohort studies within the EuroCoord CASCADE collaboration (a collaboration of 25 HIV seroconversion cohort studies across Europe).

  Dataset EGAD00001002179

• Resolving the Full Spectrum of Human Genome Variation using Linked-Reads

  Large-scale population based analyses coupled with advances in technology have demonstrated that the human genome is more diverse than originally thought. Standard short-read approaches, used primarily due to accuracy, throughput and costs, fail to give a complete picture of a genome. They struggle to identify large, balanced structural events, cannot access repetitive regions of the genome and fail to resolve the human genome into its two haplotypes. Here we describe an approach that retains long range information while harnessing the power of short reads. Starting from only 1ng of DNA, we produce barcoded short read libraries. The use of novel informatic approaches allows for the barcoded short reads to be associated with the long molecules of origin producing a novel datatype known as 'Linked-Reads'. This approach allows for simultaneous detection of small and large variants from a single Linked-Read library. We have previously demonstrated the utility of whole genome Linked-Reads (lrWGS) for performing diploid, de novo assembly of individual genomes. In this manuscript, we show the utility of reference based analysis using a single Linked-Read library for full spectrum genome analysis. We demonstrate the ability of Linked-Reads to reconstruct megabase scale haplotypes and to recover parts of the genome that are typically inaccessible to short reads, including phenotypically important genes such as STRC, SMN1 and SMN2. We demonstrate the ability of both lrWGS and Linked-Read Whole Exome Sequencing (lrWES) to identify complex structural variations, including balanced events, single exon deletions, and single exon duplications. The data presented here show that Linked-Reads provide a scalable approach for comprehensive genome analysis that is not possible using short reads alone.

  Study EGAS00001003121

• 'Targeted High Throughput Sequencing in Clinical Cancer Settings: Formaldehyde fixed-paraffin embedded (FFPE) tumor tissues, input amount and tumor heterogeneity'

  "Background: Massively parallel sequencing technologies have brought an enormous increase in sequencing throughput. However, these technologies need to be further improved with regard to reproducibility and applicability to clinical samples and settings. Methods: Using identification of genetic variations in prostate cancer as an example we address three crucial challenges in the field of targeted re-sequencing: Small nucleotide variation (SNV) detection in samples of formalin-fixed paraffin embedded (FFPE) tissue material, minimal amount of input sample and sampling in view of tissue heterogeneity.Results: We show that FFPE tissue material can supplement for fresh frozen tissues for the detection of SNVs and that solution-based enrichment experiments can be accomplished with small amounts of DNA with only minimal effects on enrichment uniformity and data variance. Finally, we address the question whether the heterogeneity of a tumor is reflected by different genetic alterations, e.g. different foci of a tumor display different genomic patterns. We show that the tumor heterogeneity plays an important role for the detection of copy number variations, but is of minor importance for the detection of somatic variations.Conclusions: The application of high throughput sequencing technologies in cancer genomics opens up a new dimension for the identification of disease mechanisms. In particular the ability to use small amounts of FFPE samples available from surgical tumor resections and histopathological examinations facilitates the collection of precious tissue materials. However, care needs to be taken in regard to the locations of the biopsies taken, which can have an influence on the prediction of copy number variations. Bearing these technological challenges in mind will significantly improve many large-scale sequencing studies and will - on a long-range - result in a more reliable prediction of individual cancer therapies."

  Study EGAS00001000136

• Analysis of error profiles in deep next-generation sequencing data

  BackgroundSequencing errors are key confounding factors for detecting low-frequency genetic variants that are important for cancer molecular diagnosis, treatment, and surveillance using deep next-generation sequencing (NGS). However, there is a lack of comprehensive understanding of errors introduced at various steps of a conventional NGS workflow, such as sample handling, library preparation, PCR enrichment, and sequencing. In this study, we use current NGS technology to systematically investigate these questions.ResultsBy evaluating read-specific error distributions, we discover that the substitution error rate can be computationally suppressed to 10-5 to 10-4, which is 10- to 100-fold lower than generally considered achievable (10-3) in the current literature. We then quantify substitution errors attributable to sample handling, library preparation, enrichment PCR, and sequencing by using multiple deep sequencing datasets. We find that error rates differ by nucleotide substitution types, ranging from 10-5 for A>C/T>G, C>A/G>T, and C>G/G>C changes to 10-4 for A>G/T>C changes. Furthermore, C>T/G>A errors exhibit strong sequence context dependency, sample-specific effects dominate elevated C>A/G>T errors, and target enrichment PCR led to aprox 6-fold increase of overall error rate. We also find that more than 70% of hotspot variants can be detected at 0.1% - 0.01% frequency with the current NGS technology by applying in-silico error suppression.ConclusionsWe present the first comprehensive analysis of sequencing error sources in conventional NGS workflows. The error profiles revealed by our study highlight new directions for further improving NGS analysis accuracy both experimentally and computationally, ultimately enhancing the precision of deep sequencing.

  Study EGAS00001003444

• SINGLE CELL ANALYSIS OF IN VITRO ERYTHROPOIESIS

  We are developing a protocol to differentiate mouse and human induced pluripotent stem (IPS) and embryonic stem (ES) cells towards the haematopoietic pathway to generate erythrocytes in vitro. This system has many applications such as the study of the role of specific genes and human polymorphisms in infectious diseases such as malaria, as well as haematological diseases such as myelodysplastic syndrome. The nature of the in vitro differentiation process means that a heterogeneous population of cells is generated. In order to understand the types of cells produced with our protocol, we have performed a single cell analysis, which has the power to reveal the different populations of cells and their characteristics. For this, a cDNA library has been made that needs to be sequenced to obtain the gene expression profiles of the different cells. With this information we will be able to assess the quality of the differentiation protocol and improve it in order to produce better cells for the downstream applications.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 EGAD00001000979

• Combination therapies for personalized cancer medicine

  It is the ambition of the team formed by members of the Netherlands Cancer Institute (NKI) and the Cancer Genome Project at the Wellcome Trust Sanger Institute (WTSI) to unravel the genomic and phenotypic complexity of human cancers in order to identify optimal drug combinations for personalized cancer therapy. Our integrated approach will entail (i) deep sequencing of human tumours and cognate mouse tumours; (ii) drug screens in a 1000+ fully characterized tumour cell line panel; (iii) high-throughput in vitro and in vivo shRNA and cDNA drug resistance and enhancement screens; (iv) computational analysis of the acquired data, leading to significant response predictions; (v) rigorous validation of these predictions in genetically engineered mouse models and patient-derived xenografts. This integrated effort is expected to yield a number of combination therapies and companion-diagnostics biomarkers that will be further explored in our existing clinical trial networks.

  Dataset EGAD00001000869

• Immune Profiling in Patients with High-Risk Smoldering Myeloma

  Single-cell RNA-sequencing identifies immune biomarkers of response to immunotherapy in patients with high-risk Smoldering Multiple Myeloma (SMM). We conducted a Phase II trial of the immunotherapeutic anti-SLAMF7 antibody, Elotuzumab, in combination with Lenalidomide and Dexamethasone (E-PRISM study), to determine the utility and safety of early immunotherapy in patients with high-risk SMM and develop biomarkers for optimal patient selection and monitoring of response to treatment.There is an overlap in subjects between this study and dbGaP accession phs001323.

  Study phs002476

• The Nephrotic Syndrome Study Network (NEPTUNE)

  The Nephrotic Syndrome Study Network (NEPTUNE) is a collaborative investigational infrastructure of 40 North American sites for conducting clinical and translational research in Focal Segmental Glomerulosclerosis (FSGS), Membranous Nephropathy (MN), and Minimal Change Disease (MCD). NEPTUNE provides prospective, un-blinded, standardized evaluation of clinical and molecular outcomes in two cohorts: A) adults and children with incident or prevalent, biopsy-proven FSGS, MN, or MCD, recruited at the time of first biopsy, and B) children with incident Nephrotic Syndrome without a diagnostic kidney biopsy.

  Study phs003210

• A Phenotypic and Genomics Approach in a Multi-Ethnic Cohort to Subtype Systemic Lupus Erythematosus

  Systemic lupus erythematosus (SLE) is a complex autoimmune disease that affects multiple organ systems and varies in severity across different populations. To examine the clinical heterogeneity of SLE, we sought to identify different lupus subtypes within our multi-ethnic cohort using a clustering approach. Additionally, with genome-wide methylation and genotype data generated for our cohort, we applied integrative methods to investigate genetic and epigenetic risk factors. This integrative and computational approach revealed molecular differences associated with phenotypic clusters.

  Study phs001850

• Therapeutic Genetics and Disease Modeling in LAMA2-CMD

  Merosin-deficient congenital muscular dystrophy (MDC1A) is an autosomal recessive disorder caused by mutations in the LAMA2 gene, resulting in a defective form of the extracellular matrix protein laminin-α2 (LAMA2). Individuals diagnosed with MDC1A exhibit progressive muscle wasting and declining neuromuscular functions. No treatments for this disorder are currently available. This study involves transcriptomic comparison analysis between MDC1A patient fibroblasts and healthy control fibroblasts to reveal changes in gene expression that contribute to LAMA2 congenital muscular dystrophy (LAMA2-CMD).

  Study phs003250