130 results for ""PhD+2001"+"cited+by"+"bioinformatics""

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• Inference of transcription factor binding from cell-free DNA enables tumor subtype prediction and early detection

  Deregulation of transcription factors (TFs) is an important driver of tumorigenesis, but non-invasive assays for assessing transcription factor activity are lacking. We Here we developed and validated a minimally invasive method for assessing TF activity based on cell-free DNA sequencing and nucleosome footprint analysis. We analyzed whole genome sequencing data for >1,000 cell-free DNA samples from cancer patients and healthy controls using a newly developed bioinformatics pipeline developed by us that infers accessibility of TF binding sites from cell-free DNA fragmentation patterns. We observe patient-specific as well as tumor-specific patterns, including accurate prediction of tumor subtypes in prostate cancer, with important clinical implications for the management of patients. Furthermore, we show that cell-free DNA TF profiling is capable of detection of early-stage colorectal carcinomas. Our approach for mapping tumor-specific transcription factor binding in vivo based on blood samples makes a key part of the noncoding genome amenable to clinical analysis.

  Study EGAS00001003206

• Mechanisms_of_patient_response_to_Dabrafenib_in_Melanoma

  Samples will be from the BRF113683 (BREAK-3) study which is a Phase III Randomized, Open-label Study Comparing GSK2118436 to Dacarbazine (DTIC) in Previously Untreated Subjects With BRAF Mutation Positive Advanced (Stage III) or Metastatic (Stage IV) Melanoma (n=250 enrolled)•NGS [Agilent capture (Sanger V2 panel): 360 genes and 20 gene fusions; Illumina HiSEQ Sequencing]•CNV: [via NGS or Affy SNP 6.0 or Illumina Omni (TBD)]Bioinformatics: Analysis will be performed using core Sanger informatics pipelines similar to those previously described (Papaemmanuil E et al. (2013) Blood. 22:3616 -3627). Briefly, copy number analysis will be performed using the ASCAT algorithm, and base substitutions, small insertions and deletions using the CAVEMAN and Pindel algorithms, respectively. Statistical approaches including generalized linear models will be used to predict clinical variables such as maximum clinical response and duration of response using genetic data. Sanger and EBI to conduct analysis; Raw data and correlation with clinical endpoints to be analyzed by both EBI/Sanger and GSK (unique pipeline analyses to increase call confidence)

  Study EGAS00001000946

• Synthetic - FEGA Sweden Heilsa synthetic dataset December 2023

  Synthetic - This submission contains a subset of a synthetic dataset derived from the project Heilsa Tryggvedottir - a Nordic collaboration on sharing sensitive human data. Heilsa Tryggvedottir is funded by the Nordic e-Infrastructure Collaboration (NeIC), the ELIXIR nodes of Finland, Norway, and Sweden, Computerome in Denmark, and the Estonian Scientific Computing Infrastructure (ETAIS). In the synthetic data creation process, it was attempted to strike a fine balance between the usability of the datasets (e.g. technical FEGA development, testing, user training, and basic bioinformatics) and compliance with GDPR. File names and file content (e.g. headers in fastq) are anonymized. Moreover, the X, Y, and mitochondrial sequences have been discarded from the original data since these data can be used for maternal, paternal, or ethnic origin tracing. The dataset does not follow natural haplotype distribution (inherent to imputation panels). The only inputs derived from real sequence data are variant distribution density per chromosome and learning sequencing error models. The synthetic dataset consists of two fastq files, a cram file, a vcf file, and two index files.

  Study EGAS50000000086

• Circulating Tumor DNA Analysis Profiles of Repotrectinib in NTRK fusion–positive advanced solid tumors: a phase 1/2 trial

  The dataset contains circulating tumor DNA (ctDNA) profiles from frozen plasma samples of 593 patients from the TRIDENT-1 clinical trial. Plasma was processed with centrifugation and automated liquid handling systems, and cell-free DNA was extracted using the Qiagen QIAsymphony platform. ctDNA libraries were constructed with the Guardant360 2.11 assay, which used targeted, hybridization-based capture and barcoding of cfDNA fragments for digital analysis, followed by high-throughput sequencing on the Illumina NextSeq 550 instrument. Sequencing data were processed and interpreted using the Bioinformatics Pipeline (BIP v3.5.2 or later) for variant calling and quantification and managed within LabVantage LIMS (v3.7 or later) to support sample tracking and workflow integration. Aligned read files are provided in bam and bai formats.

  Dataset EGAD50000002249

• CIDR Whole Exome Sequencing in Joubert Syndrome

  The purpose of this study is to identify new genetic causes of neurodevelopmental diseases (NDDs) in the Joubert syndrome (JS) spectrum. Joubert syndrome is a recessive disease characterized by cerebellar vermis hypoplasia. Our currently funded NINDS award entitled "Molecular characterization of Joubert syndrome" seeks to identify new genetic causes through a variety of molecular strategies. Although our previous strategies involved whole genome SNP-scans, followed by candidate gene sequencing to arrive at identification of new JS causes, we have recently moved to Whole Exome Sequencing (WES) as a highly efficient methodology that is optimized for recessive disease. In this pilot project, CIDR has sequenced DNA on probands from 20 inbred families with JS spectrum disorders in which known causes have been excluded, that have not previously undergone genome-wide SNP scans. These paired end reads will be subject to our established bioinformatics pipeline including HOMOZGYOSITY, SNP and INDEL callers in our lab to identify potentially deleterious sequence changes (PDSC). This is followed by analysis to include testing each PDSC for segregation in the whole pedigree, for occurrence in a ethnically-matched cohort, as well as a defined patient cohort patients, in order to validate new NDD genes.

  Study phs000382

• Adaptive Therapy Exploits Fitness Deficits in Chemotherapy-Resistant Ovarian Cancer to Achieve Long-Term Tumor Control Open Access

  Drug resistance results in poor outcomes for cancer patients. Adaptive therapy is a potential strategy to address drug resistance that exploits competitive interactions between sensitive and resistant subclones. Here, we showed that adapting carboplatin dose according to tumor response (adaptive therapy) significantly prolonged survival of murine ovarian cancer models compared to standard carboplatin dosing, without increasing mean daily drug dose or toxicity. Platinum resistant ovarian cancer cells exhibited diminished fitness when drug was absent in vitro and in vivo, which caused selective decline of resistant populations due to reduced proliferation and increased apoptosis. Conversely fitter, sensitive cells re-grew when drug was withdrawn. Using a bioinformatics pipeline that exploits copy number changes to quantify the emergence of treatment resistance, analysis of cell‐free DNA obtained longitudinally from ovarian cancer patients during treatment showed subclonal selection through therapy, and measurements of resistant population growth correlated strongly with disease burden. These preclinical findings pave the way for future clinical testing of personalized adaptive therapy regimens tailored to the evolution of carboplatin resistance in individual ovarian cancer patients.

  Study EGAS50000001142

• Heat selection enables highly scalable methylome profiling in cell-free DNA for noninvasive monitoring of cancer patients

  Genome-wide analysis of cell-free DNA (cfDNA) methylation profile has been recognized as a promising approach for sensitive and specific detection of many cancers. However, scaling such genome-wide assays for clinical translation is impractical due to the high cost of whole genome bisulfite sequencing. We have shown that the small fraction of GC-rich genome is highly enriched in CpG sites and disproportionately harbors the majority of cancer-specific methylation signature. Here, we report on the simple but effective Heat enrichment of CpG-rich regions for Bisulfite Sequencing (Heatrich-BS) platform that allows for focused methylation profiling in these highly informative regions. Our novel method and bioinformatics algorithm enable accurate tumor burden estimation with high sensitivity and quantitative tracking of colorectal cancer patient’s response to treatment, at much reduced sequencing cost suitable for frequent monitoring. We also show, for the first time, tumor epigenetic subtyping from cfDNA using Heatrich-BS, which could enable patient stratification from non-invasive liquid biopsy. As such, Heatrich-BS holds great potential for highly scalable screening and regular monitoring of cancer using liquid biopsy.

  Study EGAS00001006198

• Neurogenetics_Essen

  Neurogenetic disorders are a clinically heterogeneous group of conditions characterized by an alteration or dysfunction of the central nervous system, resulting from a change encoded in the genome of affected individuals. Most of these conditions are individually rare but collectively frequent (>1-5% of the population), manifesting during development (neurodevelopmental disorders) or during later life (neurological disorders). Currently, genetic alterations in approximately 2,500 genes have been linked to human disorders affecting the brain and collectively explain approximately 50% of patients with neurological disorders with a supposed genetic etiology. We aim to identify new genes and uncover novel mechanisms underlying neurological and neurodevelopmental disorders, with a current focus on noncoding regions and complex (repeated) regions. We use a range of complementary approaches that make use of advanced technologies and bioinformatics tools. We combine short-read genome sequencing and long-read technologies, including nanopore sequencing and optical genome mapping, with multi-omics approaches (RNA sequencing, proteomics…) to detect pathogenic variants missed by routine diagnostics and to discover new genetic diseases.

  Dac EGAC50000000329

• Variant calling dataset from the whole-exome study of familial pulmonary fibrosis in the Canary Islands-VCF files

  Sequence reads were obtained from a set of 13 families with familial pulmonary fibrosis in the Canary Islands, Spain, using Illumina paired-end reads, respectively, at the Institute of Technology and Renewable Energy (ITER). Briefly, we used bcl2fastq v2.18 to perform sample demultiplexing and BWA-MEM v0.7.15 (https://github.com/lh3/bwa) to align reads to GRCh37/hg19 reference. Resulting BAM files were assessed with SAMtools v1.3 (http://www.htslib.org) and Picard v2.10.10 (https://broadinstitute.github.io/picard/) for quality control steps. Small insertions/deletions (< 50 bp) and single nucleotide variants (SNVs) were identified using an in-house bioinformatics pipeline based on GATK HaplotypeCaller v3.8 (https://gatk.broadinstitute.org/hc/en-us/articles/360037225632-HaplotypeCaller). This pipeline follows the Best Practices recommendations for germline variant calling and its description is publicly available (https://github.com/genomicsITER/benchmarking/tree/master/WES).

  Dataset EGAD50000001152

• Anaplastic Meningioma WGS-X10

  Anaplastic meningiomas are a rare, malignant variant of meningioma. At present there is no effective treatment for this cancer. The aim of the study is to identify somatic mutations in anaplastic meningiomas. We plan to sequence a set of 500 known cancer genes in 50 anaplastic meningioma and corresponding peripheral blood DNA samples. Bioinformatics will be used to analyse the results to assess the probability of these mutations being causal and so likely of critical importance for the tumour growth. Identification of these mutations will guide selection of appropriate compounds to effectively treat the disease.

  Dataset EGAD00001001267

• Providing safe access to sensitive human data across borders: Federated EGA becomes a reality

  Enabling discovery and access to sensitive data across national boundaries is vital for improving human health. It enables more powerful and efficient research by increasing the volume and diversity of data available for analysis. It allows us to better understand the causes of diseases – cancer, rare diseases, infectious diseases like COVID-19 – and develop new medicines and treatments. Sensitive human omics data are typically generated by research initiatives and shared using specialist repositories which provide services for data submission, discovery, and access. The EGA is one such repository. Established in 2008 at EMBL’s European Bioinformatics Institute (EMBL-EBI) in the UK, since 2012 the EGA (“Central EGA”) has been jointly managed by EMBL-EBI and the Centre for Genomic Regulation (CRG) in Spain. Many countries have emerging personalised medicine programmes which generate data from national initiatives. These programmes are driving a transition in human genomics from being research-driven to receiving funding through healthcare. Data generated in a clinical context are subject to stricter governance than research data and must follow national data protection legislation. To solve these challenges, the Federated EGA provides a network of connected resources to enable transnational discovery of and access to human omics data for research, while also respecting jurisdictional data protection regulations. In this way, the Federated EGA infrastructure supports the goals of European initiatives such as the 1+ Million Genomes initiative (1+MG), the European Health Data Space, and a number of EU-funded 1+MG implementation projects including Beyond 1 Million Genomes. The Federated EGA is made up of “Nodes” – typically nationally funded and operated – which store and manage data locally while allowing global discovery within the Federated EGA network. Since 2016, multiple parallel efforts, supported by ELIXIR and other transnational and national initiatives, have created technical and legal frameworks for establishing the Federated EGA. In 2022, the first five Nodes – Finland, Germany, Norway, Spain, and Sweden – officially joined the Federated EGA by signing Federated EGA Collaboration Agreements with Central EGA. The Finnish FEGA Node is operated by CSC - IT Center for Science and provides data management services according to national laws and the requirements of the EU General Data Protection Regulation (GDPR). These services provide tools and support for the whole life-cycle of sensitive research data from collating to analysis, publication, and authorised re-use. The development of the services has been a joint effort with the other Nordic nodes within NeIC's Tryggve and Heilsa projects, and funded by Finnish Ministry of Education and Culture and projects coordinated by ELIXIR Finland. Read more about Finnish FEGA signing. The German Human Genome-Phenome Archive (GHGA) strives to provide a national infrastructure as well as an ethical and legal framework that balances FAIR omics data usage and data protection needs for Germany. As a Germany-wide consortium funded by the German Research Foundation under the umbrella of the NFDI association, GHGA combines the expertise of 21 universities and research institutions to form a federated national infrastructure. Read more about GHGA signing. The German Human Genome-Phenome Archive (GHGA) strives to provide a national infrastructure as well as an ethical and legal framework that balances FAIR omics data usage and data protection needs for Germany. As a Germany-wide consortium funded by the German Research Foundation under the umbrella of the NFDI association, GHGA combines the expertise of 21 universities and research institutions to form a federated national infrastructure. Read more about GHGA signing. In Norway, a key component of the infrastructure is the services of The Sensitive Data Service (TSD) offered by USIT at University of Oslo. The Federated EGA Norway Node is developed by ELIXIR Norway and operated by the University of Oslo as the responsible legal entity. Core software modules are developed jointly with the other Nordic nodes in the NeIC Tryggve and Heilsa projects. Read more about FEGA Norway signing. The Spanish FEGA (es-FEGA) is a national service for storing sensitive biomedical data in Spain. Supported by the Spanish Institute of Bioinformatics (INB) in collaboration with Central EGA, sensitive research datasets are primarily hosted at the Barcelona Supercomputing Centre facilities. The Swedish Sensitive Data Archive is a secure data archive and sharing platform for sensitive datasets. It was developed by the National Bioinformatics Infrastructure Sweden (NBIS) in collaboration with other Nordic ELIXIR Nodes through the Tryggve and Heilsa projects funded by NeIC and coordinated with Central EGA through ELIXIR. Read more about the Swedish Node signing. The Swedish Sensitive Data Archive is a secure data archive and sharing platform for sensitive datasets. It was developed by the National Bioinformatics Infrastructure Sweden (NBIS) in collaboration with other Nordic ELIXIR Nodes through the Tryggve and Heilsa projects funded by NeIC and coordinated with Central EGA through ELIXIR. Read more about the Swedish Node signing. By providing a solution for secure and efficient management of human omics data, the Federated EGA aims to foster data reuse, enable reproducibility, accelerate biomedical research, and improve human health. Find out more Interested in setting up your own Federated EGA Node? Check out the FEGA Onboarding Knowledge Base for more information. The ELIXIR Federated Human Data Community is a great entry point for anyone interested in learning more about the Federated EGA. You can: Join the ELIXIR Federated Human Data Community mailing list (select “Human Data”) Attend the ELIXIR Federated Human Data Community calls

  Blog safe-access-to-sensitive-human-data-federated-ega

• Gene Expression Signatures Characterized by Longitudinal Stability and Inter-Individual Variability Delineate Baseline Phenotypic Groups with Distinct Responses to Immune Stimulation

  RNA sequencing (RNAseq) of peripheral blood lymphocytes was used to develop a means to assess immune function in a way that can be used in discovery science and applied to patients individually in clinical settings. The premise is that profiles of RNA present in immune cells is reflective of the combined influence of genetic and environmental variation on immune potential of individuals and that this potential can be tapped to understand human immunity in a variety of biological contexts. CD4+ cells were isolated from fresh whole blood via positive magnetic bead selection and cell lysates were prepared using Qiazol (QIAGEN) and stored at -80&#186;C for 3 to 8 months. RNA was extracted in batches for cDNA library preparation and RNA-Seq. For this study, we developed standard operating procedures for handling human blood samples and determined: a) the best way to enrich for CD4+ T cells from whole blood and yield high quality RNA, b) the sensitivity of this RNA profiling strategy, and c) the reproducibility of generated immune profiles from healthy subjects. We then developed bioinformatics processes to establish immune response signatures and immune response phenotypes within cohorts of individuals.

  Study phs001512

• Whole Genome Profiling to Detect Schizophrenia Methylation Markers

  Schizophrenia (SCZ) is a devastating psychiatric disorder. The aim of this study was to detect DNA methylation (5meC) profiles in whole blood associated with SCZ. The sample consisted of schizophrenia cases and controls selected from national population registers in Sweden. We employed MBD protein-based enrichment of the methylated DNA fraction, followed by single end sequencing (50 bp reads) on the SOLiD platform. To improve methylome-wide coverage we used an existing protocol variant, which increases the relative number of fragments from CpG poor regions. To analyze the data, we used an analysis pipeline described elsewhere7, that was designed specifically for MBD-seq and included alignment, quality control (QC) of reads and samples, coverage calculations for the 26,752,702 autosomal CpGs in the reference genome using a non-parametric estimate of the fragment size distribution, an in silico experiment to identify CpGs in loci with alignment problems, a 2-stage adaptive algorithm to combine data from correlated adjacent CpG sites in "blocks", principal component analysis (PCA), association testing, bioinformatics annotation and network analysis. After QC, 1459 subjects with on average 32.4 million high quality reads remained. Association testing was performed on 4.3 million high quality blocks after regressing out possible assay related technical artifacts, sex/age, and unmeasured confounders through the PCA components.

  Study phs000608

• Exploring the Microbiome-Gut-Brain Axis with Respect to Psychoneurological Symptoms for Children with Solid Tumors

  The human body hosts tens of trillions of microbes, a number 10 times greater than the number of human body cells. The collection of these human-associated microbes and their genomes is called the &#8220;human microbiome&#8221;. More than 90% of these microbes live in the gastrointestinal (GI) tract, representing 500 species on average. With the development of high-throughput sequencing technology and bioinformatics tools (e.g., 16S ribosomal RNA or 16S rRNA sequencing and shotgun metagenomics analysis), recent progress has been made to elaborate the critical role that the gut microbiome plays in human health and disease. A significant disruption (dysbiosis) of the composition and function of the gut microbiome is associated with carcinogenesis, chemotherapeutic metabolism, and treatment-related symptom toxicities. The current state of science on the gut microbiome in cancer has only recently started in animal models and adults, and has not been well investigated in children diagnosed and treated for cancer, nor with respect to associations with cancer treatment-related toxicities. This proposed study will explore the hypothesis that dysbiosis of the gut microbiome is associated with cancer treatment-related GI and psychoneurological symptoms. We will collect both stool samples (for the gut microbiome) and patient-reported outcomes across chemotherapy.

  Study phs002960

• Full characterization of structural variation

  Structural variants (SVs) are important contributors to human disease. Their characterization remains however difficult due to their size and association with repetitive regions. Long-read sequencing (LRS) and optical genome mapping (OGM) can aid as their molecules span multiple kilobases and capture SVs in full. In this study, we selected six individuals who presented with unresolved SVs. We applied LRS onto all individuals and OGM to a subset of three complex cases. LRS detected and fully resolved the interrogated SV in all samples. This enabled a precise molecular diagnosis in two individuals. Overall, LRS identified 100% of the junctions at single-basepair level, providing valuable insights into their formation mechanisms without need for additional data sources. Application of OGM added straightforward variant phasing, aiding in the unravelment of complex rearrangements. These results highlight the potential of LRS and OGM as follow-up molecular tests for complete SV characterization. We show that they can assess clinically relevant structural variation at unprecedented resolution. Additionally, they detect (complex) cryptic rearrangements missed by conventional methods. This ultimately leads to an increased diagnostic yield, emphasizing their added benefit in a diagnostic setting. To aid their rapid adoption, we provide detailed laboratory and bioinformatics workflows in this manuscript.

  Study EGAS50000000520

• THE GENOMIC LANDSCAPE OF ACTINIC KERATOSIS

  Actinic keratoses (AK) are lesions of epidermal keratinocyte dysplasia and are pre-cursor lesions to invasive cutaneous squamous cell carcinoma (cSCC). Identifying the specific genomic alterations driving the progression of normal skin to AK and invasive cSCC is challenging due to the massive, ultraviolet radiation-induced mutational burden characteristic to these lesions. Here, we present the largest whole exome sequencing study to date on AK with matched cSCC and demonstrate that AK and cSCC are almost indistinguishable at the genomic level, in terms of mutational burden, patterns of driver gene mutations and copy number alterations. We identified 44 significantly mutated AK driver genes through our established bioinformatics pipeline and demonstrate these genes are similarly mutated in cSCC. We also identified mutational signature-32 exclusively in AK from patients exposed to azathioprine, providing further, compelling evidence for this drug’s role in keratinocyte carcinogenesis, likely through its blocking of transcription coupled repair. We demonstrated that cSCC had higher levels of intra-sample heterogeneity than AK and that several signaling pathways, including immune-related signaling and TGF-beta signaling were significantly more mutated in cSCC. Integrating our findings with independent gene expression data confirms that dysregulated TGF-beta signaling may represent the critical pathway in the progression from AK to cSCC.

  Study EGAS00001004243

• Whole genome, exome and transcriptome analysis to guide individualized cancer interpretation

  Study Design: The Cancer Alliance study was a collaboration of nine academic medical institutions in the New York city area and led by the New York Genome Center. Tumor-normal paired samples were submitted by the institutions for whole genome, whole exome and transcriptome sequencing and bioinformatics analysis at New York Genome Center. Relevant clinical histories were also collected. Raw sequence files were available to all collaborators. Variant call files were analyzed both manually by interpreters at NYGC and by IBM Watson Genome Analytics. This study was approved by a central institutional review board (IRB), Biomedical Research Alliance of New York, and by local IRBs, including Stony Brook University and Northwell Health. The study was supported in part by a grant from the IBM corporation (IBM Watson Health) to the New York Genome Center, New York Genome Center philanthropic funds and Rockefeller University grant # UL1TR000043 from the National Center for Advancing Translational Sciences (NCATS), and the National Institutes of Health (NIH) Clinical and Translational Science Award (CTSA) program. The principal investigator Robert B. Darnell MD, PhD., is a Howard Hughes Medical Institute Investigator. Results: Pending Publication of Analysis of Study Conclusions: Pending Publication of Analysis of Study

  Study EGAS00001004013

• Estonian Microbiome Project second time point dataset

  Out of the 2,509 Estonian Microbiome Project participants, a sub-cohort of over 300 individuals provided an additional stool sample after a median follow-up period of 4.4 years. All participants of the EstMB cohort gave informed consent for the data and samples to be used for scientific purposes. In the second time point, the participants were instructed to send the samples by post immediately after taking the sample and time their sample collection to avoid shipping on the weekends. The median time between sampling and arrival at the freezer in the core facility for the second time point was 53 hours and 1 minute (mean: 59 hours and 22 minutes; minimum: 34 minutes; maximum: 168 hours and 7 minutes [note: additionally, one outlying measurement was 508 hours and 27 minutes. The shotgun metagenomics paired-end sequencing was performed by Novogene Bioinformatics Technology Co., Ltd., using Illumina NovaSeq6000 platform, resulting in 4.62 ± 0.44 Gb of data per sample (insert size 350 bp, read length 2 × 250 bp).

  Dataset EGAD50000001686

• Synthetic - FEGA Sweden Heilsa synthetic dataset December 2023

  Synthetic - This submission contains a subset of a synthetic dataset derived from the project Heilsa Tryggvedottir - a Nordic collaboration on sharing sensitive human data. Heilsa Tryggvedottir is funded by the Nordic e-Infrastructure Collaboration (NeIC), the ELIXIR nodes of Finland, Norway, and Sweden, Computerome in Denmark, and the Estonian Scientific Computing Infrastructure (ETAIS). In the synthetic data creation process, it was attempted to strike a fine balance between the usability of the datasets (e.g. technical FEGA development, testing, user training, and basic bioinformatics) and compliance with GDPR. File names and file content (e.g. headers in fastq) are anonymized. Moreover, the X, Y, and mitochondrial sequences have been discarded from the original data since these data can be used for maternal, paternal, or ethnic origin tracing. The dataset does not follow natural haplotype distribution (inherent to imputation panels). The only inputs derived from real sequence data are variant distribution density per chromosome and learning sequencing error models. The synthetic dataset consists of two fastq files, a cram file, a vcf file, and two index files.

  Dataset EGAD50000000119

• Skin Microbiome in Disease States: Atopic Dermatitis and Immunodeficiency

  The NIH Intramural Skin Microbiome Consortium (NISMC) is a collaboration of investigators with primary expertise in genomics, bioinformatics, large-scale DNA sequencing, dermatology, immunology, allergy, infectious disease, and clinical microbiology. Atopic dermatitis (AD, "eczema") is a chronic relapsing skin disorder that affects ~15% of U.S. children and is associated with $1 billion of medical costs annually. AD is characterized by dry, itchy skin, infiltrated with immune cells. Colonization by Staphylococcus aureus (S. aureus) is ten-fold more common in AD patients and is associated with disease flares. We hypothesize that, in addition to S. aureus, AD may also be associated with additional novel microbes and/or selective shifts of commensal microbes that are relevant to disease progression. The NISMC seeks to define the microbiota that resides in and on the skin and nares of three patient groups, all of whom have eczematous lesions and are currently seen at the NIH Clinical Center: (1) AD patients; (2) Wiskott-Aldrich syndrome (WAS) patients; and (3) Hyper IgE syndrome (HIES) syndrome patients. Examination of the microbiome of patients with WAS or HIES syndromes, both rare immunodeficiencies, will advance our understanding of how an individual&#39;s immune system shapes their cutaneous microbial community. We are performing a prospective longitudinal study that follows these groups of patient thorough the cycles of eczema flares, ascertaining clinical data and samples at each stage.

  Study phs000266

• Heterogeneity in Lysosomal Storage Disorders

  Gaucher disease (GD) is an inherited lysosomal storage disorder caused by biallelic pathogenic variants in GBA1, which encodes glucocerebrosidase. Additionally, GBA1 variants are a well-established genetic risk factor for Parkinson's disease (PD), leading some PD research centers to incorporate GBA1 screening to identify patients who may benefit from targeted therapeutics. However, detecting GBA1 variants remains challenging due to the gene's high sequence homology with its pseudogene, which can give rise to complex recombinant alleles. This study evaluates the performance of Gauchian, a recently developed software tool for identifying GBA1 variants from whole genome sequencing (WGS) data. The assessment was conducted in a cohort of 90 individuals with GD and five GBA1 heterozygotes, all previously characterized through Sanger sequencing. While Gauchian successfully identified the most common genotypes, it demonstrated limitations in detecting rare or de novo variants due to its restricted internal database and heavy reliance on intergenic structural variants. These limitations led to misclassified homozygosity, incomplete genotyping, and undetected recombination events, ultimately reducing the tool's utility for comprehensive variant screening and precluding its application in diagnostic settings. The study dataset includes WGS from the 90 individuals used in the Gauchian pipeline. This data will be available through dbGaP to facilitate further research on GBA1 variant detection, genotype-phenotype correlations in GD and PD, and the development of improved bioinformatics tools for complex variant identification.

  Study phs003459

• Nala TAS-LRS PGx Study

  Pharmacogenomics (PGx) testing improves medication safety and efficacy by identifying genetic variants that affect drug response. However, current technologies often fail to resolve complex loci, detect structural variants, or phase alleles accurately. Here, we present an end-to-end PGx workflow based on Targeted Adaptive Sampling-Long Read Sequencing (TAS-LRS), integrating a streamlined laboratory protocol with a bioinformatics pipeline that includes a novel CYP2D6 caller. Using 1,000 ng of DNA and three-sample multiplexing on a single PromethION flow cell, the assay achieves consistent on-target (25x) and off-target (3x) coverage, enabling accurate, haplotype-resolved testing of 35 pharmacogenes alongside genome-wide genotyping from off-target reads. We further developed the workflow into a clinically ready service and validated its performance across 17 reference and clinical samples. The assay demonstrated high concordance for small variants (99.9%) and structural variants (>95%), with phased diplotypes and metabolizer phenotypes reaching 97.7% and 98.0% concordance, respectively. Improved calls were observed in 12 genes due to enhanced genotyping, phasing, or novel allele detection. In addition, off-target reads supported accurate genome-wide imputation, comparable to short-read sequencing and superior to microarrays. These results establish the feasibility of long-read sequencing for clinical PGx testing and position TAS-LRS as a scalable solution combining both targeted and genome-wide utility.

  Study EGAS50000001116

• Detection of mutational patterns in cell free DNA (cfDNA) of colorectal cancer by custom amplicon sequencing

  Monitoring the mutational patterns of solid tumors during cancer therapy is a major challenge in oncology. Analysis of mutations in cell free (cf) DNA offers a non-invasive approach to detect mutations that may be prognostic for disease survival or predictive for primary or secondary drug resistance. A main challenge for the application of cfDNA as a diagnostic tool is the diverse mutational landscape of cancer. Here, we developed a flexible end-to-end experimental and bioinformatics workflow to analyze mutations in cfDNA using custom amplicon sequencing. Our approach relies on open software tools to select primers suitable for multiplex PCR using minimal cfDNA as input. In addition, we developed a robust linear model to identify specific genetic alterations from sequencing data of cfDNA. We used our method to design a custom amplicon panel suitable for detection of hotspot mutations relevant for colorectal cancer and analyzed mutations in serial cfDNA samples from 34 patients with advanced colorectal cancer. Our data demonstrates that recurrent and patient-specific mutational patterns can be reliably detected for the majority of patients. Furthermore, we show that the allele frequency of mutations in cfDNA correlates well with disease progression. Finally, we demonstrate that monitoring of cfDNA can outperform the predictive power of currently used tumor markers and reveal mechanisms of resistance to anti-EGFR antibody treatment.

  Study EGAS00001003382

• The aim of this study was to identify underlying hub genes and dysregulated pathways associated with the development of HCC using bioinformatics analysis.

  In the present study, we identified differentially expressed protein-coding genes from RNA transcriptional profiling performed on 11 paired cancer tissues and adjacent non-cancerous tissues. Then, we conducted GO, KEGG, PPI network and centralities analyses to study and identify changes in pathways and hub genes. The aim of this study was to improve understanding of HCC carcinogenesis by providing information concerning the genetic changes that occur during disease progression and to uncover the expression of biomarkers with potential use for clinical diagnosis, treatment, and monitoring of disease progression.

  Study EGAS00001002526

• About

  About What's the EGA? The European Genome-phenome Archive (EGA) is a global network for permanent archiving and sharing of personally identifiable genetic, phenotypic, and clinical data generated for the purposes of biomedical research projects or in the context of research-focused healthcare systems. Jointly managed by the European Bioinformatics Institute (EMBL-EBI) in Cambridge (UK) and the Centre for Genomic Regulation (CRG) in Barcelona, we aim to advance biomedical research and promote personalised medicine worldwide by enabling discovery of and access to human genomic and health research data. The EGA contains data collected from individuals whose consent agreements authorise data release for specific research use to bona fide researchers. We ensure strict security measures to control access to the data and maintain patient confidentiality. With expertise in data management and technical infrastructure, we promote FAIR data reuse and enable researchers to share their data securely. By leveraging public funding and our strategic partnerships, the EGA provides a free service for permanent data storage, data discovery, and secure data access. In addition, we foster a federated network to provide transnational access to human research data in compliance with legal frameworks. For additional information about the EGA, please contact: Helen Parkinson and Mallory Freeberg EMBL European Bioinformatics Institute Arcadi Navarro, Roderic Guigó, and Jordi Rambla Center for Genomic Regulation History The European Genome-phenome Archive was launched in 2008 at the European Bioinformatics Institute (EMBL-EBI), an outstation of the European Molecular Biology Laboratory (EMBL), to address an identified need for archiving and sharing the results of genome-wide association studies from the Wellcome Trust Case Control Consortium. With the signing of a memorandum of understanding in 2013 and a formal agreement in 2016, the EGA became a joint project of EMBL-EBI and the Centre for Genomic Regulation (CRG). The two institutes work together to support the EGA services, including supporting submissions, website, strategic leadership, and data infrastructure developments. In 2022, the Federated EGA was officially launched with the signature of the first five countries: Finland, Germany, Norway, Spain, and Sweden. With more than 20 additional nodes worldwide preparing to join, the Federated EGA aims to become the largest human omics data sharing initiative towards understanding human health and disease. EGA overview If you're a researcher you may need to deposit, manage, or access genomic data in a secure and regulated way. The European Genome-phenome Archive (EGA) is a platform that facilitates these processes, ensuring that sensitive data is stored and shared in accordance with legal and ethical regulations. Submission process To start a submission, you need to become an EGA submitter. For that, you’ll need to sign a Data Processing Agreement (DPA) with us, that defines the terms and conditions under which your data will be processed and shared within the EGA system. The access to each study is controlled by its Data Access Committee (DAC). The DAC is responsible for managing data access requests and ensuring that the release of data is in accordance with the General Data Protection Regulation (GDPR). Please, note that once your data is released, all public metadata related to your study and dataset(s) will be searchable on the EGA website. However, the files are only accessible under controlled access, which means that a DAC has to agree to a previous data access request. Request process It is possible to submit an access request to data stored at the EGA. The DAC assigned to the study will assess the request and, if approved, grant access to the data. Requesters must provide sufficient justification for your request and comply with the intended data usage in order to get access to it. Each dataset is covered by a Data Access Agreement (DAA) that defines the terms and conditions of use for the specified dataset/s. The DAA is created and provided by the DAC, and must be signed by the individual requesting access to the given dataset/s. Download process Once the request for access is approved, the data and metadata can be downloaded. The EGA offers various download options to fit the needs: it is possible to preview files without downloading them, download specific files of interest, or even download terabytes of data. Overall, the EGA provides a secure and regulated environment for depositing, managing, and accessing human data. Regardless of the role - submitter, DAC member, or requester - the EGA provides assistance for each while ensuring that sensitive data is managed in an ethical and responsible manner. More information on how EGA handles data is available in the EGA dataflow.

  Documentation about/ega

• NIAID Centralized Sequencing Program

  Investigators at the National Institute of Allergy and Infectious Diseases (NIAID) use next-generation sequencing technologies to help determine genetic contributions to immune diseases. These efforts have increased rates of molecular diagnosis for a subset of NIAID participants as well as uncovered fundamental insights into the cellular and signaling pathways in host defense and immune regulation. Despite these successes, analysis and interpretation of genomic data remain a substantial challenge. Simply, researchers do not understand the functional and clinical consequences of most human genetic variation. Making progress in this area requires a coordinated, systematic, and transparent approach to clinical genomics research. This protocol is specific to genetic testing and explicitly aims to both strengthen clinical care and enhance research. Probands provide biological specimens for genetic testing and primary clinical and research evaluations are completed via a research protocol at NIAID. This protocol serves as a vehicle for a programmatic effort that includes standardized phenotyping, test ordering through the Clinical Research Information System (CRIS) at the National Institutes of Health Clinical Center, sample collection and isolation, nucleic acid analysis, bioinformatics, clinical interpretation, reporting in CRIS, genetic counseling, and supporting effective use of genomics as a research tool throughout the institute. Overall, increased process standardization will support data integrity and efficiency while still accommodating the need for investigator flexibility. This study aims to generate and analyze evidence regarding the genetic contribution to diverse immune diseases studied by the NIAID intramural research program.

  Study phs001899

• Esophageal Squamous Cell Carcinoma Precursor Study

  Esophageal cancer is the sixth most common cause of cancer death in the world. High-risk areas occur across central Asia and from eastern to southern Africa, and in these populations, nearly all of the cases are esophageal squamous cell carcinoma (ESCC). ESCC has a dismal prognosis, largely because symptoms usually appear late in the course of the disease, when the tumors are incurable. Early detection and treatment of curable precursor lesions and early invasive ESCC in asymptomatic high-risk adults offers the potential for long-term survival and reduced mortality from this disease. Previous studies have shown that routine cytology of non-endoscopically collected esophageal cell samples is not sensitive and specific enough to be clinically useful, so we are now looking for molecular markers of high-grade esophageal squamous dysplasia (HGD), the proven clinically important precursor lesion of ESCC, that can be used to increase the accuracy and utility of this initial screening exam. National Cancer Institute (NCI) and Cancer Institute and Hospital, Chinese Academy of Medical Sciences (CAMS) began collaborative studies of ESCC since the mid-1980s in China. The present proposal is designed to characterize the type and prevalence of DNA mutations in tissue samples of HGD and to establish biologic and questionnaire resources for further study. The proposed study has two components, a field study to obtain questionnaire data and biological samples, and a genomic analysis to perform sequencing of the biological samples and bioinformatics analysis of the sequencing.&nbsp;

  Study phs002814

• GCAT | Genomes for life: cohort study of the genomes of Catalonia

  The GCAT Study have recruited 20 000 participants aged 40–65 years. Participants who agreed to take part in the study completed a self-administered computer-driven questionnaire, and underwent blood pressure, cardiac frequency and anthropometry measurements. For each participant, blood plasma, blood serum and white blood cells are collected at baseline. A total of 5459 genomic profiles have been characterised by comprehensive genotyping. Genome-wide genotypes have been generated using Illumina Infinium SNP-bead array technology. We chose the Multi-Ethnic Global (MEGAEX, V.2) consortium array, a multipurpose, multiethnic genotyping array with two million selected markers (including previously described germline mutations, insertions-deletions (InDels) and SNPs).We have strictly followed the standard manufacturer recommended automated protocol for the Infinium HTS Assay scanned with a HiScan confocal scanner (Illumina, San Diego, California, USA). Genome Studio V.2011.1 has been used for raw data analysis. Genotyping was performed at the Genomics and Bioinformatics Unit of the PMPPC Institute for Health Science Research Germans Trias i Pujol, in Badalona, Spain. Future plans The first follow-up study started in December 2017 and will end by March 2018. Residences of all subjects will be geocoded during the following year. Several genomic analyses are ongoing, and metabolomic and genomic integrations will be performed to identify underlying genetic variants, as well as environmental factors that influence metabolites. http://dx.doi.org/10.1136/bmjopen-2017-018324

  Study EGAS00001003018

• Partner-independent fusion gene detection by multiplexed CRISPR/Cas9 enrichment and long-read Nanopore sequencing

  Fusion genes are hallmarks of various cancer types and important determinants for diagnosis, prognosis and treatment possibilities. The promiscuity of fusion genes with respect to partner choice and exact breakpoint-positions restricts their detection in the diagnostic setting, even for known and recurrent fusion gene configurations. To accurately identify these gene fusions in an unbiased manner, we developed FUDGE: a FUsion gene Detection assay from Gene Enrichment. FUDGE couples target-selected and strand-specific CRISPR/Cas9 activity for enrichment and detection of fusion gene drivers (e.g. BRAF, EWSR1, KMT2A/MLL) - without prior knowledge of fusion partner or breakpoint-location - to long-read Nanopore sequencing. FUDGE encompasses a dedicated bioinformatics approach (NanoFG) to detect fusion genes from Nanopore sequencing data. Our strategy is flexible with respect to target choice and enables multiplexed enrichment for simultaneous analysis of several genes in multiple samples in a single sequencing run. We observe on average a 508-fold on-target enrichment and identify fusion breakpoints at nucleotide resolution - all within two days. We demonstrate that FUDGE effectively identifies fusion genes in cancer cell lines, tumor samples and on whole genome amplified DNA irrespective of partner gene or breakpoint-position in 100% of cases. Furthermore, we show that FUDGE is superior to routine diagnostic methods for fusion gene detection. In summary, we have developed a rapid and versatile fusion gene detection assay, providing an unparalleled opportunity for pan-cancer detection of fusion genes in routine diagnostics.

  Study EGAS00001003964

• H3Africa - Stroke Investigative Research and Education Networks

  Although stroke is a discrete phenotype, it is the clinical culmination of several complex and interacting biological processes, precipitated by various genetic and environmental factors, thereby making ready analyses of its underlying mechanisms a challenge. However, the formulation of successful tailor-made prevention strategies for limiting the immense personal and societal burden of stroke in Sub-Saharan Africa (SSA) is contingent on a better understanding of the predisposing risk factors for stroke in the region, which may not necessarily match the nature of stroke risk factors described among other people on other continents. Indeed, the underlying risk factors for stroke in SSA have hitherto not been comprehensively characterized, and currently there is a paucity of expertise, experience, and infrastructure in genomics and environmental research in SSA, to initiate and sustain such investigation. We seek to establish the Stroke Investigative Research and Education Network (SIREN) as a multidisciplinary collaborative research network of investigators in SSA and the United States collectively focused on exploring ways to promote a better understanding of the genetic and environmental risk factors for stroke among people of African ancestry. SIREN will evaluate unique contributors to stroke in SSA and compare these findings with those in a cohort of African American stroke survivors, all while simultaneously building sustainable capacities in phenomics, biobanking, genomics, biostatistics and bioinformatics for future high-level investigation of stroke and other vascular disease entities in SSA.

  Study EGAS00001007331

• How are we funded?

  How are we funded? The EGA is provided infrastructure, administrative support, advisory input, and other relevant and necessary aspects by: CRG The EGA at the CRG is funded and supported by “La Caixa Foundation”, The Generalitat de Catalunya (Catalan Government), the Spanish Ministerio de Asuntos Económicos y Transformación Digital (Spanish Government) and the Instituto de Salud Carlos III. The Generalitat de Catalunya contributes with funding to the CRG and to the Barcelona Supercomputing Centre (BSC). The Barcelona Supercomputing Center is decisively contributing to EGA by providing the required compute, storage and networking resources, as well as key personnel for shared operations of EGA infrastructure. EMBL-EBI As part of the European Molecular Biology Laboratory (EMBL), the EGA at EMBL’s European Bioinformatics Institute receives funding from the governments of EMBL’s member states. The UK government, via UK Research and Innovation, also continues to support EMBL-EBI technical infrastructure, with EMBL supporting operational costs. A major source of funding for the EGA both at CRG and EMBL-EBI are competitive projects.The EGA resource continues to be funded through strategic engagement in collaborative partnerships and projects. Please see our projects page for more information. Other funders include the European Commission, the US National Institutes of Health, the Wellcome Trust, UK Research Councils, and our Industry Programme partners. Last updated on October, 2024.

  Documentation about/projects-and-funders/funders

• Mechanisms of patient response to Dabrafenib in Melanoma

  Samples will be from the BRF113683 (BREAK-3) study which is a Phase III Randomized, Open-label Study Comparing GSK2118436 to Dacarbazine (DTIC) in Previously Untreated Subjects With BRAF Mutation Positive Advanced (Stage III) or Metastatic (Stage IV) Melanoma (n=250 enrolled)*NGS [Agilent capture (Sanger V2 panel): 360 genes and 20 gene fusions; Illumina HiSEQ Sequencing]*CNV: [via NGS or Affy SNP 6.0 or Illumina Omni (TBD)]Bioinformatics: Analysis will be performed using core Sanger informatics pipelines similar to those previously described (Papaemmanuil E et al. (2013) Blood. 22:3616 -3627). Briefly, copy number analysis will be performed using the ASCAT algorithm, and base substitutions, small insertions and deletions using the CAVEMAN and Pindel algorithms, respectively. Statistical approaches including generalized linear models will be used to predict clinical variables such as maximum clinical response and duration of response using genetic data. Sanger and EBI to conduct analysis; Raw data and correlation with clinical endpoints to be analyzed by both EBI/Sanger and GSK (unique pipeline analyses to increase call confidence)

  Dataset EGAD00001001375

• Partner independent fusion gene detection by multiplexed CRISPR Cas9 enrichment and long read Nanopore sequencing (FUDGE)

  Targeted long-read nanopore sequencing. Abstract: Fusion genes are hallmarks of various cancer types and important determinants for diagnosis, prognosis and treatment. Fusion gene partner choice and breakpoint-position promiscuity restricts diagnostic detection, even for known and recurrent configurations. To accurately and impartially identify fusions, we developed FUDGE: FUsion Detection from Gene Enrichment. FUDGE couples target-selected and strand-specific CRISPR/Cas9 activity for fusion gene driver enrichment - without prior knowledge of fusion partner or breakpoint-location – to long-read Nanopore sequencing with the bioinformatics pipeline NanoFG. FUDGE has flexible target-loci choices and enables multiplexed enrichment for simultaneous analysis of several genes in multiple samples in one sequencing run. We observe on-average 665 fold breakpoint-site enrichment and identify nucleotide resolution fusion breakpoints - within two days. The assay identifies cancer cell line and tumor sample fusions irrespective of partner gene or breakpoint-position. FUDGE is a rapid and versatile fusion detection assay, providing unparalleled opportunity for diagnostic pan-cancer fusion detection.

  Dataset EGAD00001006111

• Disease Variant Landscape of a Large Multiethnic Population of Moyamoya Patients by Exome Sequencing

  We report a significant advance in Moyamoya disease (MMD) research through our unique access to a large MMD patient population combined with high depth exome sequencing and bioinformatics. We confirm a reported RNF213 founder mutation (FM), provide evidence for its ethnic specificity and identify novel variants in other genes that associate with Caucasian and non-RNF213 FM MMD. This work has a broader impact in vascular research by highlighting novel genetic aspects of cerebrovascular disease. Moyamoya Disease (MMD) is a rare cerebrovascular disorder characterized by unilateral or bilateral progressive stenosis or occlusion of the internal carotid artery, with frequent involvement of the anterior cerebral artery and the middle cerebral artery (MCA). Fragile collateral vessels subsequently develop and are particularly prone to hemorrhage. MMD patients are usually diagnosed angiographically following presentation of developmental delays, seizures, migraines, ischemia, and/or hemorrhage. The most common approach to re-establish normal blood flow is bypass surgery. While some progress has been made in identifying disease-associated genes, the underlying disease biology is not well understood. Moyamoya disease (MMD) is a rare cerebrovascular disease. Our unique access to a large MMD patient population combined with high depth exome sequencing and bioinformatics has led to a significant advance in the field. We confirm a reported RNF213 founder mutation (FM), provide evidence for its ethnic specificity and identify novel variants in other genes that associate with Caucasian and non-RNF213 FM MMD. This work has a broader impact in vascular research by highlighting novel genetic aspects of cerebrovascular disease. The 125 ethnically diverse, unrelated MMD patients were matched based on sex and broad ethnic category to 125 controls. Control DNA from the 1000 Genomes project was used to design a control Library by Personalis. 125 controls were selected from this Personalis Control DNA Library for our study. (The 1000 Genomes Project Consortium 2012). Genomic DNA was extracted using the Gentra Puregene kit (Qiagen, Valencia, CA). Libraries were prepared from approximately 3 &#181;g of high quality genomic DNA (50-200 ng/&#181;l) using Illumina TruSeq Genomic DNA High throughput Sample Prep Kits (Illumina, San Diego, CA) and exome enrichment (targeting 62Mb) was accomplished using the TruSeq Exome Target Enrichment kit (Illumina, San Diego, CA), all according to manufacturer&#39;s protocols. Target enrichment validation was confirmed by determining the concentration of the library by PicoGreen-based quantitation. Library yields ranged from 100-1000ng of DNA, a portion of which was run on the Bioanalyzer HS DNA chip (Agilent, Santa Clara, CA), with an average size of 300-550nt for DNA fragments. Exome Sequencing. Sequencing was performed using Illumina Hiseq2000 or HiSeq2500 sequencers with single lane, paired-end 2X100bp reads. DNA fragments were generated and amplified using Clonal Single Molecule Array technology (Illumina, San Diego, CA). The sequences were determined using the Clonal Single Molecule Array and Sequencing-by-Synthesis using Illumina&#39;s instrumentation and Reversible Terminator Chemistry. Each sequencing lane interrogated the DNA sequences of a pool of 3 individual sample libraries each carrying a unique index. Sequencing reads of at least 2x100bp in length for a total of approximately 8 Gb of sequence data were generated for each sample.

  Study phs001700

• Innate myeloid cell sbuset-specific gene expression patterns in the human colon are altered in Crohn's disease patients

  Background: There is a heterogeneous subset innate myeloid cells, such as macrophages and dendritic cells, in the human intestinal lamina propria. Several studies have demonstrated that these cells contribute to the maintenance of gut homeostasis through induction of inflammatory responses and tolerance via cell type-specific mechanisms; whereas, disrupted innate immune responses are implicated in the pathogenesis of Crohn?s disease. However, the detailed mechanisms by which each innate myeloid subset regulates gut homeostasis and inflammation largely remains unknown. Aim: We aimed to clarify the comprehensive gene expression profiles of innate myeloid cell subsets in the lamina propria from normal human colons (NC) and the inflamed colon sites from patients with Crohn?s disease (CDi). Methods: We performed RNA-sequencing analysis and precise bioinformatics analysis on three innate myeloid cell subsets, CD14-CD11c-, CD14-CD11c+, and CD14+CD11c+CD163low cells from NC and CDi. Results: Transcriptional analysis of the three subsets from the NC showed distinct gene expression patterns and gene ontology (GO) enrichment analysis revealed the associated innate myeloid subset-specific biological process (BP) terms. In addition, changes in gene expression patterns were observed in innate myeloid subsets from CDi. Furthermore, the core GO-BP terms for the genes upregulated in the innate myeloid cells from CDi were distinct from those found in NC. Conclusion: Our data identified the innate myeloid cell subset-specific transcriptomes and the associated enriched GO-BP terms in the NC and found these patterns were altered in CDi.

  Study JGAS000127

• DNA demethylation is associated with malignant progression of low-grade gliomas

  To elucidate the mechanisms of tumor progression is key to discovering strategies for treating patients with lower-grade glioma. Thus to characterize the changes in the molecular profile during malignant progression, we performed methylation array analysis (122 tumors), whole-exome sequencing (36 tumors and matched normal samples), and RNA sequencing (31 tumors). This cohort included 24 matched pairs of initial lower-grade gliomas and recurrent tumors, most of which showed malignant progression when they recurred. Nearly half of IDH-mutant glioblastomas that had progressed from lower-grade gliomas exhibited characteristic partial DNA demethylation in previously methylated genomic regions of their corresponding initial tumors, which had the glioma CpG island methylator phenotype (G-CIMP). In these glioblastomas, cell cycle-related genes were upregulated, RB and PI3K-AKT pathway genes were frequently altered. Notably, bioinformatics analysis revealed that late-replicating domain was significantly enriched in the demethylated regions that were mostly located in non-regulatory regions, suggesting that the loss of DNA methylation during malignant transformation may involve mainly passive demethylation due to a delay in maintenance of methylation during accelerated cell division. Nonetheless, a limited number of genes including IGF2BP3, which potentially drives cell proliferation, were presumed to be upregulated due to demethylation of their promoter. Thus, cell proliferation was further promoted by passive demethylation in the promoter region of these oncogenic genes. Our data indicated that demethylation of the G-CIMP profile found in a subset of recurrent gliomas reflects accelerated cell divisions accompanied by malignant transformation. Oncogenic genes activated by such epigenetic change represent potential therapeutic targets.

  Study JGAS000146

• A targeted gene panel that covers coding, noncoding, and short tandem repeat regions improves the diagnosis of patients with neurodegenerative diseases

  Genetic testing for diagnosis of neurodegenerative diseases (NDs) is highly challenging because of genotype heterogeneity and overlapping clinical manifestations. Targeted-gene panels (TGPs), coupled with next-generation sequencing (NGS), can facilitate the profiling of a large repertoire of ND-related genes and help with differential diagnosis. Due to the technical limitations inherent in NGS and TGPs, short tandem repeat (STR) variations are often ignored. However, STR expansions are known to cause such NDs as Huntington’s disease and several types of spinocerebellar ataxias, such as SCA type 3 (SCA3).Here, we studied the clinical utility of a custom-made TGP that targets 199 NDs and 311 ND-associated genes on 118 undiagnosed patients. At least one known pathogenic or likely pathogenic variation was found in 54 (45.8%) patients; 27 (22.9%) of the 118 patients demonstrated clinical profiles that matched the variants. Based on the findings from the TGP, we refined the original diagnosis of 16 patients. A high concordance of single-nucleotide polymorphisms (99.9%) and small insertions and deletions (93.2%) were observed when comparing the results from TGP and whole-exome sequencing of four patients. We tested an in-house STR detection algorithm, and it reached a specificity of 0.88 and a sensitivity of 0.82 in our SCA3 cohort. This study also uncovered a trove of novel and recurrent variants that may enrich the repertoire of ND-related genetic markers. We propose that a combination of comprehensive TGPs and the bioinformatics analysis pipeline can improve the clinical diagnosis and accelerate the diagnosis of NDs.

  Study EGAS00001003737

• Differential Presence of Exons in Cell-Free DNA Reveals Different Patterns in Colorectal Cancer Between Metastatic and Non-Metastatic Patients

  Tumor genotyping during disease follow-up is gaining an increasing interest for monitoring and clinical management in colorectal cancer (CRC). Growing evidences suggest that single biopsies are inefficient for molecular profiling, due to clonal evolution and intratumoral heterogeneity of primary tumors and/or metastasis. Circulating free DNA (cfDNA) has emerged as an alternative source of genetic material that seems to be representative of the continuously changing tumor molecular features. Thus, the so-called 'liquid biopsy' also offers the additional advantages of accessibility and non-invasiveness for the patient. Novel strategies based on next-generation sequencing (NGS) of cfDNA are being currently developed, with most studies focusing on targeted deep sequencing panels of potential clinically actionable genes. Here we describe an alternative approach, consisting of exome-sequencing in cfDNA at a relatively shallower depth to provide a more general overview of the circulating genome. In this study, two groups of colorectal cancer patients, with either disseminated (M) or localized disease (N), were compared using whole-exome sequencing and a RNA-seq bioinformatics analysis pipeline. Thus, a set of 379 exons present at different levels in cfDNA from both groups were identified, giving rise to a completely new concept in this field, termed as 'Differential Presence of Exons'. Differentially present exons (DPEs) were subsequently used for M and N patients clustering and classification, allowing for the design of a predictive algorithm with encouraging preliminary results in a small subset of patients that could not be initially classified attending to the selection criteria (U group, unassignable).

  Study EGAS00001002687

• RaScALL: Rapid screening of RNA-seq in acute lymphoblastic leukaemia

  RNA-sequencing (RNA-seq) efforts in acute lymphoblastic leukaemia (ALL) have identified numerous prognostically significant genomic alterations which can guide diagnostic risk stratification and treatment choices when detected early. However, a full RNA-seq Bioinformatics workflow is time-consuming and costly in a clinical setting where rapid detection and accurate reporting of clinically relevant alterations are essential. To accelerate the identification of ALL-specific alterations (including gene fusions, single nucleotide variants and focal gene deletions), we developed the rapid screening tool RaScALL, capable of identifying more than 100 prognostically significant lesions directly from raw sequencing reads. RaScALL uses the k-mer based targeted detection tool km and known ALL variant information to achieve a high degree of accuracy for reporting subtype defining genomic alterations compared to standard alignment-based pipelines. Gene fusions, including difficult to detect fusions involving EPOR and DUX4, were accurately identified in 98% (164 samples) of reported cases in a 180-patient Australian study cohort and 95% (n=63) of samples in a North American validation cohort. Pathogenic sequence variants were correctly identified in 75% of tested samples, including all cases involving subtype defining variants PAX5 p.P80R (n=12) and IKZF1 p.N159Y (n=4). Accurate detection of intragenic IKZF1 deletions resulting in aberrant transcript isoforms was also detectable with 98% accuracy. Importantly, the median analysis time for detection of all targeted alterations averaged 22 minutes per sample, significantly shorter than standard alignment-based approaches, ensuring accerelated risk-stratification and therapeutic triage.

  Study EGAS00001006460

• Whole genome sequencing of 198 epileptic individuals.

  This dataset includes whole genome sequencing of 198 epileptic individuals. Libraries preparation and whole-genome sequencing: gDNA was cleaned up using ZR-96 DNA Clean &amp; ConcentratorTM-5 Kit (Zymo) prior to being quantified using the Quant-iTTM PicoGreen dsDNA Assay Kit (Life Technologies) and its integrity assessed on agarose gels. Libraries were generated using the TruSeq DNA PCR-Free Library Preparation Kit (Illumina) according to the manufacturer’s recommendations. Libraries were quantified using the Quant-iTTM PicoGreen dsDNA Assay Kit (Life Technologies) and the Kapa Illumina GA with Revised Primers-SYBR Fast Universal kit (Kapa Biosystems). Average size fragment was determined using a LabChip GX (PerkinElmer) instrument. The libraries were denatured in 0.05N NaOH and diluted to 8pM using HT1 buffer. The clustering was done on a Illumina cBot and the flowcell was ran on a HiSeq 2500 for 2x125 cycles (paired-end mode) using v4 chemistry and following the manufacturer's instructions. A phiX library was used as a control and mixed with libraries at 0.01 level. Bioinformatics: The Illumina control software was HCS 2.2.58, the real-time analysis program was RTA v. 1.18.64. Program bcl2fastq v1.8.4 was used to demultiplex samples and generate fastq reads. The filtered reads were aligned to reference Homo_sapiens assembly b37. Each readset was aligned to creates a Binary Alignment Map file (.bam).

  Dataset EGAD00001004062

• Characterization of Human Transcriptome by Computational and HTS Approaches

  We have deeply sequenced total RNA and whole exome of six different post-mortem human snap-frozen tissues (brain, liver, lung, striated muscle, kidney, heart) from three unrelated healthy Caucasian individuals (males, aged 47-54) with the aim to characterize post-transcriptional events due to alternative splicing and RNA editing. In addition, to facilitate the detection of RNA editing sites we have also resequenced the entire genome of each individual. Data are also used to investigate tissue-specific abundance of mitochondrial DNA from exomes and its correlation with mitochondrial transcription, mass and respiratory activity. Tissues were obtained from Cureline (South San Francisco, CA, USA). DNA and RNA were purified using the DNeasy Blood and Tissue Kit (Qiagen, Hilden, Germany) and the RNeasy Plus Mini Kit (Qiagen, Hilden, Germany), respectively. Exome capture was performed using the TruSeq Exome Enrichment Kit (Illumina, San Diego, CA) and the sequencing was carried out at IGA Technology Services in Udine (Italy) on Illumina HiSeq 2000 (generating for each tissue approximately 40 millions of 100bp paired-end reads). Strand-oriented RNA reads (2x100bp) were generated using the TruSeq Stranded Total RNA Sample Prep Kit (Illumina, San Diego, CA, USA) and sequenced at IGA Technology Services in Udine (Italy) on Illumina HiSeq 2000 platform. Whole genome resequencing was performed at Personal Genomics in Italy on an Illumina HiSeq 2000 platform (2x100bp) with a mean coverage of 30X. MiRNA-Seq was instead performed at IBBE Institute (Italy) using the TruSeq Small RNA Sample Prep Kit on Illumina MiSeq platform. Preliminary bioinformatics analyses have been performed to check quality using FASTQC software and inconsistent read regions have been trimmed using trim_galore. All reads were mapped onto the human reference genome (version hg19) using GSNAP.

  Study phs000870

• Mitochondrial DNA deletion detection in POLG patients

  Accumulation of deletions in the mitochondrial genome is a pathological hallmark of both aging and a broad range of neurometabolic disorders. Prior mtDNA deletion studies have been limited by reliance upon either long range PCR or NGS strategies that target specific regions of the mitochondrial genome. We developed an ultrasensitive high throughput sequencing process (LostArc) to identify and quantify all deletions in circular DNA molecules while minimizing interference from linear DNA and PCR artefacts. We applied this process to skeletal muscle biopsies from 19 unaffected individuals (aged 17 to 93 years) and from 22 patients with heritable mitochondrial disease (17 to 80 years) bearing pathogenic variants in POLG, the nuclear gene encoding the catalytic subunit of the mitochondrial replicative DNA polymerase &#611; (Pol &#611;). To our astonishment, LostArc revealed 35 million mtDNA deletions with ~470,000 unique deletion spans. This unprecedented volume of data enabled detailed and highly informative bioinformatics analyses. We show that ablation of mtDNA is sufficient to explain skeletal muscle phenotypes associated with aging and POLG-derived disease, with a clear threshold separating symptomatic and asymptomatic individuals. Unsupervised hierarchical clustering and principle component analysis reveal distinct age- and disease-correlated deletion patterns that implicate DNA replication by Pol &#611; in the formation of mtDNA deletions. Deletion patterns also point to active mtDNA replication but little or no purifying selection against deleted mtDNA by mitophagy in postmitotic muscle fibers. Monte Carlo simulations of varied hypotheses for deletion formation, based in part on competing models of mtDNA replication, suggest the observed patterns are most consistent with deletions initiated by replication fork stalling during strand displacement mtDNA synthesis. Note that sequence data for HEK293 can be found under BioProject PRJNA642884.

  Study phs002052

• Whole-exome Sequencing Combined with Functional Genomics Reveals Novel Candidate Driver Cancer Genes in Endometrial Cancer

  Endometrial cancer is the most common gynecological malignancy, with more than 280,000 cases occurring annually worldwide. Although previous studies have identified important common somatic mutations in endometrial cancer, they have primarily focused on a small set of known cancer genes and have thus provided a limited view of the molecular basis underlying this disease. Here we developed an integrated systems biology approach to identifying novel cancer genes contributing to endometrial tumorigenesis. We first performed whole-exome sequencing on 13 endometrial cancers and matched normal samples, systematically identifying somatic alterations with high precision and sensitivity. We then combined bioinformatics prioritization with high-throughput screening (including both shRNA-mediated knockdown and expression of wild type and mutant constructs) in a highly sensitive cell viability assay. Our results revealed 12 potential driver cancer genes including 10 tumor suppressor candidates (ARID1A, INHBA, KMO, TTLL5, GRM8, IGFBP3, AKTIP, PHKA2, TRPS1 and WNT11) and two oncogene candidates (ERBB3 and RPS6KC1). The results in the “sensor” cell line were recapitulated by siRNA-mediated knockdown in endometrial cancer cell lines. Focusing on ARID1A, we integrated mutation profiles with functional proteomics in 222 endometrial cancer samples, demonstrating that ARID1A mutations frequently co-occur with mutations in the phosphatidylinositol 3-kinase (PI3K) pathway and are associated with PI3K pathway activation. siRNA knockdown in endometrial cancer cell lines increased AKT phosphorylation supporting ARID1A as a novel regulator of PI3K pathway activity. Our study presents the first unbiased view of somatic coding mutations in endometrial cancer and provides functional evidence for diverse driver genes and mutations in this disease.

  Study EGAS00001000318

• Whole genome sequencing of 108 epileptic patients from CENet cohort

  Epileptic patients have been sequenced as part of the CENet cohort which is composed of patients with Genetic Generalized Epilepsy (GGE) or Non-Acquired Focal Epilepsy (NAFE) collected in CHUM Research Center in Montreal. Patients were diagnosed by epileptologists. The clinical epilepsy phenotype was classified according to the current classification by the International League against Epilepsy (ILAE). Libraries preparation and whole-genome sequencing: gDNA was cleaned up using ZR-96 DNA Clean &amp; ConcentratorTM-5 Kit (Zymo) prior to being quantified using the Quant-iTTM PicoGreen dsDNA Assay Kit (Life Technologies) and its integrity assessed on agarose gels. Libraries were generated using the TruSeq DNA PCR-Free Library Preparation Kit (Illumina) according to the manufacturer’s recommendations. Libraries were quantified using the Quant-iTTM PicoGreen dsDNA Assay Kit (Life Technologies) and the Kapa Illumina GA with Revised Primers-SYBR Fast Universal kit (Kapa Biosystems). Average size fragment was determined using a LabChip GX (PerkinElmer) instrument. The libraries were denatured in 0.05N NaOH and diluted to 8pM using HT1 buffer. The clustering was done on a Illumina cBot and the flowcell was ran on a HiSeq 2500 for 2x125 cycles (paired-end mode) using v4 chemistry and following the manufacturer's instructions. A phiX library was used as a control and mixed with libraries at 0.01 level. Bioinformatics: The Illumina control software was HCS 2.2.58, the real-time analysis program was RTA v. 1.18.64. Program bcl2fastq v1.8.4 was used to demultiplex samples and generate fastq reads. The filtered reads were aligned to reference Homo_sapiens assembly b37. Each readset was aligned to create a .cram file.

  Study EGAS00001007507

• Team

  The Team Mallory Freeberg Roderic Guigó Arcadi Navarro Helen Parkinson Jordi Rambla Ana T. Alonso Silvia Bahena Àlex Bedmar Kenneth Buckley Aldar Cabrelles Ángel Carreño Marcos Casado Giulia Cellerino Amy Curwin Teresa D'Altri Abeer Fadda Teresa Garcia Sara Gregorio Max Fischer Bela Juhasz Oriol Lopez-Doriga Mireia Marin Óscar Martínez Andrea Mero Akiris Moctezuma Aurora Moreno Liina Nagirnaja Francesc de Puig Santiago Rensonnet Gabriele Rinck Aravind Sankar Andres Silva Coline Thomas Sabela de la Torre Gemma Vicente The EGA Team at the CRG co-manages the European Genome and phenome Archive together with the EGA Team at the European Bioinformatics Institute. In addition to maintaining and distributing data, we enrich the contents of the EGA contributing with our knowledge about genomics and the relationship between genomes and phenomes. Previous Team Members Alexander Vikhorev Jeff Almeida-King Mario Alberich Sergi Aguilo Pablo Arce Minjie Ding Alfred Gil Leslie Glass Jag Kandasamy Vasudev Kumanduri llkka Lapalainen Audald Lloret i Villas Sira Martinez Anand Mohan Dietmar Orth Justin Paschall Saif Ur Rehman Gary Saunders Thomas Smith Ashutosh Shimpi Marc Sitges Dhvani Solanki Giselle Kerry Nino Spataro Dylan Spalding Matthieu Vizuete-Forster Cristina Yenyxe Gonzalez Garcia Paul Flicek Anna Foix Emilio Garcia Rios Jorge Izquierdo Roberto Ariosa Marta Ferri Peradalta Daniel Barrowdale Babita Singh Umuthan Uyan Aleix Canalda Dona Shaju Mauricio Moldes Carles Garcia Frédéric Haziza Alegria Aclan Lauren Fromont Alvis Brazma Marta Huertas Arnau Soler Gemma Milla Claudia Vasallo Aina Jené Csaba Halmagyi Raül Garcia Thomas Keane Mei Gascón

  Documentation about/team

• Institut Curie Neuroblastoma Whole Genome Sequencing Diagnosis Relapse

  Neuroblastoma, a clinically heterogeneous pediatric cancer, is characterized by distinct genomic profiles but few recurrent mutations. As neuroblastoma is expected to have high degree of genetic heterogeneity, study of neuroblastoma's clonal evolution with deep coverage whole-genome sequencing of diagnosis and relapse samples will lead to a better understanding of the molecular events associated with relapse. Samples were included in this study if sufficient DNA from constitutional, diagnosis and relapse tumors was available for WGS. Whole genome sequencing was performed on trios (constitutional, diagnose and relapse DNA) from eight patients using Illumina Hi-seq2500 leading to paired-ends (PE) 90x90 for 6 of them and 100x100 for two. Expected coverage for sample NB0175 100x100bp was 30X for tumor and constitutional samples. For the seven other patients expected coverage was 80X for tumor samples with PE 100x100, 100X in the other tumor samples and 50X for all constitutional samples (see table 1). Following alignment with BWA (Li et al., Oxford J, 2009 Jul) allowing up to 4% of mismatches, bam files were cleaned up according to the Genome Analysis Toolkit (GATK) recommendations (Van der Auwera et al., Current Protocols in Bioinformatics, 2013, picard-1.45, GenomeAnalysisTK-2.2-16). Variant calling was performed in parallel using 3 variant callers: GenomeAnalysisTK-2.2-16, Samtools-0.1.18 and MuTect-1.1.4 (McKenna et al., Genome Res, 2010; Li et al., Oxford J, 2009 Aug; Cibulskis et al., Nature, 2013). Annovar-v2012-10-23 with cosmic-v64 and dbsnp-v137 were used for the annotation and RefSeq for the structural annotation. For GATK and Samtools, single nucleotide variants (SNVs) with a quality under 30, a depth of coverage under 6 or with less than 2 reads supporting the variant were filter out. MuTect with parameters following GATK and Samtools thresholds have been used to filter our irrelevant variants. .SNVs within and around exons of coding genes overlapping splice sites.. Then,variants reported in more than 1% of the population in the 1000 genomes (1000gAprl_2012) or Exome Sequencing Project (ESP6500) have been discarded in order to filter polymorphisms. Finally, synonymous variants were filtered out. MuTect focuses on somatic by filtering with constitutional sample. Mpileup comparison between constitutional and somatic DNAs allowed us to focus also on tumor specific SNVs with GATK and Samtools. Finally, every SNV called by our pipeline and also supported in any constitutional samples were filtered our in order to prevent putative constitutional DNA coverage deficiency. Then we analyzed CNVs (copy number variants) with HMMcopy-v0.1.1 (Gavin et al., Genome Res, 2012) and control-FREEC-v6.7 (Boeva et al., Bioinformatics 2011) with a respective window of 2000bp and 1000 bp, and auto-correction of normal contamination of tumor samples for Control-FREEC. Finally we explored Structural variants (SVs) including deletions, inversions, tandem duplications and translocations using DELLY-v0.5.5 with standard parameters (Rausch et al., Oxford J, 2012). In tumors, at least 10 supporting reads were required to make a call and 5 supporting reads for the sample NB0175 with a coverage of only 40X (see table 2). To predict SVs in constitutional samples for subsequent somatic filtering, only 2 supporting reads were required in order not to miss one. To identify somatic events, all the SVs in each normal sample were first flanked by 500 bp in both directions and any SVs called in a tumor sample which was in the combined flanked regions of respective normal sample was removed (see graph 1). Deletions with more than 5 genes impacted or larger than 1Mb and inversions or tandem duplications covering more than 4 genes, were removed. We focused on exonic and splicing events for deletions, inversions, and tandem duplications. For translocation, we keep all SVs that occurred in intronic, exonic, 5'UTR, upstream or splicing regions. Bioinformatics detection of variations with Deep sequencing approach Once PE reads merged and adaptors trimmed by SeqPrep with default parameters, merged reads were aligned via the BWA (Li H. and Durbin R. 2009 PMID 19451168) allowing up to 1 differences in the 22-base-long seeds and reporting only unique alignments. Only reads having a mapping quality 20 or more have been further analysed. Variant calling software was not used, since we aimed to predict variations at low frequencies, observed in less than 1% of reads. Such variants require a custom approach. Using DepthOfCoverage functions of the Genome Analysis Toolkit (GATK) v2.13.2 (McKenna A, et al., 2010 Genome Research PMID: 20644199), we focused on high quality coverage of bases A, C, G and T at the targeted variant position. Depth of coverage of each base following a mapping quality higher than 20 and a base quality higher than 10 have been taken into account in order to focus only on high quality data. Aiming to determine the background level of variability at the studied regions, 10 control samples were included in the analysis. The same approach and filtering criteria have been applied as introduced above over the entire amplicons. In order to highlight variants, for each sample the frequencies of each bases at each amplicon position were then compared to those observed in the set of controls. Statistical analyses were performed with the R statistical software (http://www.R-project.org). Fisher’s exact two-sided tests with a Bonferroni correction were performed to compare percentages of bases between the data sets, i.e. for a given base between a case and the controls. Finally, significant variations were filtered-in once (i) a significant increase in the percentage of avariant base and (ii) a significant decrease in the percentage of it's reference base following our p.values criteria was observed (p.val &lt; 0.05).

  Study EGAS00001001184

• Federated EGA

  Federated EGA Federated EGA Index Overview Map of FEGA status across the world Current status of the FEGA Network Documentation Overview Federated EGA is a global network of repositories enabling secure discovery and access to sensitive human data. Our vision is to accelerate scientific discovery and healthcare breakthroughs by creating the go-to worldwide sensitive human data resource. Our values are: Privacy & Trust: Ensuring privacy of data subjects, security of services and acting with integrity to build community trust.FAIRness: Driving reuse of data through standardisation of metadata and clear access policies.Diversity: Expanding our network and representing global diversity in the data we host to bring benefits worldwide. Federated EGA - European by name, global by nature Federated EGA collaborates with European and global initiatives (GA4GH, ELIXIR, 1+ Million Genomes Framework, GDI), demonstrating that a network for transnational discovery of and access to human data is possible. By providing a solution to emerging challenges around secure and efficient management of human omics and associated data, the FEGA Network fosters data reuse, enables reproducibility, and accelerates biomedical research. For general enquiries or to request more information about Federated EGA, please send an email to fega-info [at] lists [dot] ega-archive [dot] org. Read more about the Federated EGA in our commentary in Nature Genetics! Learn more about setting up a Federated EGA Node in our onboarding documentation. Map of FEGA status across the world Current status of the FEGA Network Node Description Joined CEGA Central EGA is managed by the Centre for Genomic Regulation (CRG) and EMBL-EBI. The EGA has been a trusted repository for sensitive data since 2008. CEGA coordinates the FEGA network, hosts central services and the EGA catalogue. 2022 Finland FEGA Finland is hosted by CSC - IT Center for Science Ltd., which is the national ELIXIR Node in Finland. CSC is a state-owned company specialising in providing high-quality IT infrastructure and services for Finnish higher education and research. 2022 Germany The German Human Genome-Phenome archive (GHGA) is part of the German National Research Data Infrastructure (NFDI). It is coordinated by the German Cancer Research Center (DKFZ) and includes over 20 academic partners. 2022 Norway FEGA Norway is a service by ELIXIR Norway, the Norwegian node of the European organisation ELIXIR. University of Oslo as a partner institution in ELIXIR Norway is hosting the service relying on the TSD infrastructure for sensitive data. ELIXIR Norway is a consortium of 5 Norwegian universities and is coordinated by University of Bergen. 2022 Spain FEGA Spain is co-hosted by the Barcelona Supercomputer Center (BSC) and the Centre for Genomic Regulation (CRG), both part of ELIXIR Spain and the Spanish National Infrastructure for Personalised Medicine associated with Science and Technology (IMPaCT). 2022 Sweden FEGA Sweden is hosted by the National Bioinformatics Infrastructure Sweden (NBIS), which legally is part of Uppsala University. NBIS forms the bioinformatics platform at SciLifeLab and constitutes the Swedish node of the European organisation ELIXIR. 2022 Poland FEGA Poland is hosted by Biobank Lodz, which is part of University of Lodz. Biobank Lodz is an element of the infrastructure of the Regional Digital Medicine Centers established by the Medical Research Agency. 2022 Portugal FEGA Portugal is managed by BioData.pt, the distributed infrastructure for Life and Health data for Portugal. This entity is a non-profit association of 15 life sciences R&I organisations spread across the country, and the home of ELIXIR Node in Portugal. 2023 Canada Canadian Genome-phenome Archive is part of the Pan Canadian Genome Library, with the mandate to allow the secure and responsible sharing and analysis of Canadian genomic information. 2024 Switzerland Swiss FEGA is hosted by SIB Swiss Institute of Bioinformatics, which is the national ELIXIR Node in Switzerland, on behalf of the Swiss FEGA Partnership which includes 4 other key partners (ETH Zurich, Health 2030 Genome Center, Swiss Data Science Center and Switch). 2025 Documentation Title Version Description Mission, vision, values Federated EGA Mission, vision, values 1.0 The official Mission-Vision-Values statement for FEGA, produced and formally approved by the FEGA Strategic Committee in September 2025. Structure and Organisation Federated EGA Structure and Organisation 2.0 The structure of the FEGA network and service expectations, responsibilities and commitments. FEGA is organised into three tiers: Central EGA, FEGA Nodes and FEGA Affiliates. Strategic Committee Federated EGA Strategic Committee 1.1 Terms of reference for the FEGA Strategic Committee describe the purpose and objectives of the committee, which is to provide direction and strategic planning for the FEGA network. The committee receives input from, and provides feedback for the EGA Strategic Committee. Operations Committee Federated EGA Operations Committee 1.1 Terms of reference for the FEGA Operations Committee describe the purpose and objectives of the committee, which is to review operational performance and coordinate technical roadmaps of FEGA Nodes and Central EGA. The committee receives advice from, and provides operational reporting to the FEGA Strategic Committee. Guidelines Node Operations guidelines 2.1 An overview of the operational areas which require resources in order to create a FEGA Node. The document is based on more than 15 years' experience of EMBL-EBI and CRG operating the EGA and initial expericences of the inaugural FEGA Nodes. The operational areas of responsability are in line with the FEGA Maturity Model, which guides establishment and operation of FEGA Nodes. Collaboration Federated EGA Node Collaboration Agreement 1.3 Nodes are welcome to make a copy of this current version of the CA to start its review with their legal teams and understand the responsibilities of joining FEGA. Nevertheless, this version (the one with a watermark) shall not be signed: the official version needs to be obtained from FEGA prior signing through its official channels.

  Documentation about/projects-and-funders/federated-ega

• Institut Curie Neuroblastoma Whole Genome Sequencing Diagnosis Relapse

  Neuroblastoma, a clinically heterogeneous pediatric cancer, is characterized by distinct genomic profiles but few recurrent mutations. As neuroblastoma is expected to have high degree of genetic heterogeneity, study of neuroblastoma's clonal evolution with deep coverage whole-genome sequencing of diagnosis and relapse samples will lead to a better understanding of the molecular events associated with relapse. Samples were included in this study if sufficient DNA from constitutional, diagnosis and relapse tumors was available for WGS. Whole genome sequencing was performed on trios (constitutional, diagnose and relapse DNA) from eight patients using Illumina Hi-seq2500 leading to paired-ends (PE) 90x90 for 6 of them and 100x100 for two. Expected coverage for sample NB0175 100x100bp was 30X for tumor and constitutional samples. For the seven other patients expected coverage was 80X for tumor samples with PE 100x100, 100X in the other tumor samples and 50X for all constitutional samples (see table 1). Following alignment with BWA (Li et al., Oxford J, 2009 Jul) allowing up to 4% of mismatches, bam files were cleaned up according to the Genome Analysis Toolkit (GATK) recommendations (Van der Auwera et al., Current Protocols in Bioinformatics, 2013, picard-1.45, GenomeAnalysisTK-2.2-16). Variant calling was performed in parallel using 3 variant callers: GenomeAnalysisTK-2.2-16, Samtools-0.1.18 and MuTect-1.1.4 (McKenna et al., Genome Res, 2010; Li et al., Oxford J, 2009 Aug; Cibulskis et al., Nature, 2013). Annovar-v2012-10-23 with cosmic-v64 and dbsnp-v137 were used for the annotation and RefSeq for the structural annotation. For GATK and Samtools, single nucleotide variants (SNVs) with a quality under 30, a depth of coverage under 6 or with less than 2 reads supporting the variant were filter out. MuTect with parameters following GATK and Samtools thresholds have been used to filter our irrelevant variants. .SNVs within and around exons of coding genes overlapping splice sites.. Then,variants reported in more than 1% of the population in the 1000 genomes (1000gAprl_2012) or Exome Sequencing Project (ESP6500) have been discarded in order to filter polymorphisms. Finally, synonymous variants were filtered out. MuTect focuses on somatic by filtering with constitutional sample. Mpileup comparison between constitutional and somatic DNAs allowed us to focus also on tumor specific SNVs with GATK and Samtools. Finally, every SNV called by our pipeline and also supported in any constitutional samples were filtered our in order to prevent putative constitutional DNA coverage deficiency. Then we analyzed CNVs (copy number variants) with HMMcopy-v0.1.1 (Gavin et al., Genome Res, 2012) and control-FREEC-v6.7 (Boeva et al., Bioinformatics 2011) with a respective window of 2000bp and 1000 bp, and auto-correction of normal contamination of tumor samples for Control-FREEC. Finally we explored Structural variants (SVs) including deletions, inversions, tandem duplications and translocations using DELLY-v0.5.5 with standard parameters (Rausch et al., Oxford J, 2012). In tumors, at least 10 supporting reads were required to make a call and 5 supporting reads for the sample NB0175 with a coverage of only 40X (see table 2). To predict SVs in constitutional samples for subsequent somatic filtering, only 2 supporting reads were required in order not to miss one. To identify somatic events, all the SVs in each normal sample were first flanked by 500 bp in both directions and any SVs called in a tumor sample which was in the combined flanked regions of respective normal sample was removed (see graph 1). Deletions with more than 5 genes impacted or larger than 1Mb and inversions or tandem duplications covering more than 4 genes, were removed. We focused on exonic and splicing events for deletions, inversions, and tandem duplications. For translocation, we keep all SVs that occurred in intronic, exonic, 5'UTR, upstream or splicing regions. Bioinformatics detection of variations with Deep sequencing approach Once PE reads merged and adaptors trimmed by SeqPrep with default parameters, merged reads were aligned via the BWA (Li H. and Durbin R. 2009 PMID 19451168) allowing up to 1 differences in the 22-base-long seeds and reporting only unique alignments. Only reads having a mapping quality 20 or more have been further analysed. Variant calling software was not used, since we aimed to predict variations at low frequencies, observed in less than 1% of reads. Such variants require a custom approach. Using DepthOfCoverage functions of the Genome Analysis Toolkit (GATK) v2.13.2 (McKenna A, et al., 2010 Genome Research PMID: 20644199), we focused on high quality coverage of bases A, C, G and T at the targeted variant position. Depth of coverage of each base following a mapping quality higher than 20 and a base quality higher than 10 have been taken into account in order to focus only on high quality data. Aiming to determine the background level of variability at the studied regions, 10 control samples were included in the analysis. The same approach and filtering criteria have been applied as introduced above over the entire amplicons. In order to highlight variants, for each sample the frequencies of each bases at each amplicon position were then compared to those observed in the set of controls. Statistical analyses were performed with the R statistical software (http://www.R-project.org). Fisher’s exact two-sided tests with a Bonferroni correction were performed to compare percentages of bases between the data sets, i.e. for a given base between a case and the controls. Finally, significant variations were filtered-in once (i) a significant increase in the percentage of avariant base and (ii) a significant decrease in the percentage of it's reference base following our p.values criteria was observed (p.val &lt; 0.05).

  Dataset EGAD00001001356

• Transcriptomic Analysis of HIV-Infected Cells

  Defining the attributes of rare CD4 T cells that harbor latent HIV under therapy with anti-retroviral agents has been challenging due to difficulties in identifying and isolating these cells. These cells represent an important barrier to cure, and characterizing them would aid the design and development of novel HIV cure strategies. In this study, we addressed this challenge using a custom microfluidic technology named Focused Interrogation of cells by Nucleic acid Detection and Sequencing (FIND-seq) that isolates HIV-infected cell transcriptomes, based on HIV DNA detection. We applied this method to HIV DNA+ memory CD4 T cells isolated from blood from five persons with HIV who were receiving anti-retroviral agents. After curating samples based on data quality, we proceeded with bioinformatics analysis from three persons. This analysis identified six transcriptomic pathways that were inhibited in HIV DNA+ cells compared to HIV DNA- cells. These included death receptor signaling, necroptosis signaling, and anti-proliferative G-alpha12/13 signaling. Furthermore, we identified two gene clusters, including 60 and 85 genes, that were significantly associated with HIV DNA+ cells identified by co-expression network analysis. These genes included negative regulators of HIV transcription that were higher in HIV DNA+ cells, positive regulators of HIV transcription that were lower in HIV DNA+ cells, and other genes involved in the negative regulation of mRNA translation, additional RNA processing functions, and the regulation of cellular state and fate. We further examined these signatures in transcriptomic data from CD4 T cell subsets sorted by flow cytometry from nine persons with HIV. We found a partial similarity between the smaller cluster of genes and the signature of CCR6- peripheral TFH cells. Raw sequencing data from all eleven human participant samples included in the study are available. These include samples from two participants who were studied only by FIND-seq, three participants who were studied by both FIND-seq and flow cytometry and six participants who were studied only by flow cytometry.

  Study phs003095

• Genome_Diversity_in_Africa_Project__Uganda

  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 SSA 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 SSA would facilitate genomic epidemiological studies in the region. The Genome Diversity in Africa Project (GDAP) aims to produce a comprehensive catalogue of human genetic variation in SSA, 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 SSA, as well as providing a global resource to help design, implement and interpret genomic studies in SSA populations and studies comprising globally diverse populations, complementing existing genomic resources. Specifically, we plan to carry out high depth whole genome sequencing of up to 2000 individuals across Africa (25 individuals from each ethnolinguistic group). Our scientific objectives are to: 1) develop a resource that provides a comprehensive catalogue of genetic variation in populations from SSA accessible to the global scientific community; 2) characterise population genetic diversity, structure, gene flow and admixture across SSA; 3) develop a cost-efficient, next-generation genotype array for diverse populations across SSA; 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 EGAS00001002523