2965 results for "愛の戦士完全破壊2-re-imagination-version-鞭首絞め・電撃・ヘソか"

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• DAC for Central African ancient demography processes NGS dataset

  Title DAC Ancient tree-topologies and gene-flow processes among human lineages in Africa Contacts: Paul Verdu +33144057317 paul.verdu@mnhn.fr CNRS-MNHN-Université de Paris UMR7206 Eco-anthropology Short description The Data Access Committee for acess to genotype and sample information used in the article "Ancient tree-topologies and gene-flow processes among human lineages in Africa" by Gwenna Breton et al. 2025 ensures that data-access requests comply with the Informed Consent provided by donors, and with ethical and deontological guidelines with respect to privacy. DNA samples from individuals were collected with the subjects’ informed consent following the Declaration of Helsinki guidelines and approved by the following Ethics committees: Institut de Recherche pour le Développement (IRD, France); Cameroon Ministry of Scientific Research and Technology; Central African Republic Ministère de l’Enseignement Supérieur et de la Recherche Scientifique; France Ministère de l’Enseignement Supérieur, de la Recherche et de l’Innovation (Comité de Protection des Personnes DC-2016-2740); French Commission Nationale Informatique et Liberté CNIL (declaration n°1972648); Uganda National Council for Science and Technology; Institutional Review Board of the University of Chicago Biological Sciences Division and the Pritzker School of Medicine University of Chicago Hospitals (protocol number 16986A); Committee for the Protection of Human Subjects of the Trustees of Dartmouth College and the Dartmouth-Hitchcock Medical Center (protocol number 22410); Human Research Ethics Committee (Medical) at the University of the Witwatersrand, Johannesburg (Protocol Numbers: M980553, M050902, M090576, M10270, M180654); Working Group of Indigenous Minorities in Southern Africa (WIMSA); South African San Council (SASC); and the Swedish Ethical Review Authority (Dnr 2019-05174). Requests for accessing all or part of the Central African data used in the article "Ancient tree-topologies and gene-flow processes among human lineages in Africa", by Gwenna Breton et al. 2024 (doi - PENDING), should be addressed to the DAC providing: 1) Data can only be used to explore human genetic evolution, evolutionary ecology and population history 2) The research institution where data will be stored and were analyses will be conducted. 3) The detailed coordinates of the researcher responsible for data storage and analyses. 4) A description (max 20 sentences) of the scientific finality and context of the project and classes of methodological analyses that will be performed to achieve it using the requested data. 5) When applicable, mention also other previously published data sets that will be used jointly with the requested datas et For accessing any or all the Central African genetic data used in the article "Ancient tree-topologies and gene-flow processes among human lineages in Africa", by Gwenna Breton et al. 2024 (doi - PENDING), the requester hereby ensures that: 1) Individual donor privacy should be respected at all times. Data cannot be made publicly available under any circumstances. 2) Data cannot be shared nor transferred to a third party without re-applying for eGA access to the DAC. 3) No direct or indirect commercial use of the data can be made 4) No individual medical genetics or disease diagnostics can be performed with the data 5) No medical or legal forensic investigations can be conducted with the data 6) No genealogical research can be conducted with the data (e.g. no paternity nor maternity testing, no genetic identification, no search of relatives using other datasets) 7) Appropriate citation of the article and data set used for any scientific communication using the data 8) Data is destroyed after the completion of the project. Any further re-use of the data is subject to a novel application to the DAC.

  Dac EGAC50000000447

• Cancer Genome Project Exome Sequencing

  Agilent whole exome hybridisation capture was performed on genomic DNA derived from cancer and matched normal DNA from the same patients. Next Generation sequencing performed on the resulting exome libraries and mapped to build 37 of the human reference genome to facilitate the identification of novel cancer genes. Now we aim to re find and validate the findings of those exome libraries using bespoke pulldown methods and sequencing the products.

  Dataset EGAD00001000289

• WGS of low-count and high-count MBL

  The cohort consisted of individuals with low-count MBL, high-count MBL as well as patients with CLL. Whole genome sequencing was initially performed in a smaller cohort of selected, representative samples, while targeted re-sequencing of CLL putative driver genes was performed in all samples of the cohort. Besides CLL cell samples, the sequencing process was performed in paired control samples including both buccal and polymorphonuclear cell samples.

  Dataset EGAD50000000629

• Acute myeloid leukemia single cell RNA-seq (Diagnosis and Relapse)

  To gain insight into the clonal heterogeneity of diagnosis (Dx) and relapse (Re) pairs, we employed single-cell RNA-seq (SORT-seq) to longitudinally profile two t(8;21) (AML1-ETO = RUNX1-RUNX1T1), and four FLT3-ITD AML cases. All the samples are Bone marrow aspirates.

  Dataset EGAD00001008373

• Chondrosarcoma Validation Study

  Agilent whole exome hybridisation capture was performed on genomic DNA derived from Chondrosarcoma cancer and matched normal DNA from the same patients. Next Generation sequencing performed on the resulting exome libraries and mapped to build 37 of the human reference genome to facilitate the identification of novel cancer genes. Now we aim to re find and validate the findings of those exome libraries using bespoke pulldown methods and sequencing the products.

  Dataset EGAD00001000392

• Metagenomic sequences from human stool samples

  ​DNA extraction from human stool samples was performed at the Center for Microbiome Innovation (CMI) at University of California, San Diego. DNA sequencing libraries were prepared using Nextera XT (Illumina). Shotgun DNA sequencing was performed on the Illumina HiSeq4000 platform. Raw fastq reads were quality-checked. Skewer (version 0.2.2) was utilized with the paired-end mode. Human reads were identified and removed by Bowtie2 mapping against the human genome reference (hg19), followed by bam2fastq with --unaligned --no-aligned --force options.

  Dataset EGAD00001006364

• Genomics of Pediatric Renal Medullary Carcinomas

  Version 1We derived faithful cancer cell lines from patients with a diagnosis of renal medullary carcinomas (RMC). These models have been sequenced with whole genome, exome and transcriptome technologies along with the patient's primary germline and tumor samples. We took these faithful models and performed loss-of-function genetic perturbation screens (e.g. RNAi and CRISPR-Cas9) along with an orthogonal small molecule screen. We identified the ubiquitin-proteasome system as an important target in RMC as well as other SMARCB1 deficient cancers.Version 2We have collaborated with the Children's Oncology Group and the study AREN03B2 to study the cohort of patients with a central diagnosis of RMC. Here, we used 10x linked read sequencing along with RNA-sequencing to profile these matched tumor-normal pairs.

  Study phs001800

• Natural Genetic Variation in the Human Genome

  In this study, we used existing whole genome sequencing data from the TOPMed Amish and Jackson Heart Study, as well as the GTEx study, to identify mobile element insertions in the genomes of these individuals.  A total of 1112 TOPMed Amish, 3331 TOPMed Jackson Heart Study, and 698 GTEx individuals were examined.  We used the Mobile Element Locator Tool (MELT) developed previously by our lab, along with a Cloud-based version of MELT (CloudMELT), to discover non-reference mobile element insertions (MEIs) in these genomes.  We discovered Alu, L1, SVA, and HERV-K MEIs and produced genotypes in individual genomes.  Build hg19 bams were used for the TOPMed samples, and build GRCh37 bams were used for the GTEx samples in this study.

  Study phs002463

• All you need to know about our new DAC Portal v2

  An updated version to boost the management of DACs (Data Access Committees), policies, and data access requests. In September of 2023, we launched new services for all EGA users, including a DAC Portal. Since then, our team has been working to refine it and improve the user experience. The version 2 of our DAC Portal is now ready, with a range of new features aimed at streamlining your workflow and boosting productivity. If you are interested in learning more about the latest version of our DAC Portal, then you are in the right place! In this post, you will find information about the implemented features and more. The full documentation is available on our website. Moreover, we highly recommend exploring the DAC Portal through the Take the Tour. This said, let's get cracking! Customise your data access requests table This table offers the possibility to tailor information about the data access requests by making visible or hiding different columns regarding the Dataset Persistent Identifier, the Organisation or the requestor's name s, among others. You can choose which columns to see, depending on your needs and interests. Additionally, we have implemented two new features columns with two new functionalities, DAC Comment and Expiration Date. You can find more details in the following sections! In this table, all data access requests are displayed by default. With the latest version of the Portal, you can now apply filters, combine them, and save them for future use. Leave internal comments on pending requests and visible to all the DAC Members We understand that many Data Access Committees (DACs) cannot instantly approve or deny data access requests. Often, there's an intermediary phase where discussions and the signing of legal documents are necessary. To facilitate this process, we've introduced the DAC comment feature! When you navigate to the DAC column, you'll find two tabs: "User" and "DAC". Under "User", you'll see the message provided by the requester at the time of requesting access to the data. As a DAC member/admin, you can then add an internal comment in the "DAC" tab. These comments are visible only to DAC members/admins, ensuring confidentiality. Requesters won't have access to DAC comments. By adding a DAC comment to a pending request, we'll know that you've begun reviewing it. This allows you to filter pending requests without DAC comments to focus on genuinely new requests, and it prevents you from receiving email notifications about ongoing data requests. Set expiration dates for permissions and revoke them automatically The latest update to the DAC Portal introduces a new feature allowing you to set expiration dates for new access requests. For instance, if you anticipate approving a data access request but only wish to grant access for a limited time, you can now specify an expiration date for the granted permissions. Once this date is reached, the permissions will automatically be revoked. By default, permissions are granted indefinitely without an expiration date. However, if you ever need to revoke permissions, you can easily do so from the History page. Audit your metadata objects Managing and auditing your metadata objects will be easier from now on as the new DAC Portal enhances and facilitates your role as a Data Controller. This is possible thanks to the tables available in each of the primary tabs visible on the homepage: DACs, Policies and Datasets. Starting with DACs, this section allows to check the policies linked to a specific DAC. In Policies, you can obtain a list of the datasets connected to a particular policy. In the Dataset tab, it is possible to group datasets by either DAC or Policy, as well as the release status. Now you have the option of deprecating metadata objects that you don’t want to use anymore, or that were created by mistake. You will find the deprecate button on the EDIT page of the DAC, as well as for registered policies. You can find more information here. What's neat about this feature is that once a DAC or policy is deprecated, it becomes hidden within the DAC Portal. This ensures that you only see information that is consistently up to date! Define your preferences regarding email notifications For pending requests, you will be able to decide how often you want to get the notifications: daily, weekly or fortnightly. Remember, you will not receive email notifications for pending requests with a DAC comment! Furthermore, you have the flexibility to decide which notifications you wish to receive, such as invitations to join a new DAC or notifications regarding DAC acceptance by the Helpdesk, among other options. Check the User preference to see the complete list of available notification settings! What’s next: incoming DAC Portal API In addition to the new DAC Portal, we are excited to announce the release of the DAC API! This enables users to programmatically manage permissions. If you are interested in learning more about the technical specifications, you can consult the DAC API specification. This post was written by Aina Jené and Ana T. Alonso.

  Blog new-dac-portal-v2

• Whole genome sequencing of multifocal ileal neuroendocrine tumors

  This dataset includes whole genome sequences of 75 synchronous primary tumors, 15 metastases, and corresponding normal samples from 13 patients with multifocal ileal neuroendocrine tumors. The whole genomes were sequenced on Illumina HiSeq X Ten to generate 151-bp paired-end reads, which were aligned to GRCh38/hg38 reference assembly using BWA–MEM and duplicate-marked with Picard tools. GATK was utilized for base score recalibration and local indel re-alignments. The whole genomes are provided as CRAM files.

  Dataset EGAD00001008831

• Dataset developed for use with EOSC4Cancer of synthetic colorectal cancer tumor/normal pairs.

  This dataset contains 10 tumor and normal pairs synthetic WGS data of colorectal cancer that were simulated in a standard format of Illumina paired-end reads. The NEAT read simulator (version 3.0, https://github.com/zstephens/neat-genreads) was utilized to synthetize these 10 pairs of tumor and normal WGS data. In the procedure of data generation, simulated parameters (i.e., sequencing error statistics, read fragment length distribution and GC% coverage bias) were learned from data models provided by NEAT. The average sequencing depth for tumor and normal samples aimed to reach around 110X and 60X, respectively. For generation of synthetic normal WGS data per each sample, a germline variant profile from a real patient was down-sampled randomly, representing 50% germline variants of a given patient. These were mixed with the other 50% in silico germline variants that were modelled randomly using an average mutation rate (0.001), finally constituting a full germline profile for normal synthetic WGS data. For generation of synthetic tumor WGS data per each sample, a pre-defined somatic short variant profile (SNVs+Indels) learnt from a real CRC patient was added to the germline variant profile used for creating the normal synthetic WGS data of the same patient, consisting of the variants for tumor sample. Neither copy number profile nor structural variation profile was introduced into the tumor synthetic WGS data. Tumor content and ploidy were assumed to be 100% and 2, respectively. For mapping/variant detection, the Sarek pipeline v3.1.2 (https://nf-co.re/sarek/3.1.2) was used, specifically: 1. BWA v0.7.17-r1188 for read mapping 2. GATK v4.3.0.0 for pre-processing BAM file (including markduplicates and recalibration). 2. Mutect2 (GATK v4.3.0.0) for somatic variant calling 3. Strelka2 v2.9.10 for germline and somatic variant calling Metadata information of 10 CRC patients used for the generation of synthetic normal and tumor WGS data: Patient_id Tumor_barcode Normal_barcode Age Sex Tissue Cancer SIM007 SIM007_T SIM007_N 71 F Rectal Primary CRC SIM008 SIM008_T SIM008_N 45 F Colon Neuroendocrine Metastasis CRC SIM010 SIM010_T SIM010_N 62 M Colon Metastasis CRC SIM011 SIM011_T SIM011_N 55 M Colon Neuroendocrine Metastasis CRC SIM012 SIM012_T SIM012_N 57 M Rectal Metastasis CRC SIM013 SIM013_T SIM013_N 69 M Colon Metastasis CRC SIM014 SIM014_T SIM014_N 68 M Colon Neuroendocrine primary CRC SIM015 SIM015_T SIM015_N 58 F Colon Primary CRC SIM016 SIM016_T SIM016_N 49 M Colon/Rectal Primary CRC SIM017 SIM017_T SIM017_N 78 M Colon Neuroendocrine primary CRC

  Dataset EGAD50000000564

• DNA-seq BAM files from 16 carriers of the BMPR2 p.Arg491Gln mutation in a family affected by HPAH

  Paired-end DNA-seq BAM files from 16 carriers of the BMPR2 p.Arg491Gln mutation in a family affected by hereditary pulmonary arterial hypertension (HPAH). Whole genome sequencing of these samples was performed in an Illumina HiSeq 4000 instrument. Libraries were prepared using the Fisher PE Kit (Kapa Biosystems). FASTQ files were processed at the CNAG (Barcelona) using the GEM short-read aligner on the human genome version hs37d5, producing a total of 16 BAM files.

  Dataset EGAD00001005757

• RNAseq on 20 samples of multiple myeloma patients and 3 normal plasma cells

  RNAseq on 20 samples of multiple myeloma patients and 3 normal plasma cells. RNAseq was performed using 200 ng of total RNA by GATC Biotech. Directional libraries were performed after mRNA selection by polyA selection using UTP method. RNA-seq libraries were sequenced on HiSeq2500 Illumina machine using 100bp paired-end reads. Reads alignment was performed using the STAR aligner (version 2.4.0f1) and human genome hg19 as reference.

  Dataset EGAD00001007813

• V(D)J and 5' Gene Expression data of bone marrow cells from patients with aplastic anemia

  Viably frozen blood MNCs from 9 cGVHD patients were sorted with BD Influx Cell sorter, to enrich for CD45+ cells. Gene and V(D)J transcript profiles were studied with 10x Genomics Chromium Single Cell Immune Profiling platform. The Chromium Single Cell 5’RNAseq run and library preparation were done using the Chromium Next GEM Single Cell Immune Profiling version 1.1 chemistry. The raw data was processed using Cell Ranger v3.1 pipelines.

  Dataset EGAD00001012117

• The Mood and Methylation Study (MMS)

  The Mood Methylation Study (MMS) is an observational study designed to understand epigenetic mechanisms underlying major depression symptoms (MDD) using a co-twin control design. Twins enrolled in the MMS were monozygotic (MZ) twin pairs discordant on lifetime history of MDD. All twins were members of the Washington State Twin Registry (WSTR), a community-based twin registry consisting of over 10,000 twin pairs. Lifetime and current MDD diagnoses were determined using the Structured Clinical Interview for DSM-IV Research Version (SCID-4-RV). DNA methylation in blood monocytes was measured using the Infinium HumanMethylationEPIC BeadChip. Monocyte gene expression was quantified by paired end RNA-seq (50 PE). Zygosity of the twin pairs included in the MMS was self-reported and confirmed using the polymorphic SNPs included in the DNA methylation array.

  Study phs002858

• Tracking Therapy-Resistant Alterations in Childhood Acute Lymphoblastic Leukemia

  Relapse is currently the major cause of death in children with B-ALL. The goal of this study is to uncover the genetic underpinnings of relapsed B-ALL in the AALL1331 cohort (PMID: 33651090) via whole genome and transcriptome sequencing of matched diagnosis, relapse, and remission specimens. We aim to discover genetic alterations that drive treatment resistance in relapsed tumors. We also aim to investigate the feasibility of detecting residual tumors from remission samples with the ultimate goal to improve risk stratification using NGS-based minimal residual disease (MRD) detection. Here we only report data from patients in remission with the aim of determining MRD. We will submit corresponding data for the diagnosis and relapse components of our cohort in a future version of our study.

  Study phs003409

• Transcriptional and DNA binding profiling of CEBPE in REH acute lymphoblastic leukaemia cells using shRNA RNA-Seq and ChIP-Seq

  ChIP-Seq - CEBPE - REH. The ETV6/RUNX1 translocated acute lymphoblastic leukaemia cell line. REH was used to perform ChIP-Seq using a CEBPE antibody. Cells were fixed in 1% formaldehyde for 10mins, prior to preparation of chromatin using Active Motif Express ChIP-IT. 2ug of antibody (anti CEBPE Atlas Antibodies HPA002928)was added to 25ug of chromatin O/N at 4C with rotation. Duplicate reactions were pooled and purified. 10ng of ChIP’d and input DNA used for Illumina NGS preparation (NEBNext ChIP-Seq Library kit; New England Biolabs), CEBPE and Input DNA ChIP samples were sequenced on a MiSeq using 150bp Kit v3 paired end and a HiSeq 2500 using 2x101 version 4 paired end (Illumina) respectively. Reactions performed in duplicate. shCEBPE RNA-Seq - REH. REH cells were lentivirally transduced with a pTRIPZ shRNA vector for transcriptional profiling of CEBPE. Two controls (empty and non-targeting) and two CEBPE shRNAs (V3THS_150517(A13), V3THS_404312(G3) Dharmacon, GE) were transduced into REH cells. Cells were treated with 1ug/ml doxycyclin for 144hrs and total RNA purified using Qiagen RNeasy. Knock down of CEBPE was validated by qRT. RNA integrity >7.7 for all samples. Libraries were prepared using NEBNext Ultra II Directional RNA Library Prep Kit and sequenced on an Illuimna HiSeq 2500 using 2x101 version 4 paired end chemistry. 3 biological replicates of each samples were prepared.

  Dataset EGAD00001004084

• CEITEC DAC

  A CEITEC Data Access Committee (DAC) is a body of one or more named individuals who are responsible for data release to external requesters. The members of DAC are selected members of research groups and their respective leaders. Multiple datasets may be affiliated to a single DAC. DAC continues communication with requester to understand the purpose of use, i.e. research setting the dataset is envisioned for by requester, to identify if data sharing is possible directly under current conditions for the dataset, or actions such as re-consenting is needed and desirable to accommodate the proposed research setting/data use.

  Dac EGAC50000000049

• TTFields Glioblastoma Data Access Committee, Charité – Universitätsmedizin Berlin

  This Data Access Committee is responsible for reviewing and approving data access requests for genomic and associated clinical data from patients with glioblastoma, IDH wildtype, treated with surgery, standard chemoradiotherapy and Tumor Treating Fields at Charité – Universitätsmedizin Berlin. The DAC ensures that any data use is consistent with the patients’ informed consent, the study’s ethical approval and applicable data protection regulations. Approved applicants may use the data only for biomedical research on brain tumours and related methods, must not attempt re-identification of participants, and may not share the data with unauthorized third parties

  Dac EGAC50000000843

• Size-tagged preferred ends in maternal plasma DNAshed light on the production mechanism and showutility in noninvasive prenatal testing

  Cell-free DNA molecules in the plasma of pregnant women exhibit nonrandom fragmentation with preferred end sites. We studied if such preferred end sites might bear any re- lationship with fragment lengths of plasma DNA. Short and long plasma DNA molecules were associated with different preferred DNA end sites. Analysis of size-tagged preferred ends could be used for measuring fetal DNA fraction and for facilitating fetal trisomy 21 detection. Fetal preferred end sites were generally located in the nucleosome cores, while the maternal ones were located in the linker regions. This con- ceptual framework provides an explanation of the relative shortness of fetal DNA in maternal plasma and brings us closer to understanding the biological mechanisms that influence plasma DNA fragmentation.

  Study EGAS00001002831

• Memory-like HCV-specific CD8+ T cells retain a chronic scar after cure of chronic HCV infection

  Chronic hepatitis C virus (HCV) infection is associated with CD8+ T-cell exhaustion characterized by limited effector functions and thus compromised anti-viral activity. Exhausted HCV-specific CD8+ T cells are comprised of memory-like and terminally exhausted CD8+ T-cell subsets. So far, little is not known about the molecular profile and fate of these cells after elimination of chronic antigen stimulation by direct acting antiviral therapy (DAA). Here, we report an antigen-driven molecular core signature underlying exhausted CD8+ T-cell subset heterogeneity in chronic viral infection with a progenitor/progeny relationship of memory-like and terminally exhausted HCV-specific CD8+ T cells via an intermediate stage. Furthermore, transcriptional profiling reveals that the memory-like cells remain after DAA-mediated cure while terminally exhausted HCV-specific CD8+ T-cell subsets are lost. Thus, the memory polarization of the overall HCV-specific CD8+ T-cell response after cure does not result from re-differentiation of exhausted T cells. Consequently, antigen elimination has little impact on the exhausted core signature of memory-like CD8+ T cells that remains clearly different from bona fide T-cell memory. These results identify a molecular signature of T-cell exhaustion that is imprinted like a chronic scar in HCV-specific CD8+ T cells even after HCV cure, highlighting the requirement of re-programming to elicit full effector potential of exhausted T cells.

  Dataset EGAD00001006259

• Whole Exome Sequencing of Congenital Diaphragmatic Hernia Patients and Trios

  Congenital diaphragmatic hernia (CDH) is a common and severe birth defect characterized by structural defects of the diaphragm and by pulmonary hypoplasia. Congenital diaphragmatic hernia patient may present either as an isolated phenotype or together with other congenital anomalies in a complex phenotype. Despite the clinical significance of CDH, the underlying genetic and developmental pathways are incompletely understood. In order to establish a catalog of human genetic variation for this condition, we performed whole exome sequencing (WES) on 275 carefully phenotyped individuals with CDH in the Pediatric Surgical Research Laboratories at the Massachusetts General Hospital (Boston, MA, USA) and Boston Children's Hospital (Boston, MA, USA). The exome data generated are valuable for comparison of candidate genes derived from WES of other CDH cohorts or affected kindreds, and to provide ideal candidates for further functional studies with the ultimate goal of enhancing our understanding of the heterogeneous and, possibly, oligogenic molecular etiology of CDH. While familial clustering has been reported in rare kindreds, the majority of probands with CDH have no family history of CDH leading to the hypothesis that de novo variants are an important and relatively frequent etiological mechanism. In the second version of the study, we performed WES analysis on 87 trios, to assess the contribution of de novo mutations in the etiology of diaphragmatic and pulmonary defects and to identify previously unknown candidate genes. This dbGaP submission includes WES data on: (a) 20 new probands, (b) 9 probands also reported in the previous version (dbGaP accession no. phs000783.v1.p1), (c) 174 unaffected parents, including the parents of 67 previously reported probands. Combined analysis with other available cohorts of congenital diaphragmatic hernia revealed a genome-wide enrichment of likely gene-disrupting de novo variants (i.e., nonsense, frameshift or splice site), and missense de novo variants predicted in silico to be damaging.

  Study phs000783

• A Genome-Wide Association Study of Sporadic ALS in an Irish Population (SIALS) and Sequencing and Analysis of an Irish Human Genome

  Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by loss of motor neurons. Epidemiological studies indicate that 2-5% of cases are familial, usually with an autosomal dominant pattern of inheritance. For a portion of these, causative genes have been identified. The remaining 95% of ALS cases are described as sporadic, and believed to result from a combination of genetic and environmental factors. This study utilized samples from Irish patients with sporadic ALS and Irish control individuals. The aim of the study was to identify susceptibility alleles for ALS in the Irish population. Genome-wide, single-nucleotide polymorphism (SNP) genotyping was undertaken using Illumina 550K version 3 chips. Genotyping data are available on 221 SALS patients and 211 controls. Future work will aim to examine ALS genetic risk at individual sequence level. As an initial step, as of September 2010 we have made available the human genome sequence from a single anonymous male control individual from the SIALS collection. Recent studies generating complete human sequences from Asian, African and European subgroupings have revealed population specific variation and illuminated disease susceptibility loci. The Irish population is of interest because of its location at the periphery of Europe and also the impact its emigrants have had on the genetics of populations in a number of other countries. We have identified sequence variants that may be specific to this population and have identified potentially novel disease associated variants. We describe a novel method for improving SNP calling accuracy at low genome coverage using haplotype information. Overall we demonstrate utility in generating whole genome sequences to test general principles and reveal specific instances of human biology. With increasing access to low cost sequencing we would predict that similar studies will emerge.

  Study phs000127

• CDK4/6 inhibition in advanced chordoma: final results of the NCT PMO-1601

  Background. Chordoma is a rare bone tumor with no approved systemic therapy. Inactivation of the tumor suppressor p16 (encoded by CDKN2A) leads to aberrant cyclin D-CDK4/6-RB pathway activity, which can be inhibited by palbociclib in chordoma cell lines. Patients and Methods. We conducted a phase II single-arm, open-labeled trial on palbociclib in adult patients with advanced chordomas with p16 loss (by immunohistochemistry) or CDKN2A loss (by genomic analysis) and CDK4/6 and RB1 presence (by immunohistochemistry or RNA-Sequencing). Patients received 125mg palbociclib once daily for 21 days in a 28-day cycle. The study used a Simon optimal 2-stage design. The primary endpoint was disease control rate after six cycles. Response was evaluated according to Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. Secondary endpoints included progression-free survival, overall survival, and biomarker analysis. The study was considered positive if 25% of patients reached the primary endpoint. Results. Between December 2017 and April 2023, 28 patients (median age 60 years, range 31-84) were enrolled. After a median follow-up of 28 months, the disease control rate was 39%, with 11 out of 28 patients achieving stable disease. No objective responses were observed. The median progression-free survival was 5.6 months, and the median overall survival was 24.6 months. Treatment was well tolerated without new safety signals. CDKN2A deletions were the most common genomic alterations underlying p16 loss and were accompanied by MTAP co-deletion. There was no correlation between responder phenotypes by immunohistochemistry and outcome. RNA-based analysis showed heterogeneous pathways deregulation at study entry suggesting underlying primary resistance mechanisms Conclusion. This trial met its primary endpoint with a 39% disease control rate, though antitumor activity was modest. Molecularly tailored combination therapies should be considered to improve efficacy. (ClinicalTrials.gov number, NCT03110744).

  Study EGAS00001007985

• Drug development for pediatric cancers: the importance of patient' s genomic data re-use

  Cancer in children is uncommon and the overall prognosis for most pediatric cancers is good. However, while the combined survival rates have improved over the last decades, certain childhood malignancies, such as high-grade gliomas or metastatic sarcomas, still remain incurable in most patients. New strategies for targeting those devastating diseases are imperative, and patient genomic data can become a key asset in the process [1]. The Pediatric Cancer Genome Project’s datasets At EGA, we store over 500 datasets with sequencing data from pediatric cancer patients. A remarkable case is the datasets belonging to the Pediatric Cancer Genome Project (PCGP) from St. Jude Children’s Research Hospital–Washington University (Table 1 - EGAS list). PCGP is an ambitious effort to identify the mutations that drive childhood cancer and find new cures. Those datasets include 600 patients with complete tumor and normal genomes from 15 different tumor types. PCGP datasets have been an important resource for various studies that pooled genomic data from different public and in-house datasets to perform extensive genomic characterizations and publish comprehensive pan-cancer pediatric studies [2],[3]. Other interesting pediatric cancer datasets are from the Pediatric Brain Tumor Consortium, Sickkids or ICGC PedBrain project. How data reuse impacts drug development for pediatric cancers Recently, in October 2024, Nature Communications published a work led by the University of Michigan where authors used data from EGA (PCGP) check the table below as part of their strategy to pursue the identification of new tumor vulnerabilities susceptible to becoming novel therapeutic targets in diffuse midline glioma. Diffuse midline gliomas (DMG) are treatment-resistant and uniformly fatal pediatric brain tumors. The prognosis of this brainstem tumor is dismal with a median overall survival of 9–12 months from diagnosis. In this study, the authors first developed a lab model of the disease and reanalyzed data from the EGA to identify genes potentially linked to tumor severity. This approach revealed involvement of specific metabolic pathways, suggesting new possibilities for therapeutic intervention. In mice, the study shows that the use of statins can improve survival. For a brief overview of the underlying science, diffuse midline gliomas present intratumor heterogeneity with subpopulations of less-differentiated oligodendrocyte precursors and more differentiated astrocytes. Authors established in vitro models to recapitulate both phenotypes and identify metabolic programs in both subpopulations. To determine the clinical relevance, the authors re-used gene expression data from 76 DMG patients identifying a gene signature predicting decreased overall survival. After extensive metabolic characterization of subpopulations, authors defined strategies to target specific metabolic vulnerabilities. Pre-clinical experiments in mice using OXPHOS inhibitors and statins showed a reduction in tumor burden and increased overall survival [4]. In this scenario, the re-use of high-quality patient’s sequencing data appeared as an advantageous strategy to support the clinical relevance of in-vitro and pre-clinical findings. On the other hand, in rare conditions such as childhood cancer, the availability of public datasets and the possibility of pooling data from different sources can be the only way to obtain impactful results that lead to improvements for patients. References Davidoff, A. M. Pediatric Oncology. Seminars in pediatric surgery 19, 225–233 (2010). Gröbner, S. N. et al. The landscape of genomic alterations across childhood cancers. Nature 555, 321–327 (2018). Venu Thatikonda et al. Comprehensive analysis of mutational signatures reveals distinct patterns and molecular processes across 27 pediatric cancers. Nature Cancer 4, 276–289 (2023). Mbah, N. E. et al. Therapeutic targeting of differentiation-state dependent metabolic vulnerabilities in diffuse midline glioma. Nature Communications 15, (2024). Datasets and Studies ID Title Access Policy Year EGAD00001000134 Driver mutations in histone H3.3 and chromatin remodelling genes in paediatric glioblastoma McGill-DKFZ Pediatric Brain Tumour Consortium 2016 EGAS0000100192 Somatic Histone H3 Mutations in Diffuse Intrinsic Pontine Gliomas and Non-Brainstem Paediatric Glioblastomas St. Jude Children's Research Hospital - Washington University Pediatric Cancer Genome Project 2017 EGAS00001000575 Whole genome sequencing and whole exome sequencing of DIPG tumors and matched normal tissue The hospital for sick children ("SickKids") 2016 EGAD00001000792 Exome sequencing reads of paediatric glioblastoma McGill-DKFZ Pediatric Brain Tumour Consortium 2016 EGAD00001002006 Whole genome sequencing of paediatric glioblastoma in the ICGC PedBrain project ICGC PedBrain project 2016

  Blog drug-development-for-pediatric-cancers

• Genetic Epidemiology Network of Arteriopathy (GENOA)

  The Genetic Epidemiology Network of Arteriopathy (GENOA): GENOA is one of four research networks that form the NHLBI Family Blood Pressure Program (FBPP). From its inception in 1995, GENOA's long-term objective was to elucidate the genetics of hypertension and its arteriosclerotic target-organ damage, including both atherosclerotic (macrovascular) and arteriolosclerotic (microvascular) complications involving the heart, brain, kidneys, and peripheral arteries. Two GENOA cohorts were originally ascertained (1995-2000) through sibships in which at least 2 siblings had essential hypertension diagnosed prior to age 60 years. All siblings in the sibship were invited to participate, both normotensive and hypertensive. These include non-Hispanic White Americans from Rochester, MN (n =1583 at the 1st exam) and African Americans from Jackson, MS (N=1854 at the 1st exam). During the second exam (2000-2005), approximately 80% of participants were re-recruited. The GENOA data consists of biological samples (DNA, serum, urine) as well as demographic, anthropometric, environmental, clinical, biochemical, physiological, and genetic data for understanding the genetic predictors of diseases of the heart, brain, kidney, and peripheral arteries. Family Blood Pressure Program (FBPP): GENOA's parent program, the FBPP, is an unprecedented collaboration to identify genes influencing blood pressure (BP) levels, hypertension, and its target-organ damage. This program has conducted over 21,000 physical examinations, assembled a shared database of several hundred BP and hypertension-related phenotypic measurements, completed genome-wide linkage analyses for BP, hypertension, and hypertension associated risk factors and complications, and published over 130 manuscripts on program findings. The FBPP emerged from what was initially funded as four independent networks of investigators (HyperGEN, GenNet, SAPPHIRe and GENOA) competing to identify genetic determinants of hypertension in multiple ethnic groups. Realizing the greater likelihood of success through collaboration, the investigators began working together during the first funding cycle (1995-2000) and formalized this arrangement in the second cycle (2000-2005), creating a single confederation with program-wide and network-specific goals.

  Study phs000379

• WES for 42 patients with pleural mesothelioma (Not matched)

  The dataset contains WES data (bam files) for 42 pleural mesothelioma samples. Nextera Flex for Enrichment solution (Illumina, San Diego, CA) in combination with SureSelect Human All Exon V7 probes (Agilent, Santa Clara, CA) was used for library preparation and generated libraries were sequenced on NovaSeq 6000 (Illumina, San Diego, CA) in 150 pair-end mode. Quality control of the raw data was conducted using fastQC (v. 0.11.8) (https://www.bioinformatics. babraham.ac.uk/projects/fastqc/). WES data were then analyzed with the DRAGEN Bio IT platform 49 (version 4.2.4) and the human GRCh38 as reference genome.

  Dataset EGAD50000002127

• WES for 45 patients with pleural mesothelioma (Matched)

  The dataset contains WES data (bam files) for 45 pleural mesothelioma samples. Nextera Flex for Enrichment solution (Illumina, San Diego, CA) in combination with SureSelect Human All Exon V7 probes (Agilent, Santa Clara, CA) was used for library preparation and generated libraries were sequenced on NovaSeq 6000 (Illumina, San Diego, CA) in 150 pair-end mode. Quality control of the raw data was conducted using fastQC (v. 0.11.8) (https://www.bioinformatics. babraham.ac.uk/projects/fastqc/). WES data were then analyzed with the DRAGEN Bio IT platform 49 (version 4.2.4) and the human GRCh38 as reference genome.

  Dataset EGAD50000002128

• Whole-exome ultra-high throughput sequencing in brain samples of suicide victims who had suffered from major depressive disorder and control subjects who had died from other causes

  Whole exome NGS data of 21 sucide victims and 23 control patients sequenced on Illumina HiSeq 2000 platform using the Agilent SureSelect Human All Exon + UTRs V5 target enrichment kit. The dataset contains the paired-end unfiltered FASTQ files, the GRCh37 (b37) aligned BAM files mapped by the BWA MEM algorithm, and the variant files in VCF 4.1 format called with the GATK HaploType caller (version 3.3).

  Dataset EGAD00001004184

• WTS and WES of Renal Cell Carcinoma Tumors From a Phase III Anti-Angiogenic Adjuvant Therapy Trial

  Archival de-identified formalin-fixed paraffin-embedded RCC tumor tissue blocks from nephrectomy or tumor biopsy were processed as per below and the same sections were used for both DNA and RNA extractions. For WES (ACE version 3; Illumina NovaSeq), samples were profiled using Personalis ACE Cancer Exome (Personalis, Inc, Menlo Park, CA) Whole-transcriptome profiles were generated by RNA-seq (Accuracy and Content Enhanced (ACE) version 3; Illumina NovaSeq) using Personalis ACE Cancer Transcriptome (Personalis, Inc, Menlo Park, CA ) Of the 615 patients in the intent-to-treat population in S-TRAC trial, 193 individual specimens were available for molecular profiling, of which 171 (27.8%) (sunitinib, n = 91; placebo, n = 80) returned results for the WES analysis, and 133 (21.6%) (sunitinib, n = 72; placebo, n = 61) returned results for the GES analysis. Of the 138 WTS samples with data, replicates for two patients were summarized by median expression, and three samples were excluded from the final analysis due to low counts.

  Dataset EGAD00001009294

• Transcriptome of CD4+ T cells and CD8+ T cells in glioblastoma multiforme

  Patients included in this study were over 18 years of age and had a histology-confirmed diagnosis of glioblastoma multiforme (GBM). Exclusion criteria were the previous administration of any anti-tumor therapy including radiation therapy. All patients gave written informed consent. The study was approved by the local ethics committee (TUM Medical school) and conducted following the Declaration of Helsinki. During resection of the tumors, tumor tissue and tissue from normal appearing brain within the operative channel was collected. Blood was drawn during the surgical procedure. Single cell suspensions were prepared from the tumor tissue, the normal appearing brain, and the blood. CD4+ T cells and CD8+ T cells were sorted by flow cytometry. Only patients with a complete set of specimens (CD4+ tumor infiltrating lymphocytes (TIL), CD8+ TIL, CD4+ T cells from normal appearing brain, CD8+ T cells from normal appearing brain, blood-derived CD4+ and CD8+ T cells) containing a minimum of 1000 cells in each sorted sample were further analyzed (n=9). Total RNA was isolated from sorted cell populations using the RNAeasy Plus micro kit (Qiagen, 74034). Quality and integrity of total RNA was controlled on a Bioanalyzer 2100 (Agilent Technologies). Library preparation for bulk-sequencing of poly(A)-RNA was done as described previously (Parekh et al., 2016). Briefly, barcoded cDNA of each sample was generated with a Maxima RT polymerase (ThermoFisher Scientific, EP0742) using oligo-dT primer containing barcodes, unique molecular identifiers (UMIs) and an adaptor. Ends of the cDNAs were extended by a template switch oligo (TSO) and full-length cDNA was amplified with primers binding to the TSO-site and the adaptor. NEB UltraII FS kit was used to fragment cDNA. After end repair and A-tailing, a TruSeq adapter was ligated and 3'-end-fragments were finally amplified using primers with Illumina P5 and P7 overhangs. In comparison to previous descriptions (Parekh et al., 2016), the P5 and P7 sites were exchanged to allow sequencing of the cDNA in read 1 and barcodes and UMIs in read 2 to achieve a better cluster recognition. The library was sequenced on a NextSeq 500 (Illumina) with 59 cycles for the cDNA in read 1 and 16 cycles for the barcodes and UMIs in read 2. Data were processed using the published Drop-seq pipeline (v1.0) to generate sample- and gene-wise UMI tables (Macosko et al., 2015). Reference genome (GRCh38) was used for alignment. Transcript and gene definitions were used according to the Genecode Annotation Version 35.

  Study EGAS50000000156

• Whole genome sequencing

  This dataset contains whole genome sequencing data from 22 samples of FACS-purified bone marrow CD34+ haematopoietic stem and progenitor cells and matched hair follicle controls collected from individuals undergoing hip replacement surgery. Additionally, it contains whole genome sequencing data from unseparated bone marrow mononuclear cells, CD3-CD34- bone marrow mononuclear cells, and peripheral blood granulocytes of the same 14 samples. Haematopoietic samples were sequenced to a target coverage of 90-120x; 19 of them were re-sequenced to a total target coverage of 270x. Hair follicle controls were sequenced to a target coverage of 30x.

  Dataset EGAD00001015178

• Single-cell RNA sequencing of 16 NPM1 mutated AML patients

  NPM1 mutated AML is the largest class in WHO classification, however several subtypes within NPM1 were already reported. Here, we sequence 16 NPM1 mutated AML samples with single-cell RNA-sequencing and multi-color flow cytometry from three major NPM1 subtypes we previously identified, totaling more than half a million cells. We show that the differences among three NPM1 subtypes are mainly driven by cell composition, and these differences are also reflected at cell surface protein level. With the re-analysis of CITE-seq data from another cohort and single cell variant calling on our data, we also report two distinct stem cell phenotypes: one with leukemic (CD34dimCD38-CD45RA+) and another with pre-leukemic (CD34+CD38-CD45RA-) origin. We believe that our data and findings provide novel insights regarding the etiology of NPM1 mutated AML.

  Study EGAS50000000332

• Longitudinal single-cell transcriptomic study in patient-derived xenografts of pediatric T-ALL

  In this study, we conducted single cell full length total RNA sequencing (VASA-seq) on 13 matched pediatric T-ALL PDX samples at initial diagnosis and relapse, along with 5 non-relapsing PDX samples collected at initial diagnosis. We identified a subpopulation of T-ALL cells with stem-like cell features that expands substantially at relapse indicating resistance to first-line therapy. Chemotherapy resistance was further validated through functional testing: Two samples were subjected to in-vitro drug testing, treated with Cytarabine for three days, followed by single-cell transcriptomic analysis using 10x Genomics. Additionally, in-vivo drug testing was conducted on one sample, involving re-engraftment into mice and treatment with a combination of Vincristine, Doxorubicin, and Dexamethasone. Single-cell transcriptomic analysis using 10x Genomics was performed after 0, 15 and 30 days of treatment.

  Study EGAS50000000582

• PanProstate Cancer Group UK data

  THE PAN PROSTATE CANCER PROJECT: Many groups around the world have generated Whole Genome DNA Sequence (WGS) data from prostate cancer patients. The pan prostate group have gathered with the idea that the accumulated data, should be collected and compared in a common format including common storage, re-analysis through a single pipeline, and investigation to achieve a variety of scientific goals. The combined collection would include the following categories: (i) cancers from different ethnic groups: eg Caucasian, Asian, Black Caribbean; (ii) cancer from different stages of progression from normal, to organ-confined disease, to metastases; (iii) early-onset prostate cancer; (iv) prostate cancer from aggressive and indolent disease; and (v) prostate cancer patients managed by different treatments with information linked to long-term clinical follow up data in many cases.This study contains the initial pilot samples (train 1) from the CRUK-ICGC prostate group.

  Study EGAS00001002876

• Acquisition_of_additional_mutations_drives_accelerated_progression_of_NPM1_positive_CMML_to_AML_

  This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/We performed exome sequencing on serial samples from a patient with CMML who progressed to AML. The exome sequencing suggests that NPM1, TET2 and DNMT3a mutations were present in the dominant clone in the CMML sample and that NRAS is a new subclonal mutation in the AML sample. Diagnostic data shows the presence of a FLT3-ITD mutation in the AML sample, which is likely to have driven progression. Here we are performing re-sequencing of the putative driver and some passenger mutations which appear to be in the same clone to validate these mutations and to verify the relative quantification of these abnormalities .

  Study EGAS00001000619

• High coverage target resequencing of coding and regulatory regions of 38 Parkinson disease genes associated either to the Mendelian or the sporadic forms of the disease

  Coding sequences and putative regulatory sequences (retrieved from Ensembl and including up to 2500 bp of promoter sequence upstream the transcription start site) were re-sequenced to a mean depth of 49X on 38 genes associated to Parkinson's disease (RAB25, NUCKS1, RAB7L1, GBA, SYT11, ACMSD, STK39, MCCC1, STBD1, GAK, DGKQ, BST1, SCARB2, HLA-DRB5, GPNMB, FGF20, ITGA8, HIP1R, STX1B, SETD1A, SREBF1, MED13, RAI1, MAPT, RIT2, GIGYF2, HTRA2, EIF4G1, SNCA, LRRK2, VPS35, PINK1, DJ1, ATP13A2, UCHL1, PARK2, FBXO7, and PLA2G6). Sequencing was performed on 249 Parkinson's idiopathic cases and 145 unrelated controls of Spanish origin. All sequencing data was generated with an Illumina Hiseq2000 instrument after enrichment with a custom NimbleGen array. Raw reads were mapped to human reference genome hg19/GRCh37 using BWA aligner.

  Study EGAS00001000973

• Detecting and quantifying clonal selection in somatic mosaicism

  In this study, we introduce a scalable method to identify clonal selection in an individual tissue, agnostic of the underlying mutations. To study clonal selection in the haematopoietic system, we performed whole genome sequencing on bone marrow CD34+ haematopoietic stem and progenitor cells from 22 non-leukemic individuals, 30-89 years of age. We performed 90x whole genome sequencing for all samples and re-sequenced 19 samples with sufficient DNA to a total target coverage of 270x. Whole genome sequencing of hair follicle DNA from the same individuals was used as germline control. In addition, we sequenced unseparated bone marrow mononuclear cells, CD3-CD34- bone marrow mononuclear cells, and peripheral blood granulocytes from 14 samples. Samples were collected from individuals undergoing hip replacement surgery as part of the Mechanisms of Age-Related Clonal Haematopoiesis (MARCH) Study (REC Ref: 17/YH/0382).

  Study EGAS00001007558

• Integrative Single-Cell Analysis of Transcriptome, Epigenome, and Lineage in HIV Latency and Activation

  With the development of triple combination antiretroviral therapy, routine HIV treatment eliminates nearly all actively infected cells. Nevertheless, the small reservoir of latently infected cells, which can remain dormant for long periods of time before becoming active and producing new virus particles, represents a crucial barrier to completely curing the disease. Identifying markers that identify latently infected cells or the biochemical factors that control latency activation could enable the effective use of a “shock and kill” strategy, where specific targeting or activation of latently infected cells eliminates the viral reservoir. Our recent work suggests that the global transcriptomic and epigenomic changes during hematopoietic differentiation affect viral latency and activation. Additionally, we recently found that global inhibition of histone deacetylase activity increases viral activation in these cells, further implicating epigenomic changes during activation. These results raise fundamental questions: What are the markers of latently infected cells? How do the transcriptomic and epigenomic states of a cell affect latency and activation? How does the differentiation state relate to viral latency? Here, we leverage our experimental platform for identifying latently and actively infected cells, single-cell transcriptome and epigenome sequencing, and our recently developed computational integration methods to investigate these questions. Our interdisciplinary team combines expertise in HIV basic science, HIV clinical treatment, and bioinformatics to develop an experimental and computational framework for integrated gene expression, chromatin accessibility, and lineage into a single picture of viral latency and activation. Specifically, this project will (1) use single-cell RNA-seq and single-cell ATAC-seq to map the diversity of infected cells, (2) investigate the relationship between hematopoietic differentiation state and viral activation, (3) determine viral integration sites through single-cell RNA-seq, (4) computationally integrate single-cell transcriptome and epigenome profiles, and (5) computationally infer cell lineage relationships among viral genomes and infected cells. To accomplish these goals, we will carry out the following aims: (1) Characterize lineage, transcriptomic and epigenomic diversity of single latently and actively infected primary cells. (2) Investigate latency and activation during in vitro differentiation. (3) Survey single cell diversity of re-activated and in vitro infected cells from cART-suppressed patients. Together, these aims will produce a comprehensive, integrated transcriptomic and epigenomic atlas of the HIV reservoir, identify DNA and RNA biomarkers of latency, and characterize clonal expansion patterns. Our work also develops a broadly applicable experimental and computational framework, laying a foundation for the discovery of novel insights into HIV latency and activation.

  Study phs002915

• Environmental Arsenic and Diabetes Mellitus (Chihuahua Cohort)

  The Chihuahua Cohort was established as part of a larger NIEHS-funded project that used laboratory models (tissue culture and mice) and human subjects to examine the association between exposure to arsenic and diabetes, including identification of mechanisms underlying this association. All procedures involving human subjects were approved by institutional review boards at the University of North Carolina at Chapel Hill and Cinvestav-IPN (Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Mexico City, Mexico). All participants provided signed informed consent. A total of 1,160 adults (≥ 18 years old) with a minimum 5-year uninterrupted residency in the Chihuahua study area were recruited between 2008 and 2012. Samples of drinking water were obtained from the participants' households. Data on residency, occupation, drinking-water sources and use, smoking, alcohol consumption, and medical history were recorded by a study questionnaire. Spot urine and fasting venous blood were collected during medical examination that included an oral glucose tolerance test (OGTT) with blood drawn 2 hr after a 75-g glucose dose. Plasma from both fasting and 2-hr blood samples was collected. Body mass index (BMI) of each participant was calculated from body weight and height. BMI cutoffs of ≥ 25.0, ≥ 30, and < 18.5 kg/m2 were used to define overweight, obese, and underweight individuals, respectively. Participants' waist circumference was also measured. Blood pressure was assessed using a manual sphygmomanometer. Three measurements were taken at intervals of at least 1 min, with a 5-min rest before obtaining the first reading; the mean of the last two measurements was used. The concentrations of arsenic in drinking water and arsenic species in urine were measured by hydride generation-atomic absorption spectrometry. The limit of detection for As in water as well as for As species in urine was 0.01 µg As/L. Creatinine concentration in urine was determined using an established colorimetric assay. DNA was isolated from blood for genotyping analysis.The following measures were taken: glucose concentration in fasting blood (FBG) and in blood collected 2 hours into OGTT (2HBG); concentrations of triglycerides, HDL and total cholesterol (TC) in fasting plasma; and LDL was calculated using the Friedewald equation. Diabetes was classified by FPG ≥ 126 mg/dL, 2HPG ≥ 200 mg/dL, or self-reported diabetes diagnosis or medication use (WHO/International Diabetes Federation 2006). Prediabetes was defined as the absence of diabetes with FPG ≥ 110 mg/ dL or 2HPG ≥ 140 mg/dL. Elevated fasting levels of each lipid were defined as plasma TG ≥ 150 mg/dL, TC ≥ 200 mg/dL, and LDL ≥ 130 mg/dL. Fasting HDL < 40 mg/dL in men and < 50 mg/dL in women were designated as low. Hypertension was defined by systolic blood pressure (SBP) > 140 mm Hg, diastolic blood pressure (DBP) > 90 mmHg, or self-reported use of anti-hypertensive medication. Genotyping analysis, which will be available in version 2 of this study, was carried out in a subset of ~600 subjects with focus on enzymes involved in the metabolism of arsenic, including AS3MT, GSTs.All data collected during the medical exams or generated by the above-described chemical and biochemical analysis of water, urine and blood/plasma, as well as results of the genotyping analysis are included in dbGaP. Data generated by metabolomics and epigenetic analyses in small subsets of the Chihuahua cohort are not included, but are available in published articles.

  Study phs002139

• Asthma in the Lives of Families Today (ALOFT)

  Social interactions and the overall psychosocial environment have a demonstrated impact on health, particularly for people living in disadvantaged urban areas. Here we investigated the effect of psychosocial experiences on gene expression in peripheral blood immune cells of 251 children with asthma in Metro Detroit. Participants were included from an ongoing longitudinal study Asthma in the Lives of Families Today (ALOFT; recruited from November 2010-July 2018. The ALOFT project was established to identify the behavioral and biological pathways through which family social environments impact youth with asthma.In version 1 we used RNA-sequencing and a new machine learning approach to identify transcriptional signatures of 19 variables including psychosocial factors, blood cell composition and asthma symptoms. Using longitudinal data collected from a subset of the participants we showed that transcriptional signatures track the longitudinal changes in measured phenotypes. Importantly, we found 169 genes associated with asthma that are regulated by psychosocial factors, and 344 significant gene-environment interactions for gene expression levels. These results demonstrate that immune gene expression mediates the link between negative psychosocial experiences and asthma risk.In version 2, we focused on pubertal development. We identified substantial gene expression changes associated with age and pubertal development. We showed that genetic effects on gene expression change dynamically during pubertal development. Gene expression changes during puberty are correlated with gene expression changes associated with asthma and may explain sex differences in prevalence. Our results show that molecular data used to study the genetics of early onset diseases should consider pubertal development as an important factor that modifies the transcriptome.Version 3 uses single cell RNA-seq to study the dynamics of the transcriptional response to glucocorticoids in activated Peripheral Blood Mononuclear Cells from 96 African American children. We employed novel statistical approaches to calculate a mean-independent measure of gene expression variability and a measure of transcriptional response pseudotime. Using these approaches, we demonstrated that glucocorticoids reverse the effects of immune stimulation on both gene expression mean and variability. Our novel measure of gene expression response dynamics, based on the diagonal linear discriminant analysis, separated individual cells by response status on the basis of their transcriptional profiles and allowed us to identify different dynamic patterns of gene expression along the response pseudotime. We identified genetic variants regulating gene expression mean and variability, including treatment-specific effects, and demonstrated widespread genetic regulation of the transcriptional dynamics of the gene expression response.Version 4 adds scATAC-seq in activated peripheral blood mononuclear cells (PBMC) from 16 children with asthma with phytohemagglutinin (PHA) or lipopolysaccharide (LPS), and treated with dexamethasone (DEX), an anti-inflammatory glucocorticoid. We analyzed changes in chromatin accessibility, measured transcription factor motif activity, and identified treatment and cell-type specific transcription factors that drive changes in both gene expression mean and variability. We observed strong positive linear dependence between motif response and their target gene expression changes, but negative in variability changes. This result suggests that an increase of transcription factor binding tightens the variability of gene expression around the mean. We then annotated genetic variants in chromatin accessibility peaks and response motifs followed by computational fine-mapping of eQTL signals from a pediatric asthma cohort. We found that eQTLs were 5-fold enriched in peaks with response motifs and refined the credible set for 410 asthma risk genes, with 191 having the causal variant in response motifs. These results enhance the understanding of molecular mechanisms for asthma risk variants mediated by gene expression.

  Study phs002182

• Submitter Portal

  Submitter Portal The Submitter Portal does not support array submissions! Please refer to the array documentationAny new objects registered in the Submitter Portal will not be available for use in an array submission For further information please check our Submission FAQs, submission quickguide as well as submission terms! The metadata submission process can be difficult and time-consuming. For this reason, the EGA has developed the Submitter Portal, a tool that was created to offer a simplified and user-friendly method of registering metadata. Our portal provides features that are intended to make the input of metadata easier, ensuring that your data is registered correctly and effectively. Our page is divided into logical sections and includes a helpful video instruction to further assist you as you complete the submission process. With the Submitter Portal, you can rest assured that the submission of your data is in good hands. Submitter Portal Index Previous steps Access to Submitter Portal Start a Submission Take The Tour Tutorial Previous steps Create your EGA account To submit data, you first need to create your EGA user. Then, once your account has been verified by the Help Desk team, you will be able to request a submitter role and send the EGA Data Processing Agreement (DPA). In the case you already have an EGA account, or an ega-box (submission account), you can skip this step. Upload your files Please note that all your files must be encrypted using the Crypt4GH tool before upload them.As soon as you are assigned with a submitter role and added your public key to your profile, you will be able to connect to the EGA inbox and upload your files. Understand the EGA metadata schema It's crucial to comprehend the EGA metadata schema, a set of rules that specify how data is organised, described, and shared inside the EGA, in order to get the most out of this resource. Learn all about EGA metadata schema! Register your DAC and policy There are two objects in the EGA metadata schema that are registered in a separate portal. All DACs and policy objects are registered using the DAC Portal, a tool developed by EGA to help data controllers manage their data stored at the EGA. You can find relevant information in the DAC Portal Guide. Access to Submitter Portal 1. Request Submitter Role Once you have created your EGA user, request a Submitter Role and complete relevant information such as submission type and size. 2. Signature of the Data Processing Agreement The Data Processing Agreement (DPA) signed by CRG and EMBL-EBI (Joint Data Processors for the EGA) can be downloaded to the submitter for its signature. Further information related to the DPA can be in the Privacy Notice section. The DPA should be signed by a legal representative of the submitter with power of attorney/appointment duly authorised to sign contracts on behalf of the institution. The submitter will return a fully-executed copy of the DPA to us. Please note that due to the high volume of submissions to EGA the DPA is non-negotiable. Once you have completed all the information and attached the DPA signed, send your Submitter request and the EGA Helpdesk Team will validate it and send a confirmation to start submitting your metadata. Start a Submission The steps below will show you how to start a submission and are accompanied with a tutorial and the Take the tour of Submitter Portal. 1. Login Go to the Submitter Portal interface and log in using your EGA account credentials. 2. Create a submission The Submitter Portal interface will show you any already existing open submission. To start a new submission click on “Create a Submission”.In this phase you have the option to add any collaborator(s) as well as their permissions (“Read only” or “Read & Write”). If you want to invite all the collaborators from a previous submission, you can add them by selecting said submission.In the event that the submission has been closed, you will need to reopen it. To do this, you will need to modify one of the objects in the submission, for example, copy and paste the title. Once the submission is open, you can add a collaborator. Further information can be found in the Submission Tour 3. Create Study Click on add Study and complete the form with the relevant information. The fields with an asterisk (*) are mandatory. Further information can be found in the Submission Tour4. Create Sample Sample registration can be done either individually or in batch by using a template. Batch upload: download the template and complete it with information about the sequencing samples. Upload the samples.csv and the sample will be created.Add single sample: complete the mandatory fields with the metadata information of your sample.  Further information can be found in the Submission Tour5. Create Experiment Register an experiment with ​information about the sequencing methods, protocols and machines. Experiments generate the connection between samples and study. Further information can be found in the Submission Tour6. Create Run Samples, experiments and files are linked through runs. To register a run, you will be asked to select the appropriate experiment and the file format. Run registration can be done either individually or in batch by using a template. Batch upload: download the template and complete it with the sample - file linkage. Upload the run.csv and the runs will register.As the INBOX is a directory, bear in mind to include the file path (/). Example: /filename.c4gh. Add single run: select the file format, sample and experiment from the drop box. For re-using submitted samples, please use the EGA accession ID (EGANXXX). Please note that files must be uploaded in your inbox before linking your files and samples. Further information can be found in the Submission Tour7. Create Analysis Analysis should be only used for BAM/BAI or CRAM/CRAI pair, VCF and phenotype linkage to samples. The analysis is an EGA specific metadata object that links samples to files. This object also stores some metadata about your experiments, such as the experiment type, genome reference, or the platform used. **If only BAM or CRAM alignment files are submitted but not the original unaligned FASTQ files, then please make sure that the BAM or CRAM files also contain the unaligned reads. This is critical to enable primary re-analysis and re-alignment of the dataset using new tools or future genome assemblies.** Start by selecting the samples to be linked to the file, and then populate the required attribute fields. Please be aware of the existence of mandatory fields that must be populated.When populating the chromosome field (mandatory), please select the chromosomes from the checkbox. Please take in consideration that EGA allows for the re-use of any registered metadata. Therefore, a previously registered study or samples can be referenced for the analysis in the current data submission.Further information can be found in the Submission Tour 8. Create Dataset Dataset contains the collection of runs/analysis data files subjected to controlled access by being associated with a Policy. Complete the mandatory fields such as title, description, type and policy. Please, take in consideration that DAC and Policy must be registered in advance in the DAC Portal. Similar to other metadata objects, you can re-use previous runs or analyses, using their EGA accessions IDs (runs - EGAR, analyses - EGAZ). Further information can be found in the Submission Tour 9. Finalise submission Once the files are ingested please delete them from your SFTP INBOX before finalising your submission. This will prevent the files from reappearing in your Submitter Portal Inbox. Once you have completed your submission, click on the Finalise button, and schedule an estimated release date. Afterwards, your submission will be sent to Helpdesk for approval. Please note that once the Helpdesk has approved your submission, you will receive the identifiers for your data. See the Frequently Asked Questions (FAQ) for more information. 10. Update closed submission If you want to append more file to a closed submission, you will need to re-open the submission where the dataset was registered. Find the Submission: go to the Submitter Portal Menu -> Datasets section and find the submission id (EGA0XXX).  Reopen the submission where the dataset is located: to reopen a submission, you must edit any object within the submission. For example, you can copy and paste the title. Take The Tour The Submitter Portal Take The Tour is a visual guidance of all the steps described previously about how to start a submission. Tutorial In this tutorial we will show you how to use the Submitter Portal to register your metadata. Please note the video focuses on the run-based submission (for raw files - fastq - and aligned data - BAM/CRAM) and analysis-based submission (for your BAM/BAI pairs, variation -VCF - and phenotype files).

  Documentation submission/metadata/submission/sequencing-phenotype/submitter-portal

• Transcriptome sequencing of hepatocellular carcinoma biopsies

  RNA-sequencing of 122 hepatocellular carcinoma biopsies and 15 normal liver biopsies. RNA-seq library prep was performed with 200 ng total RNA using the TruSeq Stranded Total RNA Library Prep Kit with Ribo-Zero Gold (Illumina) according to manufacturer’s specifications. Single-end 126-bp sequencing was performed on an Illumina HiSeq 2500 using v4 SBS chemistry at the Genomics Facility Basel according to the manufacturer’s guidelines. Primary data analysis was performed with the Illumina RTA version 1.18.66.3.

  Dataset EGAD00001008205

• Tumor and normal exomes for 95 PMBCL cases

  Whole-exome sequencing for 95 PMBCL cases (including 21 with matching normal DNA) was performed using a targeted capture approach with the SureSelect Human All Exon V6+UTR bait (Agilent Technologies) followed by massively parallel sequencing of enriched fragments on the HiSeq2500 platform (Illumina). Five libraries were pooled per lane and a 125bp paired-end mode was used. Tumor and normal DNA samples were sequenced to an average of 115X (SD 24X). All reads were aligned to the human reference genome (hg19) using bwa-mem version 0.7.5a29 with optical and PCR duplicates removed using the Picard tool.

  Dataset EGAD00001005780

• AVENIO Expanded ctDNA panel sequence alignments (BAMs) from plasma DNA of BC, CRC and NSCLC patients

  The AVENIO ctDNA Expanded Kit is a next-generation sequencing (NGS) liquid biopsy assay with a 77 gene panel (192 kb) containing genes in U.S. National Comprehensive Cancer Network (NCCN) Guidelines and emerging cancer biomarkers. This pan-cancer assay was applied to 48 plasma samples from patients with breast, colorectal and non-small cell lung cancer. After sequencing 150bp paired-end, reads were aligned to the hg38 genome with the AVENIO Oncology Analysis Software (version 2.0). These files are the deduplicated alignments generated by the analysis software used for subsequent variant, indel and CNV calling.

  Dataset EGAD00001006301

• Amplicon based re-sequencing of multi-region PDAC samples

  Multi-region tumor samples were cut from frozen sections or FFPEs and reviewed from microdissection and H&E staining in order to select ones with high cellularity. DNA extraction was done using DNeasy Blood & Tissue Kits for frozen samples following the manufacturer’s guideline. Extracted DNAs were processed on an Illumina HiSeq 2500 in a paired end mode (100x100) using a custom targeted panel based on the list of all unique somatic mutations from the original WES data by the Integrated Genomics Operation (IGO) at Memorial Sloan Kettering Cancer Center (New York, NY).

  Dataset EGAD50000000354

• Cardiogenics_Custom_Pools - Agilent SureSelect

  The Cardiogenics re-sequencing study will consist of three parts: Eight pools of 25 individuals will be sequenced using a Nimblegen hybrid-capture solution specific to miRNA sequences, 80 pools of 25 individuals will be sequenced using a custom Agilent SureSelect array covering genes associated with coronary artery disease (CAD) and myocardial infarction (MI), 10 individuals from families with a history of CAD/MI will be exome sequenced using the Sanger exome array. The experiment will use the early onset patients from the German MI cohort and the UK BHF CAD/MI cohort both of which have strong family history. For controls we will consider individuals from the UKBS and KORA cohorts.

  Dataset EGAD00001000397

• Nimblegen

  The Cardiogenics re-sequencing study will consist of three parts: Eight pools of 25 individuals will be sequenced using a Nimblegen hybrid-capture solution specific to miRNA sequences, 80 pools of 25 individuals will be sequenced using a custom Agilent SureSelect array covering genes associated with coronary artery disease (CAD) and myocardial infarction (MI), 10 individuals from families with a history of CAD/MI will be exome sequenced using the Sanger exome array. The experiment will use the early onset patients from the German MI cohort and the UK BHF CAD/MI cohort both of which have strong family history. For controls we will consider individuals from the UKBS and KORA cohorts.

  Dataset EGAD00001000398

• Tam-Seq of HGSOC samples for fixative comparison study

  Targeted amplicon sequencing of samples as part of the study "Methanol-based fixation is superior to buffered formalin for next-generation sequencing of DNA from clinical cancer samples. The amplicon panel consists of 48 amplicons in TP53, PTEN, EGFR, PIK3CA, KRAS and BRAF genes as described previously [Forshew, STM 2012]. All libraries were pooled and quantify using DNA 1000 kit on Agilent 2100 Bioanalyzer and KAPA SYBR FAST ABI Prism qPCR Kit (KAPA Biosystems) on 7900HT Fast Real-Time PCR System (Applied Biosystems) according to the supplier's recommendations. Reads were aligned using bwa-mem v0.7.12-r1039 to the 1000 genomes version of human genome build GRCh37, retaining duplicate reads.

  Dataset EGAD00001001937

• A single-chain derivative of the integrin β1-activating TS2/16 antibody potentiates organoid growth in Matrigel and Collagen hydrogels.

  Culturing epithelial organoids from Adult Stem Cells (ASCs) requires interaction of their β1-integrins with extracellular matrix (ECM) components such as laminin or collagen. Matrigel and BME® (extracts from Engelbreth-Holm-Swarm tumor cells grown in the mouse abdomen) are a popular source of ECM proteins. Yet, these preparations are costly and subject to batch-to-batch variation due to animal-derived contaminations. Here, we describe a single chain version (scFv) of antibody TS2/16, allosterically activating integrin β1 function in organoids. Addition of 5 nM of monomeric scFv to organoid medium results in up to 5-fold higher yields of organoids in Matrigel/BME®. In addition, scTS2/16 generates 6-7-fold higher yields of intestinal organoids in collagen-I hydrogel, when grown both in 3D and in 2D. Collagen is well-defined, available at clinical grade, and in combination with scTS2/16, will enable the clinical use of gastrointestinal organoids.

  Study EGAS50000001113