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• RNA_Seq_in_Patients_with_Primordial_Dwarfism

  Transcriptome studies in patients with rare genetic diseases can potentially aid in theinterpretation of likely causal genetic variation through identification of altered transcriptabundance and/or structure. RNA-Seq is the most sensitive assay for both investigatingtranscript structure and abundanceThe primary aim of this pilot project is to investigate to what degree integrating exome-Seqand RNA-Seq data on the same individual can accelerate the identification of causal allelesfor rare genetic diseases. There are two main strands to this: (i) identifying which variantsdiscovered in exome-seq appear to be having a functional impact on transcripts, and (ii)identifying transcript outliers, especially among known causal genes, that may not necessarilyhave a causal variant identified from exome sequencing. The latter may identify the presenceof causal variants that lie far from coding regions (e.g. the formation of cryptic splice sitesdeep within introns, or loss of long range regulatory elements), which can be confirmed withfurther targeted genetic assays. Just over 50% of all disease-causing variants recorded in theHuman Gene Mutation Database (HGMD) affect transcript structure and abundance (e.g.nonsense SNVs, essential splice site SNVs, frameshifting indels, CNVs).This pilot project will study RNA from lymphoblastoid cell-lines from 12 patients withprimordial dwarfism syndromes, for 10 of these samples we have previously generate exomedata as part of our collaboration with the group of Prof Andrew Jackson. The two remainingsamples are positive controls where the causal mutation is known, and is known to affecttranscript structure and/or abundance.Primordial dwarfism is a prime candidate for these RNA-seq studies because all knowncausal mutations to date have key roles in DNA replication and thus, unsurprisingly, theproducts of the causal genes are typically ubiquitously expressed.Each RNA will be sequenced, with two technical replicates (independent RT-PCR and libraries) persample, and each replicate run in 1/2 of a HiSeq lane using 100bp paired reads. Samples preparation was as follows :The cells were grown to confluency, then pellets frozen at -80. RNA samples were prepared using the Qiagen RNeasy kit, then nanodropped and analyzed using the bioanalyzer to determine concentration and purity.This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001000283

• 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

• RNAseq of PC-9 and KHM-3S with and without tarceva

  Dataset EGAD00001000843

• DAC for the study molecular biomarkers associated with the diagnosis and severity of genetic and diseases from PAIDI-BIO354 GENYO-UGR)

  Dac EGAC00001003090

• WES for cell lines UWB1.289 and COV362

  exon 11 mutated UWB1.289 and COV362 cell lines

  Dataset EGAD50000000189

• Somatic mutations in healthy and leukemic blood progenitors reveal evolutionary mechanisms underlying childhood leukemia and differential patient outcome

  Study EGAS00001004593

• Brain_Disease_Wellcome_Leap_Delta_Tissue_ATAC

  Single cell and spatial multi-omic atlas of glioblastoma

  Study EGAS00001008083

• Treatment of Pulmonary Hypertension and Sickle Cell Disease with Sildenafil Therapy (Walk-PHaSST)

  Pulmonary arterial hypertension (PAH) is a progressive condition characterized by narrowing or stiffening pulmonary arterioles resulting in increased pulmonary blood pressure and reduced delivery of oxygenated blood to the body. It is a common complication of sickle cell disease and initially presents with the symptom of shortness of breath (dyspnea) on exertion. As the condition worsens, other symptoms such as dizziness, lower extremity edema, and chest pain can develop. The drug, sildenafil, works by relaxing blood vessels in the lungs which reduces pulmonary blood pressure and allows more oxygenated blood to circulate. Increased levels of oxygenated blood allows individuals with PAH to tolerate more activity, but guidelines for using sildenafil in patients with PAH and sickle cell disease were unavailable at the time of the Walk-PHaSST trial.Participants were screened for the existence of pulmonary hypertension with a six minute walk test and a Doppler echocardiogram that assessed TRV, diastolic function, and valvular and systolic function. Subjects with TRV ≥ 2.7 m/s received further clinical evaluation for possible causes of pulmonary hypertension. Other screening data included medical history, a physical exam, and standard laboratory testing. For individuals with moderate to severe pulmonary hypertension (TRV ≥ 3.0), a cardiac catheterization was done at the baseline and week 16 data collection periods.Subjects eligible for the main intervention trial based on screening results were randomized in a 1:1 double blind fashion to receive sildenafil or placebo for 16 weeks. Subjects received 20 mg of oral sildenafil or matching placebo 3 times daily for 6 weeks, followed by 40 mg 3 times daily for 4 weeks, followed by 80 mg 3 times daily for 6 weeks, as tolerated. Participants could also receive other therapies as needed to manage sickle cell and related complications. The primary outcome measure of the trial was change in the six minute walk test, a standard indicator of a person's heart and lung function and exercise capacity, from baseline to week 16. After completing the study treatment (or placebo), participants could choose to be part of the open-label follow-up phase of the study and continue to be assessed for up to one year.The study was intended to screen about 1000 subjects and randomize 132 subjects, however it was terminated early due to the unforeseen increase in adverse events in participants treated with sildenafil as compared to placebo. When the study was stopped, 33 participants had completed the trial. Subjects continued to be monitored, but were instructed to taper sildenafil treatment over three to seven days.There was no evidence that treatment with sildenafil impacted the six minute walk distance from baseline to week 16. In addition, treatment with sildenafil appeared to increase rates of hospitalization due to sickle cell disease pain.Due to in part to the early termination of the trial, the majority of subject data was collected from the screening phase of the study (n=720), as opposed to the main intervention trial (n=74).Instructions for requesting individual-level data are available on BioData Catalyst at https://biodatacatalyst.nhlbi.nih.gov/resources/data/. Apply for data access in dbGaP. Upon approval, users may begin accessing requested data in BioData Catalyst. For questions about availability, you may contact the BioData Catalyst team at https://biodatacatalyst.nhlbi.nih.gov/contact.

  Study phs002383

• California Pacific Medical Center Research Breast Health Cohort

  Description of Cohort: The California Pacific Medical Center (CPMC) Breast Health Cohort is a cohort study based at CPMC and is linked to the San Francisco Mammography Registry, one of the sites of the NCI-funded Breast Cancer Screening Consortium (U01CA063740). CPMC is a community hospital in San Francisco, which has one of the highest volumes for mammography in San Francisco. Between September 2004 and June 2007, >90,000 mammograms were performed at CPMC. The CPMC breast health cohort collects demographic and risk factor data on women receiving mammography through participation in the San Francisco Mammography Registry, as part of the Breast Cancer Screening Consortium (U01CA063740). The SFMR database collects information from all sources, including a questionnaire on demographic and risk factor information, the clinical results of the breast examination, the measures of breast density by Dr. John Shepherd and the women who agreed to donate a blood sample. By merging these various sources of information we have very efficiently developed a large sample of women who have donated blood and have had a measure of mamographic density. Blood Collection: Dr. Steve Cummings is leading an effort to collect and archive blood samples from women who are receiving mammography screening. All women who are sent for a screening mammogram at CPMC are considered eligible. Since the cohort began collecting blood samples in July 2004 until June 2007, samples have been collected from over 11,000 women. Measurement of Breast Density: Dr. John Shepherd is currently measuring breast density in a large fraction of the cohort using an automated approach with single X-ray absorptiometry. Dr. Shepherd has established a link with the CPMC mammography center that allows him to collect routine digital mammography information. Using the data from the mammogram, Dr. Shepherd and his group have developed the single X-ray absorptiometry (SXA) technique for measuring density which is described in more detail below. The table demonstrates the distribution of demographic variables and some breast cancer risk factors of women who donated blood and had a breast density measurement in the CPMC breast health cohort. Nearly 80% of the participants are Caucasian and most of the women are post-menopausal with a median age of ~52. Since it will be difficult to accrue a large enough sample from each ethnic group, our study will focus only on Caucasian women. Table: Demographic variables, reproductive history and family history of breast cancer among 2962 women participating in the CPMC cohort study who contributed blood samples between 1994-1997. Variable Median/Percentage Age (Median/IQR) 52 (46-59) Ethnicity Caucasian/White 0.76 Asian/Pacific Islander 0.141 Hispanic 0.029 Mixed Race/Ethnicity 0.039 African American/Black 0.022 American Indian 0.001 Other 0.009 First degree relative with breast cancer 0.17 Age at first birth Nulliparous 0.39 Age<20 0.043 Age>40 0.032 Age<30, ≥20 0.251 Age>30, ≤40 0.282 Measurement of Breast Density in Cohort: Measurement of breast density is accomplished using an automated technique for all mammograms obtained by Dr. Shepherd using Single X-Ray absorptiometry (SXA). SXA measurement of breast density is done on approximately 30% of all screening mammograms. Below we describe the method for measurement of breast density by SXA by Dr. Shepherd's group and its validation and association with breast cancer. As we demonstrate below, breast density, as measured by SXA, is an automated, highly reproducible measure of the density of breast tissue and is associated with breast cancer risk. SXA for Quantifying Breast Density: Single x-ray absorptiometry (SXA) was initially developed for measuring bone density. SXA can determine the fraction of each of two densities simultaneously using the fact the sample is a constant thickness, the thickness in known, and the total attenuation is known. In applying this technique to breast density, we assume a two compartment model: fat and non-fatty (fibroglandular tissue). We use a reference material composed of various concentrations of two materials: one which is the same density as fat and another which is the same density as fibroglandular tissue. The reference material (phantom) is placed in the X-ray field with each mammogram. We have been able to implement this in a way that is unintrusive to the patient and technologist at CPMC. Assuming this two-compartment model and a constant known breast thickness, we can then calculate the percent density at any region of the breast based on the assumption that % pixel grey-scale is proportion to the mass fractions of breast fat and lean tissue. If reference materials (a phantom) of fat and fibroglandular tissue are imaged with the patient's breast and the reference materials have the same thickness as the patient's breast, then the breast's grey-scale values can be converted to fat/fibroglandular mass fractions by interpolating between those two references. The total percent density is found by averaging the volume fraction over all breast pixels. The phantom being used for breast density assessment at CPMC began to be used in September 2004. The phantom does not have to be manipulated by the technologist and stays attached on the mammography device during standard craniocaudal (CC) views. Thus it creates minimal to no interference with the clinical mammogram. Reproducibility of breast density measures: Traditional measures of mammographic density require some human interpretation. A human reader outlines the area perceived to be dense and a computer then calculates the percent area outlined as a percent of the entire image. Thus, while traditional mammographic density is associated with breast cancer risk, it has some limitations. In a study by Drs. Shepherd, Kerlikowske, et al., the correlation coefficient (Pearson's R) between different readers was 0.8-0.9. In contrast to the traditional mammographic density measurement, the SXA measurement is fully automated and, therefore, the reproducibility of the measurement is higher. Dr. Shepherd and colleagues have performed a replication study of SXA as a measurement of breast density. They have estimated the correlation coefficient of the SXA measurement of breast density to be >0.98. Thus, as expected for an automated measure, SXA is a highly reproducible measure of mammographic breast density. Drs. Shepherd and Kerlikowske have recently analyzed the association between breast cancer risk and breast density as measured by SXA (Shepherd et al., Cancer Epi Biomarkers and Prev, 2011, PMID: 21610220). They found that women in the highest quintile of % volumetric density had an odds ratio of 4.1 (95% CI: 2.3 - 7.2) for breast cancer risk compared to women in the lowest quintile of volumetric density. Thus volumetric density appears to be a highly reproducible, automated measure of breast cancer.

  Study phs000395

• Base Editing Reduces Misfolded Protein Accumulation and Toxicity in Alpha-1 Antitrypsin Deficient Patient iPSC-Hepatocytes

  This study evaluates the efficiency and biological consequences of base editing of the single base “Z” mutation in the SERIPNA1 gene responsible for causing misfolding of the alpha-1 antitrypsin protein that leads to the clinical disease “alpha-1 antitrypsin deficiency". Samples on which the editing is performed are patient-derived iPSCs from a single PiZZ patient. The iPSC line is named “PiZZ-1”. To evaluate possible off-target editing, 5 samples underwent whole genome sequencing. These samples include: 1) PiZZ-1 patient iPSCs at passage 34 (“ZZ p34”); 2) PiZZ-1 patient iPSCs at passage 42 (“ZZ p42”); 3) PiZZ-1 patient iPSCs that underwent nucleofection but not base editing at passage 34, and then were passaged until passage 42 (“ZZ nucleofected”); 4) PiZZ-1 patient iPSCs that underwent base editing of a single allele (“MZ”), and analyzed at passage 42; and 5) PiZZ-1 patient iPSCs that underwent base editing of both alleles (“MM”), and analyzed at passage 42.

  Study phs002471