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• Spatial map of microglia states across CNS diseases

  Microglia are the key immune cells involved in virtually all diseases of the central nervous system (CNS). Here, we generated a novel large-scale single-cell RNA-sequencing-derived taxonomy through the analysis of > 1 million CNS cells enriched for myeloid cells across > 15 different pathologies and conditions. Disease-associated human microglia, together with CNS-associated macrophages and monocytes, were differentiated iteratively into 27 superclusters and 192 clusters. This top-down approach, in combination with targeted and genome-wide subcellular spatial transcriptomics, enabled us to examine and compare the spatial interactome of the identified superclusters and clusters within and across pathologies. Our data provide new insights into the spatial dynamics of the endogenous CNS immune system during development, health and disease in humans.

  Study EGAS50000001289

• Center for Craniofacial and Dental Genetics (CCDG): Genetics of Orofacial Clefts and Related Phenotypes

  The purpose of this study is to investigate the genetics of orofacial clefts (OFCs) in a large study population, and importantly, to incorporate subclinical phenotypic features into these studies. Orofacial clefts (OFCs) comprise a significant fraction of human birth defects (about 1/700 live births (Rahimov et al. 2012) and represent a major public health challenge, as individuals with these anomalies require surgical, nutritional, dental, speech, medical and behavioral interventions, thus imposing a substantial economic and personal burden (Berk and Marazita 2002*). The most common forms include OFCs of the lip alone (CL, Figure 1A), CL plus cleft palate (CL+CP, Figure 1B) or of the palate only (CP, Figure 1C). Individuals born with OFC may have their first surgical repair at age 3 months, but this initial surgery is just the beginning of a lifetime of health burdens. An individual born with an OFC has a hospital use rate increased for most ages (up to 233% increase for children ages 0-10 years and 16% for middle aged adults (Wehby et al. 2012). Healthcare costs for children with OFCs are estimated to be 800% greater compared with their unaffected peers (Boulet et al. 2009). Data from Denmark show that people born with CL with or without CP (CL/P) have an increased mortality up to age 55, which may be attributed to an increased risk of suicide and/or certain cancers (Christensen et al. 2004). The focus of most OFC genetic research has been CL and/or CP. Furthermore, the majority of OFC, i.e. about 70% of CL/P and 50% of CP is considered "nonsyndromic" (Jones 1988), i.e. isolated anomalies with no other apparent cognitive or structural abnormalities. Figure 1 Sample OFC Types A: Bilateral Cleft Lip; B:Cleft Lip plus Cleft Palate; C:Cleft Palate Alone The factors leading to the majority of nonsyndromic OFCs are still unclear, particularly at an individual family level. As is true for many complex traits, substantial progress in gene identification has occurred in the OFC field in the last two years (Dixon et al. 2011; Marazita 2012). Genome Wide Association Studies (GWAS) and sequencing studies to date by our research team and others have focused on genetic risk factors for overt CL/P and CPO-and have been very successful. A major finding from this work is that OFCs exhibit significant genetic heterogeneity, i.e., multiple genetic regions have been implicated (Beaty et al. 2010; Ludwig et al. 2012). Thus, approaches are needed to understand this genetic heterogeneity. Are there GxG interactions at work? Are there subsets of families, each due to a different gene? Our research group has shown that a promising approach to dissect the etiology of OFC is to focus on subclinical phenotypic features within entire cleft families (not just in affected cases, but also in their non-cleft relatives). These subtle features are believed to represent mild manifestations of the same underlying genetic susceptibility responsible for OFCs; as such, their inclusion in case-control and family-based genetic studies can help to clarify and refine the relationship between genotype and phenotype. The study population comprises a large number of families and individuals (~12,000 individuals) from multiple populations worldwide (Caucasians from the US and Europe, Asians from China and the Philippines, Mixed Native American/Caucasians from South America, and Africans from Nigeria and Ethiopia). There are cases, case families (nuclear families and extended kindreds), as well as controls with no history of OFC nor other developmental defects. *Berk NW, Marazita ML (2002) Costs of Cleft lip and Palate: Personal and Societal Implications. In: Wyszynski DF (ed) Cleft Lip and Palate: From Origin to Treatment. Oxford University Press, Inc., New York, pp 458-467.

  Study phs000774

• CD4+ cell transcriptional profiling by RNA sequencing

  We use next generation sequencing to investigate the different transcriptomes of closely related CD4+ T-cells from healthy human donors to elucidate the genetic programs that underlie their specialized immune functions. Six cell types were included: Regulatory T-cells (CD25hiCD127low/neg with >95% FOXP3+ purity), regulatory T-cells activated using PMA/ionomycin, CD25-CD45RA+ ('naive' helper T-cells), CD25-CD45RO+ ('memory' helper T-cells), activated Th17 cells (>98% IL17A+ purity) and activated IL17-CD4+ T-cells (called 'ThPI'). Poly-T capture beads were used to isolate mRNA from total RNA, and fragment sizes of ~200 were sequenced from both ends on Illumina's genome analyzer. We confirm many of the canonical signature genes of T-cell populations, but also discover new genes whose expression is limited to specific CD4 T-cell lineages, including long non-coding RNAs. Additionally, we find that genes encoded at loci linked to multiple human autoimmune diseases are enriched for preferential expression upon T-cell activation, suggesting that an aberrant response to T-cell activation is fundamental to pathogenesis.

  Study phs000626

• 2014 sequenced Korean WES-Lung Cancer sample 36 pair

  This data is belong to WES-Lung Cancer patients' genome data which is aligned to human reference(human_g1k_v37.fasta). There are 36 paired tumor/normal samples from Samsung Hospital. All samples has passed QC and recalibration steps while aligning to reference.

  Dataset EGAD00001004027

• 2015 AML-ETO WGS additional analysis result

  This data is belong to additional 2015 AML-ETO patients' genome data which is aligned to human reference(human_g1k_v37.fasta). There are 2 paired tumor/normal samples from SNUH. All samples has passed QC and recalibration steps while aligning to reference.

  Dataset EGAD00001004012

• 2014 Lung squamous cell carcinoma WES analysis result

  This data is belong to 2014 Lung squamous patients' exome data which is aligned to human reference(human_g1k_v37.fasta). There are 104 paired tumor/normal samples from SMC. All samples has passed QC and re-calibration steps while aligning to reference.

  Dataset EGAD00001003960

• 2014 sequenced WGS-Lung Cancer sample 30 pair

  This data is belong to WGS-Lung Cancer patients' genome data which is aligned to human reference(human_g1k_v37.fasta). There are 30 paired tumor/normal samples from Samsung Hospital. All samples has passed QC and recalibration steps while aligning to reference.

  Dataset EGAD00001003978

• 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

• Dilgom_Exome

  The Finrisk sample sets are part of the National FINRISK Study. It is a large population survey on risk factors of chronic, noncommunicable diseases. The survey is carried out since 1972 every five years using independent, random and representative population samples from different parts of Finland. The main results from the previous FINRISK 2007 survey are published.The National FINRISK Study Survey was carried out in 5 areas in Finland and 2000 inhabitants aged 25-75 years were invited to participate in each year. Among findings were that Finns continue to gain weight.Data from FINRISK surveys are used for many different research projects and for national health monitoring needs. The recent research activities deal, in addition to cardiovascular diseases and the classical risk factors, also with e.g. asthma and allergy, alcohol, socioeconomic factors and genetic epidemiology.The FINRISK study is part of the MORGAM Project (MONICA Risk, Genetics, Archiving and Monigraph), sponsored by the EU and MDECODE (Molecular Diversity and Epidemiology of Common Disease) program coordinated by the University of Michigan.The exome sequencing study will be part of the Dilgom study, which is a part of the larger Finrisk population based health study performed in Finland. It consists of 5000 individuals with a prospective aspect of metabolic traits. The cohort has been extensively phenotyped for their cardiovascular and metabolic status. So far, we have performed a 660K Illumina GWAS and a full genome wide expression study from peripheral blood cells of 500 individuals. The cohort has also been in total genotyped by the cardiometabochip

  Study EGAS00001000086

• Programmatic submission based on XML

  Programmatic submissions (XML based) For further information please check our Submission FAQs, submission quickguide as well as submission terms! Introduction Besides the Submitter Portal tool, EGA supports programmatic sequence and clinical data metadata submissions. If you are not sure what this means, you may want to explore our brief metadata introduction. Programmatic submissions are recommended for array-based submission. Moreove, it may be of help if your submission is recurrent or it is difficult to manage manually due to its sheer size. Otherwise, we highly recommend using the Submitter Portal to perform submissions. In this page we will guide you through the required steps to programmatically submit data to the EGA. Programmatic submissions require your metadata to be structured for an easy and straightforward validation and archival. It basically consists in formatting your metadata as Extensible markup language (XML) files and submitting them to the EGA using the WEBIN Before submitting metadata to the EGA, it is important to ensure that the information in your XML files is compliant with our standards. You can see further details on how these standards are maintained at EGA at our EGA Schemas documentation page. Using WEBIN, you can validate your XML files against EGA's schemas to ensure that your metadata is compliant before submission. WEBIN services WEBIN production service WEBIN test service We advise you to submit your metadata to the test service when submitting to the production service for the first time. The test service is identical to the production service except that all submissions will be discarded in the following 24 hours. This allows you to learn about the submission process without having to worry about data being submitted. Authentication Authentication is required each time a submission is made. The submission service uses HTTPS protocol for metadata encryption and identification to provide a secure submission environment. Data file upload Both Runs and Analyses reference files (e.g. FASTQ need to be uploaded to the EGA before these metadata objects are submitted. In other words, if you submit a Run that references a file that we cannot find associated with your account, the metadata submission will fail. See further details on how to upload your files in our File Upload documentation. Metadata model of the EGA Our metadata model is formed by multiple metadata objects. Check further details in our documentation at our EGA Schema documentation page. Working with EGA XMLs files Now that the basic concepts of the EGA metadata have been described, you can start preparing your programmatic submission through XML. Here you will find the guidance on how to prepare the XML files. Programmatic Submission Tutorial Video Take a look at the Programmatic Submission Tutorial Video, which explains the workflow of a programmatic submission and goes over an example metadata submission. Programmatic Submission Tutorial Video. When building your XML files, we recommend using text editors (e.g.Sublime Text or VisualStudio) that allow you to visualise the structure of the XML with ease. Furthermore, these editors constantly check the consistency of the XML structure. Alternatively, and if the submission consists of a big number of objects (specially analyses), you may find the tool star2xml handy. This tool allows for a direct conversion between metadata in a tabular format (e.g. a spreadsheet) into XMLs. Identifying objects: Aliases and center names Every EGA object must be uniquely identified within the submission account using their alias attribute. The aliases can be used in submissions to make references between EGA objects. Let us dig into EGA's use of aliases and center names: alias: every object should have a name that is unique within your submission account. Once submitted successfully, every alias will be assigned a unique and permanent accession (EGA ID). refname: when an object references another by its alias, the alias of the referenced object goes into the "refname" attribute of the referencing object. For example, if a sample has the alias "sample1", and an experiment uses this sample, then the experiment's "EXPERIMENT/SAMPLE/refname" attribute should be "sample1". center_name: The "center_name" attribute is required within the submission XML and, if not provided when the object is submitted, it will be automatically filled using your default EGA account center_name. This element is the "controlled vocabulary acronym or abbreviation that is provided to the account holder when the account is first generated". If the submitter is brokering a submission for another institute, the submitter should use their special broker account name in broker_name while the data centre acronym remains in center_name. Log-in details should have been provided when you requested a submission account. Please contact our Helpdesk team if you have any questions. run_center: Many submitting centers contract out the actual sample sequencing to another center. In these cases, the sequencing center should be acknowledged in the run_center attribute. Again, this is controlled vocabulary and the acronym should be sought from EGA helpdesk before submitting. Please contact our Helpdesk team if you have any questions. Prepare your XMLs The goal of this section is to provide sufficient information to be able to create the metadata XML documents required for programmatic submissions. Please note, the EGA utilises the XML schemas maintained at the European Nucleotide Archive (ENA). It is important due to the fact that by using a similar system, some pieces of documentation from the ENA's programmatic submission can also help you with your programmatic submission to the EGA. For example, you can submit programmatically without using a Submission XML by following the steps at Submission actions without submission XML. A submission does not have to contain all different types of XMLs. For example, it is possible to submit only a few samples; or a study that is later to be referenced. You can submit each object one by one, or submit all in a batch: you choose what method of submission works best for you. We do recommend, nevertheless, that you submit the objects to be referenced (e.g. samples or studies) first, and the objects that reference these (e.g. experiments or datasets) afterwards. You can see a graphical view of these objects and their relationships at our EGA Schemas page. Independently of the submission scenario, you will always require a Dataset XML. The entity of a dataset is what is used to control access to the given data, in the form of runs or analyses. In other words, when a requester is granted access, it is through the dataset and the objects (e.g. runs or analyses) that the dataset contains, granting access to them in one go. Given the nature of the EGA, a dataset XML will always be required for the data access. First, we will differentiate between submissions of "raw" and "processed" data: Runs and Analyses, respectively. Run data submissions Raw data derives from instruments "as is". For example, a plain sequence file (e.g. FASTQ or unaligned BAM files) would be considered raw data. A typical raw (unaligned) sequence read submission consists of 8 XMLs: Submission Study Sample Experiment Run DAC Policy Dataset When technical reads (e.g. barcodes, adaptors or linkers) are included in the submitted raw sequences, a spot descriptor must be submitted to describe the position of the technical reads so that they can be removed. The following data files can be submitted without providing spot descriptor information in the experiment/run XML: BAM files (single reads) SFF files (single reads without barcodes) FastQ files (single reads without any technical reads) Complete Genomics files Analysis data submissions Processed data is, in some way, refined raw data. This includes raw data that has been processed by some form of analysis method (e.g. alignment, noise reduction, etc.). For example, an aligned sequence (e.g. BAM file), that was created using raw FASTQ files, would be a processed file. This category includes most types of data: sequence alignment files (e.g. BAM or CRAM), clinical data (e.g. phenopackets), sequence variation files (e.g. VCF), sequence annotation, etc. A typical EGA analysis data submission consists of 7 EGA XML: Submission Study Sample Analysis DAC Policy Dataset We accept three different types of analysis data submissions: BAM files (for multiple read alignments) VCF files (for sequence variations) Phenotype files (in any format) In anycase, keep in mind that samples must be created in order to be referenced in the analyses. In other words, the provenance of the information within the BAM, VCF and phenotype files Example XMLs Below you can find a non-extensive list of example XMLs with descriptive fields (i.e. explaining what to provide in each field). Furthermore, you can also find real examples (i.e. the true value of the provided fields) in our GitHub repository. Submission XML The submission XML is used to validate, submit or update any number of other objects. The submission XML refers to other XMLs. New submissions use the ADD action to submit new objects. Object updates are done using the MODIFY action and objects can be validated using the VERIFY action. Descriptive submission XML example True values submission XML example Study XML The study XML is used to describe the study containing a title, a study type and abstract as it would appear in a publication. Descriptive study XML example True values study XML example Please use the following notation within the property "STUDY_LINKS" when including PubMed citations in the Study XML: <STUDY_LINKS> <STUDY_LINK> <XREF_LINK> <DB>PUBMED</DB> <ID>18987735</ID> </XREF_LINK> </STUDY_LINK> </STUDY_LINKS> Sample XML The sample XML is used to describe the samples used to obtain the data, whether they were sequenced, measured in any other way, or have an associated phenotype. The mandatory fields include information about the taxonomy of the sample, sex, subject ID and phenotype. For example, the mandatory attribute fields for each sample would look like these, within the array of "SAMPLE_ATTRIBUTES": <SAMPLE_ATTRIBUTES> <SAMPLE_ATTRIBUTE> <TAG>subject_id</TAG> <VALUE>free text!</VALUE> </SAMPLE_ATTRIBUTE> <SAMPLE_ATTRIBUTE> <TAG>sex</TAG> <VALUE>female/male/unknown</VALUE> </SAMPLE_ATTRIBUTE> <SAMPLE_ATTRIBUTE> <TAG>phenotype</TAG> <VALUE>Free text, EFO terms (e.g. EFO:0000574) are recommended</VALUE> </SAMPLE_ATTRIBUTE> </SAMPLE_ATTRIBUTES> Sample is one of the most important objects to be described biologically, it is highly recommended that “TAG-VALUE” pairs are generated as SAMPLE_ATTRIBUTES to describe the sample in as much detail as possible. For example, were we to give the population ancestry of the sample, we could add a new attribute to the array, in which, for example, we would indicate that the sample derives from an individual of "Mende in Sierra Leone" (MSL), with an african ancestry: <SAMPLE_ATTRIBUTE> <TAG>Population</TAG> <VALUE>MSL</VALUE> </SAMPLE_ATTRIBUTE> Given that VALUE and TAG are free text, the combinations are limitless in order to give you full flexibility on the information you want to provide. We recommend you use the Experimental Factor Ontology (EFO) to describe the phenotypes of your samples. You can provide more than one phenotype by adding more items to the array of SAMPLE_ATTRIBUTES. Phenotypes considered essential for understanding the data submission should be provided. Each phenotype described should be listed as a separate sample attribute <SAMPLE_ATTRIBUTE> </SAMPLE_ATTRIBUTE>. There is no limit to the number of phenotypes that can be submitted. If a suitable EFO accession cannot be found for your phenotype attribute, please consider using another controlled ontology database (e.g. HPO, MONDO, etc.) before using free text. Descriptive sample XML example True values sample XML example Experiment XML The experiment XML is used to describe the experimental setup, including instrument platform and model details, library preparation details, and any additional information required to correctly interpret the submitted data. Where any of these values differ between runs, a new experiment object must exist, since runs are grouped by experiments. Each experiment references a study and a sample by alias, or if previously-submitted, by accession. Pooled data must be demultiplexed by barcode for submission. Descriptive experiment ( Illumina paired read ) XML example True values experiment ( Illumina paired read ) XML example Run XML The run XML is used to associate data files with experiments and typically comprises a single data file (e.g. a FASTQ file). Please note that pooled samples should be de-multiplexed prior submission and submitted as different runs. Descriptive run XML example True values run XML example Analysis XML Given that an analysis can be used to submit any type of processed data to the EGA, we will list below an example of each of the three most common types of analysis XMLs submitted to the EGA: sequence alignments (e.g. BAM files); sequence variation (e.g. VCF files); and clinical metadata or phenotypes (e.g. phenopackets). Regardless of the type of processed data submitted in the analysis, the analysis must be associated with a Study and can reference multiple types of other objects, from samples to experiments, if they are available at the EGA. Just like with Runs, whenever a file is submitted to the EGA through an analysis object, the file MD5 checksums must be present, in order for the EGA to validate file integrity upon transfer. This also includes index files when applicable (e.g. .bai.md5 files). Ideally, any analysis that uses a reference sequence for some kind of alignment (e.g. BAM, CRAM or VCF files), would contain metadata about the alignment, such as INSDC reference assemblies and sequences, by either using accessions (e.g. CM000663.1) or common labels (e.g. GRCh37). Read alignment (BAM) Analysis XML The Analysis can be used to submit BAM alignments to EGA. Only one BAM file can be submitted in each analysis and the samples used within the BAM read groups must be associated with Samples. Descriptive bam alignments XML example True values bam alignments XML example Sequence variation (VCF) Analysis XML The Analysis can be used to submit VCF files to EGA. Only one VCF file can be submitted in each analysis and the samples used within the VCF files must be associated with Samples. Download analysis XML (VCF) Phenotype files The Analysis XML can be used to submit phenotype files to the EGA. Only one phenotype file can be submitted in each analysis and the samples used within the phenotype files must be associated with EGA Samples. Download analysis XML (Phenotype) DAC XML The DAC XML describes the Data Access Committee (DAC) affiliated to the data submission. The DAC may consist of a group or a single individual and is responsible for the data access decisions based on the application procedure described in the POLICY.XML. As with any other object, if it was already submitted to the EGA, there is no need to submit it again: you can reference an existing object within the EGA. Hence, A DAC XML does not need to be provided if your submission is affiliated to an existing EGA DAC.. Further information on DACs can be found here, and you can always contact our Helpdesk team if you have further inquiries. Descriptive dac XML example True values dac XML example Policy XML The Policy XML describes the Data Access Agreement (DAA) to be affiliated to the named Data Access Committee. Descriptive policy XML example True values study XML example Dataset XML The dataset XML describes the data files, defined by the Run.XML and Analysis.XML, that make up the dataset and links the collection of data files to a specified Policy. The dataset xml is commonly the last metadata object to be submitted, since it references multiple other entities. Please consider the number of datasets that your submission consists of. For example, a case-control study is likely to consist of at least two datasets. In addition, we suggest that multiple datasets should be described for studies using the same samples but different sequence technologies. Descriptive dataset XML example True values dataset XML example Validating and submitting your EGA Validating EGA's XMLs through Webin After you have ensured that the XMLs are properly formatted and contain all the required information. You can proceed to validate and submit your data. Use the curl command to validate your XML file: Once you have prepared your XML file and asserted you have access to Webin, you can validate your XML file programmatically against EGA's schemas using the curl command. There are multiple ways in which you can validate your XMLs. This variety has to do with the fact that: (1) there are 2 instances of Webin (test and production); and (2) that validation is a default step during submission. In other words, any time that you submit your data through Webin, it will be validated automatically before being accepted. This allows for 4 possible routes of validation, all having the same validation result: validating or submitting to either the production service or the test service of Webin. For example, directly validating a "study" object XML in the testing service (wwwdev…) would look like the following: curl -u <USERNAME>:<PASSWORD> -F "ACTION=VALIDATE" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" In this command, you would need to replace <USERNAME> and <PASSWORD> with your EGA account username and password, respectively. You would also replace <INPUT_FILE> with the path to your XML file. A mock example would look like the following: curl -u ega-test-data@ebi.ac.uk:egarocks -F "ACTION=VALIDATE" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" The validation attempt can have different results depending on the given arguments: If your XML file is valid according to EGA's schemas, you will see a message indicating that your XML file is compliant. For example, see below for our mock example, where the "success" was "true" (i.e. no validation errors found). Nevertheless, notice how the "<STUDY accession=" is empty: it is because we were simply validating, so the study did not get an accession or ID. <?xml version="1.0" encoding="UTF-8"?> <?xml-stylesheet type="text/xsl" href="receipt.xsl"?> <RECEIPT receiptDate="2023-04-11T15:19:28.850+01:00" submissionFile="submission-EBI-TEST_1681222768850.xml" success="true"> <STUDY accession="" alias="Mock example" status="PRIVATE"/> <SUBMISSION accession="" alias="SUBMISSION-11-04-2023-15:19:28:840"/> <MESSAGES> <INFO>VALIDATE action has been specified.</INFO> <INFO>Submission has been rolled back.</INFO> <INFO>This submission is a TEST submission and will be discarded within 24 hours</INFO> </MESSAGES> <ACTIONS>VALIDATE</ACTIONS> <ACTIONS>PROTECT</ACTIONS> If there are any errors or warnings, the tool will display them, allowing you to correct them before submitting your data to EGA. For example, in the following response, it is said that the object we were trying to submit was already existing, and therefore the "success" was "false". <?xml version="1.0" encoding="UTF-8"?> <?xml-stylesheet type="text/xsl" href="receipt.xsl"?> <RECEIPT receiptDate="2023-04-11T15:12:35.609+01:00" submissionFile="submission-EBI-TEST_1681222355609.xml" success="false"> <STUDY alias="Example!_Human Microbiome Project SP56J" status="PRIVATE" holdUntilDate="2023-03-11Z"/> <SUBMISSION alias="SUBMISSION-11-04-2023-15:12:35:576"/> <MESSAGES> <ERROR>In study, alias: "Example!_Human Microbiome Project SP56J". The object being added already exists in the submission account with accession: "ERP127584".</ERROR> <INFO>VALIDATE action has been specified.</INFO> <INFO>Submission has been rolled back.</INFO> <INFO>This submission is a TEST submission and will be discarded within 24 hours</INFO> </MESSAGES> <ACTIONS>VALIDATE</ACTIONS> <ACTIONS>PROTECT</ACTIONS> If the curl command retrieves no response at all, please double check if your username and password are correctly provided. Also notice the "ACTION=..." argument passed to the Curl command. This specifies the action to take during the call to Webin, so we do not need a "Submission" XML just for a validation attempt. See more at submission actions without submission XML. Furthermore, validation of multiple files or objects (e.g. sample, experiment, study…) can be done in a single command by adding more arguments (i.e. '-F'). For example: curl -u <USERNAME>:<PASSWORD> -F "ACTION=VALIDATE" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" -F "SAMPLE=@sample.xml" -F "DATASET=@dataset.xml" As mentioned above, beside "validate" action in the test environment, you can also validate your metadata by three other methods: "Validate" in the production server. From our example above, you simply need to take the "dev" away from the URL. curl -u <USERNAME>:<PASSWORD> -F "ACTION=VALIDATE" "https://www.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" "Add" in the development server. From our example above, you would simply need to replace the action: from "validate" to "add". Whatever is submitted to this service will be discarded in 24h, so whether something gets submitted or not would not matter in the long run. curl -u <USERNAME>:<PASSWORD> -F "ACTION=ADD" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" "Add" in the productionserver. A combination of the previous two methods, which would render this attempt into a submission. This path is just to be taken when you are sure your metadata is compliant and what you want to submit. curl -u <USERNAME>:<PASSWORD> -F "ACTION=ADD" "https://www.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" What happens after the submission of a dataset XML? Once you have completed the registration of your dataset/s please contact our Helpdesk Team to provide a release date for your study. Please note that all datasets affiliated to unreleased studies are automatically placed on hold until the authorised submitter or DAC contact contact the EGA Helpdesk for the study to be released. We strongly advise you not to delete your data until EGA Helpdesk confirms that your data has been successfully archived.

  Documentation submission/metadata/submission/programmatic-submission-xml

• Genes-Environments and Admixture in Latino Asthmatics (GALA II) Study

  A case-control pharmacogenetic study of bronchodilator drug response among racially admixed Latino children with asthma. Each participant had two spirometry measurements using the KoKo PFT System. With the first spirometry test, participant was administered with 4 puffs of HFA Albuterol. The second albuterol measurement was based on age, for participants under 16 years of age, additional 2 puffs were administered and for those over 16 years of age, additional 4 puffs were administered. The overall goal is to identify genetic factors which are predictive of drug response in children with asthma. The principal tools include a questionnaire and biological specimen collection. Participants are 8-21 years old at time of recruitment. Children with asthma have physician-diagnosed asthma, symptoms and medications. The GALAII Study is utilized in the following dbGaP substudies. To view genotypes, analysis, other molecular data, and derived variables collected in these substudies, please click on the following substudies below or in the "Substudies" box located on the right hand side of this top-level study page phs001180 GALAII Study. phs001274phs001274 GALAII GWAS

  Study phs001180

• LEF1 knockdown effects on human T cell transcriptome and chromatin accessibility profiles.

  LEF1 (Lymphoid Enhancer-Binding Factor 1) is a key transcription factor in T cell development and function, and its dysregulation has been implicated in various immune-related disorders. This study aimed to comprehensively characterize the transcriptional and epigenetic changes induced by LEF1 knockdown in human T cells. CD3+ T cells isolated from three healthy donors were transfected with either LEF1-specific siRNA or control siRNA, followed by activation with CD3/CD28 Dynabeads and culture in IL-2-containing media. Cells were harvested at 24, 48 and 72 hours post-transfection for multi-omics analysis including bulk RNA-seq, bulk ATAC-seq, single-cell CITE-seq (For CITE-seq, 48 and 72 hours only). This dataset provides insights into LEF1-dependent gene regulatory networks and chromatin accessibility patterns in activated human T cells, contributing to our understanding of T cell biology and potential therapeutic targets. This dataset is part of a comprehensive study investigating functional roles of risk polymorphisms in autoimmune diseases, particularly focusing on the rs58107865 variant at the LEF1 locus.

  Study JGAS000818

• Egypt Genome Project - high coverage whole genome sequencing

  All humans outside Africa are descendants of the same single exit, usually dated at 50-70 thousand years ago. However, the route taken out of Africa is still debated. The two main candidates are a northern route via Egypt and the Levant, or a southern route via Ethiopia and the Arabian Peninsula. We are generating genetic data to evaluate these two possibilities. In this study we propose to generate high-coverage sequencing data for 3 Egyptian samples.

  Dataset EGAD00001001380

• WGS data from LCNEC patient derived tumor organoids (PDTOs) and matched parental tumors - SA

  The dataset includes cram files from WGS of 2 LCNEC tumors and matched PDTOs. For all tumors, cram files from WGS of matched normal tissue derived organoids from the corresponding patients is included. Analysis VCF files are also included in the dataset. The sequencing was done with a NovaSeq 6000 instrument.

  Dataset EGAD00001009990

• Patient-derived neuroblastoma model system OHC-NB1

  The dataset is comprised of seven samples, one blood sample (germline control) of the patient, one neuroblastoma metastasis from the bone marrow and five derived cell models. The models include the primary culture, the first xenogenograft passage, a monolayer culture derived from the first xenograft passage and two samples of the fourth xenograft passage, cells and supernatant. For all these samples, whole-exome sequencing data have been generated. The BAM files contain the alignments against the human genome, assembly GRCh37, and also the unaligned reads.

  Dataset EGAD00001004138

• Egypt Genome Project - low coverage whole genome sequencing

  All humans outside Africa are descendants of the same single exit, usually dated at 50-70 thousand years ago. However, the route taken out of Africa is still debated. The two main candidates are a northern route via Egypt and the Levant, or a southern route via Ethiopia and the Arabian Peninsula. We are generating genetic data to evaluate these two possibilities. In this study we propose to generate low-coverage sequencing data for 100 Egyptian samples.

  Dataset EGAD00001001372

• Non-invasive prenatal diagnosis by genome-wide haplotyping of cell-free plasma DNA

  This dataset contains a total of 10 families and 51 samples presented in 'Non-invasive prenatal diagnosis by genome-wide haplotyping of cell-free plasma DNA' study, in which 9 of them are cfDNA samples and the rest of samples are gDNA samples. All the samples are targeted sequencing data with a custom 45Mb capture library . Raw pair-end fastq files for each sample is available.

  Dataset EGAD00001004989

• Center for Education and Drug Abuse Research (CEDAR)

  Under its programmatic name, the Center for Education and Drug Abuse research (CEDAR), this Center of Excellence project was completed in 2015. The CEDAR sample consists of 775 nuclear families comprising an adult male (proband), his spouse/mate, and their biological child who is 10-12 years of age at recruitment. This child is designated as index child (IC), and is followed through age 30. Minimal exclusionary criteria were applied to maximize the sample&#39;s representativeness. Follow-up evaluations on ICs were conducted at ages 14, 16, 19, 22, 25, 27 and 30. Interim questionnaires were mailed annually beginning at age 20. CEDAR utilizes the family/high-risk paradigm. The offspring thus comprise high average risk (HAR) and low average risk (LAR) groups, constituting the majority of the CEDAR children sample: 250 HAR males, 100 HAR females, 250 LAR males and 94 LAR females. The remainder of the IC sample, 50 males and 31 females, are the offspring of fathers who have a lifetime non-SUD psychiatric dis-order. The follow-up rate from baseline to the final assessment is &#126;65%. The cohort of children is 76% White and 22% Black. The representation of females among ICs is lower than males, due to the later start of recruit-ment (2nd funding cycle; the study originally focused on males). The CEDAR genetic study has been augmented by the inclusion of its sample into the whole-genome genotyping effort conducted under the Genes, Environment and Development Initiative (GEDI). DNA from the blood samples of target children (index cases) and their siblings submitted to the NIDA Genetics Depository a Rutgers University was obtained and genotyped on Illumina Human660W-Quad Beadchips, with a high average genotyping rate of 99.8%. The sample comprised 158 females and 271 males (37 and 63 %), &#126;70:30 European- and African- American, with approximately 1:1 ratio of children of affected fathers and normal controls.

  Study phs001649

• Single-cell genome-wide concurrent haplotyping and copy-number profiling through genotyping-by-sequencing

  scGBS is a single-cell sequencing-based methodology to haplotype and copy-number profile single cells. Genomic size and complexity is reduced through restriction enzyme digestion and DNA is genotyped through sequencing of the restriction fragments. scGBS data serves as the input for haplarithmisis, an algorithm we previously developed for SNP array-based single-cell haplotyping (Zamani Esteki et al., 2015). We established technical parameters and developed an analysis pipeline enabling accurate concurrent haplotyping and copy-number profiling of single cells with the use of a HapMap cell line pedigree (7 single cells). A clinical validation of the methodology with a total of 14 single blastomeres and 3 trophectoderm samples biopsies from human preimplantation embryos for 6 PGT-M families were processed with scGBS and were previously haploptyped via SNP array.

  Dataset EGAD00001007794

• How to use the EGA search box

  Tips on how to search Tip 1: Use simple and clear keywords No matter what you are searching: a study, a dataset or a committee; the search will look recursively in all the fields of all the entities in the database. Thus, there is no need to specify which type of data you are looking for. Try to use single and specific words and avoid long and complex expressions. Try it out: cancer rna-seq instead of cancer studies using RNA-seq. Tip 2: Narrow your results using search operators Search operators give you more control over the results. They manage the way keywords are used to restrict the search. By default, the search engine applies an OR operator when a space is inserted between two words. Include AND (capitalised) between the keywords in your search in order to show only pages containing both terms. Try it out: Search all the studies based on transcriptomic data, including those ones that use RNA sequencing to obtain this data. Tip 3: Do not worry about details The search engine automatically checks for the most common spelling mistakes of given words and suggests the use of the correct version. It also ignores capital letters and most punctuation so none of these mistakes will hamper the accuracy of your search. Besides, when using a combination of words, the search engine will suggest similar combinations with a higher number of results. Tip 4: Use words that are likely to appear within the text Unfortunately, our search engine is not able to interpret words yet. Therefore, words which do not appear in the body text will not return any result despite them denoting a group of present terms. For instance, if we are looking for studies focused on neurological diseases, using neurological as a keyword would not be the best option. Instead, you should use the name of each neurological disease such as bipolar disorder, schizophrenia or depression. In the same fashion, incomplete words do not return any result. Please include an asterisk (*) before and/or after the incomplete word in order to allow for partial matching. Try it out: canc* instead of canc(without wildcard). Also you can try out *oma, or even *geneti* Tip 5: Narrow down your search by filtering the results You can filter the results by the following options: general, study, and dataset. Under these categories, you will find more sub-filters to refine your search. Make sure that you click on Apply in order to filter the results. You can clear the filtering anytime. Advanced Searches Additional example queries: Search for DUO:0000005 and word lung example query Or write: DUO:0000005 AND lung Search for an exome sequencing study in dbGaP example query Or write: study_type: "exome sequencing" AND repository:dbGaP Search for an exome sequencing study in EGA example query Or write: study_type: "exome sequencing" AND repository:ega Search for a dataset that is at the EGA beacon and with type "Whole genome sequencing" and with number of samples between 100 and 2000 and with technology "Illumina HiSeq 2000" example query Or write: is_in_beacon:true AND dataset_types:"Whole genome sequencing" AND samples:[100 TO 2000] AND dataset_technologies:"Illumina HiSeq 2000" Search for PMIDS example query Or write: pmids:32025007

  Documentation discovery/metadata/search-box

• M116 Proteome Extracellular Vesicle Profiling

  For extracellular vesicles (ECV) extraction from plasma, 1M ammonium acetate was added to precipitate ECVs on ice for 45 min. Then, 100 mM ammonium acetate was added to the mixture, and ECVs were precipitated by centrifugation at 20,000g for 30 min. ECVs were washed with 50 mM ammonium bicarbonate (Sigma-Aldrich, Cat. 1066-33-7). Then, 600 µl of 1% ammonium deoxycholate (Sigma Aldrich, Cat. K2755-1MG) were added. The concentration of protein in each sample was measured using a bicinchoninic acid assay (BCA assay). Ammonium bicarbonate was used to dilute 500 µg of protein into a final volume of 500 µl. Next, dithiothreitol (Sigma-Aldrich, Cat. D9779) was added to obtain a final concentration of 20 mM, followed by iodoacetamide (Sigma-Aldrich, Cat. I6125) to a final concentration of 40 mM. Next, trypsin (Roche, Cat. RTRYP-RO) was added to the sample in a 1:25 protein ratio and incubated at 37°C overnight. Next day, formic acid (ThermoFisher Scientific, Cat. 28905) in a final concentration of 0.1% was added and extraction of proteins was done using Empore™ Solid Phase Extraction Cartridges (3M), following manufacturer’s instructions. The eluted samples were then centrifuged for 90 min. using a speed vacuum centrifuge (Thermo, RC1010), followed by snap freezing in liquid nitrogen. Then, the samples were kept in a freeze dryer (LyoDry Compact Benchtop, MechaTech) overnight. Next, the samples were reconstituted in 30 µl of 0.1% formic acid (FA) and an o-Phthaladehyde (Oparil) assay was performed to determine the concentration of each sample. After that, the sample was prepared in a concentration of 0.5 µg/µl using 0.1% FA and alcohol dehydrogenase (ADH). The samples were prepared in glass mass spectrometry vials for proteomic analysis using a Waters Synapt G2Si High-Definition Mass Spectrometry (Waters Corporation) operated by the MassLynx 4.1., 110 min. running time with 2 µl of an injection containing 1 µg of peptide. Quality controls were also run along with samples to guarantee consistency. Pooled quality controls were made from all samples, in which the samples were run at the beginning, middle and end of the mass spectrometry run. Samples were randomized before running the experiment. The proteomic data was then imported into Progenesis software 4.2 (Nonlinear Dynamic, UK) to identify and quantify peptides and proteins.

  Dataset EGAD50000001679

• Dynamics of sequence and structural cell-free DNA landscapes in small-cell lung cancer

  Patients with small-cell lung cancer (SCLC) have an exceptionally poor prognosis, calling for improved real-time non-invasive biomarkers of therapeutic response. We performed targeted error-correction sequencing on 171 serial plasmas and matched white blood cell (WBC) DNA from 33 patients with metastatic SCLC who received systemic treatment with chemotherapy or immunotherapy-containing regimens. Tumor-derived sequence alterations and plasma aneuploidy were evaluated serially and combined to assess changes in total cell-free tumor load (cfTL). Longitudinal dynamic changes in cfTL were monitored to determine circulating cell-free tumor DNA (ctDNA) molecular response during therapy. Combined tiered analyses of tumor-derived sequence alterations and plasma aneuploidy allowed for assessment of ctDNA molecular response in all patients. Molecular responses captured the therapeutic effect and long-term clinical outcomes in a more accurate and rapid manner compared to radiographic imaging. Patients with sustained molecular responses had longer overall (log-rank p=0.0006) and progression-free (log-rank p&lt;0.0001) survival, with molecular responses detected on average 4 weeks earlier than imaging. ctDNA analyses provide a rapid approach for the assessment of early on-therapy molecular responses and have important implications for the management of patients with SCLC, including the development of improved strategies for real-time tumor burden monitoring.

  Study EGAS00001006831

• Assessment_of_epigenetic_variation_in_human_iPS_cells_Medip

  n order to progress human induced pluripotent stem cells (hiPSCs) towards the clinic, several outstanding questions must be addressed. It is possible to reprogram different somatic cell types into hiPSCs and from studies in the mouse, it appears that an epigenetic memory of the starting cell type is carried over to hiPSCs. However a comprehensive comparative study of the characteristics of these hiPSCs has been missing from the literature. Importantly studies which aimed to address these aspects of hiPSCs have used cells from different patients. In order to avoid this important confounding variable and to keep the genetic background constant, tissue samples were procured from the patients and reprogrammed to iPS cells. The methylation status of these iPS cells will be compared.Protocol:Primary cell cultures were generated and reprogrammed to iPS cells. DNA was extracted and immunoprecipitated using anti-methyl cytosine and anti-hydroxymethyl cytosine antibodies.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 EGAS00001000741

• Circulating RNA profiles of healthy and preeclamptic pregnancies

  This prospective study investigated whether circulating RNA (C-RNA; cell free RNA present in the bloodstream) measurements could non-invasively monitor pregnancy status, with emphasis on detecting molecular markers associated with preterm preeclampsia. Whole blood samples were collected from pregnant women at the time of diagnosis with early-onset preeclampsia with severe features. Blood samples were also obtained normotypical control pregnancies and from patients diagnosed with late-onset preeclampsia for comparison. For all samples, C-RNA was extracted from 4 mL blood and whole transcriptome enrichment performed on cDNA libraries prior to sequencing to a target depth of 50M reads on an Illumina HiSeq 2000 platform. Throughout the course of this work, whole transcriptome RNA sequencing data was obtained from 289 C-RNA samples collected from 178 subjects. The complete dataset consists of samples from the following cohorts: Illumina Preeclampsia Cohort (iPEC): 40 pregnancies at the time of early-onset preeclampsia diagnosis and 73 gestational age-matched controls PEARL preeclampsia cohort: 12 early-onset and 12 late-onset preeclampsia patients PEARL healthy control cohort: 152 longitundinal samples collected from 41 healthy pregnancies

  Study phs002017

• E5103 Correlative Studies

  ECOG-ACRIN E5103 (E5103) was a phase III adjuvant breast cancer trial that randomized 4994 patients with node-positive or high-risk node-negative breast cancer to intravenous doxorubicin and cyclophosphamide every 2 or 3 weeks (at the discretion of treating physician), for four cycles of adriamycin and cyclophosphamide (AC) followed by 12 weeks of weekly paclitaxel (80 mg/m2) alone (Arm A), or to the same chemotherapy with either concurrent bevacizumab (Arm B), or concurrent plus sequential bevacizumab (Arm C). To develop predictive genetic biomarkers for chemotherapy efficacy and toxicities, we have successfully performed genome-wide genotyping and whole exome sequencing in the germline (blood) DNA of 3215 and 672 patients, respectively, from the E5103 trial to investigate the association of genetic variants with chemotherapy efficacy and toxicities. Case and control studies were performed with genome-wide analyses (GWAS) and whole exome analyses (WES) across all arms of E5103 to identify genotypes at single-nucleotide polymorphisms (SNPs) and deleterious gene mutations that were associated with paclitaxel-induced peripheral neuropathy (TIPN). Through GWAS, we have identified a SNP, rs3125923, and through WES, Cytochrome P450 oxidoreductase (POR), that was significantly associated with grade 3-4 TIPN.

  Study phs003201

• RNA sequencing in primary human macrophages overexpressing ETS2

  This dataset consists of RNA sequencing data from an ETS2 overexpression experiment in M0 macrophages. Controlled overexpression of ETS2 mRNA or control mRNA (an equivalent amount of mRNA encoding the reverse complement of ETS2 – thereby controlling for the quantity, length and purine/pyrimidine composition of the transfected RNA but with a transcript that would not be translated) was induced in resting, non-activated (M0) macrophages by transfecting predefined amounts of in vitro transcribed mRNA. To minimise non-specific activation due to the transfected RNA, in vitro transcription was performed using co-transcriptional capping (to minimise uncapped products), and incorporating modified, minimally immunogenic nucleotides (replacing uridine with N1-methyl-pseudouridine and cytidine with methylcytidine). After 18 hours, transfected cells were were activated with low dose LPS and harvested for RNA-sequencing 6 hours later. Sequencing libraries were prepared from 10ng RNA using the SMARTer Stranded Total RNA-Seq Kit v2 - Pico Input Mammalian (Takara) following the manufacturer’s instructions. The quality and molarity of all libraries was assessed using a BioAnalyzer 2100 and the libraries were sequenced on a NovaSeq6000. Raw data are provided as 100 bp paired-end Illumina reads from n = 8 donors.

  Dataset EGAD00001011341

• SNP array analysis of spondylocostal dysostosis patient iPSCs and gene edited isogenic controls

  This study, "SNP Array Analysis of Gene Edited SDV Patient iPSCs" is part of a larger study where we applied our recently established experimental in vitro model system of the human segmentation clock, for the analysis of human segmentation defects of the vertebrae (SDV) including spondylocostal dysostosis (SCD). To this end we generated patient-derived iPSC lines, identified putative disease-causing mutations and corrected these mutations using CRISPR/Cas9-based genome editing technology. We identified in the patient-sample originally obtained from the NIGMS Human Genetic Cell Repository at the Coriell Institute for Medical Research (#: GM13539) heterozygous compound mutations in MESP2. These mutations were corrected accordingly and the resulting clones were genotyped by SNP array analysis.

  Study phs001975

• RNAseq samples of patients with anti-PD-1 resistant HNSCC from a non-randomized, open-label phase 1b clinical trial

  Immune checkpoint blockade (ICB) is an effective treatment for a subset of patients with head and neck squamous cell carcinoma (HNSCC); however, the majority remain refractory. In a nonrandomized, open-label Phase 1b clinical trial, participants with recurrent and/or metastatic (R/M) HNSCC who had progressed on ICB were treated with low-dose 5-azacytidine (5-aza) daily for either 5 or 10 days, in combination with durvalumab and tremelimumab. The primary objective was to determine the biologically effective dose of 5-aza. In this study, RNA sequencing (RNA-seq) was performed on baseline and on-treatment samples from eight participants, and transcriptomic changes were analyzed to assess the impact of low-dose 5-aza treatment.

  Study EGAS50000000728

• WES samples of patients with anti-PD-1 resistant HNSCC from a non-randomized, open-label phase 1b clinical trial

  Immune checkpoint blockade (ICB) is an effective treatment for a subset of patients with head and neck squamous cell carcinoma (HNSCC); however, the majority remain refractory. In a nonrandomized, open-label Phase 1b clinical trial, participants with recurrent and/or metastatic (R/M) HNSCC who had progressed on ICB were treated with low-dose 5-azacytidine (5-aza) daily for either 5 or 10 days, in combination with durvalumab and tremelimumab. The primary objective was to determine the biologically effective dose of 5-aza. In this study, whole exome sequencing (WES) was performed on baseline samples from 12 participants, and genomic alterations were analyzed to access the genetic background of these participants.

  Study EGAS50000000729

• WGS_of_healhy_mesothelial_cells_and_primary_mesothelima_cell_lines

  Mesothelioma is a rare and aggressive cancer associated with previous exposure to asbestos. Currently there are no effective treatments for mesothelioma and majority of patients will die within a year after diagnosis. Although a large number of preclinical and clinical trials assessed the efficacy of various therapuetic modes, none of the tested molecules entered the clinic. Thus, to better understand what drives mesothelioma carcinogenesis and to identify novel targets for therapy, here we aim at performing WGS of heathy mesothelial cells and a panel of mesothelioma cells lines. Primary mesothelial cell lines were established in prof. Marciniak lab from human pleural specimens, while primary mesothelioma cell lines were obtained from MesobanK. Both models constitute low passage cell lines and therefore should closely represent genetics of the original tissue.

  Study EGAS00001005559

• Genomics of Kidney Transplantation

  We have an established consortium consisting of 5 clinical centers which do a total of >800 kidney transplants/year. We have developed a quality-controlled multicenter database with high quality data, and obtained DNA samples and clinical data on 962 recipients and 442 donors. Using our developed infrastructure, we propose to study whether genetic variants are, in part, responsible for the differing outcomes of transplant recipients treated with contemporary immunosuppressive protocols. Our central hypotheses are that the genetic variation is associated with a) kidney transplant outcome, and b) immunosuppressive drug disposition and toxicity. Our long-term goals are to determine whether it is possible to individualize immunosuppressive therapy, based on genetics. At the same time, our studies may elicit new information on pathways important in the immune response or in immunosuppressive drug efficacy, disposition, and toxicity.Our application consists of 3 cores (Genetics Core, Clinical Core, and Administrative Core). We will do a genome wide association study (GWAS). Significant single nucleotide polymorphisms (SNPs) will be identified using the GWAS.Project 1 has 2 specific aims.Aim 1: To identify recipient SNPs that are associated with kidney allograft outcomes, including acute rejection, chronic graft dysfunction, and graft failure. Aim 2: To identify living donor SNPs that are associated with kidney allograft outcomes. Project 2 has 4 specific aims. Aim 1: To identify SNPs that are associated with tacrolimus blood levels.Aim 2: To identify SNPs that are associated with early tacrolimus-related nephrotoxicity and immune suppressant-related new onset diabetes.Aim 3: To identify SNPs that are associated with mycophenolate-related toxicity. Aim 4: To establish the relationships between candidate recipient SNPs, enzyme activity and mRNA expression of the pharmacologic targets of mycophenolate and calcineurin inhibitors. The projects use the genotyping data, which were provided by the Genetics Core, and clinical information, which were collected and entered into the multicenter database by the Clinical Core. Further, biostatistical support (Clinical Core) and administrative oversight and support (Administrative Core) will facilitate data collection, data entry, and data analyses for both projects. The Administrative Core will also facilitate interaction between Sites, Cores, and Projects.Technical note: Regarding to the study design, all transplant recipients should be unrelated. Given the nature of transplantation, some donors may be related to recipients. The donor-recipient relationships are reported in the demographic file under the "related" column. As we did not collect full pedigree information for the related donor-recipient pairs, we have included a IBD file (calculated using PLINK) with the genotype data.

  Study phs001667

• Dataset for B-ALL scRNA-seq

  10x Genomics 5' library scRNA-seq data for 85 B-ALL patients with different subtypes

  Dataset EGAD00001011327

• Illumina Omni5 SNP chip genotyping of MacTel cases and unaffected controls for a genome-wide association study

  Macular telangiectasia type 2 (MacTel) is a rare neurovascular degenerative retinal disease. To identify genetic susceptibility loci for MacTel, we performed a genome-wide association study (GWAS). We genotyped 704 samples, of which 678 have consented to be available here. These samples mainly include MacTel cases but also some unaffected controls (often spouses). The samples were collected through the MacTel Project (funded by the Lowy Medical Research Institute), which includes a registry and extensive clinical analysis to confirm the presence of MacTel in affected individuals. Additional controls were obtained from dbGap [phs000429.v1.p1]. After QC steps, the GWAS utilised 476 cases and 1733 controls of European ancestry.

  Study EGAS00001002249

• Role of Tobacco Smoke in Clear Cell Renal Cell Carcinoma

  Background. Clear cell renal cell carcinoma (ccRCC) is the most common histologically defined renal cancer. However, it is not a uniform disease and includes several genetic subtypes with different prognoses. ccRCC is also characterized by distinguished metabolic reprogramming. Tobacco smoking (TS) is an established risk factor for ccRCC with unknown effects on tumor pathobiology.Methods. We investigated the landscape of ccRCCs and paired normal kidney tissues (NKTs) using integrated transcriptomic, metabolomic and metallomic approaches in a cohort of never smokers (NS) and long-term current smokers (LTS) Caucasian males. Results. All three Omics domains consistently identified a distinct metabolic subtype of ccRCCs in LTS, characterized by activation of oxidative phosphorylation (OxPhos) coupled with reprogramming of the malate-aspartate shuttle and metabolism of aspartate, glutamate, glutamine and histidine. Cadmium, copper and inorganic arsenic accumulated in LTS tumors showing redistribution among intracellular pools, including relocation of copper into the cytochrome c oxidase complex. Gene expression signature based on the LTS metabolic subtype provided prognostic stratification of The Cancer Genome Atlas (TCGA) ccRCC tumors that was independent from genomic alterations.Conclusions. The work identified the TS related metabolic subtype of ccRCC with vulnerabilities that can be exploited for precision medicine approaches targeting metabolic pathways. The results provided rationale for the development of metabolic biomarkers with diagnostic and prognostic applications using evaluation of OxPhos status. The metallomic analysis revealed the role of disrupted metal homeostasis in ccRCC highlighting the importance of studying effects of metals from e-cigarettes and environmental exposures.

  Study phs002083

• Colorectal adenomas, carcinomas and adjacent normal NKI-AvL TGO series NGS-ProToCol

  Cancer is caused by changes in the genome of cells, resulting in aberrant transcripts and proteins. In order to understand why normal cells become cancer cells and how we can identify and destroy them, the CTMM-NGS-ProToCol project aimed to understand prostate and colorectal cancer through DNA and RNA molecular profiling, to further improve diagnosis, prognosis and treatment.The state-of-the-art within the field of genome sequencing makes it possible to identify all the modifications in our genome that encode for RNA and of the transcripts themselves. The latest development in RNA sequencing is that if one does not select for polyA RNA, but first removes rRNA from the total RNA and then performs ‘random’ RNA sequencing, 30-60% of the reads are intronic. Since about 40% of the genome consists of genes (and only 1.6% is exonic), deep RNA sequencing will cover not only the exons, but also a large portion of the introns and therefore much of the genome. The power of the combined sequencing of genome and transcriptome is the full view of copy number aberrations (CNA), exon mutations and the consequence and relevance of the mutations at the RNA level in the form of differential gene expression, alternative splicing, alternative promoter usage, novel transcripts, fusion transcripts, read-through transcripts and mutations. For the colorectal (tumor) tissue dataset, we applied molecular profiling to 30 colorectal adenomas, 30 colorectal carcinomas and 18 normal adjacent colon tissues.

  Study EGAS00001002854

• Genetic Analysis of Limb Malformation Disorders: Miller Syndrome Sequencing Study (LMD-MS)

  The overall goal of this project is to investigate the etiology and pathogenesis of malformations (i.e., birth defects) of the limb, concentrating on abnormalities of limb patterning such as limb deficiency/duplications and multiple congenital contractures. The exome sequences of two siblings and two unrelated individuals were obtained by massively parallel DNA sequencing. The four individuals were affected with Miller syndrome (OMIM: 263750). Additionally, the whole-genome sequences of a family of four were obtained with the method of Complete Genomics Incorporated (CGI). The two offspring were both affected with Miller syndrome and is the same sibling pair mentioned previously from whom exome sequences were also obtained.

  Study phs000244

• Data generated by the Drier Lab at HUJI

  This Data Access Committee is responsible to all data generated by and deposited by the Drier Lab at the Faculty of Medicine of the Hebrew University of Jerusalem (http://yotamdrier.ekmd.huji.ac.il/)

  Dac EGAC50000000060

• Profiling_molecular_heterogeneity_in_human_primary_microglia

  In the brain the cells that control inflammation are called a type of white blood cell called microglia. Microglia are located throughout the brain and spinal cord and account for 10–15% of all cells found within the brain. As the resident white blood cells, they are the main active immune defence in the central nervous system (CNS). Microglia are part of an important class of cells known as macrophages that have two main states: M1 and M2. M1 cells are pro- inflammatory, leading to more inflammation, while M2 are anti-inflammatory, and drive wound healing. In this study, we will collect primary microglia from surgical biospies of 100 individuals. 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 EGAS00001002494

• Exploration of CNV’s and SNV’s in cancers with well-known genetic rearrangements: Identification of additional genetic changes in rearrangements-driven cancer

  Specific cancers are well known to have specific gene rearrangements. Representative cancer is chronic myeloid leukemia (CML) which is famous for BCR/ABL gene rearrangements. By the way, also genetic rearrangements are not infrequently found in acute leukemia, and those include CBFB/MYH11 rearrangements, PML/RARA rearrangements, AML/ETO rearrangements, and BCR/ABL rearrangements. The situation is same is for lung adenocarcinoma of non-smokers and prostate cancers, and ALK and RET gene rearrangements are recently found. However, not all these rearrangements are sufficient to develop cancer, and it is suggested that some genetic aberrations are required for the development and progression of cancer in addition to these genetic rearrangements. In this study, we have a plan to explore the existence of additional genetic changes including single nucleotide variants (SNV’s) and copy number variations (CNV’s) in cancers with specific genetic rearrangements so as to define additional genetic changes that confer full oncogenic potential to cancer cells. So as to look at SNV’s and small indels, we are going to use whole exome sequencing (WES) data. And as for CNV’s, we are going to use array-CGH data. Also, to check the functionality of these genetic changes, we are going to use whole transcriptome data (WTS). In addition, in samples with whole genome sequencing (WGS ) data, we are going to explore genetic changes in intronic regions also. In fact, we sequenced 10 AML samples which are known to have specific genetic rearrangements. Analysis of these WGS, WES and WTS data are under the way. If we have a chance to access WES, array-CGH and WTS data of other cancer samples with specific genetic rearrangements, we would be able to compare additional genetic changes required for oncogenesis in cancer cells with various genetic rearrangements. We hope this result will enlighten our understanding of cancer genetics further.

  Study EGAS00001000673

• DNA Methylation Analysis of Prostate Cancer Cell Lines and Tissues Using Next Generation Sequencing

  Aberrant DNA methylation changes are known to occur during prostate cancer progression beginning with precursor lesions. Utilizing fifty nanograms of genomic DNA in Methylplex-Next Generation Sequencing (M-NGS) we mapped the global DNA methylation patterns in prostate tissues (n=17) and cells (n=2). Peaks were located from mapped reads obtained in each sequencing run using a Hidden Markov Model (HMM)-based algorithm previously used for Chip-Seq data analysis(http://www.sph.umich.edu/csg/qin/HPeak). The total methylation events in intergenic/intronic regions between benign adjacent and cancer tissues were comparable. Promoter CGI methylation gradually increased from -12.6% in benign samples to 19.3% and 21.8% in localized and metastatic cancer tissues and approximately 20% of all CpG islands (CGIs) (68,508) were methylated in tissues. We observed distinct patterns in promoter methylation around transcription start sites, where methylation occurred directly on the CGIs, flanking regions and on CGI sparse promoters. Among the 6,691 methylated promoters in prostate tissues, 2481 differentially methylated regions (DMRs) are cancer specific and several previously studied targets were among them. A novel cancer specific DMR in WFDC2 promoter showed 77% methylation in cancer (17/22), 100% methylation in transformed prostate cell lines (6/6), none in the benign tissues (0/10) and normal PrEC cells. Integration of LNCaP DNA methylation and H3K4me3 data suggested a role for DNA methylation in alternate transcription start site utilization. While methylated promoters containing CGIs had mutually exclusive H3K4me3 modification, the histone mark was absent in CGI sparse promoters. Finally, we observed difference in methylation of LINE-1 elements between transcription factor ERG positive and negative cancers. The comprehensive methylome map presented here will further our understanding of epigenetic regulation of the prostate cancer genome. Overall Design: We mapped the global DNA methylation patterns in prostate tissues (n=17) and cells (n=2) from fifty nanograms of genomic DNA using Methylplex-Next Generation Sequencing (M-NGS). For replicate analysis in cell lines, a total of 4 runs were completed for PrEC prostate normal cell line, and 5 runs were completed for LNCaP prostate cancer cell line. For tissue samples, 2 benign prostate samples were sequenced twice on Illumina next generation sequencing platform to access overall repeatability of M-NGS.

  Study phs000597

• Genomic Studies of Gilles de la Tourette Syndrome

  This study consists of three components. The first component includes genome-wide association study (GWAS) data on 695 TS cases and 198 ancestry matched controls from the first TS GWAS of 1285 TS cases and 4964 ancestry matched controls. The second component includes genome-wide association study (GWAS) data on 2106 TS cases from the second TS GWAS of 2716 TS cases and 3762 ancestry matched controls. The third component consists of 438 individuals representing 146 probands with DSM-IV-TR diagnosed Tourette Syndrome and their parents (146 complete parent-offspring trios). These individuals are part of the whole exome sequencing study, aiming to use whole exome sequencing of TS parent-offspring to identify de novo protein-truncating variants (PTVs) that are present in the child with TS but not in either parent. All subjects were collected by the Tourette Association of America International Consortium for Genetics (TAAICG) at seven sites in the United States and Canada. Both affected individuals and unaffected relatives were assessed for the presence of Tourette Syndrome and Chronic (Persistent) Tic Disorder (CTD) using a standardized, semi-structured interview, which has high clinical validity and reliability for the diagnoses of TS and CTD (TSAICG, Am J Hum Genet, 2007 (PMID: 17304708)); Darrow et al., Psychiatric Research, 2015 (PMID: 26054936)).

  Study phs001380

• Mutation signatures in melanocytic nevi reveal characteristics of defective DNA repair

  Nevi are common benign melanocytic neoplasms that can be found on any skin location and share many of the genomic hallmarks of melanoma. In our recent report involving whole-exome sequencing of acquired nevi (Stark et al., 2018), ultra-violet radiation (UVR) related somatic mutation signatures (“signature 7”) (Alexandrov et al., 2013) were frequently observed in all lesions that were located on sun-exposed sites. In the study presented herein, we sought to investigate whether an independent Spanish cohort (Valencia, Spain) of minimally sun exposed nevi had genomic features consistent with our previous findings.

  Study EGAS00001004274

• Genetic landscape of pediatric Infant Acute Lymphoblastic leukemia

  We performed whole genome sequencing of tumor and normal DNA samples obtained from 22 infant ALL cases with MLL rearrangements. In addition, we sequenced 2 paired diagnostic-relapse samples. Using complementary pipelines, somatically acquired genetic changes were analyzed, including single nucleotide variations (SNVs), insertion/deletions, structural variations and copy number variations in the cancer genomes. In addition, exome sequencing was performed on paired diagnostic and normal DNA samples obtained from 20 cases of non-infant MLL rearranged leukemias and somatic mutations in the coding regions were identified.

  Study EGAS00001000246

• Whole exome sequencing on Pediatric MDS patients

  WES was performed on 126 cases (SAMD9/9Lmut: 64/67; GATA2mut 24/35, MDS wildtype 38/471) using SureSelect Human All Exon V6 enrichment (Agilent, cat# 5190-8863). The generated libraries were sequenced on the Illumina Hiseq 2500 with 150bp paired-end reads. FASTQ files were processed using SeqNext platform (JSI medical system, Germany), with gene-based alignment to a virtual panel of 300 genes consisting of genes relevant to bone marrow failure, MDS predisposition, and hematological cancers as per the Pan-Cancer studies with cohorts of &gt;10,000 cancers. The generated BAM files are submitted here.

  Study EGAS00001005432

• Persistence of circulating tumor DNA in breast cancer patients during neoadjuvant treatment is a significant predictor of poor tumor response

  Purpose:Accurate assessment of response during neoadjuvant systemic treatment (NST) poses a major clinical challenge. Therefore, a minimally-invasive assessment of tumor response based on cell-free circulating tumor DNA (ctDNA) may be beneficial to guide treatment decisions.Experimental Design: We profiled 93 genes in tissue from 193 early breast cancer patients. Patient-specific assays were designed to track ctDNA during NST from 145 patients with available plasma. ctDNA presence and levels were correlated with complete pathological response (pCR) and residual cancer burden (RCB) as well as with clinicopathologic characteristics of the tumor to identify potential proxies for ctDNA release. Moreover, we tested the predictive value of driver events identified in the tumor.Results:At baseline, ctDNA could be detected in 63/145 (43.4%) patients and persisted in 25/63 (39.7%) patients at mid-therapy (MT) and 15/63 (23.8%) patients at the end of treatment. ctDNA detection at MT was significantly associated with higher RCB (OR 0.062, 95% CI 0.01-0.48, P=0.0077). Out of 31 patients with detectable ctDNA at MT, 30 patients (96.8%) were non-responders (RCB II, n=8; RCB III, n=22) and only one patient responded to the treatment (RCB I). Considering all 145 patients with baseline (BL) plasma, none of the patients with RCB 0 and only 6.7% of patients with RCB I had ctDNA detectable at MT, while 30.6% and 29.6% of patients with RCB II/III, respectively, had a positive ctDNA result. Conclusion:Overall, our results demonstrate that the detection and persistence of ctDNA at mid-therapy may have the potential to negatively predict response to neoadjuvant treatment and identify patients who will not achieve pCR or be classified with RCB II/III.

  Study EGAS00001005798

• Gut microbiome dynamics unravelled with metagenomics sequencing

  It has been already several years since we discovered that microbial therapeutics like Faecal Microbiota Transplantation can be used to treat Clostridium difficile infection (CDI) and Inflammatory bowel disease (IBD) with great success. The concept has challenged us to test the limits of our knowledge with the perspective to improve health in the most diverse conditions like Parkinson disease, depression, autism and obesity, just to mention a few. Note: the figure belongs to the original paper and is owned by the authors. Allyson L.Bird and colleagues from Genentech Inc. (San Francisco) recently published an interesting study building up on the key concept that “As microbial therapeutics are increasingly being tested in diverse patient populations, it is essential to understand the host and environmental factors influencing the microbiome.” The authors of this work analysed 1359 gut microbiome samples using shotgun metagenomics sequencing.  The results of the comparison done between microbiota composition across the different groups showed that biological sex is the strongest driver of diversity, while aging (age range studied: 20–69) appears to be the second factor. As smartly outlined in the authors’ own graphical abstract (figure to the left), Bacteroidota species consistently increased with donors' age while Actinobacteriota species, including Bifidobacterium, decreased. This study also integrated data from oncological patients to draw cancer-related evidences. After correcting the biases for technical and geographic differences, they found that cancer patients show significantly altered gut bacterial communities compared with their healthy counterparts. Eg. E.coli was found to be increased in cancer patients together with Bacteroidota/Firmicutes ratios. By comparing sequential samples taken from the same donors (413) within short-distance time points, the authors found that in the short term, intra-individual differences were less than the inter-individual ones, indicating a certain community stability being the norm. One of the pivotal points from this interesting study is that the data proceeds from extremely well-defined and selected donors. Specifically, the data for 949 selected healthy donors came from the Milieu Intérieur (MI) Consortium and extensive metadata, including demographic variables, serological measures, dietary information, and systemic immune profiles, are available. This precise stratification allowed for the robust detection of smaller changes, compared to former works. All sequencing data has been deposited in the EGA archive under the accession number EGAS00001004437. Enjoy this amazing open access paper published in the Experimental Journal of Medicine on January 2021.

  Blog gut-microbiome-dynamics-unravelled

• This study generated Oxford Nanopore long read sequencing data of cancer cell line mixtures for validating long-read variant calls in cancer genomics

  Long-read sequencing (LRS) improves genome alignment and facilitates resolving variants in complex genomic regions, making it a promising approach for cancer variant detection and biomarker discovery. Here, we evaluate the performance of LRS to detect somatic variants across different tumour purities and sequencing depths by comparing to somatic variants from short-read sequencing. We generated experimental mixtures of cancer cell lines and matched normal cell lines to simulate 10 tumour purities (ranging from 0 to 100% tumour content). This resulted in 22 samples which were sequenced using whole genome LRS to a targeted depth of 60x and somatic variants were identified. We downsampled sequencing data to explore the optimal read depth for somatic variant detection. Our results show that long-read variant calling tools achieve recall rates comparable to short-read gold standards. While a tumour sequencing depth of 30x to 60× is generally sufficient for detecting common variants, particularly structural variants (SVs), sequencing the matched normal sample at adequate depth is crucial for accuracy. Notably, we found variants unique to LRS that may represent real and previously undetected events. This study highlights key factors for optimising cancer genome sequencing with LRS and provides a comprehensive dataset of cell line mixtures for future research and tool development.

  Study EGAS00001008107

• B-cell precursor acute lymphoblastic leukemia acute lymphoblastic leukemia Micro-C sequencing data Data Access Committee

  This dataset included 33 childhood B-cell precursor (BCP) acute lymphoblastic leukemia Micro-C samples. All samples were subjected to Illumina pair-end sequencing.

  Dac EGAC50000000467

• Whole exome sequence of intratumor heterogeneity study

  This dataset contains whole exome sequence data from 86 samples from 6 patient. All the experiments were performed on Illumina HiSeq platform with raw reads stored in fastq format.

  Dataset EGAD00001005453

• Type 1 Diabetes Genetics Consortium (T1DGC): Multi-Ethnic ImmunoChip Study

  Type 1 diabetes (T1D) is characterized by an autoimmune attack that destroys the insulin-producing pancreatic beta cells and is driven by diverse genetic and environmental factors. Between 8-100 chromosome regions are known to contain variants that alter T1D risk, substantially fewer than many other common diseases. Less is known about the contribution of T1D risk alleles in non-European populations, despite recent increases in T1D diagnoses in multiple non-European ancestry groups. To address these gaps in T1D genetics, we doubled the sample size from the previous largest T1D association study, genotyped ancestrally diverse T1D cases, controls, and affected families with the Illumina ImmunoChip array, and imputed additional variants using a large haplotype reference panel. After quality filtering, a total of 61,427 participants and 140,333 genotyped ImmunoChip variants were included in analysis, providing dense coverage in 188 autosomal regions ("ImmunoChip regions") and sparse genotyping in other regions of the genome. Each participant was assigned to one of five ancestry groups using k-means clustering of ImmunoChip genotype principal components: European (EUR, N = 47,319), African Admixed (AFR, N = 4,290), Finnish (FIN, N = 6,991), East Asian (EAS, N = 588) and Other Admixed (AMR, N = 2,239). In total, 16,159 T1D cases, 25,386 controls (unrelated) and 6,143 trio families (i.e., an affected child and both parents) were genotyped and included in association analyses. Imputation with the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) multi-ethnic reference panel was used to improve discovery power and fine-mapping resolution. After imputation, the number of variants in ImmunoChip regions with imputation R-squared &gt; 0.8 and MAF &gt; 0.005 in each ancestry group was 322,084 (AFR), 166,274 (EUR), 163,612 (FIN), 137,730 (EAS) and 188,550 (AMR). Imputed genotypes were compared in the same participants who have whole genome sequencing data, resulting in high concordance. Unrelated cases and controls (N = 41,545) were analyzed initially, assuming an additive inheritance model. T1D trio families (N = 6,143 trios, some trio families were multiplex and analyzed as multiple trios) were analyzed separately, with meta-analysis of case-control and trio results used for discovery of regions associated with T1D (p &lt; 5&#215;10-8). Summary-level data at each SNP (genotype and imputed) for type 1 diabetes (outcome) are provided in dbGaP.

  Study phs002468