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

in 25.66 milliseconds.

• Epigenetic memory of SARS-CoV-2 mRNA vaccination in monocyte-derived macrophages

  Immune memory is key to effective antimicrobial responses, but the impact of mRNA vaccines on this process is not fully understood. Our research shows that SARS-CoV-2 mRNA vaccines alter the epigenetic profile of human macrophages, specifically enhancing histone acetylation, which is linked to immune training. Significant epigenetic changes, along with increased cytokine release, require two vaccine doses. However, these effects diminish over time but can be restored with a booster dose six months later, maintaining a strong pro-inflammatory response.

  Dataset EGAD50000000495

• Combination pembrolizumab and radiotherapy induces systemic anti-tumor immune responses in immunologically-cold non-small cell lung cancer

  This data set includes sequence files from whole exome sequencing and RNA sequencing from serial tissue and blood biospecimens from a phase 2 clinical trial of stereotactic body radiation therapy (SBRT) followed by pembrolizumab in metastatic non-small cell lung cancer (NSCLC; NCT02492568).

  Study EGAS50000000277

• Single cell RNA-seq from AD and PS patients

  We applied single cell immune profiling to gain a holistic view from immune cells infiltrating the skin to circulating immune cells, facilitating a better understanding of the two diseases in their entirety, and the identification of potential type-2 or type-3-disease-driving mechanisms.

  Study EGAS00001007055

• Succession Of Transiently Active Tumour-Initiating Cell Clones inHuman Pancreatic Cancer

  To elucidate whether newly acquired genetic alterations during serial transplantation of patient derived primary pancreatic cancer cultures contribute to the observed clonal dynamics in vivo, all coding genes of two patient derived primary cultures and derived genetically marked serial xenografts (1°/2°/3°) were sequenced.

  Study EGAS00001000882

• DKFZ-HIPO Project H021/NCT MASTER

  Clinical genome and transcriptome sequencing in younger adults with advanced-stage cancer across all histologies: DKFZ-HIPO Project H021 (http://www.hipo-heidelberg.org/hipo2/index.php/menu-item-2/32-021-prospective-genetic-characterization-of-tumors-from-individual-patients-of-special-interest) / NCT MASTER (http://www.nct-heidelberg.de/forschung/nct-master.html)

  Study EGAS00001000948

• V2_panel_bait_design_test

  This study comprises of three different datasets. 1) 57 samples from the 1243 canapps cell line study,2) 91 FFPE normal samples and 3) 87 samples from the SCORT WS2 dataset. The aim is to sequence these 235 samples in order to test the new V2 Colorectal bait design.

  Study EGAS00001001780

• RNA-Seq on OCIAML-22 Fractions

  The donor AML patient (unsorted) that was used to generate OCI-AML22 was expanded for the indicated number of days (Donor: 0 day, 12d: 12 days, 2m: 2 months, 3m: 3 months), then sorted for the indicated fraction (CD34p: CD34+; CD34m: CD34-, CD38m: CD38-, CD38m: CD38-).

  Study EGAS00001006512

• BLUEPRINT release August 2016, ChIP-Seq for monocyte - T=0days, on genome GRCh38

  ChIP-Seq data for 2 monocyte - T=0days sample(s). 7 run(s), 7 experiment(s), 7 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002491

• BLUEPRINT release August 2016, ChIP-Seq for monocyte - RPMI_BG_T=1hr, on genome GRCh38

  ChIP-Seq data for 2 monocyte - RPMI_BG_T=1hr sample(s). 6 run(s), 6 experiment(s), 6 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002297

• BLUEPRINT release August 2016, ChIP-Seq for monocyte - RPMI_T=1hr, on genome GRCh38

  ChIP-Seq data for 2 monocyte - RPMI_T=1hr sample(s). 4 run(s), 4 experiment(s), 4 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002453

• BLUEPRINT release August 2016, ChIP-Seq for monocyte - RPMI_LPS_T=1hr, on genome GRCh38

  ChIP-Seq data for 2 monocyte - RPMI_LPS_T=1hr sample(s). 8 run(s), 6 experiment(s), 6 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002376

• BLUEPRINT release August 2016, Bisulfite-Seq for effector memory CD8-positive, alpha-beta T cell, terminally differentiated, on genome GRCh38

  Bisulfite-Seq data for 2 effector memory CD8-positive, alpha-beta T cell, terminally differentiated sample(s). 34 run(s), 3 experiment(s), 4 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002346

• Human lung cell atlas 10x and SS2 sequencing data (2 of 3)

  Contains FASTQs for cells from 20 10x channels across 2 NovaSeq runs, 42 384-well plates across 6 NovaSeq runs, and 12 96-well plates across 4 NextSeq runs.

  Dataset EGAD00001006127

• Concatenated long-read single-cell RNA sequencing of momentum biopsies of ovarian cancer patients

  Concatenated long-read single-cell RNA sequencing samples prepared using 10X and the HIT-scIsoSeq protocol. The sequencing was performed on Sequel II Pacbio machine. 3 ovarian cancer patients, 5 omentum biopsies samples: 3 metastasis samples (one per patient), 2 healthy samples (one per patient except Patient2). 4 bam files per metastasis sample, 2 bam files per healthy sample.

  Dataset EGAD00001009814

• BLUEPRINT release August 2016, ChIP-Seq for adult endothelial progenitor cell, on genome GRCh38

  ChIP-Seq data for 2 adult endothelial progenitor cell sample(s). 16 run(s), 14 experiment(s), 14 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002487

• BLUEPRINT release August 2016, ChIP-Seq for endothelial cell of umbilical vein (proliferating), on genome GRCh38

  ChIP-Seq data for 2 endothelial cell of umbilical vein (proliferating) sample(s). 14 run(s), 14 experiment(s), 14 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002424

• BLUEPRINT September 2016, ChIPmentation for central memory CD8-positive, alpha-beta T cell from venous blood, on Genome GRCh38

  ChIPmentation data for 2 sample(s) for central memory CD8-positive, alpha-beta T cell from venous blood, on Genome GRCh38. 11 run(s), 7 experiment(s), 7 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002924

• BLUEPRINT release August 2015, ChIP-Seq for endothelial cell of umbilical vein (resting), on genome GRCh38

  ChIP-Seq data for 2 endothelial cell of umbilical vein (resting) sample(s). 11 run(s), 11 experiment(s), 11 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001559

• BLUEPRINT release January 2015, ChIP-Seq for endothelial cell of umbilical vein (resting)

  ChIP-Seq data for 2 endothelial cell of umbilical vein (resting) sample(s). 10 run(s), 10 experiment(s), 10 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

  Dataset EGAD00001001183

• BLUEPRINT release August 2015, ChIP-Seq for endothelial cell of umbilical vein (proliferating), on genome GRCh38

  ChIP-Seq data for 2 endothelial cell of umbilical vein (proliferating) sample(s). 12 run(s), 12 experiment(s), 12 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001487

• BLUEPRINT release August 2015, ChIP-Seq for germinal center B cell, on genome GRCh38

  ChIP-Seq data for 2 germinal center B cell sample(s). 12 run(s), 11 experiment(s), 11 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001502

• BLUEPRINT release January 2015, ChIP-Seq for effector memory CD8-positive, alpha-beta T cell

  ChIP-Seq data for 2 effector memory CD8-positive, alpha-beta T cell sample(s). 10 run(s), 10 experiment(s), 10 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

  Dataset EGAD00001001127

• BLUEPRINT release August 2014, ChIP-Seq for CD8-positive, alpha-beta T cell

  ChIP-Seq data for 2 CD8-positive, alpha-beta T cell sample(s). 13 run(s), 13 experiment(s), 13 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

  Dataset EGAD00001000936

• BLUEPRINT September 2016, ChIPmentation Activated B-Cell-Like Diffuse Large B-Cell Lymphoma from lymph node, on Genome GRCh38

  ChIPmentation data for 2 sample(s) Activated B-Cell-Like Diffuse Large B-Cell Lymphoma from lymph node, on Genome GRCh38. 3 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002944

• BLUEPRINT September 2016, ChIPmentation Germinal Center B-Cell-Like Diffuse Large B-Cell Lymphoma from lymph node, on Genome GRCh38

  ChIPmentation data for 2 sample(s) Germinal Center B-Cell-Like Diffuse Large B-Cell Lymphoma from lymph node, on Genome GRCh38. 6 run(s), 6 experiment(s), 6 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002946

• BLUEPRINT release January 2015, ChIP-Seq for endothelial cell of umbilical vein (proliferating)

  ChIP-Seq data for 2 endothelial cell of umbilical vein (proliferating) sample(s). 13 run(s), 13 experiment(s), 13 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

  Dataset EGAD00001001136

• BLUEPRINT release August 2016, ChIP-Seq for endothelial cell of umbilical vein (resting), on genome GRCh38

  ChIP-Seq data for 2 endothelial cell of umbilical vein (resting) sample(s). 13 run(s), 13 experiment(s), 13 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002488

• Pervasive H3K27 acetylation and ERV expression in H3.3K27M gliomas present a therapeutic vulnerability

  We sequenced a total of 2 H3.3K27WT (pcGBM2, G477; 3 replicates total) and 2 H3.3K27M (DIPGVI, DIPGXIII; 6 replicates total) patient-derived cell lines as well as Crispr/Cas9 H3.3K27M-KO clones for one of the cell lines (3 replicates total; DIPGXIII-KO) using ATAC-Seq. P-XX designates passages of replicates. These samples can be found at GEO under accession number GSE128744. This repository contains 1 replicate of G477 to be released under controlled access.

  Dataset EGAD00001004961

• limbal stem cells from Aniridia patients

  Limbal stem cells obtained during penetrating keratoplasty from aniridia patients with congenital aniridia (Lagali Stage 4), limbal tissue was digested in collagenase A solution (4 mg/ml) in keratinocyte serum-free medium (KSFM) (Thermo Fisher Scientific; Waltham, MA) for 20 h at 37 °C. Cell suspensions were filtered through a use of Flowmi® micro strainer (SP Bel-Art; Wayne, NJ). LSC clusters were dissociated with trypsin-EDTA (0.05%) solution and cultivated in KSFM. Medium was refreshed every other day. Subconfluent (80–90%) limbal epithelial cells were harvested at passage 2

  Dataset EGAD00001011124

• PyEGA3 download client

  PyEGA3 Checks prior to download - Is the dataset available for download? Most datasets are available using the PyEGA3 Download client, except for specific legacy datasets. For these ones, refer to Live Outbox distribution, an alternative way to download these datasets.If the dataset you've been granted access to begins with the ID EGAD5XXXXXXX, please refer to the Live Outbox distribution. If the dataset you have been granted access to contains encrypted data (filename ending in .gpg), contact EGA Helpdesk for detailed information on how to download these files.General Information The PyEGA3 download client is available at its GitHub repository. In its README you will find a step by step guide on how to use it to download files you have been granted access to. PyEGA3 implements the GA4GH-compliant HTSGET protocol supporting requests over genomic ranges. This enables the download of specific regions of interest rather than the entire file. HTSGET is not possible for all the files held by EGA: if the dataset does not contain index files (e.g. filename ending in *.crai), then genomic range requests cannot be performed. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Check out our video tutorial! Frequently Asked Questions General questions How do I download the datasets to which I have been granted access? After setting up your EGA download account, you can proceed to download using the EGA download client – PyEGA3. The pyEGA3 download client is a python-based tool for viewing and downloading files from authorised EGA datasets. This download client is continuously being developed for more user-friendly download experiences. Can I download the dataset’s metadata via the download client? No, the download client cannot be used to download metadata. Registered EGA users can download metadata of an authorised dataset by logging into the EGA webpage and navigating to the dataset of choice. Approximately two thirds down the page you will find the option to download the metadata as a zip file. How do I download large datasets as quickly as possible? The main EGA download client is pyEGA3 download client, a python-based tool for viewing and downloading files from authorised EGA datasets.We could exceptionally consider the option of an Aspera download for users experiencing very slow download rates or facing particular issues.Contact the EGA Helpdesk for further information. Pre-requirement and Installation What are the major requirements for the PyEGA3 download client installation and usage? The PyEGA3 client is compatible with any OS with Python 3.6+ installed. The client requires a connection to the internet, sufficient space on the destination drive, and the EGA download account credentials. Where can I find the PyEGA3 download client installation and download instructions? We strongly recommend checking our video tutorial demonstrating the usage of PyEGA3 from installation to file download. More detailed information is available at PyEGA3 GitHub. What ports are required for the PyEGA3 download client to function? PyEGA3 makes HTTPS calls to the EGA Data API (https://ega.ebi.ac.uk:8443). This port 8443 must be reachable from the location where PyEGA3 is executed to avoid timeouts. PyEGA3 download client How to update the PyEGA3 download client to be on the latest version? Updating the client to the latest version can be achieved by running the following command: pip3 install pyega3 --upgrade I do not have an active EGA account. Can I test the download with the PyEGA3 download client? An EGA download test account (ega-test-data@ebi.ac.uk) has been created for troubleshooting and training purposes. The test account does not require an EGA username and password because it contains publicly accessible files from the 1000 Genomes Project. More information about the use of this account and log in details are available at pyEGA3. I lost my connection while the download was in progress. Does this mean that I have to download the file again from the start? The PyEGA3 download client supports automatic resumption of downloads. To enable quicker download speeds, the API breaks files into up to four segments and downloads them in parallel. With the resume feature, downloads will automatically resume if you encounter any errors or if the connection is interrupted. How can I improve the overall download speed of the PyEGA3 download client? Download speeds can be optimised using the --connections parameter which will parallelise download at the file level. If the --connections parameter is provided, all files >100Mb will be downloaded using the specified number of parallel connections. It is important to note that files are still downloaded sequentially, so using multiple connections does not mean downloading multiple files in parallel. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Why is my file taking a long time to be saved? Please note that when a file is being saved, it goes through two processes. First, the downloaded file "chunks" are pieced back together to reconstruct the original file. Secondly, PyEGA3 calculates the checksum of the file to confirm that the file was downloaded successfully. Larger files will take more time to reconstruct and validate the checksum. Why is the HTSGET protocol not working with my dataset of interest? PyEGA3 implements the GA4GH-compliant htsget protocol for supporting requests over genomic ranges. This exciting new feature means that for data files with accompanying index files (e.g. .crai for CRAM files) users can download specific regions of interest rather than the entire file, saving both time and storage space. Please note that in order for the genomic range requests to work for a BAM, CRAM or VCF file there must be an associated BAI, CRAI or TBI file, respectively. If the dataset does not contain these index files then genomic range requests cannot be performed. After download Do I need to decrypt data files downloaded through the pyEGA3 download client? Files are transferred over secure HTTPS connections and received unencrypted, thus there is no need for decryption after data download. What is the purpose of an MD5 file? After the download completes, file integrity is verified using checksums. The PyEGA3 download client automatically verifies each file’s unencrypted md5 after download to ensure that the file was downloaded correctly from the EGA. Errors Why do I get a “400 Client Error” while trying to access the download client? Ensure that your credentials are formatted correctly. Please contact the EGA Helpdesk if you are unsure about your account’s username (i.e. your email address). Why do I get a “500 Server Error”? It is important to note that files are still downloaded sequentially, so using multiple connections does not mean downloading multiple files in parallel. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Otherwise, 500 server errors are internal server errors from EGA’s end. Retry after some time and, if the 500 error persists: check our homepage or Twitter for any outage information or report it to EGA Helpdesk. Why do I get a “slice error”? This type of error occurs when one slice download was prematurely interrupted. Please re-start the same download. The download client will pick up any partial download and download any missing slices. Why do I get the error “Dataset' EGADXXXXXXXXXXX' is not in the list of your authorised datasets”? Please note that the data access was given to the email (username) provided to the Data Access Committee (DAC) in your official request. Therefore, you shall check that the account that you are using is the same one that you sent to the DAC. Why do I get “ERROR:root:Failed to obtain IP address”? This might mean that the connection ports are blocked. Check that the port 8443 is reachable.In case the port is reachable and the error persists, please try to modify the file pyega3.py on line 44 in the python module installation: Replace endpoint = 'https://ipinfo.io/json' with endpoint = 'https://api.myip.com' Why do I get the error “Invalid username, password or secret key - please check and retry”? Make sure that you are using the correct credentials (username and password) and there is no typo. Contact EGA Helpdesk if the problem persists.

  Documentation access/download/files/pyega3

• Transcriptional effect of 4HTBZ on Caco-2 cells

  This study was performed to investigate the transcriptional effect of the H2S donor 4HTBZ on intestinal epithelial cells upon inflammatory stimulation. Caco-2/TC7 cells were pre-stimulated with interferon gamma (IFNG, 2.5 ng/mL) and tumor necrosis factor alpha (TNF, 10 ng/mL), followed by treatment with 4HTBZ (50 µM) or vehicle (0.1% DMSO) for 72 hours. Total RNA was extracted using the RNeasy Mini Kit (Qiagen, Hilden, Germany), and cDNA libraries were prepared with the QuantSeq 3′ mRNA-Seq Kit (Lexogen, Inc.) and sequenced on an Illumina HiSeq4000 system.

  Study EGAS50000001237

• GoNL release 5 haplotype panel

  The samples in this panel come from 250 families: 248 parents-child trios and 2 parent-child duos. As the children do not provide additional haplotypes or population information, they were excluded from the panel. The samples present in the release are composed of 248 couples, 2 single individuals and 1 sample composed from the 2 haplotypes from the duo's children transmitted by their missing parent. The composed sample is named gonl-220c_223c.The files contain a total of 18.9M SNVs and 1.1M INDELs in autosomal chromosomes. They were generated by phasing/imputing the SNVs (a) and INDELs (b) using MVNCall. Only sites passing filters are reported. Sites filtered as part of the GoNL inaccessible genome were kept (but flagged as filtered) and still may contain true positive calls but should be used with care as they are located in parts of the genome that are less well captured (systematic under or over-covered or low-mapping quality)

  Dataset EGAD00001000744

• DNA polymerase and mismatch repair deficient cancers exert distinct genome-wide microsatellite signatures 2

  Study EGAS00001004816

• Human skeletal muscle CD90+ fibro-adipogenic progenitors are associated with muscle degeneration in type 2 diabetic patients

  Study EGAS00001005599

• Summary statistics for cervical cancer GWAS

  Summary statistics from GWAS meta-analysis of cervical cancer

  Dataset EGAD00001004361

• Clinical Outcomes and ctDNA Correlates for CAPOX BETR: A phase II trial of Capecitabine, Oxaliplatin, Bevacizumab, Trastuzumab in Previously Untreated Advanced HER2+ Gastroesophageal Adenocarcinoma

  We performed circulating tumor DNA (ctDNA) analysis on plasma of patients with advanced HER2 positive gastroesophageal adenocarcinomas who were treated on a phase 2 clinical trial testing the addition of bevacizumab to capecitabine, oxaliplatin, and trastuzumab. ctDNA was analyzed using panel based next generation sequencing assay (Predicine CARE), low-pass whole genome sequencing (LP-WGS), and methylation based panel assay (Predicine ALERT). This allowed us to derive tumor mutational profiles, circulating tumor fractions in plasma, and changes in both over time. We show that tumor fraction and the presence of mutations in the MAPK pathway are prognostic in our patient cohort. LP-WGS data from baseline and cycle 2 of treatment is available through dbGaP. Additional data is submitted alongside the manuscript.

  Study phs003706

• WGoxBS and SureSelect data of the POPS placenta samples

  We carried out whole-genome oxidative bisulfite sequencing (WGoxBS) in the placentas of two healthy female and two healthy male pregnancies generating an average genome depth of coverage of 25x. The sex-specific differential methylation pattern observed in this region was validated in additional 8 healthy placentas (including 2 from the WGoxBS) using SureSelect in-solution target capture. For WGoxBS, placental genomic DNA (4 µg) from 4 healthy pregnancies was processed to achieve 10 kb fragments with the g-Tube (Covaris), according to the manufacturer's instructions. To increase the number of uniquely sequenced reads, two independent libraries were generated for each individual. Multiplexed sequencing was carried out on the Illumina MiSeq, HiSeq 2000, and HiSeq 2500 instruments with 2x100, 2x50 and 2x125 cycles using MiSeq Reagent Kit v3, HiSeq SBS Kit v3 and HiSeq SBS Kit v4, respectively. For SureSelect in-solution capture, placental genomic DNA (3.5 µg) from 8 healthy pregnancies (including 2 from the WGoxBS) was fragmented by the Covaris S220 system according to the SureSelect Methyl-Seq target enrichment protocol (Agilent). All 8 libraries were pooled and sequenced on the Illumina HiSeq 2500 instrument with 2 × 125 cycles using HiSeq SBS Kit v4 and a single lane of the Illumina HiSeq 4000 instrument with 2 × 150 cycles using HiSeq 3000/4000 SBS Kit following Illumina's guidelines (Illumina Application Note: Epigenetics February 2016).

  Dataset EGAD00001003136

• Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA) - OncoArray Genotypes

  The data come from 40 studies participating in the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA). CIMBA recruits individuals with pathogenic mutations in BRCA1 or BRCA2. The majority of carriers were recruited through cancer genetics clinics offering genetic testing, and were enrolled into national or regional studies. The remainder were identified by population-based sampling of cases, or community recruitment. Eligibility to participate is restricted to carriers of pathogenic BRCA1/2 mutations who were 18 years or older at recruitment. Information collected included amongst other variables: age at recruitment; ages at breast and ovarian cancer diagnosis; and estrogen receptor (ER) status. Samples were genotyped using the Illumina OncoArray beadchip 500K SNP custom array. Details of the genotyping process and sample selection are included in Phelan et al, Identification of twelve new susceptibility loci for different histotypes of epithelial ovarian cancer, Nat Genet. 2017 May;49(5):680-691 (PMID:28346442), and Milne et al, Identification of ten variants associated with risk of estrogen receptor negative breast cancer, Nat Genet (in press).

  Study phs001321

• Northwestern NUgene Project: Type 2 Diabetes

  The ability to correlate genetic variation with disease susceptibility and response to drug therapy depends on genotype or sequence analysis of large numbers of richly characterized DNA samples. Eight years agoWe are a part of NHGRI's electronic Medical Records and Genomics (eMERGE) Network, whose goal is to conduct genome-wide association studies in thousands of individuals using EMR-derived phenotypes and DNA from linked biorepositories. For eMERGE, Northwestern University (NU) is studying type 2 diabetes as a phenotype. In addition, in order to explore race differences in the prevalence of type 2 diabetes, NU collaborated with Vanderbilt University to study a mix of both Caucasian and African-Americans. Northwestern University: In 2002, Northwestern committed to the development of a DNA repository to serve as a platform for the identification and validation of genotype-phenotype associations that will impact healthcare. The NUgene Project is a repository with longitudinal medical information from participating patients at affiliated hospitals and outpatient clinics from the Northwestern University Medical Center. Participants' DNA samples are coupled with data from a questionnaire (2 versions were used, 1 before and 1 after February 2006, both are included) and continuously updated data from our Electronic Medical Record (EMR) representing actual clinical care events. Northwestern has a state-of-the art, comprehensive inpatient and outpatient EMR system of over 2 million patients. NUgene has broad access to participant data for all outpatient visits as well as inpatient data via a consolidated data warehouse. NUgene participants consent to distribution and use of their coded DNA samples and data for a broad range of genetic research by third-party investigators. Vanderbilt University: BioVU, Vanderbilt's DNA databank, is an enabling resource for exploration of the relationships among genetic variation, disease susceptibility, and variable drug responses, and represents a key first step in moving the emerging sciences of genomics and pharmacogenomics from research tools to clinical practice. BioVU acquires DNA from discarded blood samples collected from routine patient care. The biobank is linked to de-identified clinical data extracted from Vanderbilt's EMR, which forms the basis for phenotype definitions used in genotype-phenotype correlations.

  Study phs000237

• EGAD00010000474

  blood-based gene expression from breast cancer cases and age-matched controls in case-control serie 2 (CC2)

  Dataset EGAD00010000474

• WES for cell lines UWB1.289 and COV362

  exon 11 mutated UWB1.289 and COV362 cell lines

  Dataset EGAD50000000189

• Datasets 929/938

  Datasets Galaxy 929/938 describe the amplified single chromosome sequencing data.

  Dataset EGAD00001004457

• RNAseq files

  Illumina Nextseq 500 whole transcriptome RNAseq from PBMCs - run1

  Dataset EGAD00001006006

• Data Access Commitee for Dietrich and Wendisch et al., SARS-CoV-2 infection triggers profibrotic macrophage responses and lung fibrosis (Submitted)

  Dac EGAC00001001884

• Data Access Commitee for Dietrich and Wendisch et al., SARS-CoV-2 infection triggers profibrotic macrophage responses and lung fibrosis (Submitted)

  Dac EGAC00001002335

• Gentoypes_SouthAfrica

  Individuals genotyped on the Illumina Omni2.5. Autosome and X chromosome.

  Dataset EGAD00010002467

• BC WGS Dataset 4

  Frequent somatic transfer of mitochondrial DNA into the nuclear genome of human cancer cells

  Dataset EGAD00001001353

• BC WGS Dataset 3

  Frequent somatic transfer of mitochondrial DNA into the nuclear genome of human cancer cells

  Dataset EGAD00001001351

• WGS Huh7 cell lines

  Whole Genome Sequencing of Huh7 cell lines

  Dataset EGAD00001001386

• RNA-seq data for NRF2 study

  RNA-seq data for clinical samples

  Dataset EGAD00001002243

• The Sea Islands Genetic Network (SIGNET): Identifying genetic contributors to diabetes and dyslipidemia in African Americans

  Recent genome-wide association studies (GWAS) have successfully identified genetic variants that influence diabetes risk in European populations, however most do not have a major impact on diabetes risk in populations of African descent. The African American (AA) population from the Sea Islands of coastal South Carolina and Georgia has high rates of type 2 diabetes, low levels of admixture, and in general, consume a diet rich in saturated fats. We postulate that this unique combination of ancestral and environmental factors results in a more consistent penetrance of diabetes risk alleles, as well as enrichment of risk alleles of African origin. The existing DNA samples and rich phenotypic data from the Sea Island Families Project comprise a unique resource for genetic studies of type 2 diabetes and related metabolic traits such as dyslipidemia. Our central hypothesis is that the increased risk for T2DM in AA compared with European American (EA) is due, in part, to susceptibility alleles of African origin, and that these alleles can be identified using a GWAS. The Specific Aims are to: 1) Identify genetic risk factors for type 2 diabetes utilizing DNA samples and data from the Sea Island Families Project, Reasons for Geographic and Racial Differences in Stroke (REGARDS) Study recruited from SC, GA, NC, and AL; and a GWAS approach; 2) Identify genetic contributors to lipoprotein subclasses in African Americans using the lipoprotein subclass profile (particle size and concentration for multiple subclasses of VLDL, LDL, and HDL) assessed by NMR at LipoScience, Inc., and the GWAS data from Aim 1. The rationale for this project is that identification and validation of novel pathophysiological pathways and informed selection of candidate genes for diabetes risk will inform development of new, targeted prevention and treatment strategies in this underserved, high risk population.

  Study phs000433

• OurHealth - Cardiovascular Disease in South Asians

  The OurHealth Study aims to build a biobank comprised of South Asian samples from participants residing in the United States. Primary objectives are to (1) identify genetic and non-genetic drivers of cardiometabolic disease in South Asians, (2) engage participants and researchers in scientific advances and research opportunities to accelerate discovery, and (3) assess the impact of returning polygenic risk scores for cardiovascular-related health conditions. Participants use a web-based platform to complete electronic informed consent, and contribute data via (1) online health surveys, (2) mailed saliva biospecimens for sequencing, and (3) optional electronic health record sharing. Surveys capture medical history, medication data, lifestyle factors, family history, mental health history, and detailed information about cultural and ancestral background. Biospecimens, procured via saliva self-collection kits, undergo DNA isolation and are sequenced using the "blended genome exome" (BGE) method, which consists of sequencing on the NovaSeqX 10B Flowcell followed by Dynamic Read Analysis for GENomics (DRAGEN) analysis for alignment to GRCh38, mapping, and variant calling. BGE uses low-coverage whole genome sequencing (lcWGS) (2-3x) combined with deep-coverage exome sequencing (30-40x), improving on SNP arrays for common variant imputation across European and non-European populations while also capturing rare coding variants. Relative to array genotyping, coverage, genotyping breadth, imputation generalizability, and cost are more optimal.BGE requires imputation for which the NHLBI TOPMed reference panel was used, and GLIMPSE2 was used for imputation emitting genotyping dosages (i.e., 0-2 multimodal continuous). Quality control (QC) included the following procedures. Sample QC excluded those with mean coverage 3% contamination from FREEMIX/VerifyBamID, sex aneuploidy and genotype/phenotype sex-mismatch due to potential phenotyping errors, fingerprinting discordance, and sample metric-level outliers were ancestry-aware, given expected distribution differences. Variant QC metrics including allele frequencies, imputation INFO scores, and Hardy-Weinberg Equilibrium (HWE) p-values are provided with the data; the genotype files provided are not filtered.

  Study phs003821

• A Perioperative Study of Safusidenib in Patients with IDH1-Mutated Glioma

  10 participants with a radiological or pathological diagnosis of WHO Grade 2 lower-grade glioma (LGG) were recruited at a single center starting in December, 2022. Eligibility criteria permitted participants who had undergone prior surgery for WHO Grade 2 LGG, but excluded those with a history of prior radiation or chemotherapy.In Part A of the study, participants underwent an initial craniotomy with tumor biopsy and a lumbar puncture for cerebrospinal fluid (CSF) collection at study entry. Following sample collection, participants received safusidenib at the recommended phase 2 doses of 250 mg twice daily (b.i.d.) for a period of 22–36 days. This was followed by a second craniotomy for maximal safe tumor resection and a repeat lumbar puncture within 5 hours of the final pre-resection dose of safusidenib. Tumor, blood, and CSF samples were collected at both surgical time points for comprehensive translational research.Molecular analyses were performed on samples collected at both time points. These included pharmacokinetics (PK), targeted metabolomics, whole-genome sequencing (WGS) of tumor tissue, single-nucleus RNA sequencing (snRNA-seq), and DNA methylation profiling on all patients. Targeted metabolomics was conducted on samples from 9 participants, while spatial transcriptomics and spatial metabolomics were performed on tumor tissue from 3 participants each. Histone methylation profiling was conducted on samples from 8 participants.

  Study phs003976

• Control of Focal Adhesion Kinase Activation by RUNX1-regulated miRNAs in high-risk AML

  We recently described a 16-gene expression signature for improved risk stratification of acute myeloid leukemia (AML) patients called the AML Prognostic Score (APS). A subset of APS high-risk AML patients showed increased levels of focal adhesion kinase (FAK), encoded by the Protein Tyrosine Kinase 2 (PTK2) gene, which was correlated with RUNX1 mutations. RUNX1 mutant cells are more sensitive to PTK2 inhibitors. As we were not able to detect RUNX1 binding sites in the PTK2 promoter, we hypothesized that RUNX1 might regulate micro(mi)RNAs that repress PTK2, such that loss-of-function RUNX1 mutations would result in reduced miRNA expression and derepression of PTK2. Examination of paired RNA-seq and miRNA-seq data from 301 AML cases revealed two miRNAs that positively correlated with RUNX1 expression, contained RUNX1 binding sites in their promoters and were predicted to target PTK2. We show that the hsa-let7a-2-3p and hsa-miR-135a-5p promoters are regulated by RUNX1, and that PTK2 is a direct target of both miRNAs. Even in the absence of RUNX1 mutations, hsa-let7a-2-3p and hsa-miR-135a-5p regulate PTK2 expression, and reduced expression of these two miRNAs sensitizes AML cells to PTK2 inhibition. These data explain how RUNX1 regulates PTK2, and identify potential miRNA biomarkers for targeting AML with PTK2 inhibitors.

  Study EGAS00001006491

• TP53_KO_RPE1_SNPs

  Allelic imbalance data for cell lines derived from RPE1 with TP53 knockout

  Dataset EGAD00010001566

• Transcriptome of (monocytes and dendritic cells ), from donor S9, replicate 2 control

  Dataset EGAD00001009023

• Transcriptome of (monocytes and dendritic cells ), from donor S5, replicate 2 control

  Dataset EGAD00001009015

• Transcriptome of (monocytes and dendritic cells ), from donor S6, replicate 2 control

  Dataset EGAD00001009017

• Transcriptome of (monocytes and dendritic cells ), from donor S14, replicate 2 control

  Dataset EGAD00001009033

• Transcriptome of (monocytes and dendritic cells ), from donor S7, replicate 2 patient

  Dataset EGAD00001009019

• Transcriptome of (monocytes and dendritic cells ), from donor S8, replicate 2 patient

  Dataset EGAD00001009021

• Transcriptome of (monocytes and dendritic cells ), from donor S15, replicate 2 patient

  Dataset EGAD00001009035

• Transcriptome of (monocytes and dendritic cells ), from donor S16, replicate 2 patient

  Dataset EGAD00001009037

• Transcriptome of (monocytes and dendritic cells ), from donor S13, replicate 2 control

  Dataset EGAD00001009031

• Transcriptome of (monocytes and dendritic cells ), from donor S1, replicate 2 control

  Dataset EGAD00001009007

• Transcriptome of (monocytes and dendritic cells ), from donor S2, replicate 2 control

  Dataset EGAD00001009009

• Transcriptome of (monocytes and dendritic cells ), from donor S3, replicate 2 patient

  Dataset EGAD00001009011

• Transcriptome of (monocytes and dendritic cells ), from donor S4, replicate 2 patient

  Dataset EGAD00001009013

• Transcriptome of (monocytes and dendritic cells ), from donor S10, replicate 2 control

  Dataset EGAD00001009025

• Transcriptome of (monocytes and dendritic cells ), from donor S11, replicate 2 patient

  Dataset EGAD00001009027

• Transcriptome of (monocytes and dendritic cells ), from donor S12, replicate 2 patient

  Dataset EGAD00001009029

• chr17_allsamples_imputated

  Chromosome 17 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002526

• chr15_allsamples_imputated

  Chromosome 15 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002533

• chrX_allsamples_imputated

  Chromosome X imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002522

• Study2OrangeFiber

  SOMAscan plasma proteome datasets generated from participants consuming the orange fiber snack prototype (study 2)

  Dataset EGAD00010002191

• TSO500 on RNA

  TruSight Oncology 500 RNA probes panel on case 521

  Dataset EGAD50000000271

• Preoperative ipilimumab plus nivolumab in locoregionally advanced urothelial cancer (NABUCCO Cohorts 1 and 2)

  Study EGAS00001004521

• Durvalumab Plus Tremelimumab Alone or in Combination with Low-Dose or Hypofractionated Radiotherapy in Metastatic Non-Small-Cell Lung Cancer Refractory to Previous PD(L)-1 Therapy: an Open-Label, Multicentre, Randomised, Phase 2 Trial

  Brief SummaryThis randomized phase II trial studies the side effects of durvalumab and tremelimumab and to see how well they work with or without high or low-dose radiation therapy in treating patients with colorectal or non-small cell lung cancer that has spread to other parts of the body (metastatic). Immunotherapy with durvalumab and tremelimumab, may induce changes in body's immune system and may interfere with the ability of tumor cells to grow and spread. Radiation therapy uses high energy x-rays to kill tumor cells and shrink tumors. Giving durvalumab and tremelimumab with radiation therapy may work better in treating patients with colorectal or non-small cell lung cancer.Detailed DescriptionPRIMARY OBJECTIVES:I. To assess safety and tolerability of combined checkpoint blockade with MEDI4736 (durvalumab) and tremelimumab alone or with high or low-dose radiation in non-small cell lung cancer (NSCLC). (NSCLC Cohort) II. To compare the overall response (excluding the irradiated lesion[s]) between combined checkpoint blockade with MEDI4736 and tremelimumab alone or combined checkpoint blockade with low or high dose radiation. (NSCLC Cohort) III. To assess safety and tolerability of combined checkpoint blockade with MEDI4736 and tremelimumab with high or low-dose radiation. (Colorectal Cohort) IV. To determine the overall response rate (excluding the irradiated lesion[s]) with combined checkpoint blockade with MEDI4736 and tremelimumab with either low or high dose radiation. (Colorectal Cohort)SECONDARY OBJECTIVES:I. To estimate median progression-free survival and overall survival. (NSCLC Cohort) II. To determine local control within the irradiated field(s) and abscopal response rates. (NSCLC Cohort) III. To evaluate associations between PD-L1 expression as well as levels of infiltrating CD3+, CD8+ T-cells and overall response. (NSCLC Cohort) IV. To explore changes in PD-L1 expression, circulating T-cell populations, T-cell infiltration, ribonucleic acid (RNA) expression, spatial relationship of immune markers, and mutational burden as a result of low or high dose radiation. (NSCLC Cohort) V. To estimate median progression-free survival and overall survival. (Colorectal Cohort) VI. To determine local control within the irradiated field and abscopal response rates. (Colorectal Cohort) VII. To evaluate associations between PD-L1 expression as well as levels of infiltrating CD3+ CD8+ T-cells and overall response. (Colorectal Cohort) VIII. To evaluate changes between PD-L1 expression as well as levels of infiltrating CD3+, CD8+ T-cells induced by targeted low or high dose radiation. (Colorectal Cohort) IX. To explore changes in circulating T-cell populations, T-cell infiltration, RNA expression, spatial relationship of immune markers, and mutational burden as a result of low or high dose radiation. (Colorectal Cohort)OUTLINE: Patients are assigned to 1 of 2 cohorts. NOTE: This Study is only for Cohort 1. Please see phs003294 for Cohort 2COHORT 1: Patients with NSCLC are randomized to 1 of 3 arms.ARM A: Patients receive tremelimumab and durvalumab and as in Arm C. Beginning at week 2, patients receive high dose radiation therapy once per day (QD) over 10 days for up to 3 fractions.ARM B: Patients receive tremelimumab and durvalumab and as in Arm C. Beginning at week 2, patients receive low dose radiation therapy every 6 hours twice per day (BID) on weeks 2, 6, 10 and 14.ARM C: Patients receive tremelimumab intravenously (IV) and durvalumab IV over 60 minutes every 4 weeks for up to 16 weeks in the absence of disease progression or unacceptable toxicity. Patients then receive durvalumab IV over 60 minutes 4 weeks after last combination dose for up to 9 additional doses.COHORT 2: Patients with CRC are randomized to 1 of 2 arms.ARM A: Patients receive tremelimumab intravenously (IV) and durvalumab IV over 60 minutes every 4 weeks for up to 16 weeks in the absence of disease progression or unacceptable toxicity. Patients then receive durvalumab IV over 60 minutes 4 weeks after last combination dose for up to 9 additional doses. Beginning at week 2, patients receive high dose radiation therapy once per day (QD) over 10 days for up to 3 fractions.ARM B: Patients receive tremelimumab intravenously (IV) and durvalumab IV over 60 minutes every 4 weeks for up to 16 weeks in the absence of disease progression or unacceptable toxicity. Patients then receive durvalumab IV over 60 minutes 4 weeks after last combination dose for up to 9 additional doses. Beginning at week 2, patients receive low dose radiation therapy every 6 hours twice per day (BID) on weeks 2, 6, 10 and 14.After completion of study treatment, patients are followed for up to 12 weeks.Official TitleA Phase 2 Study of MEDI4736 (Durvalumab) and Tremelimumab Alone or in Combination With High or Low-Dose Radiation in Metastatic Colorectal and NSCLC Conditions Metastatic Colorectal CarcinomaMetastatic Lung Non-Small Cell CarcinomaStage IV Colorectal Cancer AJCC v7Stage IV Lung Non-Small Cell Cancer AJCC v7Stage IVA Colorectal Cancer AJCC v7Stage IVB Colorectal Cancer AJCC v7 Intervention/Treatment Biological: DurvalumabRadiation: Radiation TherapyBiological: Tremelimumab Other Study ID Numbers NCI-2016-01325NCI-2016-01325 ( Registry Identifier ) (REGISTRY: CTRP (Clinical Trial Reporting Program))17-71910021 ( Other Identifier ) (OTHER: Dana-Farber - Harvard Cancer Center LAO)10021 ( Other Identifier ) (OTHER: CTEP) Molecular Technologies / AssaysCyTOFIHCmIFOlinkRNAseqWES

  Study phs003295

• Profiling of 27 type 2 diabetes GWAS loci using next-generation (NG) capture C in a human beta-cell model

  Most type 2 diabetes association signals are due to variants that impact gene regulation but identifying the genes they impact is challenging. We generated interaction profiles at 27 T2D GWAS loci using next-generation (NG) capture C (6 replicates) in the human beta-cell model, EndoC-betaH1, along with chromatin accessibility profiling using ATAC-Seq (9 replicates).

  Dataset EGAD00001008655

• Single Cell Sequencing of Medulloblastoma Samples

  This dataset contains the results of sequencing 9 samples (7 human/PDX, 2 cell lines) of medulloblastoma tumours (or 2 Neural Stem Cell lines). The data was collected to characterize the intratumour heterogeneity of chromothripsis in this tumour type. The 7 tumour samples underwent single cell DNA and RNA sequencing using the 10x CNVkit and 10xRNA protocols, and 1 of the tumour samples as well as the Neural Stem Cell lines underwent StrandSeq sequencing. All experiments were sequenced on Illumina sequencers. The provided files are paired-end fastq files from the sequencing experiments.

  Dataset EGAD50000000909

• Lung cancer Early Molecular Assessment

  This study aimed to validate the potential utility of a clinically accessible, highly sensitive tumor-informed circulating tumor DNA (ctDNA) assay. The study considers a ‘landmark’ time period (between two weeks and four months after definitive treatment), as well as longitudinal sampling (>2 weeks) and compares two independent, retrospectively collected real-world cohorts.

  Study EGAS50000000896

• Elucidation of the association of the HPV integration and oropharyngeal cancer

  Whole-genome sequencing was performed using tumor and blood samples from 14 patients with HPV-associated oropharyngeal cancer. Bulk RNA sequencing was performed using tumor samples from 19 patients with HPV-associated oropharyngeal cancer and 17 patients with non-HPV-associated oropharyngeal cancer and normal tonsil samples from 2 healthy controls.

  Study JGAS000751

• mRNA-seq data from ALL patients with NUP214::ABL1 disease

  Dataset contains mRNA-sequencing data (fastq files) from 2 patients with NUP214::ABL1 disease (one T-ALL and one B-ALL patient). Patient samples (peripheral blood or bone marrow) were taken at diagnosis (B-ALL) or relapse (T-ALL). mRNA was extracted from lymphoblasts and underwent paired-end sequencing (75bp) using the Illumina NextSeq platform. Transcriptomic data was used for identification of gene fusions, SNVs and InDels. mRNA-seq data for an additional B-ALL patient with NUP214::ABL1 disease utilised in this study has already been published (EGAS00001006460).

  Dataset EGAD50000001395

• Investigation of 3D chromatin structure in clear cell renal cell carcinoma

  In order to investigation the 3D chromatin structure in clear cell renal cell carcinoma (ccRCC), we performed ultra high-resolution chromatin capture using MNase (Micro-C) in 786-O, A-498, UM-RC-2 ccRCC cell lines, as well as HK-2 normal renal epithelium cell line, cultured under normoxic (20% O2) and hypoxic (0.05% O2) conditions. Data are provided in raw fastq and the chromatin interactions in hic format. Data were processed using the Juicer pipeline.

  Dataset EGAD50000001884

• Investigation of chromatin accessibility in clear cell renal cell carcinoma using ATAC-seq

  To identify regions of open and accessible chromatin in clear cell renal cell carcinoma (ccRCC), we performed ATAC-seq on the 786-O, A-498, UM-RC-2 ccRCC cell lines, as well as HK-2 normal renal epithelium cell line, cultured under normoxic (20% O2) and hypoxic (0.05% O2) conditions. Data are provided in raw fastq and processed narrowPeak fomats. Data were processed using the nf-core atacseq pipeline.

  Dataset EGAD50000001885

• Epigenetic analysis of clear cell renal cell carcinoma using ChIP-seq

  To investigate the epigenetic landscape of clear cell renal cell carcinoma (ccRCC), we performed ChIP-seq data for the CTCF, H3K4me1, H3K4me3, H3K27ac, H3K27me3, H3K36me3, and HIF1A marks in the 786-O, A-498, UM-RC-2 ccRCC cell lines, as well as the HK-2 normal renal epithelium cell line, cultured under normoxic (20% O2) and hypoxic (0.05% O2) conditions. Data are provided in raw fastq and narrowPeak formats. Data were processed using the nf-core chipseq pipeline.

  Dataset EGAD50000001886

• A distinct monocyte cellular state links systemic immune dysregulation to pulmonary impairment in long COVID

  This is an independent cohort of Long COVID patients recruited from a rehabilitation centre. These patients had Long COVID from 8 months to more than 2 years since their acute COVID-19 disease. PBMC from these patients were collected and single cell transcriptomics dataset was generated.

  Study EGAS50000001216

• Patients with metastatic urothelial carcinoma

  Samples from three patients with urothelial carcinoma and paired lymph node metastases and distant metastases. Patient 1: Muscle invasive tumor and lymph node metastasis. Patient 2: Muscle invasive tumor, lymph node metastasis, and distant metastasis in the small intestine. Patient 3: T1b tumor, lymph node metastasis, and distant metastasis in the lung

  Study EGAS00001002114

• Whole exome sequencing in familial Multiple Sclerosis

  The purpose of the study is to determine whether variants of genes are associated with greater risk of Multiple Sclerosis (MS). We used whole-exome sequencing in 138 individuals from 23 families including at least 2 members with MS. We compare patients with MS, patients with other autoimmune diseases (AID), and unaffected individuals.

  Study EGAS00001004204

• The_life_history_of_colorectal_cancer_metastases_study_WGS_X10

  Whole genome sequencing of a series of metastasis collected from 2 patients at autopsy will be used to construct a life history of the cancer, including phylogenetic relationship between the different metastases an also whether there is evidence of convergence towards common biological processes that underpin the metastatic process but though different mutational events

  Study EGAS00001000864

• Whole Exome Sequencing of cohorts of Mutant Braf mouse model melanoma DNA and germline DNA.

  Whole Exome Sequencing of cohorts of Mutant Braf mouse model melanoma DNA and germline DNA. The cohorts are (1) Mutant Braf mouse model melanomas, (2) Mutant Braf mouse model melanomas from UVR exposed mice and (3) Mutant Braf mouse model melanomas from UVR exposed, sunscreen protected mice.

  Study EGAS00001000729

• Anal SCC cell line and parent tumour comparative whole exome sequencing

  1) To perform whole exome sequencing on a panel of five human anal squamous cell carcinoma lines and their respective parent tumours. 2) To perform a comparative analysis between the lines and their parent tumours. 3) Specifically assess the somatic nucleotide variants, copy number variants, mutational burden and mutational signatures.

  Study EGAS00001005077

• Evaluating the immune response in treatment-naive hospitalised patients with influenza and COVID-19

  Clinical characteristics and peripheral blood were acquired upon hospital admission from two well characterised cohorts, a cohort of patients infected with influenza and a cohort of patients infected with SARS-CoV-2 during the first wave of the pandemic and prior to availability of COVID-19 treatments and vaccines.

  Study EGAS00001005971

• Targeted sequencing of patients affected by familial or sporadic Alzheimer's disease

  The study aimed at identifying variants related to familial cases of Alzheimer's disease by comparing the mutational state of approximately 4000 genes associated to many known diseases ("clinical exome") in members of a family (n=7) of with members with dementia (n=2) and unaffected members (n=5)

  Study EGAS00001003856

• Oxford Nanopore RNA sequencing for HLA typing

  A targeted RNA-based method for typing of 12 classical HLA genes using Oxford Nanopore sequencing. In the method, were enriched HLA genes from cDNA of 50 individuals using gene-specific reverse primers. The library molecules were then barcoded, pooled into 2 separate gene pools, and sequenced on MinION R9.4 SpotON flow cells.

  Study EGAS00001004918

• Tyrol Lifestyle Atlas: Intermittent Fasting Methylation Data

  This study explored methylation, clinical, and other molecular alterations longitudinally over the course of intermittent fasting in healthy women aged 30 to 60. Here, we present methylation data from three matched sample types - buccal, cervical, and blood - longitudinally over the course of six months (baseline, month 2, month 4, and month 6).

  Study EGAS00001007840

• Tyrol Lifestyle Atlas: Smoking Cessation Methylation Data

  This study explored methylation, clinical, and other molecular alterations longitudinally over the course of smoking cessatio in healthy women aged 30 to 60. Here, we present methylation data from three matched sample types - buccal, cervical, and blood - longitudinally over the course of six months (baseline, month 2, month 4, and month 6).

  Study EGAS00001007841

• BLUEPRINT release August 2016, ChIP-Seq for effector memory CD8-positive, alpha-beta T cell, terminally differentiated, on genome GRCh38

  ChIP-Seq data for 2 effector memory CD8-positive, alpha-beta T cell, terminally differentiated sample(s). 6 run(s), 6 experiment(s), 6 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002503

• Raw microarray gene expression data from NPC samples_Cohort 2

  We retrospectively collected 150 non-metastatic, pretreatment, formalin-fixed, paraffin-embedded (FFPE) nasopharyngeal carcinoma (NPC) samples as validation cohort 1. Also, we prospectively collected 32 FFPE samples from NPC patients enrolled in a trial evaluating anti-PD-1 antibody as validation cohort 2. Total RNA was extracted and hybridised to an Affymetrix HTA 2.0 microarray. In this study, we investigated the immune status of the tumour microenvironment (TME) based on gene expression profiles to classify NPC into biologically distinct immune subtypes, and clarify their associations with prognosis and immunotherapy response.

  Dataset EGAD00001006272

• WGS data of fetal stem cells (15x) and culture-associated mutations of iPSCs and ISC

  This data set includes WGS data of the in vivo acquired mutations in fetal ISCs and HSPCs of 4 foetuses ( T21=2, D21= 2). In addition, this data set includes sub-clonal fetal ISCs to determine the culture-associated mutations of fetal ISCs. Also, it concludes clone +subclone WGS data of iPSCs derived of the fetal ISCs

  Dataset EGAD00001008475