23083 results for "ega"

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• RCC genomic data

  whole genome sequencing data

  Dataset EGAD00001009866

• WGS short read and 10X linked read sequencing of HR Deficient breast cancers

  A set of tumor/normal paired sequencing experiments, performed in short read WGS and 10X linked read whole genomes. BAM files aligned to hg19 are provided. Sample Alias number and Subject ID reflects patient of origin. T/N distinction discriminates between tumor and normal (peripheral blood) tissue.

  Dataset EGAD00001010326

• Machine Learning Guided Signal Enrichment for Plasma Tumor-burden Monitoring Dataset

  Includes 4 Datasets: 1. 35 Control Plasma WGS samples sequenced on NovaSeq V1.0 Chemistry at the New York Genome Center. Denoted by CTRL-2XX naming scheme. 2. Plasma WGS from 17 patients with Small Cell Lung Cancer. Samples extracted at either Pretreatment or Postoperative at weeks 2 or 3. Denoted by SCLC-XX naming scheme. 3. Plasma WGS from a synthetic mixing study of a high-burden melanoma plasma sample with plasma bag, at estimated tumor concentrations of 10e-3, 10e-4, 10e-5, and 10e-6 for 2 replicates. Denoted by SM-repX naming scheme. 4. Assorted Plasma, Tumor, and Normal WGS from patients with NSCLC expressing high-burden for use in model training. Denoted by NSCLC-2XX naming scheme.

  Dataset EGAD00001011352

• Effects of Metformin on Transcriptomic and Metabolomic Profiles in Breast Cancer Survivors Enrolled in the Randomized Placebo-Controlled MetBreCS Trial

  Metformin reduces the incidence of breast cancer in patients with obesity and type 2 diabetes. However, our knowledge of the effects of metformin on breast cancer recurrence is limited. Within the randomized double-blind placebo-controlled phase II trial MetBreCS, we examined changes in breast tissue from breast cancer survivors with BMI >25 kg/m2 after treatment with metformin. To identify metformin-regulated signaling pathways, we integrated the transcriptomic, metabolomic and steroid hormone profiles using bivariate and functional analyses. We identified MS4A1, HBA2, MT-RNR1 and MT-RNR2 expression to be differentially expressed in breast tissues from metformin-treated women. We also found metformin-associated down-regulation of EGFL6 and FDCSP in postmenopausal women. The integration of transcriptomic and metabolomic profiles revealed down-regulation of immune response genes associated with reduced levels of arginine and citrulline in the metformin-treated group. The integration of transcriptomic and steroid hormone profiles showed an enrichment of steroid hormone biosynthesis and metabolism pathways with highly negatively correlated CYP11A1 and CYP1B1 expression in breast tissue from postmenopausal metformin-treated women. Our results indicate that breast cancer survivors treated with metformin have specific changes in breast tissue gene expression that may prevent the development of new tumors. Within the randomized double-blind placebo-controlled phase II trial MetBreCS, we examined changes in breast tissue from breast cancer survivors with BMI >25 kg/m2 after treatment with metformin using bulk RNAseq (total mRNA sequencing), that is deposited in Federated EGA. The steroid hormones, and metabolomics and the insulin sensitivity serum biomarker data underlying this article may be shared upon reasonable request to the PI of the MetBreCS study (Dr. Bernardo Bonanni), following approval by the Data and Safety Monitoring Board at IEO, Milan.

  Study EGAS50000000874

• 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

• How upcycled prostate cancer sequences enabled key findings on telomeres length

  The length of telomeres, the chromosomes’ end tails, as depicted in the figure below, physiologically shorten with aging. This means that younger cells have longer telomeres compared to their aged counterparts; one could say that the length of cells’ telomeres is a measure of their replicative power. Cancer cells are known to be able to turn this into their favour, and expand their replicative immortality through telomere lengthening and maintenance. Despite being a well-studied pan-cancer hallmark, the relation of the telomeres length to specific cancer genomic features is poorly understood. Julia Livingstone and her colleagues from P.C. Boutros' team in Los Angeles focused their work on prostate cancer. To investigate the relations between telomere length and the clinico-genomics of prostate tumours, they used whole genome sequencing (WGS) of 392 published tumour–reference pairs previously deposited at the EGA under accession numbers EGAS00001000400 and EGAS00001000900. Their work was published on Nature Communications in November 2021. They determined the telomere length of the sequencing sample, from both tumour and non-tumour adjacent tissues, and subsequently ran statistical analysis to investigate possible correlation between the telomeres length with several cancer features. The team discovered that telomeres length does not correlate with any specific driver gene mutation, but it does with the overall frequency of single nucleotides variants in a sample. This finding suggests that tumours with shorter telomeres accumulate more mutations without strong selective pressures for specific ones. While proliferation rate did not correlate directly with telomere length, methylation of almost half of all genes strongly did. The team’s results are massive and reveal a complicated and fascinating relation of different clinical and genomic features of prostate cancer with telomere length. In the paper discussion, they smartly consider different possibilities. As noted above, all these amazing results have been obtained analysing previously sequenced samples. Remarkably, the researchers who generated the samples did not focus at all on telomeres length. They indeed studied specific genes mutations and the features of early and late-onset prostate cancer. This proves, once again, the power of sequencing data re-use. This data is tremendously rich in information, and can hardly be exhausted with any number of analyses. Generating sequencing data, especially Whole Genome Sequencing, even better if tumor-reference paired, is like shopping every last article at the grocery store. You can then cook several recipes, or prepare a seven-course dinner, and still have ingredients left for someone else to borrow and cook something you have not thought about, a recipe that is just not in your cookbook. Noteworthy, the data retrieved from the EGA for this paper is associated with rich and complete metadata, meaning pheno-clinical information regarding the sample donors. As Julia Livingstone and her team wrote, using rich clinical annotations enabled them to assess the relationship between telomere length and patients outcome. This highlights once again the importance of reporting and annotating metadata correctly; It might feel like extra work for the researchers depositing their data, but it really has a huge value. Our team at the EGA is currently working to improve the metadata annotation process, with the aim to maximize the benefits for the scientific community.

  Blog prostate-cancer-sequences-enabled-key-findings-on-telomeres-length

• Advancing fast in the analysis of circulating tumour DNA

  In cancer patients, solid tumours also release DNA fragments into the blood, and they are called Circulating Tumours DNA (ctDNA). Nowadays, the standard procedure for cancer monitoring is analysis of surgically obtained biopsies. They generate a big amount of material, but the operation is highly invasive and provide medical doctors with a single-time snapshot of the tumours, which are often highly changeable. Analysis of ctDNA, on the other hand, can provide continuous information regarding the tumour itself without the need of invasive procedures. This is why interest in this method is growing, and with it, the need for its technological optimization. Guanhua Zhu and colleagues, at the Genome Institute of Singapore, developed a methodology to quantify ctDNA burden, which means to estimate the amount of tumours derived circulating DNA among all cell-free DNA in the blood of individuals. Their work, published in Nature Communications in april 2021 and summarized in a sketch below, uses state-of-the art methods to analyse cfDNA degradation patterns at promoters and exon regions, to discern it from the DNA coming from the tumours. The bright point of their approach is that simultaneously enable for profiling of actionable cancer mutations and quantitative estimation of ctDNA burden. Their sequencing data of ctDNA has been deposited in the EGA under the accession number EGAS00001004657. This study sums up to a total of 37 of cfDNA and 36 about ctDNA present up to date in the EGA archive. The existence of cfDNA was firstly described back in 2002, while the use of ctDNA analysis in the oncological field exploded only after 2016. The approach is rapidly evolving, moving in parallel with the ever-improving sequencing technologies. Especially, the possibility to obtain high quality sequences from minimal amount of material is a crucial factor. Being able to alleviate cancer patients of disturbing and often slow-recovery procedures, while at the same time improving the quality of diagnosis and disease monitoring seems like a lot to ask. Thankfully, we are blessed with brilliant and dedicated scientists from all around the world who collaborate to make this a reality, sharing their results, knowledge and data.

  Blog advancing-fast-in-the-analysis-of-circulating-tumour-dna

• Reading about genomic analysis of pan-neuroblastoma

  There are virtually no research fields that do not benefit from the large-scale genomic analysis that are so popular nowadays. For research into diseases with highly diverse clinical behavior like pediatric neuroblastoma, nevertheless, this type of approach has been a game-changer. Samuel W. Brady and colleagues, from St. Jude Children’s Research Hospital in Memphis, have gathered data from 702 neuroblastomas which were profiled by whole genome sequencing (WGS, n = 205), whole exome sequencing (WES, n = 539), and/or RNA-Seq. Less than half of the samples were generated during this study, while the others were obtained from previous work. Part of the WES data was indeed deposited in EGA in 2012 (EGA study EGAS00001000213). A scratchy summary about the huge work from Samuel W. Brady and colleagues, published in Nature Communications this October 2020 “These findings suggest that the sympathetic nervous system, the tissue from which neuroblastoma arises, is susceptible to different oncogenic insults at different times during development, which could be explored in future investigations using animal models.” Conclude the authors, highlighting the importance of their findings. Pediatric neuroblastomas are classified according to clinical and molecular features into low, intermediate, and high risk. Age plays a critical role in the classification, with younger children having an overall better prognosis. The samples included in this study belonged to all ages and risk-groups: the aim of such set up is the identification of age-associated molecular aberrations. The authors defined a mutational landscape of pediatric neuroblastoma including rare driver events, impossible to pick with a smaller data set. Interestingly, most driver mutations' frequency was found to be age-related. Those include alterations on MYCN, TERT, PTPRD, and Ras pathway, in addition to the previously reported ATRX. In addition, the large data set allowed the authors to define co-existence relations between the mutations (mutual exclusion or co-occurrence) which is a crucial puzzle piece to unravel the mechanisms driving the pathogenesis. Enjoy this really interesting article, open source on Pubmed.

  Blog genomic-analysis-of-pan-neuroblastoma

• Youth-GEMs, the project using data to define the mental health trajectories of young people

  Mental health trajectories are shaped by genetic, cognitive, social, and environmental factors that are particularly influential during human development. How exactly the interplay between these factors is impacting mental health, however, is yet unknown. The project “Gene Environment interactions in Mental health trajectories of Youth (Youth-GEMs)”, funded by the European Commission under the Horizon Europe programme, aims to help reducing mental suffering among European youth. How do Youth-GEMs contribute to bettering mental health in young people? Youth-GEMs aims to identify specific neurological, (epi)genetic and environmental drivers on mental health trajectories of young people. To do so, the project is working on a public knowledge base on functional genomics of the developing human brain, between birth and 24 years old. Reliable predictive models for trajectories of onset and course of mental illness in young people of the general population in Europe will be developed. Additionally, a secure and FAIR data infrastructure in mental health is being produced. Engaging and empowering young people Youth-GEMs is also working on the engagement of young people in the scientific field of Psychiatry. For that, the project is developing initiatives such as Science Café, intended to create a learning community where ideas and stories are shared. The 10th Science Café will take place today, with the participation of Lauren Fromont from the EGA. How do the EGA contribute to Youth-GEMs? The contributions require careful data management and harmonisation: we are dealing with an extraordinary amount of data types, sources, and time points (since the studies are longitudinal). To analyse them together and to determine which factors and interactions are truly key to account for mental health trajectories, the variables need to be transformed in a common “language”, i.e., harmonised under a common data model. Further, to guarantee FAIRness of data storage and sharing for future projects that will further advance our knowledge in the mental health field, we are ensuring that data are processed securely but can also be discovered easily. Find out more information about the project in the Youth-GEMs website.

  Blog youth-gems-mental-health

• Breast Cancer Follow Up Series

  This study's purpose is to use data generated in whole genome and exome Breast Cancer sequencing to target specific areas in multiple other Breast Cancer samples. These areas will then be subjected to PCR in multiple cases, tagged and then pool the resultant amplicons. These amplicons will then be sequenced on the Illumina GAII. This is hoped to show the prevalence of previous findings in multiple individuals in a high throughput method.

  Study EGAS00001000002

• Illumina Human Exome (ExomeChip) genotype data from the Pomak villages in Greece (HELIC Pomak Isolate). 1040 samples all >=16 years old.

  Isolated populations can empower the identification of rare variation associated with complex traits through next generation association studies. Here we have genotyped individuals from a Greek population isolate on the Illumina HumanExome Beadchip. The HELIC (Hellenic Isolated Cohorts) Pomak collection focuses on the Pomak villages in Greece. Recruitment of this population-based sample was primarily carried out at the village medical centres. www.helic.org

  Study EGAS00001000658

• The complex SNP and CNV genetic architecture of the increased risk of congenital heart defects in Down syndrome

  Here we aimed to contribute to the description of the genetic architecture of Congenital heart defect (CHD) in Down syndrome (DS), and report the results of a genome-wide association study using samples from DS individuals with and without CHD. CHD is a common developmental defect of DS occurring in 40% of cases. This case-control GWAS includes 187 DS with CHD as cases, and 151 DS without CHD as controls.

  Study EGAS00000000129

• DO NOT USE - Whole genome sequencing of SI-NETs from five patients

  Small intestine neuroendocrine tumor (SI-NET), the most common cancer of the small bowel, often displays a curious multifocal phenotype with several intestinal tumors centered around a regional lymph node metastasis, yet the typical path of evolution of these lesions remains unclear. Here, we determined the complete genome sequences of 37 tumors from 5 patients with multifocal SI-NET, allowing elucidation of phylogenetic relationships between multiple intestinal tumors and metastases in individual patients.

  Study EGAS00001004435

• Genome-wide association study in multiple human prion diseases suggests genetic risk factors additional to PRNP

  We conducted GWAS of sporadic CJD, variant CJD, iatrogenic CJD, inherited prion disease, kuru and resistance to kuru despite attendance at mortuary feasts. After quality control we analysed 2000 samples and 6015 control individuals (provided by the Wellcome Trust Case Control Consortium and KORA-gen), for 491032-511862 SNPs in the European study. Association studies were done in each geographical and aetiological group followed by several combined analyses.

  Study EGAS00000000097

• Department of Human Genetics, Radboud University Nijmegen Medical Centre, Data Access Committee

  Dac EGAC00001000076

• Frequent mutation of the major cartilage collagen gene, COL2A1, in chondrosarcoma

  Chondrosarcoma (CHS) is a heterogeneous collection of malignant bone tumours and is the second most common primary malignancy of bone after osteosarcoma. Recent work has identified frequent, recurrent mutations in IDH1/2 in nearly half of central CHS. However, there has been little systematic genomic analysis of this tumour type and thus the contribution of other genes is unclear. Here we report comprehensive genomic analyses of 49 cases of CHS. We identified hypermutability of the major cartilage collagen COL2A1 with insertions, deletions and rearrangements identified in 37% of cases. The patterns of mutation were consistent with selection for variants likely to impair normal collagen biosynthesis. In addition we identified mutations in IDH1/2 (59%), TP53 (20%), the RB1 pathway (27%) and hedgehog signaling (22%).

  Dataset EGAD00001000358

• 13K T2D-GENES analysis files

  This data set includes the following summary level data files used for the 13k analysis of T2D-GENES data: wes.variants.list: list of variants to keep for any analysis of the exomes data wes.assoc.samples.list: list of samples to keep for association analysis wes.assoc.variants.list: list of variants to keep for association analysis wes.sv.assoc.txt: single variant association analysis results wes.gene.ptv.variants.list.txt: list of protein truncating variants to use in gene-level analysis wes.gene.ptv.assoc.txt: results from gene-level tests of protein truncating variants wes.gene.nsstrict.variants.list.txt: list of NSstrict variants to use in gene-level analysis wes.gene.nsstrict.assoc.txt: results from gene-level tests of NSstrict variants wes.gene.nsbroad.variants.list.txt: list of NSbroad variants to use in gene-level analysis wes.gene.nsbroad.assoc.txt: results from gene-level tests of NSbroad variants wes.gene.ns.variants.list.txt: list of non synonymous variants to use in gene-level analysis wes.gene.ns.assoc.txt: results from gene-level tests of non synonymous variants

  Dataset EGAD00010001188

• Counts: Brain transcriptome of hereditary cerebral haemorrhage with amyloidosis–Dutch type (HCHWA-D)

  These are the log2CPM (log2 counts per million) fragments per gene counts associated with the BAM files in EGAD00001003806, in tab separated format. Counts for 36 postmortem brain samples from 9 non-demented control subjects and 9 Hereditary cerebral hemorrhage with amyloidosis-Dutch type subjects are included (1 Frontal cortex sample and 1 Occipital cortex sample per subject). RNA samples were depleted for ribosomal RNA with the Ribo Zero Gold Human kit (Illumina) and strand specific RNA-Seq libraries were generated. Paired-end sequencing was performed on a HiSeq2500 Illumina system (2x50bp reads). Alignments were performed using GSNAP v2014-12-23 with setting "--npaths 1" on GRCh38 reference genome without the alternative contigs. Fragment per gene counting was performed using HTSeq-count v0.6.1p1 with setting "--stranded reverse". The gene annotation used for quantification were UCSC RefSeq genes for GRCh38 downloaded on 2015-07-13.

  Dataset EGAD00010001457

• UK_RCC_GWAS

  Renal cell carcinoma (RCC) cases comprised adult patients with histologically proven RCC were collected through two sources within the UK. First, 856 cases from SORCE, a MRC collection of surgically treated RCC cases ascertained through UK clinical oncology centres. Second, 189 RCC cases collected through the ICR and Royal Marsden NHS Hospitals Trust. Cases included 590 clear cell carcinomas (CCCs), 42 papillary carcinomas (PCs), 33 chromophobe carcinomas (CCs) and 19 mixed or other histological subtypes. DNA was extracted from EDTA-venous blood samples using the conventional methods and quantified using PicoGreen (Invitrogen). Cases were genotyped using the Human OmniExpress-12 BeadChip according to the manufacturer's recommendations (Illumina Inc, San Diego, CA, USA). After strict QC, 944 cases were retained. Data provided in plink format. Controls used were data from the Wellcome Trust Case Control Consortium 2 (WTCCC2) 1958 birth cohort and the UK Blood Service Control Group (available as EGAS00000000028). 

  Dataset EGAD00010002310

• GSA QCed data

  QC-ed data of 9,534 DDD Study participants, including 8,879 individuals with inferred GBR ancestry. Details of genotype QC can be found in https://www.medrxiv.org/content/10.1101/2023.04.20.23288860v1.full.pdf. Genome builds are indicated in the file name. Related individuals have not been removed. Of the 9,534 samples there are 3,148 mothers, 3,138 fathers and 3,248 probands, which form 3,099 trios. Of the 8,879 GBR samples, there are 2,931 mothers, 2,937 father and 3,011 probands, which form 2,788 trios. Most of the probands have been previously genoyped on the llumina HumanCoreExome BeadChip (EGAD00010001598) or the Illumina InfiniumCoreExome Beadchip (EGAD00010001600). All samples were genotyped on the Illumina Global Screening Array.

  Dataset EGAD00010002568

• ONGOING CHROMOSOMAL INSTABILITY ACROSS ANEUPLOID SUBTYPES OF CHILDHOOD B-CELL ACUTE LYMPHOBLASTIC LEUKEMIA ASSOCIATES WITH DISEASE PROGRESSION

  Single-cells from primary cB-ALL samples were isolated using an inverted microscope coupled to a micromanipulator equipped with glass capillary for cell collection. A minimum of 20 cells were isolated per sample in microdrops of 2.5 μl of phosphate buffered saline (PBS) with 0.5% polyvinylpyrrolidone. Cell lysis and DNA amplification was performed using the SurePlex DNA Amplification System (Illumina). Genomic DNA was subsequently fragmented and tagged with the VeriSeq PGS transposome and the TruSeq Index adapters by PCR for library preparation (VeriSeq PGS Library Prep Kit, Illumina). Equal volumes of normalized libraries were pooled and sequenced on an Illumina MySeq platform with 1×75-bp single-end sequencing. Reads were subsequently aligned to the human reference genome (GRCh38/hg38) using Bowtie2 (version 2.2.4).

  Dataset EGAD50000000029

• Differential gene expression in the colon mucosa of irritable bowel syndrome patients with diarrhea-predominant symptoms

  The aim of this study is to characterize the pathophysiology of the irritable bowel syndrome (IBS) on the functional level and in parallel on the gene expression level. For this purpose RNAseq of mucosal biopsies was performed. The data from the differential expression analysis will be further processed by Ingenuity Pathway Analysis (IPA) software to gather Information on activated or inhibited signaling pathways in the disease.

  Study EGAS50000000046

• Multi-omic characterisation of PBMCs in IBD

  We generated 10X, droplet-based Multiome (paired RNA+ATAC) data of PBMCs in 38 IBD patients. Patients are either UC or CD patients. To test the responsiveness of the cells to bacterial stimuli, we stimulated samples with either RPMI, LPS or S. Salmonella (total: 80 samples). Single-nuclei libraries are multiplexed across donors. Genotypes are provided in this dataset to perform genetic demultiplexing, phenotypes providing mapping information to retrace donors / pools / stimulations.

  Study EGAS50000000140

• Correlative Genomic Analysis of Durvalumab plus Pazopanib Combination in Patients with Advanced Soft Tissue Sarcomas

  We conducted an open-label, phase 2 study to determine the activity of the anti-VEGF receptor tyrosine-kinase inhibitor, pazopanib, combined with the anti-PD-L1 immune checkpoint inhibitor, durvalumab, in unselected advanced sarcomas. In addition, we conducted whole exome and transcriptomic sequencing with pre-treatment tissue biopsy to correlate clinical outcomes with molecular and genomic biomarkers to identify patients who would most likely benefit from the combination treatment.

  Study EGAS50000000082

• Arcagen – thoracic malignancies

  Arcagen is an EORTC/SPECTA pan-European project that aims to recruit 1000 rare cancer patients from different tumour domains of EURACAN. This study collected samples from advanced or metastatic rare cancer from patients older than 12, and analysed them using Foundation Medicine next-generation sequencing (NGS) panels (FoundationOne CDx for FFPE samples or FoundationOne Liquid CDx for blood samples). Here we are submitting the dataset that contain NGS files from rare thoracic malignancies (n=102)

  Study EGAS50000000123

• Single cell and spatial transcriptomics of adult human adrenal glands

  Adrenal glands are endocrine organs secreting a variety of hormones, known to undergo tissue remodelling through constant cellular turnover. Here we employed single cell and spatial transcriptomics to investigate thoroughly adult human adrenals from 14 individuals with a focus on adrenocortical cell types and cell states. By including both male and female tissue donors of various ages, we extended the study to sexual dimorphic features of the adult gland.

  Study EGAS50000000269

• Platinum-based chemotherapy induces opposing effects on immunotherapy response-related spatial and stromal biomarkers in the bladder cancer microenvironment

  The goal of the current project was to investigate the modulation of the tumor-immune microenvironment (TME) by neo-adjuvant platinum-based chemotherapy in muscle-invasive bladder cancer. Furthermore, we aimed to focus on how this modulation affects biomarkers of response to immune checkpoint inhibitors. Additionally, we aimed to explore the differences between MVAC and gemcitabine-containing platinum-based regimens regarding their effects on the bladder TME.

  Study EGAS50000000309

• scRNA-seq of CD4+ T cells from blood and tumor of NSCLC patients

  This study explore the CD4+ T cells diversity in paired blood and tumor of 5 different NSCLC patients. After FACS-sorted of Tregs (DAPI- CD45+ CD4+ CD25hi CD127lo) and Tconvs (DAPI- CD45+ CD4+CD25lo CD127lo/hi), cells were mixed in equal proportion (fifty/fifty ratio) and processed with 10X Chromium (10X Genomics) gene expression kit (3 bloods and 2 tumors) or immunoprofiling kit (2 bloods and 3 tumors) and sequenced using Illumina platforms.

  Study EGAS50000000293

• RNA-sequencing on thyroid samples from fetuses with Down syndrome and fetuses with no genetic/developmental abnormality

  The current data pertains RNA-sequencing reads obtained from thyroid samples acquired from fetuses with Down syndrome and fetuses with no genetic/developmental abnormality. Total RNA was isolated from left lobe from thyroid samples using a hand-held homogenizer and the Promega ReliaPrep RNA Miniprep System (Thermo Fisher Scientific). RNA yield was determined with the NanoDrop Microvolume Spectrophotometer (Thermo Fisher Scientific). Fragmentation and mRNA library preparation was performed using the Kapa mRNA Hyperprep Kit (Roche, Basel, Switzerland). Libraries were equimolar pooled and quality was checked on a TapeStation system using the DNA1000 ScreenTape (Agilent Technologies, Santa Clara, CA, USA). Libraries were sequenced with poly(A) selection to sequence all messenger RNA for gene expression analysis on the NovaSeq6000 PE150 (Illumina, San Diego, CA, USA), producing at least 40M 150-bp paired-end reads per library.

  Dataset EGAD50000000387

• Genomic profiles associated with response to immunotherapy in adolescent and young adult patients with melanoma

  The patterns of immune infiltration and treatment responses are unclear due to genomic and clinicopathological differences in AYA patients compared to those of older patients, and these patterns and differences may contribute to the lack of response to immunotherapy. This study fills the research gaps in understanding the molecular profiles of AYA melanoma and the key immunosuppressive signalling pathways contributing to tumour growth and resistance to immunotherapy

  Study EGAS50000000238

• The epigenomic response of skeletal muscle in life-long high-level trained and untrained men and women after acute exercise

  To investigate the influence of lifelong exercise training on the response of skeletal muscle to a bout of acute exercise we generated targeted epigenomic data from long-term endurance (8 men) and strength (8 men) trained individuals and healthy age-matched untrained controls (8 men). Skeletal muscle biopsies were taken from M. vastus lateralis before, directly after, and 3hrs following acute exercise. Control subjects completed one bout of acute endurance exercise and one bout of acute resistance exercise, separated by 4-8 weeks, athletes completed one bout in their respective form of sports. All 96 samples were used for DNA extraction and targeted library construction using a custom Twist Biosciences panel and following EM-methylation tranformation were sequenced (2x150bp paired end) on the Illumina NovaSeq 6000.

  Dataset EGAD50000000512

• Panel sequencing of endocrine-resistant breast cancer

  Patients with endocrine-resistant breast cancer in Stockholm Sweden. DNA obtained from patients primary and relapse tumors, and tumor-free lymph nodes used as germline control. DNA was extracted from formalin-fixed paraffin-embedded tissue and sequenced by 370-gene panel-based sequencing with Kapa HyperPlus library preparation and Twist Bioscience hybrid capture. Custom bait sets (panels) from Twist Bioscience. Paired end 2x150 bp using NovaSeq X. The data is presented as fastq-files.

  Study EGAS50000000236

• scRNAseq of ARID1B+/+ (control) and ARID1B+/- human telencephalic organoids at D120

  This dataset contains scRNAseq data of human telencephalic organoids, at day 120, from 4 different sequencing runs (libraries), as follows (please see cell line clone nomenclature in original publication): 1. Library 177136: Pat.2 ARID1B+/+ clone 2c (2 organoids, E11rep_1, E11rep_2), Pat.2 ARID1B+/- clone 2a (2 organoids: B002orig_1, B002orig_2) 2. Library 178119: Pat.2 ARID1B+/+ clone 2c (3 organoids: E11rep_1, E11rep_2, E11rep_3), Pat.2 ARID1B+/- clone 2a (3 organoids: B002orig_1, B002orig_2, B002orig_3) 3. Library 178120: Pat.2 ARID1B+/+ clone 2d (3 organoids: A3rep_1, A3rep_2, A3rep_3), Pat.2 ARID1B+/- clone 2b (3 organoids: B002_F7_1, B002_F7_2, B002_F7_3) 4. Library 184337: Pat.1 ARID1B+/- clone 1a (3 organoids: B001orig_1, B001orig_2, B001orig_3), HD.1 ARID1B+/+ clone 3a (3 organoids: 176_1, 176_2, 176_3), HD.1 ARID1B+/- clone 3b (3 organoids: F10_1, F10_2, F10_3)

  Dataset EGAD50000000498

• RNA sequencing from patient-derived intestinal organoids

  uman duodenal tissues for establishing organoid cultures used in this study were sourced from de-identified surgical specimens (n = 3) of the duodenum obtained from patients who had undergone biopsy procedures unrelated to CeD at Tampere University Hospital. The protocol was approved by the Ethics Committee of Tampere University Hospital, Tampere, Finland (ETL code R18082). RNA from the duodenal organoids was isolated using an RNeasy Kit (Qiagen, Hilden, Germany) following the manufacturer’s instructions. RNA purity and concentration were measured using a NanoDrop One spectrophotometer (NanoDrop Technologies, Wilmington, Delaware, USA). Preparation of the RNA library and transcriptome sequencing was conducted by Novogene Co., LTD (Cambridge, UK). Messenger RNA was purified from total RNA using polyA selection and subjected to library construction. Sequencing was performed on an Illumina platform, and 150 bp paired-end reads were generated.

  Dataset EGAD50000000492

• LCM-isolated buccal epithelial cell sequencing

  Single buccal epithelial cells were dissected and collected on an adhesive cap with laser capture microdissection (LCM) with approximately 13-60 cells per cap. Each adhesive cap containing 13-60 cells was independently processed with QIAamp DNA micro kit (Qiagen) for DNA extraction. The NEBNext ultra II FS DNA library kit was used to prepare DNA libraries with low starting material. Paired-end whole genome sequencing reads were obtained with the NovaSeq.

  Study EGAS50000000098

• DNA methylation database for gynecological cancer detection, classification and assay development

  DNA methylation changes are associated with cancer the best predictor of the presence of cancer. In order to detect and classify gynecological cancer, we performed MeD-seq to generate DNA methylation profiles of vulvar, cervical, endometrial, fallopian tube and ovarian cancers and their associated healthy tissues. DNA methylation profiles were used to find general cancer, regional cancer and specific cancer biomarkers that can be used for cancer detection, classification and treatment monitoring.

  Study EGAS50000000417

• HGG panel sequencing

  NGS panel sequencing of pediatric high-grade gliomas (HGG), including diffuse midline gliomas and hemispheric HGG. This is a population-based dataset from the Hospital for Sick Children (Toronto, Canada), of patients <18 years of age at diagnosis, and diagnosed between 2000-2022. The goal of the study was to define the genetic drivers in pediatric HGG, including the rates of different genetic drivers, such as H3 and BRAF mutations, in a single-site population based cohort.

  Study EGAS50000000221

• Single-nuclei RNA sequencing of pheochromocytoma and paraganglioma arising from germline SDHB mutations

  10x genomics single-nuclei RNA sequencing of 9 SDHB-deficient non-metastatic and metastatic pheochromocytoma and paraganglioma. snRNA-seq was performed using the ‘Frankenstein’ protocol (dx.doi.org/10.17504/protocols.io.bqxymxpw). Briefly, nuclei were extracted from frozen tissues and subjected to fluorescence-activated nuclei sorting (FANS) using 4′,6-diamidino-2-phenylindole (DAPI). Sorting was performed using a BD FACSaria 2 instrument, sorting between 3000 and 10,000 nuclei per sample, capturing both diploid and tetraploid populations. FAN-sorted nuclei were immediately processed using either the 10x Chromium Single Cell 5’ (PN-1000006, 4 samples) or 3’ (PN-1000075, 4 samples) Library & Gel Bead Kit (10x Genomics, USA). Once processed, snRNA-seq libraries were sequenced on the Illumina Nova-Seq 6000 (Illumina, USA) using 150bp paired-end sequencing. This dataset contains raw sequencing reads in FASTQ format.

  Dataset EGAD50000000505

• Cergentis FFPE-TLC sequencing of colorectal carcinoma

  In this study, formalin-fixed paraffin-embedded targeted locus capture (FFPE-TLC) sequencing is used as a novel technology for targeted detection of tumor-specific genomic structural variants (SVs) in the primary tumor of 29 colorectal cancer patients with metastatic disease. The tumor region was macrodissected and sequenced for 29 patients, and the "normal" region of the same slide was macrodissected and sequenced for 8 of these patients. SVs were found in the common fragile site (CFS)-associated genes MACROD2, PRKN, FHIT and WWOX as well as SVs caused by three LINE transposable elements. Tumor-specificity of selected SVs was independently verified by droplet digital PCR of tumor tissue DNA and their applicability as plasma circulating tumor DNA biomarkers was demonstrated. This dataset contains the hg19 reference alignment of the FFPE-TLC sequences for all 29 colorectal cancers and 8 adjacent normals.

  Dataset EGAD50000000618

• Single-cell RNA sequencing of pediatric Hodgkin Lymphoma to study the inhibition of T cell subtypes

  Here, we applied single-cell RNA-sequencing (scRNA-seq) on isolated HRS cells and the immune cells from the same pediatric classical Hodgkin Lymphoma (cHL) tumors. Specifically, 13 cHL patients and 3 reactive lymph node control samples were included in this cohort. This allowed us to identify genes of cell surface proteins that are consistently overexpressed in HRS cells and can potentially be used as targets for antibody-drug conjugates or CAR T cells. Finally, we identify potential interactions by which HRS cells inhibit T cells, among which the Galectin-1/CD69 and HLA-DRA/LAG3 interactions. However, high levels of inter-patient heterogeneity of the interaction strength were observed. In conclusion, this study identifies new potential therapeutic targets for cHL and highlights the importance of studying heterogeneity when identifying therapy targets.

  Dataset EGAD50000000626

• Bulk RNA sequencing of human T and B cells before and after interleukin-2 immunotherapy

  Bulk RNA sequencing of flow cytometry sorted human CD4+ regulatory T (Treg), CD4+ conventional T (Tcon), CD8+ T, and CD19+ B cells from systemic lupus erythematosus patients collected at baseline (day 0, before interleukin-2 immunotherpay), day 5 (after 1 treatment cycle of interleukin-2 immunotherapy), and day 68 (after 4 treatment cycles of interleukin-2 immunotherapy). The dataset comprises files from the above-mentioned 4 immune cell subsets, collected at 3 time points (day 0, day 5, and day 68) of 12 systemic lupus erythematosus patients. Due to technical reasons, day 5 samples of patient SLE_012 could not be processed and are thus missing. The complete dataset totals in 140 raw sequencing files (fastq format).

  Dataset EGAD50000000661

• Whole genome and transcriptome sequencing of cancer of unknown primary tumours

  Whole genome and transcriptome sequencing of cancer of unknown primary tumours was used to determine yield of clinical biomarkers for a molecular guided trial or for resolving cancer type of origin. This study includes profiling of germline DNA and tumour DNA and RNA by whole genome and transcriptome sequencing. All samples are in BAM format aligned with GRCh38 reference genome. This dataset includes: 1. Whole genome sequencing (WGS) of 78 cancer of unknown primary tumour samples and 73 matched germline DNA. 2. Whole transcriptome sequencing (WTS) of 69 cancer of unknown primary tumour samples (matched to WGS cases) 3. Whole genome sequencing of 22 cell-free DNA samples from cancer of unknown primary patients and matched Germaine DNA(8 samples matched to tumour WGS)

  Dataset EGAD50000000656

• Prognostic whole-genome and transcriptome signatures in colorectal cancers

  Colorectal cancer (CRC) is caused by a sequence of somatic genomic alterations affecting driver genes in core cancer pathways1. To understand the functional and prognostic impact of cancer-causing somatic mutations, we analysed the whole genomes and transcriptomes of 1,063 primary CRCs in a population-based cohort with long-term follow-up. This study constitutes the largest integrated genome and transcriptome analysis of CRC to date, and links mutations, gene expressions and patient outcomes.

  Study EGAS50000000124

• RNA-Seq of human longitudinal whole blood samples from PCR-positive and PCR-negative recent household contacts of COVID-19 index cases.

  Little is known about the transcriptomic profile of individuals who are exposed to SARS-CoV-2 yet resist becoming PCR positive. To investigate this, longitudinal whole-blood samples were taken (0, 7, 14, and 28 days after enrolment) from PCR positive and PCR negative SARS-CoV-2-naïve household contacts who were recently exposed to a COVID-19 index. Samples were also taken from pre- and post-pandemic unexposed controls. Total RNA was extracted from PAXgene tubes before undergoing poly(A) selection followed by globin and rRNA depletion. DNA libraries were constructed using the NEBNext® Ultra™ II Directional RNA Library Prep Kit for Illumina. All samples were then sequenced across 2 flowcells of an Illumina HiSeq 4000.

  Dataset EGAD50000000684

• Characterisation of Cyr61-enriched myeloid angiogenic cells

  We previously demonstrated the enrichment of an angiogenic precursor cell population from peripheral blood mononuclear cells by CYR61, a protein of the CCN family. In vivo, CYR61 mediates the binding of leukocytes and particular monocyte populations at sites of vascular inflammation. Here, we present an in vitro enrichment system mimicking (patho)physiology and at the same time representing an effective tool for enrichment of the CYR61-binding blood cells and analyzed this cell population in depth.

  Study EGAS50000000533

• Genomic Profile of Multiple Localised Spiradenoma and Spiradenocarcinoma

  Multiple spiradenomas and spiradenocarcinoma from a single donor were subjected to exome sequencing. Tumor samples were microdissected from formalin-fixed, paraffin-embedded tissue, and genomic DNA was extracted. The DNA samples were subjected to library generation using the xGen ssDNA & Low-Input DNA Library Preparation Kit, followed by exome capture using the xGen Exome Research Panel v2. The libraries were sequenced on DNBSEQ G400RS. Reads were aligned to GRCh37 with BWA.

  Study EGAS50000000554

• Transcriptome Sequencing PPGL (2)

  RNA-Seq data (n=81) library preparation was performed as previously reported by Calsina et al 2023 using QuantSeq 3' mRNA-Seq Library Prep Kit FWD for Illumina (Lexogen, 015). The prepared libraries were subsequently sequenced on NovaSeq™ system (Illumina). To combine the new CNIO samples (n=81) with the CNIO+TCGA dataset generated in Calsina et al., 2023, RNA data were processed using ComBat-seq as described in that same article.

  Study EGAS50000000013

• Whole genome sequencing of pancreatic cystic fluid for early detection and diagnosis of pancreatic cancer

  Pancreatic cancer (PC) is a leading cause of cancer-related deaths globally. Accurate PC detection at early or premalignant stage, when surgery is effective, would increase survival rates and prevent unnecessary surgery or surveillance. Molecular diagnostic attempts using PCy fluid (PCyF), as a liquid biopsy, to analyze common mutations associated with PC development are yet to contribute to early diagnosis. Whole genome sequencing (WGS) is utilized in various cancers, PC inclusive, for detecting genetic changes associated with carcinogenesis, but not in PCyF due to technical limitations, including isolated DNA purity. The goal of the study was to achieve high quality of WGS from PCyF from 9 patients to enable development of signatures of malignancy. The WGS was performed using Illumina sequencing. Experimental design was at a 50x depth.

  Dataset EGAD50000000869

• Cas9-targeted-based long-read sequencing for genetic screening of RPE65 locus

  In order to compare sequencing performance of different technologies in a specific locus, we sequenced blood samples of five inherited retinal dystrophies (IRD) patients harboring a monoallelic variant in RPE65 that remained uncharacterized with: 1) Clinical exome targeted sequencing (CES), 2) whole genome sequencing (WGS), and 3) Long-read sequencing on a MinION platform where CRISPR-Cas9 guide RNA probes were designed to target a 31 kb region including the entire RPE65 locus.

  Study EGAS50000000596

• Targeting Heterochromatin Eliminates Malignant Stem Cells in Chronic Myelomonocytic Leukemia Through Reactivation of Retroelements and Innate Immune pathways

  Hematopoietic stem cell (HSC) aging involves heterochromatin reorganization. These repressive marks together with DNA methylation are essential for suppressing transposable elements (TEs). In this study we analyze heterochromatin and transcriptomic changes occurring at genes and TEs in HSCs from chronic myelomonocytic leukemia, a severe myeloid malignancy affecting the elderly, compared to age-matched cells, and in the presence or absence of a combination of epidrugs.

  Study EGAS50000000367