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• Genome-Wide Association Study of Patients with Coccidioidomycosis

  BackgroundValley Fever is typically an infection of the lungs caused by the fungi Coccidioides immitis and Coccidioides posadasii. The incidence of Coccidioidomycosis (CM), or infection with Coccidioides, has dramatically increased over the last 20 years. This is particularly true in the Southwest of the United States, where people often breathe fungal spores that arise from the soil. Reasons for increased infection rates are thought to include population growth and construction in these endemic regions, an increase in the number of people whose immune systems are compromised due to infection or treatment with drugs required for organ transplants, climate change, as well as improved testing practices and greater physician awareness. Mild CM most commonly presents itself with flu-like symptoms and rashes, which can last weeks to months. Individuals with compromised immune systems, specifically-- substantial suppression of the immune cells known as T cells, can develop severe pulmonary and disseminated disease. Infection that remains localized to the lungs is referred to as pulmonary disease, but when the infection spreads out of the lungs into other parts of the body it represents a more serious condition referred to as a disseminated disease, or disseminated CM. In nature, Coccidioides spp. exists as mold and lives in dust and soil. When the contaminated soil or dust is disturbed by human activity, animals, or weather, the Coccidiodies spores are released into the air. Airborne spores are taken up by breathing and settle in the lungs. Once in the moist and warm environment of the lung, spores transform into spherules, which divide and become filled with smaller spores, called endospores. When the spherules get large enough, they rupture and release these endospores, which can spread and disseminate to surrounding tissue. The cycle then repeats itself as these endospores develop into new spherules3. Different ethnic groups have been described to vary in their susceptibility to developing disseminated CM after initial infection with Coccidioides. For example, evidence suggests that African-American and Filipino patients suffer the disseminated disease at a greater rate than other ethnicities. The suggestion that race plays a role in the clinical expression of the disease is still a source of debate amongst the scientific community and any genetic mechanisms responsible for these differences have yet to be fully elucidated. If our genetic makeup influences our ability to limit the spread of infection, finding which DNA differences cause these variances could provide clues to how the body successfully fights infection, and provide opportunities to boost the body’s ability to do this. Further, if we are able to identify the specific genetic risk factors that correlate with the development of disseminated infection, physicians could perform genetic screenings to identify high-risk patients and provide them with preemptive antifungal therapy prior to developing disseminated disease.The genome, made up of DNA, contains all of the information needed for humans to develop and grow. Genome-wide association studies (GWAS) allow us to look for inherited differences that are more common between people who share a particular trait, for example, height or susceptibility to certain diseases, compared to those who do not share the trait. Although some traits and diseases are controlled by a single gene, the majority are influenced by contribution from several, or even many, different genes. To find evidence of genes that contribute to specific traits, GWAS typically compares genome information from large numbers of people who have a particular disease (referred to as “cases”) looking for DNA sequences that are common among these samples, and are different from DNA sequences seen in large numbers of people who lack the trait, but are as much like the cases as possible (referred to as “controls”). The DNA sequence data from each group, cases versus controls, are analyzed to see if there are specific genomic differences that tend to be associated with the disease. MethodsTwo separate GWAS approaches were taken to look for genetic differences that could be responsible for the observed differences between the different patient populations we are studying. The first method, known as genotyping, scans for differences at a set of positions across the genome, which includes both the genes that encode our proteins and the larger amount of DNA that does not. The second method, known as exome sequencing, allows us to compare the entire sequence of the portion of the genome that codes for proteins.  For this study, DNA from patients with either pulmonary or disseminated CM were genotyped and exome sequenced to look for DNA differences that are associated with one condition or the other. All patients were at least 18 years old, had no evidence of immunosuppression, and had proven or probable pulmonary coccidioidomycosis according to established diagnostic criteria. Of these patients, a subset demonstrated disseminated disease, i.e., they showed evidence of coccidioidal infection outside of the thorax by biopsy/aspiration, had radiographic imaging, and show positive coccidioidal serology. Our criteria for including patients with the pulmonary disease were that they must not require ongoing antifungal treatment or show evidence of active CM (in skin test positive patients), show no evidence of extrapulmonary dissemination, and have no evidence of ongoing pulmonary infection (pulmonary nodules are accepted) beyond six months from diagnosis.Patient DNA was purified from blood or from sputum samples by the labs of our collaborators, Drs. George Thompson (UC Davis School of Medicine) and John Galgiani (University of Arizona Health Sciences). Genome-wide association (GWAS) analysis was carried out to look for candidate loci associated with pulmonary versus disseminated disease, taking into account the population structure of the samples. Single nucleotide or insertion/deletion variants were identified from whole-exome sequences (WES) using the Picard/BWA/GATK pipeline. ResultsTable 1. Pulmonary versus Disseminated Cases of Coccidiomycosis for GWAS, Sorted by EthnicityEthnicityPulmonary CasesDisseminated CasesAsian85Black/African American1664Caucasian/White4015Filipino03Hispanic/Latino3414Indian21Mexican American1039Pacific Islander01Samoan03Vietnamese10Unknown16917More than one race02Total373134Table 1 shows the number of samples analyzed from patients with pulmonary versus disseminated disease, and patient ethnicity, where known. In all, we worked with 507 samples, including 134 samples from patients with disseminated disease and 373 samples from patients with pulmonary disease. Of these, 505 samples were genotyped using the Multi-Ethnic Global Array from Illumina Inc. In addition, we were able to generate whole-exome sequence from 498 patient samples. No significant associations were detected that differed between samples from patients with pulmonary versus disseminated disease; that is, no particular DNA sequences were found to be significantly enriched in patients with disseminated disease compared to patients with pulmonary disease. The ability to detect genetic association between specific sequences and genetically determined traits is influenced by several factors, including how many patient samples are available to compare, how many different genes contribute to the trait and how strong their contributions are. When the number of genes is small and the contribution of each gene is great, smaller numbers of patient samples are needed to detect an association. When more genes are involved, or the contribution from each gene is more modest, larger numbers of patient samples must be examined. While we were not able to detect any associated within this study, it does not mean that subsequent studies would not find this connection. Our study suggests significantly more samples should be analyzed in further studies.Whole-exome sequences were generated from 498 samples and were aligned to reference sequences to identify positions where the sequences differed from the reference. These data are being analyzed to determine if any variants are associated with pulmonary, versus disseminated, disease. 

  Study phs002170

• M116 scRNA-seq

  scRNA-seq was performed using Chromium Next GEM Single Cell 3’ Kit v3.1 (10X Genomics) according to the manufacturer’s instructions. Briefly, M116's PBMCs were thawed and quantified in order to calculate the number of cells to be loaded. Then, barcoded Single Cell 3ʹ Gel Beads, a master mix containing PBMCs, and Partitioning Oil were combined onto the Chromium Next GEM Chip G to generate Gel Beads-In-Emulsion (GEMs), where polyadenylated mRNAs were reverse transcribed. This way, all generated cDNAs from the same cell shared a common 10X Barcode. Following the reverse transcription, GEMs were broken and cDNAs were amplified and cleaned up with SPRIselect beads (Beckman Coulter®). Next, a portion of these cDNAs were enzymatically fragmented and subjected to adaptor ligation before using them as a PCR template for the incorporation of i5/i7 indexes. Finally, libraries were purified with SPRIselect beads, quantified and quality checked by using the 2200 TapeStation (Agilent®), and subjected to paired-end 150-bp sequencing (Novaseq systems, Illumina®) following standard practices at external facilities (Novogene®). Files types included in this dataset are raw Fastq files.

  Dataset EGAD50000001289

• MutWP5: CRUK Mutographs of Cancer: Breast: Reduction Mammoplasty (WG)

  Sequencing of LCM-derived microbiopsies from 10 women who underwent reduction mammoplasty. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Whole-genome sequencing will be conducted on samples identified as promising from the initial targeted data. Results from this portion of the study will be compared to women who had breast cancer and those who are BRCA 1/2 carriers. This dataset contains all the data available for this study on 2023-03-08.

  Dataset EGAD00001010109

• Weighing Risks and Benefits of Laparoscopic Anti-Reflux Surgery in Patients With Idiopathic Pulmonary Fibrosis (WRAP-IPF-BioLINCC)

  Data Access NOTE: Please refer to the "Authorized Access" section below for information about how access to the data from this accession differs from many other dbGaP accessions. Objectives: To determine if the reduction of abnormal gastro-esophageal reflux (GER) with laparoscopic anti-reflux surgery will slow the progression of idiopathic pulmonary fibrosis (IPF) as measured by forced vital capacity (FVC).Background: Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease of unknown cause and increasing prevalence in the United States. Aside from lung transplantation, which approximately only 1% of participants will receive, there is no FDA-approved therapy. Abnormal acid gastro-esophageal reflux (GER) has been well described in participants with IPF and is thought to play a role in the progression of the disease. The several retrospective cohort studies that have assessed the association of anti-acid therapies for GER with clinical outcomes in IPF have had inconsistent results.Participants: A total of 58 participants were enrolled.Design: The WRAP-IPF trial was a multicenter, unblinded randomized clinical trial with participants randomized to either laparoscopic anti-reflux surgery or no surgery. Participants in the no-surgery group had the option of receiving laparoscopic anti-reflux surgery from 24 weeks after randomization if their clinician deemed it medically necessary. Medications for acid GER were allowed in both groups if the clinician felt it was necessary.Participants were followed up from time of randomization to 52 weeks. All participants had study visits at baseline, 12, 24, 36, and 48 weeks, during which spirometry, 6-min walk testing, and participant-related outcome assessments were done. Participants in the surgery group had clinical visits for preoperative evaluation, laparoscopic anti-reflux surgery, and postoperative management as clinically indicated. Surgery participants additionally underwent repeat 24-hour pH testing at 24 weeks to assess the efficacy of the surgery. All participants were contacted by telephone for safety assessments at weeks 4, 8, 16, 20, 28, 32, 40, 44, and 52. All participants completed an exploratory questionnaire on reflux symptoms at baseline and 48 weeks. The primary endpoint was change in FVC from randomization (baseline) to 48 weeks. Secondary endpoints included acute exacerbation, non-elective hospitalization (both all-cause and respiratory-related), death, change in cough severity, change in dyspnea severity, change in health-related quality of life, change in 6-min walk distance, and time to selected event-driven composite endpoints of disease progression. Conclusions: In participants with IPF and abnormal acid GER, laparoscopic anti-reflux surgery is safe and well tolerated but did not significantly slow the rate of FVC decline. Further research is needed, particularly with a larger study in order to achieve sufficient statistical power, regarding the possible benefits of anti-reflux surgery in this population.Raghu G, Pellegrini CA, Yow E, et al. Laparoscopic anti-reflux surgery for the treatment of idiopathic pulmonary fibrosis (WRAP-IPF): a multicentre, randomised, controlled phase 2 trial. Reference: Raghu, et al., 2018; PMID: 30100404.

  Study phs003968

• Characterisation of the genomic landscape of CRLF2-d ALL

  To determine the clinical and genetic landscape of CRLF2 deregulated acute lymphoblastic leukaemia (CRLF2-d ALL). We identified 172 patients with a CRLF2 rearrangement treated on either the UKALL2003 trial for children and adolescents (1-24 years) or the UKALLXII trial for adolescents and adults (15-59 years). Genomic technologies from conventional karyotyping, and FISH through to whole genome and exome sequencing were used to characterise the genomes of patients with CRLF2-d ALL. This is the largest study to date to investigate the genomic landscape of CRLF2-d ALL and define CRLF2-d as a unique subgroup of B-other ALL. We have confirmed the high incidence of CRLF2-d in Down syndrome-ALL and demonstrated the co-existence of CRLF2-d with other primary chromosomal rearrangements, suggesting that in these patients CRLF2-d can be a secondary genetic abnormality. Other defining features included enrichment of IKZF1, BTG1 and ADD3 deletions in IGH-CRLF2 patients and specific chromosomal gains seen at much higher frequencies than B-other ALL . We report recurrent established and new co-operating abnormalities and the novel involvement of USP9X and DDX3X in CRLF2-d ALL. It is clear from these data that CRLF2-d ALL is heterogenoeus, requiring a combination of genetic abnormalities in functionally relevent genes, to work alongside the deregulated expression of CRLF2 in order to initiate and drive leukaemogenesis in this subtype. Although the functional relevance of many of the abnormalities presented here are currently unknown, many are likely to activate alternate pathways or sensitize patients to current therapies.

  Dataset EGAD00001002007

• Characterisation of the genomic landscape of CRLF2-d ALL

  To determine the clinical and genetic landscape of CRLF2 deregulated acute lymphoblastic leukaemia (CRLF2-d ALL). We identified 172 patients with a CRLF2 rearrangement treated on either the UKALL2003 trial for children and adolescents (1-24 years) or the UKALLXII trial for adolescents and adults (15-59 years). Genomic technologies from conventional karyotyping, and FISH through to whole genome and exome sequencing were used to characterise the genomes of patients with CRLF2-d ALL. This is the largest study to date to investigate the genomic landscape of CRLF2-d ALL and define CRLF2-d as a unique subgroup of B-other ALL. We have confirmed the high incidence of CRLF2-d in Down syndrome-ALL and demonstrated the co-existence of CRLF2-d with other primary chromosomal rearrangements, suggesting that in these patients CRLF2-d can be a secondary genetic abnormality. Other defining features included enrichment of IKZF1, BTG1 and ADD3 deletions in IGH-CRLF2 patients and specific chromosomal gains seen at much higher frequencies than B-other ALL . We report recurrent established and new co-operating abnormalities and the novel involvement of USP9X and DDX3X in CRLF2-d ALL. It is clear from these data that CRLF2-d ALL is heterogenoeus, requiring a combination of genetic abnormalities in functionally relevent genes, to work alongside the deregulated expression of CRLF2 in order to initiate and drive leukaemogenesis in this subtype. Although the functional relevance of many of the abnormalities presented here are currently unknown, many are likely to activate alternate pathways or sensitize patients to current therapies.

  Dataset EGAD00001002008

• miRNA expression data from primary tumors, metastasis and matched normals.

  MicroRNAs (miRs) have been recognized as promising biomarkers. It is unknown to what extent tumor-derived miRs are differentially expressed between primary colorectal cancers (pCRCs) and metastatic lesions, and to what extent the expression profiles of tumor tissue differ from the surrounding normal tissue. Next-generation sequencing (NGS) of 220 fresh-frozen samples, including paired primary and metastatic tumor tissue and non-tumorous tissue from 38 patients, revealed expression of 2245 known unique mature miRs and 515 novel candidate miRs. Unsupervised clustering of miR expression profiles of pCRC tissue with paired metastases did not separate the two entities, whereas unsupervised clustering of miR expression profiles of pCRC with normal colorectal mucosa demonstrated complete separation of the tumor samples from their paired normal mucosa. Two hundred and twenty-two miRs differentiated both pCRC and metastases from normal tissue samples (false discovery rate (FDR) <0.05). The highest expressed tumor-specific miRs were miR-21 and miR-92a, both previously described to be involved in CRC with potential as circulating biomarker for early detection. Only eight miRs, 0.5% of the analysed miR transcriptome, were differentially expressed between pCRC and the corresponding metastases (FDR <0.1), consisting of five known miRs (miR-320b, miR-320d, miR-3117, miR-1246 and miR-663b) and three novel candidate miRs (chr 1-2552-5p, chr 8-20656-5p and chr 10-25333-3p). These results indicate that previously unrecognized candidate miRs expressed in advanced CRC were identified using NGS. In addition, miR expression profiles of pCRC and metastatic lesions are highly comparable and may be of similar predictive value for prognosis or response to treatment in patients with advanced CRC.

  Dataset EGAD00001001644

• Single cell characterization of T-cell lymphoma: RNA (2025-07-31)

  Lymphomas are types of blood cancer derived from lymphocytes, and can be classified into two main categories, Hodgkin's lymphomas (HL) and non-Hodgkin lymphomas (NHL). T-cell lymphoma is a type of NHL that develops from T lymphocytes and accounts for 10-15% of total NHL cases. As a rare disease, T-cell lymphoma is functionally, pathologically, and clinically complex and heterogeneous. The 2016 revision of the WHO classification has listed 29 subtypes of mature T/NK-cell neoplasms, among which over 25 are T lymphocyte in origin. These subtypes of T-cell lymphomas have different biological behaviours and clinical prognosis. Clinically, T-cell lymphoma ranges from indolent (slow-growing) to aggressive (fast-growing and spreading) disease. According to the guideline booklet provided by the Lymphoma Research Foundation (https://lymphoma.org/aboutlymphoma/nhl/), indolent T-cell lymphoma subtypes include adult T-cell leukaemia/lymphoma (ATLL), cutaneous T-cell lymphoma (CTCL), and mycosis fungoides (MF), while the aggressive subtypes include anaplastic large cell lymphoma (ALCL), angioimmunoblastic T-cell lymphoma (AITL), peripheral T-cell lymphoma (PTCL), and T lymphoblastic lymphoma (TLBL). PTCL and ALCL represent the most common subtypes of aggressive T-cell lymphoma whereas TLBL is a rare and aggressive subtype mostly diagnosed in children. In this study, we will use single cell RNA sequencing and single cell multiomic methods three aggressive T-cell lymphoma subtypes (ALCL, PTCL and TLBL) and try to gain a deeper insight into the genotypic and phenotypic features of these T-cell lymphomas. . This dataset contains all the data available for this study on 2025-07-31.

  Dataset EGAD00001015669

• This committee is composed on individuals who have access to the data corresponding to this project: Gut microbiome modulates response to anti PD-1 immunotherapy in melanoma patients

  Dac EGAC00001000758

• Average_5mC_Roadmap_histoneMarks

  Average methylation levels based on nanopolish calls from Roadmap Epigenomics histone mark annotations using different cell types. Data from 8 individuals at different time points.

  Dataset EGAD00010002422

• Average_5hmC_Roadmap_histoneMarks

  Average hydroxymethylation levels based on megalodon calls from Roadmap Epigenomics histone mark annotations using different cell types. Data from 8 individuals at different time points.

  Dataset EGAD00010002417

• Average_5hmC_Roadmap_chromatinStates

  Average hydroxymethylation levels based on megalodon calls from Roadmap Epigenomics chromatin state annotations using different cell types. Data from 8 individuals at different time points.

  Dataset EGAD00010002414

• Average_5mC_Roadmap_chromatinStates

  Average methylation levels based on nanopolish calls from Roadmap Epigenomics chromatin state annotations using different cell types. Data from 8 individuals at different time points.

  Dataset EGAD00010002413

• WP8_DNA_Methylation_450K_UCL

  BLUEPRINT DNA methylation profiles of monocytes, T cells and B cells in type 1 diabetes-discordant monozygotic twins

  Dataset EGAD00010001025

• HipSci - Hereditary Spastic Paraplegia - Exome Sequencing - July 2017

  HipSci - Hereditary Spastic Paraplegia - Exome Sequencing - July 2017

  Dataset EGAD00001003522

• IfGH-10772

  Clinical and genetic information of an individual with RVOT-VT and a KCNK2 (TREK1) gene mutation obtained after whole exome sequencing.

  Dataset EGAD00001003328

• Promoter capture HiC on KMS11 (multiple myeloma)

  Promoter capture HiC on KMS11 (multiple myeloma)

  Dataset EGAD00001003597

• Cloning of the breakpoint of a novel translocation associated with T-acute lymphoblastic leukaemia

  Single case of T-ALL carrying t(4;6), a novel translocation.

  Dataset EGAD00001002193

• Reference epigenome ADMSC02 mRNA-Seq data generated from KEP study

  ADMSC02 mRNA-Seq paired end data

  Dataset EGAD00001007151

• Reference epigenome ADMSC05 miRNA-Seq data generated from KEP study

  ADMSC05 miRNA-Seq single end data

  Dataset EGAD00001007177

• Reference epigenome ADMSC06 miRNA-Seq data generated from KEP study

  ADMSC06 miRNA-Seq single end data

  Dataset EGAD00001007178

• Reference epigenome ADMSC07 miRNA-Seq data generated from KEP study

  ADMSC07 miRNA-Seq single end data

  Dataset EGAD00001007179

• Reference epigenome ADMSC08 miRNA-Seq data generated from KEP study

  ADMSC08 miRNA-Seq single end data

  Dataset EGAD00001007180

• WGS data of multiple myeloma patients

  48 samples of individuals with rare germline variants in familial multiple myeloma, whole genome sequencing

  Dataset EGAD00001007747

• Reference epigenome Islet-derived_iPSC04 h3k4me1 ChIP-Seq data generated from KEP study

  Islet-derived_iPSC04 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007891

• Reference epigenome Pancreas-Islet06 WGBS data generated from KEP study

  Pancreas-Islet06 WGBS paired end data

  Dataset EGAD00001007884

• Reference epigenome Pancreas-Islet08 WGBS data generated from KEP study

  Pancreas-Islet08 WGBS paired end data

  Dataset EGAD00001007887

• Reference epigenome Islet-derived_iPSC04 h3k27ac ChIP-Seq data generated from KEP study

  Islet-derived_iPSC04 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007888

• Reference epigenome Islet-derived_iPSC04 h3k27me3 ChIP-Seq data generated from KEP study

  Islet-derived_iPSC04 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007889

• Reference epigenome Islet-derived_iPSC04 h3k36me3 ChIP-Seq data generated from KEP study

  Islet-derived_iPSC04 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007890

• Reference epigenome Islet-derived_iPSC04 h3k4me3 ChIP-Seq data generated from KEP study

  Islet-derived_iPSC04 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007892

• Reference epigenome Islet-derived_iPSC04 h3k9me3 ChIP-Seq data generated from KEP study

  Islet-derived_iPSC04 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007893

• Reference epigenome Islet-derived_iPSC04 input ChIP-Seq data generated from KEP study

  Islet-derived_iPSC04 input ChIP-Seq paired end data

  Dataset EGAD00001007894

• Reference epigenome Islet-derived_MSC06 h3k27ac ChIP-Seq data generated from KEP study

  Islet-derived_MSC06 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007895

• Reference epigenome Islet-derived_MSC06 h3k27me3 ChIP-Seq data generated from KEP study

  Islet-derived_MSC06 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007896

• Reference epigenome Islet-derived_MSC06 h3k36me3 ChIP-Seq data generated from KEP study

  Islet-derived_MSC06 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007897

• Reference epigenome Islet-derived_MSC06 h3k4me1 ChIP-Seq data generated from KEP study

  Islet-derived_MSC06 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007898

• Reference epigenome Islet-derived_MSC06 h3k4me3 ChIP-Seq data generated from KEP study

  Islet-derived_MSC06 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007899

• Reference epigenome Islet-derived_MSC06 h3k9me3 ChIP-Seq data generated from KEP study

  Islet-derived_MSC06 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007900

• Reference epigenome Islet-derived_MSC06 input ChIP-Seq data generated from KEP study

  Islet-derived_MSC06 input ChIP-Seq paired end data

  Dataset EGAD00001007901

• Reference epigenome Islet-derived_MSC08 h3k27ac ChIP-Seq data generated from KEP study

  Islet-derived_MSC08 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007902

• Reference epigenome Islet-derived_MSC08 h3k27me3 ChIP-Seq data generated from KEP study

  Islet-derived_MSC08 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007903

• Reference epigenome Islet-derived_MSC08 h3k36me3 ChIP-Seq data generated from KEP study

  Islet-derived_MSC08 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007904

• Reference epigenome Islet-derived_MSC08 h3k4me1 ChIP-Seq data generated from KEP study

  Islet-derived_MSC08 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007905

• Reference epigenome Islet-derived_MSC08 h3k4me3 ChIP-Seq data generated from KEP study

  Islet-derived_MSC08 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007906

• Reference epigenome Islet-derived_MSC08 h3k9me3 ChIP-Seq data generated from KEP study

  Islet-derived_MSC08 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007907

• Reference epigenome Islet-derived_MSC08 input ChIP-Seq data generated from KEP study

  Islet-derived_MSC08 input ChIP-Seq paired end data

  Dataset EGAD00001007908

• KUI7051

  Nanopore pericentromere normal methylation

  Dataset EGAD00001009885

• STAG1 and STAG2-ChIP-seq in RAD21-mutant adult acute myeloid leukemia

  STAG1 and STAG2-ChIP-seq in RAD21-mutant adult acute myeloid leukemia

  Dataset EGAD00001015361

• Single-cell multiomics reveals the interplay of clonal evolution and cellular plasticity in hepatoblastoma

  spatial transcriptomics

  Dataset EGAD00001015262

• Systematic immune cell dysregulation and molecular subtypes revealed by single cell RNA-seq of subjects with type 1 diabetes

  Type 1 diabetes mellitus (T1DM) is a prototypic endocrine autoimmune disease resulting from an immune-mediated destruction of pancreatic insulin-secreting beta-cells. A comprehensive immune cell phenotype evaluation in T1DM has not been performed thus far at the single. In this cross-sectional analysis, we generated a single-cell transcriptomic dataset of peripheral blood mononuclear cells (PBMCs) from 46 manifest T1DM (Stage 3) cases and 31 matched controls.Our study reveals a surprisingly strong systemic dimension at the level of immune cell network in T1DM, defines disease-relevant molecular subtypes and has the potential to guide non-invasive test development and patient stratification.

  Study EGAS50000000231

• Providing access to COVID-19 data: one year later

  response efforts On 11 March 2020 the World Health Organization declared the novel coronavirus outbreak a global pandemic. Four months later, the European Genome-phenome Archive (EGA) released its first COVID-19 dataset. This dataset – single cell RNA and VDJ sequencing of B cells from 60 COVID-19 patients – showed that neutralizing antibodies could be identified by high-throughput sequencing in response to SARS-CoV-2 infection. That was one year ago. Today, the EGA provides access to fifteen COVID-19 datasets from researchers across seven countries in Asia, Europe, and North America. These studies represent almost 17,000 individuals and have resulted in at least sixteen publications and preprints. Researchers deposit controlled access COVID-19 data at EGA The global research community has come together rapidly to investigate the SARS-CoV-2 coronavirus and better understand the related disease, COVID-19. These research efforts generate valuable genetic and phenotypic data from patients and research participants that can be shared with approved researchers. The EGA enables sharing of this research data by providing a service for archiving and controlled distribution of sensitive data. Over the past year, the EGA has worked with researchers to archive and distribute COVID-19 data from high-throughput sequencing experiments, genotyping studies, and phenotypic information. These datasets investigate the immune system, blood, and cells and tissues of the lung, which are relevant for studying a contagious respiratory illness caused by a viral infection. *Study Spotlight. In January 2021, Ancestry.com demonstrated the utility of deep phenotyping based on self-reported outcomes from a large population of mild and asymptomatic COVID-19 cases. They identified genetic associations with eight COVID-19 phenotypes and showed distinct patterns of association, most notably related to the chr3/SLC6A20/LZTFL1 and chr9/ABO regions. The supporting data is available at the EGA to approved researchers and includes both genotype and phenotype data for 15,000 individuals. EGA collaborates with global COVID-19 community Since the coronavirus outbreak, the EGA has collaborated with other life science resources to support discovery and access to COVID-19 datasets. COVID-19 Host Genetics Initiative. With the NHGRI’s Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) platform, the EGA enables sharing of individual-level genetic and phenotypic data from the COVID-19 Host Genetics Initiative (HGI). This initiative aims to generate, share, and analyze data from COVID-19 host genetics research projects to better understand the genetic determinants of COVID-19 susceptibility, severity, and outcomes. In response to COVID-19, the EGA actively supports COVID-19 data submissions and integration of data access and flow into the COVID-19-HGI analysis platform. *Study Spotlight. The COVID-19 HGI has combined individual-level data for 13,868 COVID-19 positive patients (N=7,167 hospitalized) from 17 cohorts in nine countries. The data were used to assess the association of the major common COVID-19 genetic risk factor (chromosome 3 locus tagged by rs10490770) with mortality, COVID-19-related complications, and laboratory values. The genotype and phenotype data for 10 of these cohorts is available at the EGA to approved researchers under accession EGAS00001005304. Fig 1: EGA COVID-19 studies are searchable in the European COVID-19 Data Portal alongside other COVID-19 and SARS-CoV-2 public datasets. European COVID-19 Data Portal. EGA-archived COVID-19 data are discoverable via the European COVID-19 Data Portal (Fig. 1), which brings together public and controlled access data to accelerate coronavirus research for the international community. By indexing all COVID-19-related data in one place, researchers can more easily discover relevant datasets of interest, thus increasing the “FAIRness” (Findability, Accessibility, Interoperability, Reusability) of this valuable data. Fig 2: SARS-CoV-2 viral sequences are imported from ENA and analysed in Galaxy to detect variants. Results are accessible to researchers through the COVID-19 Viral Beacon. COVID-19 Viral Beacon. The COVID-19 Viral Beacon tool was developed in collaboration with the European Nucleotide Archive and Galaxy to enable near real-time browsing of SARS-CoV-2 variability at genomic, amino acid, and motif levels (Fig. 2). The COVID-19 Viral Beacon allows researchers to (i) perform detailed searches about genomic variants, (ii) filter queries and find unique cases, (iii) filter data based on strain-specific variants, and (iv) explore associated metadata. It uses the Global Alliance for Genomics and Health (GA4GH) Beacon standard including new Beacon v2 features. With this tool, researchers can study intra-host mutations on genomic regions of interest, or trace any variant frequency over time using raw read data. More than 200,000 SARS-CoV-2 analysed genomic data files are now available to researchers for further exploration. Ongoing COVID-19 efforts at EGA Addressing the COVID-19 pandemic is a global effort. Federated resources are necessary to support transnational deposition, access, and analysis of sensitive COVID-19 host genetics and other related data. At the same time, many countries now have emerging personalized medicine programmes which are generating data from national or regional healthcare initiatives. These data are subject to more stringent information governance than research data and often must comply with national data protection legislation. To address this need, the Federated EGA was established to serve as the primary global resource for discovery and access of sensitive human omics and associated data consented for secondary use. The Federated EGA will comprise a network of national human data repositories and will implement community standards and common interfaces. Launching Federated EGA promises to accelerate not only global research efforts to understand, diagnose, and treat COVID-19, but also to foster data reuse, enable reproducibility, and accelerate biomedical and disease research to ultimately improve human health.

  Blog providing-access-to-covid-19

• Variant call of single Jakun Individual

  One Jakun individual was sequenced on IIllumina HiSeq 2000. Variant calling was performed through GATK

  Dataset EGAD50000001024

• Average_5hmC_TF_sites

  Average hydroxymethylation levels on transcription factor binding sites obtained from ENCODE (ChIP-sequencing of GM12878 lymphoblastoid cell line). Data from 6 individuals at different time points.

  Dataset EGAD00010002421

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells - untreated) from donor D2, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008048

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells - untreated) from donor D1, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008038

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells - untreated) from donor D3, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008058

• RNA-sequençing of 21 inflammatory hepatocellular adenomas and 1 non-tumoral sample

  Dataset EGAD00001005284

• OT2_Solid_WXS_T

  Whole-exome sequencing on AB 5500xl Genetic Analyzer of colorectal cancer primary tumor sample (OT2_cohort)

  Dataset EGAD00001003386

• HipSci - Monogenic Diabetes - Exome Sequencing - July 2017

  HipSci - Monogenic Diabetes - Exome Sequencing - July 2017

  Dataset EGAD00001003516

• SF11310

  SF11310 Oligodendroglioma, IDH-mutant.Tumor Location: Frontal. Age:22. Sex: Female

  Dataset EGAD00001006313

• CSC DDR dataset

  CSC DDR dataset contains 4 bam files of two pairs of colorectal CSCs sensitive and resistant to ATR or CHK1 inhibitor

  Dataset EGAD00001006774

• Reference epigenome Pancreas-Islet07 WGBS data generated from KEP study

  Pancreas-Islet07 WGBS paired end data

  Dataset EGAD00001007146

• Reference epigenome Fat-adipocyte03 WGBS data generated from KEP study

  Fat-adipocyte03 WGBS paired end data

  Dataset EGAD00001007147

• Reference epigenome Fat-adipocyte04 WGBS data generated from KEP study

  Fat-adipocyte04 WGBS paired end data

  Dataset EGAD00001007148

• Reference epigenome Fat-adipocyte05 WGBS data generated from KEP study

  Fat-adipocyte05 WGBS paired end data

  Dataset EGAD00001007149

• Reference epigenome ADMSC01 mRNA-Seq data generated from KEP study

  ADMSC01 mRNA-Seq paired end data

  Dataset EGAD00001007150

• Reference epigenome ADMSC03 mRNA-Seq data generated from KEP study

  ADMSC03 mRNA-Seq paired end data

  Dataset EGAD00001007152

• Reference epigenome ADMSC05 mRNA-Seq data generated from KEP study

  ADMSC05 mRNA-Seq paired end data

  Dataset EGAD00001007153

• Reference epigenome ADMSC06 mRNA-Seq data generated from KEP study

  ADMSC06 mRNA-Seq paired end data

  Dataset EGAD00001007154

• Reference epigenome ADMSC07 mRNA-Seq data generated from KEP study

  ADMSC07 mRNA-Seq paired end data

  Dataset EGAD00001007155

• Reference epigenome ADMSC08 mRNA-Seq data generated from KEP study

  ADMSC08 mRNA-Seq paired end data

  Dataset EGAD00001007156

• Reference epigenome SMC01 mRNA-Seq data generated from KEP study

  SMC01 mRNA-Seq paired end data

  Dataset EGAD00001007169

• Reference epigenome SMC02 mRNA-Seq data generated from KEP study

  SMC02 mRNA-Seq paired end data

  Dataset EGAD00001007170

• Reference epigenome SMC03 mRNA-Seq data generated from KEP study

  SMC03 mRNA-Seq paired end data

  Dataset EGAD00001007171

• Reference epigenome SMC04 mRNA-Seq data generated from KEP study

  SMC04 mRNA-Seq paired end data

  Dataset EGAD00001007172

• Reference epigenome SMC05 mRNA-Seq data generated from KEP study

  SMC05 mRNA-Seq paired end data

  Dataset EGAD00001007173

• Reference epigenome SMC07 mRNA-Seq data generated from KEP study

  SMC07 mRNA-Seq paired end data

  Dataset EGAD00001007174

• Reference epigenome SMC08 mRNA-Seq data generated from KEP study

  SMC08 mRNA-Seq paired end data

  Dataset EGAD00001007175

• Reference epigenome SMC09 mRNA-Seq data generated from KEP study

  SMC09 mRNA-Seq paired end data

  Dataset EGAD00001007176

• Germline variant analysis in childhood AML

  This dataset contains one vcf file with variants from whole exome sequencing of 24 paediatric AML samples at diagnosis.

  Dataset EGAD00001008783

• Reference epigenome Islet-derived-MSC06 WGBS data generated from KEP study

  Islet-derived_MSC06 WGBS paired end data

  Dataset EGAD00001007875

• Reference epigenome Islet-derived-MSC08 WGBS data generated from KEP study

  Islet-derived_MSC08 WGBS paired end data

  Dataset EGAD00001007876

• Reference epigenome Islet-derived-iPSC04 WGBS data generated from KEP study

  Islet-derived_iPSC04 WGBS paired end data

  Dataset EGAD00001007877

• scRNA-seq of placental immune cells

  scRNA-seq analysis of 384 placental immune cells

  Dataset EGAD00001010040

• STAG1-ChIP-Seq of STAG2-mutated and cohesin wildtype adult AMLs

  ChIPseq targeting the cohesin subunit STAG1 in STAG2-mutated AMLs or cohesin wildtype AMLs

  Dataset EGAD00001011206

• Covacta OLINK LOD

  Explore 1536 dataset from serum of patients enrolled in the COVACTA trial. This dataset includes limit of detection values provided by Olink.

  Dataset EGAD00001011161

• Covacta OLINK NPX

  Olink Explore 1536 dataset from serum of patients enrolled in the COVACTA trial. This dataset includes the NPX values provided by Olink.

  Dataset EGAD00001011169

• Whole Genome Sequencing of clonal expansions of single healthy somatic cells (human, female)

  HSC73_clone: Bone marrow mononuclear cells from the healthy 73 years old female were thawed and labeled with Alexa-Fluor 488-conjugated anti-CD34 (581, Biolegend), Alexa-Fluor 700-conjugated anti-CD38 (HIT2, eBioscience), a cocktail of APC-conjugated lineage antibodies consisting of anti-CD4 (RPA-T4), anti-CD8 (RPA-T8), anti-CD11b (ICRF44), anti-CD20 (2H7), anti-CD56 (B159, all BD Biosciences), anti-CD14 (61D3), anti-CD19 (HIB19) and anti-CD235a (HIR2, all eBiocience) and 1 micro-gram/ml propidium iodide (Sigma). Using a BD FACSAria cell sorter, single Lin-CD34+CD38-PI- cells were individually sorted into low-adhesion 96-well tissue culture plates (Corning) containing 100micro-litre of StemSpan Serum-Free Expansion Medium (Stemcell technologies) supplemented with 100ng/ml of human SCF and FLT-3L, 50ng/ml of human TPO, 20ng/ml of human IL-3, IL-6 and G-CSF (all cytokines from Peprotech) and 50U/ml of penicillin and 50μg/ml of streptomycin (Sigma). Cells were incubated at 37 degrees C in a humidified atmosphere with 5% CO2 in air. After 5 days in culture, another 100micro litres of cytokine-containing medium were added. 13 days after seeding, clones B6 and G2 had expanded to approx. 105 cells and were selected for whole genome sequencing (2x101bp, paired-end, Illumina HiSeq2500) after tagmentation-based library preparation (see Extended Experimental Procedures) for clone B6 and standard library preparation for clone G2. For germline-control ~106 unsorted bone marrow mononuclear cells from the same donor were used for sequencing. An average of 30-fold sequence coverage for each the clones and the matching control were obtained.L4clone: A progenitor cell clone was raised from a peripheral blood sample of the 39 year old healthy female. Frozen peripheral blood mononuclear cells (PBMCs) were isolated from 2 ml heparinised peripheral blood via Ficoll Paque density centrifugation. A methylcellulose assay was performed as described earlier (Weisse et al., 2012). In brief, non-adherent mononuclear cells were incubated in the presence of the recombinant human cytokines IL-3, IL-5 and GM-CSF (R&D systems) over 14 days to induce colony formation. Colonies were detected under an inverted light microscope, and plucked by a pipette when colonies had approximately 10,000 cells/CFU. Each colony was washed three times in PBS and finally frozen as a cell pellet in -80 degrees C. Genomic DNA was isolated using the QIAamp DNA micro kit according to the instructions of the manufacturer (Qiagen, Hilden, Germany). Whole genome sequencing (2x101bp, paired-end, Illumina HiSeq2500) was performed for colony 4 after tagmentation-based library preparation and resulted in 15-fold sequence coverage for each the colony and the matching whole blood.

  Dataset EGAD00001000666

• Kids First: Genomic Analysis of Esophageal Atresia and Tracheoesophageal Fistulas and Associated Congenital Anomalies

  The Gabriella Miller Kids First Pediatric Research Program (Kids First) is a trans-NIH effort initiated in response to the 2014 Gabriella Miller Kids First Research Act and supported by the NIH Common Fund. This program focuses on gene discovery in pediatric cancers and structural birth defects and the development of the Gabriella Miller Kids First Pediatric Data Resource (Kids First Data Resource). All of the WGS and phenotypic data from this study are accessible through dbGaP and kidsfirstdrc.org, where other Kids First datasets can also be accessed. Esophageal atresia/tracheoesophageal fistula (EA/TEF) is a rare and complex aerodigestive congenital anomaly with an estimated incidence of 1 in 2500 to 1 in 4000 live births. We propose to elucidate the underlying genomic architecture of EA/TEF by performing whole genome sequencing to characterize new clinical syndromes associated with EA/TEF to provide more accurate clinical prognostic information.

  Study phs002161

• Exome sequencing in HIV+ Viremic Non-Progressors (VNPs) and HIV+ Progressors

  Viremic Non-Progressors (VNPs) maintain normal CD4+ T-cell counts despite uncontrolled HIV-1 replication, but mechanisms leading to CD4+ T-cell preservation are incompletely characterized. We aim to generate a comprehensive understanding of this infrequent phenotype by comparing rates of cellular infection, immunophenotype, genomics, single-cell transcriptomics, metabolomics, and levels of plasma biomarkers in 16 VNPs and 29 HIV+ Progressors. During chronic infection VNPs show lower frequency of HIV-1-infected cells in periphery, which is associated with reduced CCR5 expression and higher prevalence of CCR5Δ32 heterozygosity. The CD8+ T-cell compartment displays a less cytotoxic and less activated phenotype in VNPs. Moreover, despite similar viremia, we found lower bystander CD4+ T-cell death in the VNPs, together with weaker IFN responses, reduced plasma levels of zonulin (a biomarker of intestinal permeability), altered tryptophan catabolism, and preserved LPS responsiveness in vitro. Overall, a complex multifactorial mechanism, suggesting gut-associated lymphoid tissue preservation, underlies resistance to HIV pathogenesis in VNPs.

  Study EGAS50000000079

• Recurrent adeno-associated virus 2-related insertional mutagenesis in human hepatocellular carcinomas

  Hepatocellular carcinomas (HCC) are liver tumors related to various etiologies including alcohol intake, hepatitis B (HBV) or C (HCV) virus infection. Additional risk factors remain to be identified, particularly in patients who develop HCC without cirrhosis. We identified clonal integration of adeno-associated virus type 2 (AAV2) in 11 out of 193 HCC. These AAV2 integrations occurred within known cancer driver genes, namely, CCNA2 (Cyclin A2, 4 cases), TERT (Telomerase Reverse-Transcriptase, 1 case), CCNE1 (Cyclin E1, 3 cases), TNFSF10 (Tumor Necrosis Factor member 10, 2 cases) or KMT2B (Lysine (K)-Specific Methyltransferase 2B, 1 case) leading to over-expression of the target genes. Tumors with viral integration mainly developed in non-cirrhotic liver (9 out of 11 cases) and without known risk factors (6 out of 11) suggesting a pathogenic role of AAV2 in these patients. In conclusion, AAV2 is a DNA virus associated with oncogenic insertional mutagenesis in human HCC.

  Study EGAS00001001284

• A Clinical Trial of Pembrolizumab in Patients with Hepatitis B Virus-related Hepatocellular Carcinoma, with Parallel Study on Baseline and Serial Change in the Immune Environment

  This is a single-arm Phase II trial of pembrolizumab in patients with hepatitis B virus-related hepatocellular carcinoma with parallel study on baseline and serial change in the immune environment. Subjects should have a confirmed diagnosis of HCC (in accordance with the AASLD guideline) and confirmed chronic infection with hepatitis B virus as defined by positivity for HBsAg. Antiviral therapy for HBV must be given for at least 12 weeks and HBV viral load must be less than 100 IU/mL prior to first dose of study drug. They must have disease not amenable to a curative treatment approach or loco-ablation. Subject must be fit and agreeable with baseline and post-treatment biopsy of tumor. Subjects must have at least one measurable lesion per Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, an Eastern Cooperative Oncology Group (ECOG) performance score of 0 or 1 and adequate organ functions. 30 subjects will be enrolled to receive pembrolizumab 200 mg IV every 3 weeks(Q3W). Pre-treatment and on-treatment biopsy after 2 cycles of Pembrolizumab will be preformed. Treatment will be stopped when progression of disease or intolerable toxicity occurs. The primary objectives of this trial are to study the efficacy and safety of pembrolizumab in patients with HBV-related HCC and to study the serial change in RNA expression of immune-related gene panel in post-treatment biopsy tissue. The secondary objectives of this trial are to study the serial change in cytokine profile between pre-treatment and post-treatment samples, to study the PD-L1 immunohistochemical (IHC) expression in tumor sample at baseline and post-treatment tissue samples and to study the presence of tumor infiltrating lymphocytes in the baseline and post-treatment tumor samples. The exploratory objective of this trial is to evaluate the possibility of using baseline and the serial change in RNA expression of immune-related gene panel or PD-L1/2 IHC to predict treatment response.

  Dataset EGAD50000001872

• RNAseq data to study PRPF6 regulated splice forms in colon cancer cell lines

  Alternative splicing plays critical roles in differentiation, development, and cancer (Pettigrew et al., 2008; Chen and Manley, 2009). The recent identification of specific spliceosome inhibitors has generated interest in the therapeutic potential of targeting this cellular process (van Alphen et al., 2009). Using an integrated genomic approach, we have identified PRPF6, an RNA binding component of the pre-mRNA spliceosome, as an essential driver of oncogenesis in colon cancer. Importantly, PRPF6 is both amplified and overexpressed in colon cancer, and only colon cancer cells with high PRPF6 levels are sensitive to its loss. Our data clearly point to an important role for PRPF6 in colon cancer growth and suggest that a better understanding of its role in alternative splicing in colon cancer is warranted. To determine the specific alternative splice forms that PRPF6 regulates in colon cancer, we plan three experiments: 1. The first involves knocking down expression of PRPF6 in two different cancer cell lines with 3 different siRNAs, and then completing RNA-seq to determine the gene expression changes that occur relative to a non-targeting control siRNA. Because of the role for PRPF6 in pre-mRNA splicing, we especially want to quantify the changes in splice-specific forms of all genes genome-wide to identify genes whose splicing is altered upon PRPF6 knockdown. 2. The second involves immunoprecipitating PRPF6 from two different cancer cell lines and isolating any RNA that is bound to PRPF6, since PRPF6 is an RNA-binding protein. We then want to carry out RNA-seq to identify which RNA molecules co-immunoprecipitated with PRPF6. This will help us determine possible functions for PRPF6 in regulating colon cancer growth. 3. The third involves overexpressing PRPF6 in cell lines and then carrying out RNA-seq to identify any changes in splice-specific gene expression. This will allow us to determine whether increased PRPF6 expression is sufficient to drive alternative splicing changes.

  Dataset EGAD00001000817

• Fecal microbiome predicts treatment response after the initiation of semaglutide or empagliflozin uptake

  To investigate the effect of semaglutide and empagliflozin initiation on the gut microbiome of type 2 diabetes patients. In addition, to analyze whether the pre-treatment gut microbiome can predict the treatment efficacy. Gut microbiome fecal samples donated at four timepoints (Baseline, Month 1, Month 3; Month 12) were studied using 16S ribosomal RNA gene sequencing and analysis.

  Study EGAS50000000531

• Circulating RNAs prior to endometrial cancer diagnosis

  We analyzed circulating RNA levels using small RNA sequencing, targeting RNA in the size range 17 to 47 nucleotides, in samples collected prior to endometrial cancer diagnosis compared to cancer-free controls. The analysis included 316 cases with samples collected 1-11 years prior to diagnosis , and 316 matched controls, both from the Janus Serum Bank Cohort in Norway.

  Study EGAS50000000267

• Whole-Genome Sequencing of 128 Ashkenazi Jewish individuals

  We sequenced 128 individuals of Ashkenazi Jewish ancestry. All individuals were controls in other studies: (1) Longevity (Gil Atzmon's lab; Albert Einstein College of Medicine), n=74. (2) Parkinson's disease (Lorraine Clark's lab; Columbia University Medical Center), n=54. Whole-genome, high-coverage (50x) sequencing was performed by Complete Genomics in 2012 and 2013.

  Study EGAS00001000664

• Intellance-2: omics data on recurrent glioblastoma patients participating in the Intellance-2 clinical trial, prior to treatment.

  This study contains omics data from patients from the Intellance-2 clinical trial, in which effectiveness of ABT414 in EGFR amplified recurrent glioblastoma patients was tested. Patients were randomly assigned to one of three arms: (1) standard of care treatement (TMZ or CCNU), (2) Only ABT414 and (3) ABT414 + (TMZ|CCNU)

  Study EGAS00001005437

• Bulk RNA sequencing data of peripheral blood mononuclear cells obtained from 7 ATL patients and 9 HTLV-1-infected asymptomatic carriers.

  Bulk RNA sequencing (RNA-seq) data of peripheral blood mononuclear cells (PBMCs) obtained from 7 ATL patients (9 samples) and 9 HTLV-1-infected asymptomatic carriers.

  Dataset EGAD00001007018

• HGP-clean

  Himalayan population genetic study QC filtered data

  Dataset EGAD00010001471