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• Raw DNA and RNA data from breast cancer organoids, control samples and biopsies

  Whole genome sequencing data for primary tumors, matching control material from blood and their corresponding organoid. Whole transcriptome data for organoids.

  Dataset EGAD00001003751

• Sequencing data for oesophageal and related samples - Xiaodun Li et al (WGS, RNA)

  Data supporting: "Esophageal adenocarcinoma organoid cultures recapitulate human disease heterogeneity and provide a model for clonality studies and precision therapeutics." Li et al. WGS (BAM files) RNAseq (BAM files) Tumours, organoids, normals

  Dataset EGAD00001004007

• Ovarian Cancer Organoid Biobank - RNASeq data

  Ovarian cancer (OC) is a heterogeneous disease usually diagnosed at a late stage. Experimental in vitro models that faithfully capture the hallmarks and tumor heterogeneity of OC are limited and hard to establish. We present a novel protocol that enables efficient derivation and long-term expansion of OC organoids. Utilizing this protocol, we have established 56 organoid lines from 32 patients, representing the spectrum of ovarian neoplasms, including non-malignant borderline tumors, as well as mucinous, clear-cell, endometrioid, low- and high-grade serous carcinomas. OC organoids recapitulate histological and genomic features of the pertinent lesion from which they were derived, illustrating intra- and inter-patient heterogeneity, and can be genetically modified. We show that OC organoids can be used for drug screening assays and capture different tumor subtype responses to the gold standard platinum-based chemotherapy, including acquisition of chemoresistance in recurrent disease. Finally, OC organoids can be xenografted, enabling in vivo drug sensitivity assays. Taken together, this demonstrates their potential application for research and personalized medicine.

  Dataset EGAD00001004509

• The landscape of somatic mutation in normal colorectal epithelial cells

  The colorectal adenoma-carcinoma sequence has provided a paradigmatic framework for understanding the successive somatic genetic events and consequent clonal expansions leading to cancer. As for most cancer types, however, understanding of the earliest phases of colorectal neoplastic change, which may occur in morphologically normal tissue, is comparatively limited because of the difficulty of detecting somatic mutations in normal cells. Each colorectal crypt is a small clone of cells derived from a single recently-existing stem cell. Here, we sequenced hundreds of normal crypts from 42 individuals. Signatures of multiple mutational processes were revealed, some ubiquitous and continuous, others only found in some individuals, in some crypts or during some phases of the cell lineage from zygote to adult cell. Likely driver mutations were present in ~1% of normal colorectal crypts in middle-aged individuals, indicating that adenomas and carcinomas are rare outcomes of a pervasive process of neoplastic change across morphologically normal colorectal epithelium.

  Dataset EGAD00001004193

• Sequencing component for the whole genome methylation analysis in PBMCs and cell subsets (pilot study)

  DNA methylation has been shown to play a major role in determining cellular phenotype by regulating gene expression. Moreover, dysregulation of differentially methylated genes has been implicated in disease pathogenesis of various conditions including cancer development as well as autoimmune diseases such as systemic Lupus erythematosus and rheumatoid arthritis. Evidence is rapidly accumulating for a role of DNA methylation in regulating immune responses in health and disease. However, the exact mechanisms remain unknown. The overall aim of the project is to investigate the role of epigenetic mechanisms in regulating immunity and their impact on autoimmune disease pathogenesis.The aim of this pilot study is to perform whole genome methylation analysis in peripheral blood mononuclear cells (PBMCs) and cell subsets (CD4, CD8, CD14, CD19, CD16 and whole PBMCs) obtained from 6 healthy volunteers. Whole genome methylation analysis will be performed using two methodological approaches, the Infinium Methylation Bead Array K450 (Illumina) and MeDIP-seq. mRNA expression arrays will also be performed in order to correlate DNA methylation with gene expression as well as genotyping on the Illumina OmniExpress chip

  Dataset EGAD00001000394

• FASTQ files of the small RNA-Seq dataset from the POPS cohort

  Placental biopsies were collected within 30 minutes of birth and flash frozen in RNAlater(ThermoFisher). For each biopsy,total placental RNA was extracted from approximately 5 mg of tissue using the “mirVana miRNA Isolation Kit” (Ambion) followed by DNase treatment (“DNA-free DNA Removal Kit”, Ambion). RNA quality was assessed with the Agilent Bioanalyzer and all the samples with RIN values ≥ 7.0 were used in the downstream experiments. Total RNA-libraries were prepared from 300-500ng of total placental RNA with the “TruSeq Stranded Total RNA Library Prep Kit with Ribo-Zero Human/Mouse/Rat”(Illumina). Small RNA-libraries were prepared from 150ng of total placental RNA with the “NEBNext Multiplex Small RNA Library Prep Kit for Illumina”(New England Biolabs)and concentrated using the “QIAquick PCR purification kit”(Qiagen). Paired libraries were combined and size selected using the Pippin Prep and 3% Agarose Gel Cassette with marker F (Sage Science),pooled and sequenced (single-end, 50bp) using a Single End V4 cluster kit and HiSeq4000 instrument.

  Dataset EGAD00001004860

• Analysis of the B cell receptor repertoire in six immune-mediated diseases

  Our understanding of the BCR repertoire in the context of immune-mediated diseases is incomplete, and defining this could provide new insights into pathogenesis and therapy. Here, we compared the BCR repertoire in systemic lupus erythematosus, anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis, Crohn’s disease, Behçet’s disease, eosinophilic granulomatosis with polyangiitis, and immunoglobulin A (IgA) vasculitis by analysing BCR clonality, use of immunoglobulin heavy-chain variable region (IGHV) genes and—in particular—isotype use. An increase in clonality in systemic lupus erythematosus and Crohn’s disease that was dominated by the IgA isotype, together with skewed use of the IGHV genes in these and other diseases, suggested a microbial contribution to pathogenesis. Different immunosuppressive treatments had specific and distinct effects on the repertoire; B cells that persisted after treatment with rituximab were predominately isotype-switched and clonally expanded, whereas the inverse was true for B cells that persisted after treatment with mycophenolate mofetil. Our comparative analysis of the BCR repertoire in immune- mediated disease reveals a complex B cell architecture, providing a platform for understanding pathological mechanisms and designing treatment strategies.

  Dataset EGAD00001005431

• Somatic mutation and clonal evolution in premalignant lung disease - WGS (2020-01-15)

  Cancer is a genetic disease caused by an accumulations of mutations, however many of these mutations have been identified in pathologically normal tissue. We aim to use laser-capture microscopy (LCM) to sample individual clones from the lung tissue of individuals with a variety of lung diseases (COPD, UIP, IPF, Emphysema, pulmonary hypertension). This will allow us to identify whether cancer-associated mutations appear in this normal tissue, assess the mutational burden present, and identify the mutational processes causing these mutations. Smoking is a large risk factor for developing many of these lung diseases so we are particularly keen to determining whether there is evidence of a smoking signature in these patients. . This dataset contains all the data available for this study on 2020-01-15.

  Dataset EGAD00001005786

• MutWP5: CRUK Mutographs of Cancer: Lung: PD38234 (Targeted) (2020-01-29)

  Sequencing of LCM-derived microbiopsies from explanted lung from pulmonary fibrosis patient. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Deep sampling throughout multiple regions of the lung will determine whether there are differences in smoking-related mutation burden in different portions of the lung. Targeted sequencing will be conducted on samples to identify drivers of interest and clonality of the samples, well-performing samples will be sent for subsequent whole-genome sequencing. Results from this portion of the study will be compared to other individuals with smoking-related diseases (COPD, pulmonary fibrosis, lung cancer), and normal, non-smoking lungs. . This dataset contains all the data available for this study on 2020-01-29.

  Dataset EGAD00001005925

• RNA-seq study of a Princess Margaret Cancer Centre human acute myeloid leukemia patient cohort

  The PMCC AML RNAseq dataset consists of 81 AML patient samples (clinical data in Supplemental Table 11 of manuscript), processed in two batches. These patient samples are able to engraft in the NSG (NOD.Cg PrkdcscidIl2rgtm1Wjl /SzJ) mouse model. Five patients (90543, 598, 90240, 110484, 100500) were included in both batches. Viaably frozen material from the Leukemia Tissue Bank at Princess Margaret Cancer Centre/ University Health Network were thawed by dropwise addition of X-VIVO + 50% fetal calf serum supplemented with DNase (100μg/mL final concentration, Roche). RNA was extracted from bulk peripheral blood mononuclear cells (PBMC) using the RNeasy Micro Kit (Qiagen Inc.). A paired-end 76 base-pair flow-cell lane Illumina High seq 2000 yielded an average of 240 million sequence reads aligning to genome per sample at the Genome Sciences Centre, BC Cancer Agency for cohort 1. Cohort 2 was subjected to 125 bp, paired-end RNA-sequencing on the Illumina HiSeq 2500 with an average of 50 million reads/sample at the Centre for Applied Genomics, Sick Kids Hospital.

  Dataset EGAD00001006576

• A108735A

  Whole genome sequencing for single cells for library A108735A 714 cells; filetype=bam

  Dataset EGAD00001007593

• Dataset for the Pearl study

  Biomarkers to identify patients without benefit from adding everolimus to endocrine treatment in metastatic breast cancer (MBC) are needed. We report the results of the Pearl trial conducted in five Belgian centers assessing 18F-FDG-PET/CT non-response (n=45) and ctDNA detection (n=46) after 14 days of exemestane-everolimus (EXE-EVE) to identify MBC patients who will not benefit. Metabolic non-response rate was 66.6%. Median PFS in non-responding patients (using as cut-off 25% for SUVmax decrease) was 3.1 months compared to 6.0 months in those showing response (HR: 0.77, 95% CI: 0.40-1.50, p=0.44). Difference was significant when using a “post-hoc” cut-off of 15% (PFS 2.2 months vs 6.4 months). ctDNA detection at D14 was associated with PFS: 2.1 months vs 5.0 months (HR-2.5, 95% CI: 1.3-5.0, p=0.012). Detection of ctDNA and/or the absence of 18F-FDG-PET/CT response after 14 days of EXE-EVE identifies patients with a low probability of benefiting from treatment. Independent validation is needed.

  Dataset EGAD00001007979

• Upper cortical layer-driven network impairment in schizophrenia - 10x genomics v3 snRNA-seq and Visium spatial transcriptomics datasets

  This dataset contains 10x Genomics v3 3’ single nuclei RNA sequencing (24 human schizophrenia and control samples) and 10x Genomics Visium spatial transcriptomics (14 human schizophrenia and control samples) datasets. Files are in .bam format, output of the cellranger v3.1.0 (https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/3.1/using/count) for snRNA-seq and spaceranger v1.1.0 (https://support.10xgenomics.com/spatial-gene-expression/software/pipelines/1.1/using/count) for Visium samples. Reads were mapped against Release 97 of human genome from Ensembl (http://ftp.ensembl.org/pub/release-97/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna.primary_assembly.fa.gz http://ftp.ensembl.org/pub/release-97/gtf/homo_sapiens/Homo_sapiens.GRCh38.97.gtf.gz). More details on sample processing are available in the biorXiv pre-print (https://doi.org/10.1101/2020.11.17.386458) and upcoming publication in Science Advances. BAM files can be converted to fastq files using bamtofastq tool (https://support.10xgenomics.com/docs/bamtofastq) with downstream remapping using tools and genomes of choice.

  Dataset EGAD00001009173

• Single Cell Genome Sequence for DLP+ library A118389B

  Single Cell Genome Sequence for Telomerase immortalized breast epithelium cell line 184-hTERT 85.14 p20 cell line AT135 on DLP+ library A118389B

  Dataset EGAD00001009433

• Single Cell Genome Sequence for DLP+ library A118368B

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA605 passage 3 on DLP+ library A118368B

  Dataset EGAD00001009432

• Single Cell Genome Sequence for DLP+ library A118425B

  Single Cell Genome Sequence for Immortalized breast epithelium BRCA2+/- Tp53-/- cell line 184-hTert L9 116.66 cell line SA1188 on DLP+ library A118425B

  Dataset EGAD00001009434

• Single Cell Genome Sequence for DLP+ library A118782A

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1050B passage 1, patient-derived xenograft SA1050E passage 1 on DLP+ library A118782A

  Dataset EGAD00001009435

• Single Cell Genome Sequence for DLP+ library A118784A

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1050B passage 1, patient-derived xenograft SA1050E passage 1 on DLP+ library A118784A

  Dataset EGAD00001009436

• Single Cell Genome Sequence for DLP+ library A118790A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA610 passage 3, patient-derived xenograft SA1096C passage 1 on DLP+ library A118790A

  Dataset EGAD00001009437

• Single Cell Genome Sequence for DLP+ library A118797B

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1184D passage 1 on DLP+ library A118797B

  Dataset EGAD00001009438

• Single Cell Genome Sequence for DLP+ library A118804A

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1162B passage 1, patient-derived xenograft SA1096B passage 1 on DLP+ library A118804A

  Dataset EGAD00001009439

• Single Cell Genome Sequence for DLP+ library A118808B

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1096C passage 1 on DLP+ library A118808B

  Dataset EGAD00001009441

• Single Cell Genome Sequence for DLP+ library A118812B

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1180C passage 1 on DLP+ library A118812B

  Dataset EGAD00001009442

• Single Cell Genome Sequence for DLP+ library A118814B

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1162B passage 1 on DLP+ library A118814B

  Dataset EGAD00001009443

• Single Cell Genome Sequence for DLP+ library A118857B

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1184D passage 1 on DLP+ library A118857B

  Dataset EGAD00001009445

• Single Cell Genome Sequence for DLP+ library A95663A

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1053F passage 1 on DLP+ library A95663A

  Dataset EGAD00001009446

• Single Cell Genome Sequence for DLP+ library A95668A

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient SA1162SA on DLP+ library A95668A

  Dataset EGAD00001009447

• Single Cell Genome Sequence for DLP+ library A95717A

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1096A passage 1 on DLP+ library A95717A

  Dataset EGAD00001009450

• Single Cell Genome Sequence for DLP+ library A96109A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA501 passage 2 on DLP+ library A96109A

  Dataset EGAD00001009452

• Single Cell Genome Sequence for DLP+ library A96142B

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient SA1162SA on DLP+ library A96142B

  Dataset EGAD00001009455

• Single Cell Genome Sequence for DLP+ library A96171A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA501 passage 2, patient-derived xenograft SA611 passage 3 on DLP+ library A96171A

  Dataset EGAD00001009458

• Single Cell Genome Sequence for DLP+ library A96173A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA501 passage 15 on DLP+ library A96173A

  Dataset EGAD00001009459

• Single Cell Genome Sequence for DLP+ library A96174A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA501 passage 11 on DLP+ library A96174A

  Dataset EGAD00001009460

• Single Cell Genome Sequence for DLP+ library A96177C

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA604 passage 6 on DLP+ library A96177C

  Dataset EGAD00001009462

• Single Cell Genome Sequence for DLP+ library A96187A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA501 passage 11, patient-derived xenograft SA609 passage 7 on DLP+ library A96187A

  Dataset EGAD00001009464

• Single Cell Genome Sequence for DLP+ library A96189A

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1049C passage 1 on DLP+ library A96189A

  Dataset EGAD00001009465

• Single Cell Genome Sequence for DLP+ library A96192A

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1093C passage 1, patient SA1147 on DLP+ library A96192A

  Dataset EGAD00001009467

• WGS data for manuscript titled: Multi-omic features of oesophageal adenocarcinoma in patients treated with preoperative neoadjuvant therapy

  WGS data for manuscript titled: Multi-omic features of oesophageal adenocarcinoma in patients treated with preoperative neoadjuvant therapy

  Dataset EGAD00001008646

• Targeted myeloid panel DNA-Sequencing Mutations Matrix Validation Cohort

  Profiling of co-mutations was done by targeted resequencing using the TruSight Myeloid assay (Illumina, Chesterford, UK) covering 54 genes recurrently mutated in AML: BCOR, BCORL1, CDKN2A, CEBPA, CUX1, DNMT3A, ETV6, EZH2, IKZF1, KDM6A, PHF6, RAD21, RUNX1, STAG2, ZRSR2, ABL1, ASXL1, ATRX, BRAF, CALR, CBL, CBLB, CBLC, CDKN2A, CSF3R, FBXW7, FLT3, GATA1, GATA2, GNAS, HRAS, IDH1, IDH2, JAK2, JAK3, KIT, KRAS, MLL, MPL, MYD88, NOTCH1, NPM1, NRAS, PDGFRA, PTEN, PTPN11, SETBP1, SF3B1, SMC1A, SMC3, SRSF2, TET2, TP53, U2AF1 and WT1. For each reaction, 50 ng of genomic DNA was used. Library preparation was done as recommended by the manufacturer (TruSight Myeloid Sequencing Panel Reference Guide 15054779 v02, Illumina). Samples were sequenced paired-end (150 bp PE) on NextSeq- (Illumina) or (300 bp PE) MiSeq-NGS platforms, with a median coverage of 3076 reads (range 824–30565). Sequence data alignment of demultiplexed FastQ files, variant calling and filtering was done using the Sequence Pilot software package (JSI medical systems GmbH, Ettenheim, Germany) with default settings and a 5% variant allele frequency (VAF) mutation calling cut-off. Human genome build HG19 was used as reference genome for mapping algorithms.

  Dataset EGAD00001008506

• Alignment BAM files and gene count files of the Illumina Sequencing Data (10 tumor samples)

  Reads were processed with the RNA-seq workflow 1.3.0 developed by the DKFZ Omics IT and Data Management Core Facility (https://github.com/DKFZ-ODCF/RNAseqWorkflow). First, FASTQ reads were aligned via two-pass alignment using STAR 2.5.3a. The STAR index was generated from the 1000 Genomes assembly and GENCODE Version 19 gene models with a sjdbOverhang of 200. Duplicate marking of the resultant main alignment file was done with sambamba 0.6.5. Gene-specific read counting was performed using featureCounts (from Subread 1.5.1) over exon features based on GENCODE Version 19 gene models. Both reads of a paired fragment were used for counting, and the quality threshold was set to 255, indicating that STAR found a unique alignment. Strand-specific counting was also used. For RPKM and TPM calculations, all genes on chromosomes X and Y, the mitochondrial genome, as well as rRNA and tRNA genes were omitted as they are likely to introduce library size estimation biases.

  Dataset EGAD00001008969

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

  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. Exome data will be used as a driver and clonality screen, highly clonal or driver-containing samples will subsequently be sent for whole-genome sequencing. Results from this portion of the study will be compared to women who are BRCA1/2 germline carriers and those with cancer. . This dataset contains all the data available for this study on 2023-03-08.

  Dataset EGAD00001010110

• MutWP5: CRUK Mutographs of Cancer: BRCA Carriers (WG)(Novaseq)

  Sequencing of LCM-derived microbiopsies from 20 women who underwent risk-reducing reduction mastecomies due to germline BRCA1/2. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Exome data will be used as a driver and clonality screen, highly clonal or driver-containing samples will subsequenctly be sent for whole-genome sequencing. Results from this poriton of the study will be compared to women who had cosmetic breast reduction surgeries and those with cancer. . This dataset contains all the data available for this study on 2023-03-08.

  Dataset EGAD00001010112

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

  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. Exome data will be used as a driver and clonality screen, highly clonal or driver-containing samples will subsequenctly be sent for whole-genome sequencing. Results from this poriton of the study will be compared to women who are BRCA1/2 germline carriers and those with cancer. . This dataset contains all the data available for this study on 2023-03-08.

  Dataset EGAD00001010113

• MutWP5: CRUK Mutographs of Cancer: BRCA Carriers (Exome)(Novaseq)

  Sequencing of LCM-derived microbiopsies from 20 women who underwent risk-reducing reduction mastecomies due to germline BRCA1/2. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Exome data will be used as a driver and clonality screen, highly clonal or driver-containing samples will subsequenctly be sent for whole-genome sequencing. Results from this poriton of the study will be compared to women who had cosmetic breast reduction surgeries and those with cancer. . This dataset contains all the data available for this study on 2023-03-08.

  Dataset EGAD00001010115

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

  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. Exome data will be used as a driver and clonality screen, highly clonal or driver-containing samples will subsequenctly be sent for whole-genome sequencing. Results from this poriton of the study will be compared to women who are BRCA1/2 germline carriers and those with cancer. . This dataset contains all the data available for this study on 2023-03-08.

  Dataset EGAD00001010116

• Whole blood RNA sequencing of individuals from Nepal

  This study utilized blood samples collected in Lalitpur, Nepal as part of the Strategic Typhoid Alliance across Africa and Asia (STRATAA) study. The dataset comprises whole blood RNAseq of 376 febrile individuals from Nepal. Blood was collected in PAXgene tubes and sent to Monash University (Melbourne, Australia) where RNA was extracted using the PAXgene Blood RNA kit before being sent to the Wellcome Sanger Institute (Hinxton, UK) for sequencing. Library prep used NEBNext Ultra II RNA custom kits on an Agilent Bravo WS automation platform with poly(A) pulldown. After PCR, plates were purified using Agencourt AMPure XP SPRI beads and libraries were quantified using Biotium Accuclear Ultra high sensitivity dsDNA Quantitative kits. Pooled libraries were normalised to 2.8 nM. Samples were globin depleted using KAPA RNA HyperPrep with RiboErase. Libraries were then subjected to 2x100bp paired-end sequencing on Illumina NovaSeq. Each library was sequenced to an average of 80 million reads. The STRATAA study was approved by the Nepal Health Research Council (NHRC, ref 283 306/2015) and OxTREC (Oxford Tropical Research Ethics Committee, ref 39-15). All participants provided informed consent for human genetic tests.

  Dataset EGAD00001011131

• Processed Chromium Single Cell GEX, CSP and VDJ data from intestinal plasma cells of celiac disease patients

  The dataset contains processed sequencing data from Chromium Single Cell 5’ gene expression, human B cell VDJ and feature barcode (CSP) sequencing from transglutaminase 2-specific and other small intestinal and bone marrow plasma cells isolated from four celiac disease patients. The raw sequencing data has been processed with Cell Ranger v.6.0.2 with the multi and aggr functions using the pre-built Cell Ranger references GRCh38 version 2020-A for gene expression and GRCh38-alts-ensembl-5.0.0 for V(D)J analysis. The dataset consists of a gene expression and antibody capture expression matrix (cell barcodes and feature names in tsv.gz file, expression matrix in mtx.gz file) and VDJ sequences in AIRR format (csv file). A metadata file (csv file) details cells passing our custom quality control based on number of detected genes, UMIs, mitochondrial genes, immunoglobulin genes and a productively rearranged immunoglobulin heavy chain.

  Dataset EGAD50000002778

• Childhood B-cell acute lymphoblastic leukaemia whole-genome sequencing (Sanger Institute and Great Ormond Street Hospital)

  This project comprises whole-genome sequencing (matched tumour–normal and tumour-only) from children with leukaemia, enriched for B-cell acute lymphoblastic leukaemia treated at Great Ormond Street Hospital (London, UK). Where available, serial bone marrow timepoints across therapy were profiled. Clinical metadata include whether blinatumomab was administered at diagnosis or relapse. This submission includes all data available for this study as of 02/03/2026.

  Dataset EGAD00001016144

• Methylation biomarker study of magnesium deficiency and colorectal cancer

  Five US studies using the Mg tolerance test, the "gold standard" test of Mg status, indicated that >50% participants had Mg deficiency. In our ongoing US trial, we have found a similar result. In growing recognition of the importance of Mg in human health, very recently, Mg was selected by the US Federal Dietary Reference Intake (DRI) Committee to update the DRI. In US and other populations at high risk of Mg deficiency, high Mg intake has been linked to reduced risk of colorectal neoplasia, insulin resistance, metabolic syndrome, type 2 diabetes, and cardiovascular disease (CVD). Conversely, in populations not at high risk of Mg deficiency, high Mg intake has been related to increased risk of total mortality (e.g. total cancer, colorectal cancer, and CVD), particularly when calcium intake is low. These observations suggest that the associations between high Mg intake and disease risks may completely differ by the underlying Mg status. Due to major limitations, the Mg tolerance test is not used in conventional clinical practice and rarely used in research. Instead, serum Mg is used for clinical diagnosis. However, serum Mg performs very poorly at identifying those with Mg deficiency. There is a great need to develop implementable, sensitive, and specific biomarkers which can be easily used for identifying people with Mg deficiency. Without such a marker, it is impossible to develop effective strategies that minimize adverse effects by targeting those who, as a result of Mg deficiency, are at risk of common diseases. It is known that DNA methylation changes are inducible by environmental exposures, including nutrients, and reversible when the exposure disappears. There are two major types of cytosine modifications in DNA, 5-hydroxymethylcytosine (5-hmC) and 5-methylcytosine (5-mC). 5-mC is often associated with suppressed gene expression. 5-hmC, generated by the oxidation of 5-mC, is specifically enriched in expressed genes and plays a critical role in activating and/or maintaining gene expression. However, current epigenomic profiling platforms cannot distinguish 5-hmC from 5-mC. Just recently, our collaborator, Dr. Chuan He (HHMI Investigator), established a novel and state of the art technique, TAB-Seq and TAB-Array protocol, which can separate 5-hmC from 5-mC in the genome. Our pilot studies indicate that Mg intake or treatment significantly affects methylation capacity, 5-mC and 5-hmC biomarkers. Our findings suggest blood leukocyte 5-hmC/5-mC biomarkers may serve as more sensitive biomarkers to identify Mg deficiency than serum Mg. Thus, a comprehensive epigenome-wide association study (EWAS) may help to identify the most sensitive 5-hmC/ 5- mC biomarkers for Mg deficiency. We propose to identify 5-hmC/5-mC biomarkers for Mg deficiency by a 4- phase EWAS study in the "Personalized Prevention of Colorectal Cancer Trial [PPCCT, R01CA149633; PI, Dai and Yu]" with a total of 240 participants. Mg tolerance test will be used as the gold standard. Finally, using newly identified biomarkers, we will evaluate if 12-week Mg treatment reduces TRPM7 expression, essential in Mg homeostasis and colorectal carcinogenesis, in rectal tissues only among those with Mg deficiency.

  Study phs002037

• Environmental Influences on Child Health Outcomes (ECHO) - PATHWAYS - Conditions Affecting Neurocognitive Development and Learning in Early Childhood (CANDLE)

  Pregnant women are exposed daily to multiple chemical and non-chemical stressors, including air pollutants, phthalates, and psychosocial stress. The Developmental Origins of Health and Disease model (DOHaD) states that exposure to these stressors in pregnancy affects fetal development in a manner that impacts offspring health across the life course. Yet epidemiologic data in these areas is limited, particularly with U.S. samples. It is also poorly understood whether these exposures prenatally affect childhood neurodevelopment and airway health in an independent or combined manner, and likely moderators of effects, such as the sex of the child, are infrequently addressed. We propose to unify three diverse extant pregnancy cohorts: TIDES (N=717, 2010-12), GAPPS (N=1133, 2012-16), and CANDLE (N=1385, 2007-11), for a combined sample size of 3235 mother-child dyads in the PATHWAYS study. PATHWAYS will investigate how chemical (air pollutants and phthalates) and non-chemical (psychosocial stress) exposures during pregnancy are related to placental gene expression (transcriptome) and childhood neurodevelopment and airway health (at ages 4-6, 8-9, and 10-11 years). Each cohort has rich resources of prenatal data and banked specimens (urine, blood, and placenta) that will be harmonized for the PATHWAYS study and will contribute to the ECHO consortium. We will develop a national model with high spatiotemporal resolution of key air pollutants and assess urinary markers of maternal exposure. A composite measure will capture multilevel maternal psychosocial stress across pregnancy, and urinary phthalate and blood stress hormone [Corticotropin-releasing hormone (CRH)] levels in the second and third trimesters will provide individual assessment of those exposures in potential critical periods. We will characterize the placental transcriptome using RNA sequencing (RNA-Seq) and will assess neurodevelopment and airway health prospectively into middle childhood. PATHWAYS will examine how these prenatal exposures are related to the placental transcriptome and child health outcomes in main effect and interactive models, with emphasis on sex-specific associations. For both neurodevelopment and airway health, we propose to measure both phenotypic precursors of health outcomes (i.e. fluid cognition, lung function growth), which yield dimensional tests of proposed associations, as well as clinically meaningful and policy relevant outcomes (i.e. asthma, mental health). Our study is powered to assess interactive effects of chemical and non-chemical stressors and will be the first study to characterize how prenatal environmental exposures relate to placental transcriptome pathways in relation to childhood health outcomes. This represents a significant scientific advance in testing DOHaD hypotheses. Major contributions to the ECHO Consortium include: 1) the development of a state of the art national model of air pollution, 2) a large, diverse pregnancy cohort with extensive biorepositories and extant prenatal and postnatal biomarkers, placental transcriptome, psychosocial and environmental data, and 3) an experienced, interdisciplinary team that will contribute meaningfully to the ECHO program of work.The sequencing data in this submission pertains only to the 794 subjects from the CANDLE study for whom placental tissue transcriptomics was performed.

  Study phs003619