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• Genome-wide prediction of human embryos

  This dataset includes whole genome sequence information for three individuals (Mother, Father and Newborn) used in this study. Genomes were sequenced using Illumina HiSeq technology. Files included are fastq files in paired read format.

  Dataset EGAD00001002257

• Small variant calling for 110 Egyptian individuals

  This data set comprises WGS small variants and structural variants called in a cohort of 110 Egyptian individuals (10 individuals have been sequenced as part of this study and 100 are from EGAD00001001372/EGAD00001001380).

  Dataset EGAD00001006039

• Genomic profiling of BRCA- mutant breast cancer tumors

  This data set contains whole exome sequencing (WXS) and RNA-Seq on germline BRCA- mutant tumors from 18 patients. BAM files are provided for WXS on tumor and germline samples. FASTQ files are provided for the RNA-Seq samples. Sequencing was done on an Illumina Hi-Seq 2500.

  Dataset EGAD00001008276

• DNA Replication Speed controls Epigenome Integrity and T Cell Fate

  Epigenetic mechanisms maintain cellular identities by enforcing stable, cell type-specific transcriptional programs while also allowing regulated plasticity for adaptations to environmental cues like differentiation signals. Dysregulation of this dual capacity has been linked to cancer and chronic inflammation, with the regulatory mechanisms remaining poorly understood. Here, we identify DNA replication speed as a physiological regulator controlling epigenome stability and plasticity. Using T-lymphocytes as a model, we demonstrate that fast DNA replication speed impaired epigenome stability, causing DNA methylation maintenance errors at cell type-specific enhancers and heterochromatin, ultimately leading to compromised cell function. However, accelerated DNA replication speed supported epigenome plasticity during differentiation, promoting successful cell-type transitions

  Study EGAS50000001273

• Single-cell transcriptome of T-ALL P1

  In this study, we profiled single-cell transcriptome (10X genomics) of Patient-derived xenografts (PDX) T-ALL replase samples from P1 patient. Primary human T-ALL cells were recovered from cryopreserved bone marrow aspirates of patients enrolled in the ALL-BFM 2009 study. Patient-derived xenografts (PDX) were generated as previously described by intrafemoral injection of 1 Million viable primary ALL cells in NSG mice110 PDX-derived (P1)28 cells were frozen until processing. For scRNA-seq library preparation, cryopreserved cells were thawed rapidly at 37 ℃ and resuspended in 10 ml warm Roswell Park Memorial Institute (RPMI) medium with 100 μg/ml Dnase I. Cells were centrifuged for 5 mins at 300 g, and resuspended in ice-cold phosphate buffered saline (PBS) with 2% foetal bovine serum (FBS) and 5mM EDTA. Cells were stained on ice with anti-murine-CD45-PE (mCD45)(clone 30-F11; BioLegend; 1:20) in the dark for 30 mins. 1:100 DAPI was added and incubated in the dark for 5 mins before sorting. Triple negative cells (DAPI-mCD45-GFP-) were sorted (Fig. S27) using a BD FACSAria™ Fusion Cell Sorter into ice cold 0.03% bovine serum albumin (BSA) in PBS. All isolated cells were immediately used for scRNA-seq libraries, which were generated as per the standard 10x Genomics Chromium 3′ (v.3.1 Chemistry) protocol. Completed libraries were sequenced on a NextSeq5000 sequencer (HIGH-mode, 75 bp paired-end).

  Dataset EGAD00001008325

• ROCHE PD 92 Multiome dataset

  This dataset contains 10X Multiome data generated on post-mortem brain from 92 individuals (70 PD, 22 controls). For the Roche_PD dataset, nuclei were isolated using Nuclei Pure Prep Nuclei Isolation Kit (Sigma Aldrich) with the following modifications. The tissue was lysed in Nuclei Pure Lysis Solution with 0.1% Triton X, 1mM DTT and 0.4U/ul SUPERase-In™ RNase Inhibitor (ThermoFisher Scientific) freshly added before use and homogenized with the help first of a 23G and then of a 29G syringe. Cold 1.8M Sucrose Cushion Solution, prepared immediately before use with the addition of1mM DTT and 0.4U/ul RNase Inhibitor, was added to the suspensions before they were filtered through a 30μm strainer. The lysates were then carefully and slowly layered on top of 1.8M Sucrose Cushion Solution previously added in new Eppendorf tubes. Samples were centrifuged for 45 minutes at 16000xg at 4°C. Pellets were re-suspended in Nuclei Storage Buffer with RNase Inhibitor, transferred in new Eppendorf tubes and centrifuged twice for 5 minutes at 500xg at 4°C. Finally purified nuclei were re-suspended in Nuclei Storage Buffer with RNase Inhibitor, stained with trypan blue and counted using Countess II (Life technology). After count, nuclei permeabilization was carried out following the demonstrated protocol for single cell multiome ATAC + Gene Expression sequencing from 10x Genomics. A total of 12,000 estimated nuclei from each sample was used for the transposition step and then loaded on the Chromium Next GEM Single Cell Chip J. ATAC library and gene expression library construction was performed using the Chromium Next GEM Single Cell Multiome ATAC + Gene Expression kit according to the manufacturer’s instructions. Libraries were sequenced using Illumina NovaSeq 6000 System and NovaSeq 6000 S2 Reagent Kit v1.5 (100 cycles), aiming at a minimum sequencing depth of 30K reads/nucleus. Genotype was generated as previously described (Bryois et al, Nature Neuroscience, 2022).

  Dataset EGAD50000000964

• Plasma MicroRNA Signatures of Aging

  Background. MicroRNAs are small non-coding RNAs that regulate gene expression post-transcriptionally and show differential expression in various tissues with aging phenotypes. Detectable in circulation, extracellular microRNAs reflect (patho)physiological processes and hold promise as biomarkers for healthy aging and age-related diseases. This study aimed to explore plasma extracellular microRNAs as biological aging indicator and their associations with health outcomes using population-level data. Methods. We quantified plasma expression levels of 2,083 extracellular microRNAs using targeted RNA-sequencing in 2,684 participants from the population-based Rotterdam Study cohort. The training and test sets included 1,930 participants from the advanced-aged initial and second subcohort (RS-I/RS-II; median age: 70.6), while the validation set comprised 754 participants from the middle-aged fourth subcohort (RS-IV; median age: 53.5). Based on 591 microRNAs well-expressed in plasma, we examined differential expression of microRNAs with chronological age, PhenoAge –a composite score of age and nine multi-system blood biomarkers–, the frailty index, and mortality. Next, elastic net models were employed to construct composite microRNA-based aging biomarkers predicting chronological age (mirAge), PhenoAge (mirPA), frailty index (mirFI), and mortality (mirMort). The association of these aging biomarkers with different age-related health outcomes was assessed using Cox Proportional Hazard, linear regression, and logistic regression models in the test and validation sets. Results. We identified 188 microRNAs differentially expressed with chronological age within the RS-I/RS-II advanced-aged population (ntraining=1158, ntest=772), of which 177 microRNAs (94.1%) were replicated in the middle-aged RS-IV subcohort (nvalidation=754). Moreover, 227 miRNAs showed robust associations with PhenoAge, 61 with FI, and 16 with ten-year mortality independent of chronological age. Subsequently, we constructed four plasma microRNA-based aging biomarkers: mirAge with 108, mirPA with 153, mirFI with 81, and mirMort with 50 miRNAs. Elevated scores on these microRNA-based aging biomarkers were associated with unfavorable health outcomes, including lower subjective physical functioning and self-reported health and increased mortality and frailty risk, but not with first- or multi-morbidity. Overall, larger effect estimates were observed for mirPA, mirFI, and mirMort compared to mirAge. Conclusions. This study describes distinct plasma microRNA-aging signatures and introduces four microRNA-based aging biomarkers with potential to identify accelerated aging and age-related decline, providing insights into the intricate process of human aging.

  Study EGAS00001008117

• Elucidation of the pathomechanism of inflammatory muscle diseases using multi-omics analysis

  This study aims to elucidate the pathomechanism of inflammatory muscle disease. We performed multi-omics analysis of muscle biopsy samples from the patients.

  Study JGAS000636

• scRaCH-seq fastq files

  We base called the raw data of nanopore sequencing using the Guppy software (v3.1.5) with “dna_r9.4.1_450bps_sup_prom.cfg” configuration. The passed reads (Qscore > 9) are deposited in this EGA study.

  Study EGAS50000000165

• The genomic landscape of relapsed infant and childhood KMT2A-rearranged acute leukemia

  To study the mechanisms of relapse in KMT2A-rearranged (KMT2A-r) acute lymphoblastic (ALL) and acute myeloid leukemia (AML), we performed whole-genome and exome sequencing of infants and children with relapsed ALL/AML (n=36), and longitudinal deep-sequencing of 257 samples in 30 patients. Somatic alterations in drug-response genes, most commonly in TP53 and IKZF1 (64%), were highly enriched in early relapse ALL (79%, 9-36 months after diagnosis), but rare in very early relapse ALL (<9 months, 9%). A marked chemotherapy-exposure signature was detected for mutations in early relapse ALL but not in very early ALL or AML relapse, in line with different mechanisms of relapse. Longitudinal analyses could track residual leukemia cells, clonal drug responses, and the upcoming relapse. These results highlight that KMT2A-r ALL and AML evade therapy differently and provide new insights into the mechanisms of relapse in this highly lethal form of pediatric acute leukemia.

  Study EGAS00001008197