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• Screening for tryptophan conversion in human stool samples

  The goal of this study was to compare the tryptophan metabolism of different human fecal microbiota. To this end we enriched human stool samples in culture medium and measured growth and metabolite concentrations after 48h of culture. We quantified the consumption of tryptophan and the production of typical tryptophan derivaties.

  Study EGAS50000000548

• Research for biological features by gene expression analysis and biomarkers at diagnosis/therapeutics in lung cancer

  WGS, WES, ATAC-seq and methylation analysis with DNA harvested from lung cancer organoids established from surgery, biopsy, pleural effusion, sputum and blood samples of patients. RNA-seq analysis with RNA harvested from lung cancer organoids established from surgery, biopsy, pleural effusion, sputum and blood samples of patients.

  Study JGAS000610

• Research for biological features by gene expression analysis and biomarkers at diagnosis/therapeutics in lung cancer

  WGS, WES, ATAC-seq and methylation analysis with DNA harvested from lung cancer organoids established from surgery, biopsy, pleural effusion, sputum and blood samples of patients. RNA-seq analysis with RNA harvested from lung cancer organoids established from surgery, biopsy, pleural effusion, sputum and blood samples of patients.

  Study JGAS000609

• Research for biological features by gene expression analysis and biomarkers at diagnosis/therapeutics in lung cancer

  WGS, WES, ATAC-seq and methylation analysis with DNA harvested from lung cancer organoids established from surgery, biopsy, pleural effusion, sputum and blood samples of patients. RNA-seq analysis with RNA harvested from lung cancer organoids established from surgery, biopsy, pleural effusion, sputum and blood samples of patients.

  Study JGAS000557

• Establishing multiple omics baselines for three Southeast Asian populations in the Singapore Integrative Omics Study

  In this study, we established population reference measurements across multiple omic technologies and investigated the inter-population variations in each omic and phenotypic measurements in three major populations in Singapore. The measurements include genome-wide SNPs, gene probesets expression, lipid species and circulating miRNAs.

  Study EGAS00001002527

• Angiopredict: predicting response for bevacizumab treatment

  In ANGIOPREDICT, academic cancer biologists and industry-based biotechnology researchers worked with clinicians to identify biomarkers to predict whether individual metastatic colorectal cancer patients will respond positively to bevacizumab containing therapy. In this retrospective study, copy number profiles were determined using whole genome shallow sequencing, and progression free survival and treatment data was collected.

  Study EGAS00001002724

• EM-seq datatset of primary human thymocyte subsets

  FASTQ-files of EM-seq experiments of 6 primary human thymocyte subsets, isolated by flow-cytometry (FACS) from neonatal thymus biopsies. All samples were sorted to be negative for the expression of the lineage surface markers CD11c, CD19, CD56, CD141 and CD303 and additionally had the following surface phenotypes: DN2 (CD4-CD8-CD7+CD1a-CD161-CD38+CD3-TCRab-); DN3 (CD4-CD8-CD7+CD1a+); ISP (CD4+CD8-CD3-TCRab-CD1a+); DPE (CD4+CD8+TCRab-CD69-); DPL (CD4+CD8+TCRab+CD69+); SP4 (CD4+CD8-). Samples were sorted from 1-2 pediatric patients undergoing heart surgery.

  Dataset EGAD50000001600

• Oxford Nanopore long-read sequencing data of high-grade serous ovarian cancer

  This dataset comprises whole-genome Oxford Nanopore long-read sequencing data from six high-grade serous ovarian cancer (HGSOC) patients, including cryopreserved, pre-treatment tumor tissues and matched peripheral blood samples (12 samples in total). High-molecular-weight DNA was prepared using the SQK-LSK114 Ligation Sequencing Kit and sequenced on FLO-PRO114M flow cells (R10.4.1). Basecalling was conducted using the super-accurate (SUP) model, enabling simultaneous detection of 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC). The dataset includes BAM files containing base modification information for downstream genomic and epigenomic analyses.

  Dataset EGAD50000001509

• Visium CytAssist Spatial Gene Expression analysis for glioblastoma

  The glioblastoma spatial transcriptomics dataset was generated from 4 FFPE fixed adult glioblastoma samples, using the 10x Genomics Visium platform, producing raw FASTQ files, spatial feature matrices (.h5, .mtx, .tsv), and paired histology images (.tif). Spot-level transcript counts were aligned to tissue architecture and processed in Seurat, with results stored as .rds objects. Data visualization was performed using SpatialFeaturePlot, and quantitative analyses were supported by custom Matlab scripts. The dataset provides both raw and processed files, offering reproducible spatially resolved transcriptomic profiles of glioblastoma tissue sections

  Dataset EGAD50000001767

• Transcriptome data of WS patients and controls

  Fibroblasts of 4 WS patients and 4 sex matched controls were transformed into iPSCs, which were then differentiated to Neuroepithelial Stem Cells (NESCs), and further differentiated into neurons. RNA sequencing was performed at the NESC stage and after 4 and 8 weeks of neural development for 3 replicates per individual using the Illumina TruSeq stranded library polyA method on the NovaSeq6000 platform at NGI Stockholm. The data was preprocessed using the nf-core/rnaseq (https://nf-co.re/rnaseq) that includes quality control, data cleaning, and read alignment to GRCh38 (STAR, v2.6.1d)

  Dataset EGAD50000001728

• Sequencing data for oesophageal and related samples - BOs release 4 (RNA)

  Dataset EGAD00001004023

• RNA sequencing data of pediatric B-other acute lymphoblastic leukemia (set 1)

  The dataset includes RNA sequencing FASTQ files from diagnostic tumor samples of four B-other ALL patients (ALLT-314, ALLT-319, ALLT-327, ALLT-334), and from relapse sample of one B-other ALL patient (ALLT-316) . NEBNext Ultra Directional RNA Library Prep Kit (New England Biolabs) was used in library preparation, and Globin-Zero Gold rRNA Removal Kit (Illumina) was utilized for rRNA removal. The diagnostic and relapse tumor samples were sequenced using Illumina Novaseq 6000, and the target sequencing depth was 21 Gb per sample.

  Dataset EGAD50000002598

• Finding structural variation from the patient-derived xenografts of pediatric T-cell leukemia at the single-cell level

  In this study, we aimed to identify somatic structural variation of T-cell acute lymphoblastic leukemias (T-ALLs_ from patient-derived xenografts (PDX) at the single-cell level. For this purpose, we performed strand-specific single-cell sequencing of PDX-derived T-ALL relapse samples from two juvenile patients (P1, P33). To validate structural variation detected via scTRIP, we profiled whole exome sequencing (WES) data from P33 (samples taken during initial disease, remission, relapse), and mate-pair sequencing data from P1 (relapse).

  Dataset EGAD00001004499

• ICGC Prostate Cancer Whole Genome Sequencing

  Whole Genome Sequencing Illumina HiSeq data from 20 men with prostate cancer. 20 samples were taken from primary tissue obtained at prostatectomy (target sequencing depth 50X) with matched blood control (target sequencing depth 30X). These were submitted for use in the ICGC Pan-Cancer Analysis of Whole Genomes project. Same raw data submitted in EGAD00001001116. As of September 2020, some of the studies using these data include: Wedge et al, Nature Genetics 2018 (PMID: 29662167) Pan-Cancer Analysis of Whole Genomes, Nature 2020 (PMID: 32025007)

  Dataset EGAD00001000892

• Sequencing data for ICGC Oesophageal Adenocarcinoma tissue samples - mobile_elements

  Samples from Edwards et al 2015 - doi:10.1186/s12864-015-1685-z

  Dataset EGAD00001001048

• Sequence Data for DEEP Release August 2016

  DEEP (German Epigenome Project) sequence data of following samples (Sequencing Types: Chip-Seq, WGBS-Seq, RNA-Seq, sncRNA-Seq, NOMe-Se, DNase-Seq): 41_Hf01_LiHe_Ct, 41_Hf02_LiHe_Ct, 41_Hf03_LiHe_Ct, 01_HepG2_LiHG_Ct1, 01_HepG2_LiHG_Ct2, 01_HepaRG_LiHR_D31, 01_HepaRG_LiHR_D32, 01_HepaRG_LiHR_D33, 43_Hm01_BlMo_Ct, 43_Hm03_BlMo_Ct, 43_Hm05_BlMo_Ct, 43_Hm03_BlMa_Ct, 43_Hm05_BlMa_Ct, 43_Hm03_BlMa_TO, 43_Hm05_BlMa_TO, 43_Hm03_BlMa_TE, 43_Hm05_BlMa_TE, 51_Hf01_BlCM_Ct, 51_Hf03_BlCM_Ct, 51_Hf04_BlCM_Ct, 51_Hf02_BlCM_Ct, 51_Hf05_BlCM_Ct, 51_Hf06_BlCM_Ct, 51_Hf06_BlCM_T1, 51_Hf06_BlCM_T2, 51_Hf03_BlEM_Ct, 51_Hf04_BlEM_Ct, 51_Hf02_BlEM_Ct, 51_Hf05_BlEM_Ct, 51_Hf06_BlEM_Ct, 51_Hf06_BlEM_T1, 51_Hf06_BlEM_T2, 51_Hf03_BlTN_Ct, 51_Hf04_BlTN_Ct, 51_Hf02_BlTN_Ct, 51_Hf05_BlTN_Ct, 51_Hf06_BlTN_Ct, 51_Hf06_BlTN_T1, 51_Hf06_BlTN_T2, 51_Hf07_BmTM4_Ct, 51_Hf08_BlTM4_Ct, 51_Hf08_BmTM4_SP1, 51_Hf08_BmTM4_SP2, 51_Hf05_BlTA_Ct, 44_Mm01_WEAd_C2, 44_Mm03_WEAd_C2, 44_Mm02_WEAd_C2, 44_Mm07_WEAd_C2, 44_Mm04_WEAd_C1, 44_Mm05_WEAd_C1

  Dataset EGAD00001002527

• Deep exome sequencing

  Bam files from deep exome sequencing of blood DNA samples from five TET2 mutation carriers (Ly1, Ly2, Ly9, Ly11, Ly14) and three wild-type family members (Ly8, Ly10, Ly13) extracted at multiple time points. Library preparations were performed with SeqCap EZ Exome v3 (Roche, Switzerland) using six different index primers per sample for which paired-end Illumina sequencing was done with 75bp read length and HiSeq4000 sequencer. After alignment (bwa version 0.7.12), base recalibration (GATK 3.5), realignment around indels (GATK 3.5) and duplicate removal (MarkDuplicates; Picard Tools version 1.79), data from libraries with six different indexes were merged.

  Dataset EGAD00001004780

• Low input LC (WGS) (2019-04-01)

  R&D project to develop low input library construction methods. . This dataset contains all the data available for this study on 2019-04-01.

  Dataset EGAD00001004878

• Evolution of the cancer epigenome in myeloproliferative neoplasms. (2019-04-01)

  Evolution of the cancer epigenome in myeloproliferative neoplasms. . This dataset contains all the data available for this study on 2019-04-01.

  Dataset EGAD00001004879

• small RNA-sequencing and RNA-sequencing of human brain tissue with temporal lobe epilepsy

  This data set contains the raw .fastq files from two RNA-sequencing experiments and two small RNA-sequencing experiments. Both control brain tissue and tissue from sufferers of mesial temporal lobe epilepsy were sequenced. Two different brain regions were sequenced; the cortex and the hippocampus. For more details please see: Mills, James D., et al. "Coding and non-coding transcriptome of mesial temporal lobe epilepsy: Critical role of small non-coding RNAs." Neurobiology of disease 134 (2020): 104612.

  Dataset EGAD00001005735

• Sequencing data for oesophageal and related samples - Nowicki-Osuch, Zhuang et al (scRNA)

  Data supporting: “Deep molecular phenotyping reveals the identity of Barrett’s esophagus and its malignant transition.” Nowicki-Osuch, Zhuang et al. scRNAseq (BAM files) 38 Barrett's and normal samples

  Dataset EGAD00001005438

• Circulating cell-free DNA analyses for Patient Monitoring in Small Cell Lung Cancer

  This study consists of over 200 data files from cfDNA and germline DNA from 69 patients and 32 healthy normal volunteers discussed in this publication.

  Dataset EGAD00001007070

• Sequencing data for oesophageal and related samples - Nowicki-Osuch, Zhuang et al (WGS)

  Data supporting: “Deep molecular phenotyping reveals the identity of Barrett’s esophagus and its malignant transition.” Nowicki-Osuch, Zhuang et al. WGS (BAM files) 5 Barrett's samples 5 normal oesophageal samples 5 normal gastric cardia samples 5 normal duodenal samples

  Dataset EGAD00001006874

• Sequencing data for oesophageal and related samples - Nowicki-Osuch, Zhuang et al (bulk RNA)

  Data supporting: “Deep molecular phenotyping reveals the identity of Barrett’s esophagus and its malignant transition.” Nowicki-Osuch, Zhuang et al. RNAseq (BAM files) 12 Barrett's samples 12 normal oesophageal samples 11 normal gastric cardia samples

  Dataset EGAD00001006882

• Dataset for identification of the dismal subtype of B-ALL with dysregulation of CDX2 and UBTF

  WGS (tumor and germline samples) was performed to identify structural variants in the UBTF/CDX2 subgroup. RNA-seq was performed to detect gene fusion in the UBTF/CDX2 subgroup. HiChIP was performed to investigate 3D chromatin architecture and enhancer landscapes of representative patient samples and cell lines harboring Type I and II FLT3-PAN3 deletions and amplifications.

  Dataset EGAD00001008416