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• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0079_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0079_002 for Triple negative breast cancer patient-derived xenograft SA1035X6XB03211

  Dataset EGAD00001011242

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0145_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0145_001 for Triple negative breast cancer patient-derived xenograft SA1035X7XB03338

  Dataset EGAD00001011244

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0145_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0145_002 for Triple negative breast cancer patient-derived xenograft SA1035X7XB03340

  Dataset EGAD00001011245

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0151_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0151_001 for Triple negative breast cancer patient-derived xenograft SA1035X8XB03425

  Dataset EGAD00001011247

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0175_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0175_002 for Triple negative breast cancer patient-derived xenograft SA1035X8XB03631

  Dataset EGAD00001011249

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0014_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0014_001 for Triple negative breast cancer patient-derived xenograft SA604X6XB01979

  Dataset EGAD00001011251

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0015_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0015_001 for Triple negative breast cancer patient-derived xenograft SA604X6XB01979

  Dataset EGAD00001011252

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0019_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0019_001 for Triple negative breast cancer patient-derived xenograft SA604X7XB02089

  Dataset EGAD00001011253

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0020_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0020_002 for Triple negative breast cancer patient-derived xenograft SA604X8XB02164

  Dataset EGAD00001011254

• 10x Single Cell Gene Expression Library TENX063

  10x Single Cell Gene Expression library TENX063 for Triple negative breast cancer patient-derived xenograft SA609X3XB01584

  Dataset EGAD00001011268

• Circulating tumor DNA, pathological and immunologic responses to neoadjuvant nivolumab or nivolumab plus relatlimab and chemoradiotherapy in resectable esophageal/gastroesophageal junction cancer

  The dataset for the study “Circulating tumor DNA, pathological and immunologic responses to neoadjuvant nivolumab or nivolumab plus relatlimab and chemoradiotherapy in resectable esophageal/gastroesophageal junction cancer” includes 173 bam files from hybrid capture targeted error-correction next-generation sequencing (TEC-Seq) from plasma cell-free DNA and matched white blood cell DNA from 32 individuals with esophageal/gastroesophageal junction cancer, who received immunotherapy-containing regimens.

  Dataset EGAD00001011822

• Sequencing data for oesophageal and related samples - Linossi et al

  Data supporting: "Utility of ctDNA assessment after six weeks of immunotherapy to predict radiological response in advanced oesophageal cancer" Linossi et al

  Dataset EGAD00001015434

• Resistance to Latent Mycobacterium tuberculosis Infection in South Africa: Immunologic Profiling

  Despite significant exposure to Mycobacterium tuberculosis (Mtb), some humans remain uninfected, as defined by persistently negative immune sensitization tests (tuberculin skin test, TST; interferon gamma release assays, IGRA). To explore mechanisms of natural resistance to Mtb infection, we profiled tissues from highly-exposed Mtb contacts from South Africa. The cohort in this submission were gold miners in South Africa who had worked in the mining industry ≥ 15 years with presumed high Mtb exposure, based on extreme tuberculosis incidence rates and high prevalence of latent Mtb infection (Chihota et al. 2022, PMID: 35302992; Ntshiqa et al. 2021, doi: 10.12688/gatesopenres.13191.1). TST and IGRA testing were performed at enrollment of gold miners, some of whom remained available for repeat testing at 12 months.

  Study phs002746

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library for library A95728A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 844 samples; filetype=bam

  Dataset EGAD00001004758

• A108879A

  Whole genome sequencing for single cells for library A108879A 1567 cells; filetype=bam

  Dataset EGAD00001007093

• A95732B

  Whole genome sequencing for single cells for library A95732B 655 samples; filetype=bam

  Dataset EGAD00001008231

• A96228B

  Whole genome sequencing for single cells for library A96228B 1125 samples; filetype=bam

  Dataset EGAD00001008218

• A95707A

  Whole genome sequencing for single cells for library A95707A 1359 samples; filetype=bam

  Dataset EGAD00001008228

• A96145A

  Whole genome sequencing for single cells for library A96145A 642 samples; filetype=bam

  Dataset EGAD00001008232

• A96149A

  Whole genome sequencing for single cells for library A96149A 751 samples; filetype=bam

  Dataset EGAD00001008234

• A96155B

  Whole genome sequencing for single cells for library A96155B 1028 samples; filetype=bam

  Dataset EGAD00001008236

• A96157C

  Whole genome sequencing for single cells for library A96157C 938 samples; filetype=bam

  Dataset EGAD00001008237

• A96165A

  Whole genome sequencing for single cells for library A96165A 779 samples; filetype=bam

  Dataset EGAD00001008239

• A96174B

  Whole genome sequencing for single cells for library A96174B 1447 samples; filetype=bam

  Dataset EGAD00001008242

• A96177B

  Whole genome sequencing for single cells for library A96177B 683 samples; filetype=bam

  Dataset EGAD00001008244

• The immunological characterization of expanded tumor-infiltrating lymphocytes in renal cell carcinoma patients

  In this study, we comprehensively characterized the non-expanded and expanded tumor-infiltrating lymphocytes (TILs) from treatment-naïve RCC patients, as well as the corresponding “young” TILs (pre-REP TILs) and “rapidly expanded” TILs (REP TILs) following a clinical-grade TIL production protocol. From our scRNA+TCRαβ-seq and TCRβ-seq analyses, we show that the REP protocol preferentially favors the expansion CD4+ T-cells that originate from small T-cell clones in the tumor microenvironment (TME), indicating that the large, exhausted T-cell clones in the tumor do not expand during the protocol. We further identified RCC-associated TCR motifs that were validated in multiple TCRαβ-seq and scRNA+TCRαβ-seq datasets, as well as quantified the RCC-associated TCRs from the REP protocol. Overall, the T-cells carrying the RCC-associated TCRs remained high in the tumors and corresponding pre-REP TILs, but the frequency was reduced in the REP TILs. Furthermore, the overall anti-viral TCRs remained low throughout the REP protocol. Our results provide an in-depth understanding of the origin, phenotype, and specificity of the TCRs in RCC TILs.

  Study EGAS00001006952

• Covid19 Transcriptomic Data analysis in Irish Population

  The Samples were collected from Saint James's Hospital, Dublin and after informed consent. RNA-Sequencing library preparation was performed using the Illumina TruSeq Stranded Total RNA Library Prep Globin kit (20020612) with an input amount of 100 – 1000 ng. Library preparation was automated and processed using a Hamilton NGS Star. Library quality was assessed using the Roche KAPA Library Quantification Kit (7960298001) and GX Caliper HS Assay (CLS760672, 760517), run on Roche Lightcycler 480 II and Perkin Elmer LabChip GX Touch analyser respectively. Libraries were pooled and sequenced (75bp PE) on an Illumina NovaSeq 6000 instrument using NovaSeq 6000 S2 Reagent Kit v1.5 (20028314) targeting 50M paired reads. Raw data (BCL format) were demultiplexed and converted to FASTQ format using BCL2FastQ (Illumina). Adapters were trimmed using Skewer (PMID: 24925680) and QC assessed using FASTQC. STAR (PMID: 23104886) was used to align reads to the reference genome (GRCh38/hg38) as well as to the transcriptome (GENCODE v. 25). Quality of the RNA alignment was assessed using Picard QC. Gene and isoform quantification was performed using RSEM (PMID: 21816040). The study shares expression data (gene.results) and chimeric.junction.out.

  Study EGAS00001007116

• Further details about data access are available at KNIH DAC.

  Dac EGAC00001000487

• Gut metagenome from Estonian Microbiome Cohort

  Stool samples were collected from 2,509 Estonian Biobank participants. The shotgun metagenomic paired-end sequencing was performed by Novogene Bioinformatics Technology Co., Ltd. using the Illumina NovaSeq6000 platform, resulting in 4.62 ± 0.44 Gb of data per sample (insert size, 350 bp; read length, 2 × 250 bp). A total of 2,513 samples belonging to 2,509 individuals were sequenced, including 4 biological replicates from one individual. First, the reads were trimmed for quality and adapter sequences. The host reads that aligned to the human genome were removed using SOAP2.21 (parameters: -s 135 -l 30 -v 7 -m 200 -x 400).

  Dataset EGAD00001008448

• Myeloma Genome Project Targeted Panel Validation dataset

  372 samples consisting of 185 patient paired CD138+ tumor and non-involved DNA pairs, plus 5 Horizon Diagnostic known mutation standards (HD). Samples were processed using the KAPA HyperCap protocol and hybridized onto a targeted panel for multiple myeloma and associated diseases. Reference Sudha et al Clinical Cancer Research, 2022.

  Dataset EGAD00001008689

• Exploration of predictive biomarkers for postoperative recurrence of stage II/III colorectal cancer using genomic sequencing

  To identify biomarkers that can predict treatment outcomes, we compared the mutational profiles of surgically resected specimens from patients with recurrent cancer with those from patients with non-recurrent cancer. Target sequencing, whole-exome sequencing (WES), or whole-genome sequencing (WGS) was perfomed.

  Study JGAS000335

• Chemotherapy accelerates genomic aging of normal blood in children treated for cancer

  Whole genome sequencing (WGS) data obtained from clonally expanded hematopoietic stem and progenitor cells (HSPCs) from pediatric cancer patients before and after therapy exposure. In addition, WGS data of cord-blood derived HSPCs clones exposed to various chemotherapeutics and radiation was obtained.

  Study EGAS00001005141

• Single-cell RNA sequencing of metastatic colorectal cancer patient-derived xenografts treated with cetuximab

  Tumor implantation and expansion were performed in two 6-week-old NOD/SCID mice. Once tumors reached an average volume of approximately 700 mm³, mice were randomly assigned to one of two treatment arms: vehicle (physiological saline, intraperitoneally, twice weekly) or cetuximab (20 mg/kg, intraperitoneally, twice weekly). After 1 week of treatment, mice were sacrificed, and tumors were harvested and preserved in sCelLiVE® Buffer for downstream single-cell RNA sequencing analysis.

  Dataset EGAD50000002103

• Log2-CPM (voom)-normalised read-count matrix of the RNAseq Datasets of CLUSTER JIA pre-MTX cohort

  Samples were sequenced with NovaSeq6000 and the sequencing files were processed using the RSSnextflow workflow (https://gitlab.com/b8307038/rssnextflow). The raw read counts from all batches were combined. Analysis-ready batch-corrected read counts were generated using ComBat-seq. Read counts normalisation for DGE analysis was performed with limma-voom function (log2 CPM) as seen in the pipeline.

  Dataset EGAD50000002170

• Homologous recombination repair status in metastatic prostate cancer by next-generation sequencing and functional tissue-based immunofluorescence assays

  - WES files from primary or metastatic prostate cancer biopsies, from patients with metastatic prostate cancer. - This dataset includes those cases profiled by WES and included in the manuscript Homologous recombination repair status in metastatic prostate cancer by next-generation sequencing and functional tissue-based immunofluorescence assays, Arce-Gallego et al, Cell Rep Med 2024 - The dataset includes WES for 80 tumor biopsies from 65 different patients, including paired tumor/normal samples.

  Dataset EGAD50000001018

• Transcriptomic changes in placenta associated with anesthesia, delivery mode and maternal diabetes

  Samples were from 34 mothers, 6 with gestational diabetes, 14 with type 1 diabetes and 14 without diabetes history with either vaginal (N=17) or cesarean section (n=17) delivery, comprising primary planned section (N=9) or secondary emergency section (N=8). Anesthesia methods during delivery included spinal (N=11), epidural (N=11), or local anesthesia (N=6), with unknown anesthesia information for six cases. Samples from each other were from the Villous and the Decidua part of the Placenta

  Dataset EGAD50000000701

• Single-cell RNA and TCR sequencing of 16 PSCC and 6 non-malignant samples

  We included 16 fresh tumor samples of biopsy-proven invasive penile squamous cell carcinomas and 6 adult non-malignant inner prepuce samples. Single-cell RNA sequencing was performed (10x genomics). An HPV reference genome of 15 high-risk HPV types was generated and we mapped all single cell reads to the GRCh38 human and HPV reference genome using CellRanger. Targeted next-generation sequencing (tNGS) was performed for the detection of TP53 loss-of-function mutations.

  Dataset EGAD50000000317

• Whole-genome enzymatic DNA methylation sequencing and single-cell RNA sequencing data from nasal cells of COVID-19 patients

  Nasal swabs were collected from COVID-19 patients during the acute phase of infection as well as 3 and 12-months post-infection. Cells isolated from nasal swabs were subjected to whole-genome enzymatic DNA methylation sequencing (n=33) and scRNA-seq (n=24). For n=12 former COVID-19 pateints, follow-up samples were collected at 3 and 12 months post-infection and subjected to scRNA-seq.

  Dataset EGAD50000000398

• bfast CohortD OAPL

  ctDNA mutation calls (one mutation per sample per line) for BFAST Cohort D including mutation-level data like coding change, allele frequency, etc.

  Dataset EGAD50000000148

• AWI-Gen Phase 1 GWAS Genotype data

  The samples were genotyped on the H3Africa array (~2.3M SNPs) using the Illumina FastTrack Sequencing Service2. The default Illumina pipeline was used for the genotype calling (build GRCh37/hg19). The data was converted to PLINK using the h3abionet/h3agwas/call2plink pipeline and QC done using the h3abionet/h3agwas/qc pipeline

  Dataset EGAD00010001996

• ETMR_Nanostring

  Total RNA (100ng) from 21 ETMRs with C19MC structural alterations and 28 other PBTs was prepared with nCounter miRNA Sample Prep Kit according to standard protocol. miRNA expression profiling was conducted with human v1, v2, or v3 miRNA panel on nCounter miRNA expression platform (NanoString Technologies, Seattle, WA) according to manufacturer’s protocol. Signal normalization was done using nSolver Analysis and batch corrected using ComBat (Johnson et al. Biostatistics. 2007). 565 miRNAs overlapped between all three versions and was used for further analyses. Fold change and supervised t-test with FDR correction was calculated between the ETMRs and other PBTs.

  Dataset EGAD00010001701

• Sequencing data for oesophageal and related samples - Normals release 5 (RNA)

  Dataset EGAD00001005384

• BLUEPRINT September 2016, ChIPmentation Germinal Center B-Cell-Like Diffuse Large B-Cell Lymphoma from lymph node, on Genome GRCh38

  ChIPmentation data for 2 sample(s) Germinal Center B-Cell-Like Diffuse Large B-Cell Lymphoma from lymph node, on Genome GRCh38. 6 run(s), 6 experiment(s), 6 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002946

• BLUEPRINT September 2016, ChIP-Seq T-cell Acute Lymphocytic Leukemia from capillary blood, on Genome GRCh38

  ChIP-Seq data for 4 sample(s) T-cell Acute Lymphocytic Leukemia from capillary blood, on Genome GRCh38. 7 run(s), 7 experiment(s), 7 alignment(s). Part of BLUEPRINT (September 2016).Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

  Dataset EGAD00001002952

• BLUEPRINT September 2016, ChIPmentation Activated B-Cell-Like Diffuse Large B-Cell Lymphoma from lymph node, on Genome GRCh38

  ChIPmentation data for 2 sample(s) Activated B-Cell-Like Diffuse Large B-Cell Lymphoma from lymph node, on Genome GRCh38. 3 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002944

• Deep WGS of matched tumor-normal pairs for HGSOC copy-number signatures study

  This dataset includes 111 bam files from WGS sequence data aligned to human genome assembly GRCh37 (hg19) from 56 tumour and matched normal samples. Libraries were constructed with ~350-bp insert length using the TruSeq Nano DNA Library prep kit (Illumina) and sequenced on an Illumina HiSeq X Ten System in paired-end 150-bp reads mode. The average depth was 60× (range 40-101×) in tumours and 40× (range 24-73×) in matched blood samples.

  Dataset EGAD00001004189

• Primary breast tumor heterogeneity through therapy

  We performed whole-exome sequencing on multiple regions (n=2-3) from four primary untreated breast tumors (n=1 HER2+, n=2 ER+/HER2-, n=1 triple-negative), as well as matched normal. We also performed whole-exome sequencing on one region from the pre-treatment diagnostic core biopsy and multiple regions (n=2-6) from the post-treatment surgical specimen for five HER2+ primary breast tumors, as well as matched normal; all were treated with combination chemotherapy and trastuzumab. Analysis of these specimens allows characterization of breast tumor heterogeneity and clonal evolution.

  Dataset EGAD00001004306

• DNA Methylation-based diagnostic biomarkers for ESCC in Han Chinese population

  To validate the methylation status of the four candidate tumor suppressor genes (ADHFE1, EOMES, SALL1, TFPI2) in Han Chinese ESCC patients, we recruited 103 patients and obtained the paired tumors(entitled as T) and adjacent normal tissues (entitled as N) as well. Targeted bisulfite sequencing was conducted to detect the methylation profiles of these four genes in these 103 paired tissues. Furthermore, the raw sequence data (fastq files) was aligned using the BSseeker2 and this dataset included all of the bam file after alignment.

  Dataset EGAD00001004288

• Whole genome sequence of liver cancers displaying biliary phenotype

  RIKEN collection WGS reads for 61 liver cancer and matched blood samples from 30 donors displaying biliary phenotype

  Dataset EGAD00001000809

• Integrated analysis of whole genome and RNA sequencing in 22 HBV-associated HCCs

  RIKEN collection WGS and RNA-seq reads for 66 HBV-associated HCC and matched blood or liver samples from 22 donors.

  Dataset EGAD00001001035