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• Siberia.Pakendorf

  The tar archive contains a) the txt file with the genotypes, b) illumina annotation file with info on SNPs, c) sample info file unfiltered illumina data, autosomes only data from Pugach et al MBE 2016 The Complex Admixture History and Recent Southern Origins of Siberian Populations

  Dataset EGAD00010002304

• Paired Biopsy Project: West Coast Dream Team

  This project contains the 16 WGS tumor samples and the corresponding 15 WGS normal tissue samples (germline control) not yet deposited in the public domain (e.g., EGA, dbGaP) at the time of submission of the manuscript (Zhu et al.)

  Dataset EGAD50000000473

• Mucosal Melanoma transcriptomes

  This is an RNA sequencing dataset of 101 mucosal melanoma tumors. The original corresponding study is titled "Diversity of the immune microenvironment and response to checkpoint inhibitor immunotherapy in mucosal melanoma" by J.L. Vos et al., published in JCI Insight.

  Dataset EGAD50000000892

• DNA methylation and transposable element landscapes in human regulatory and conventional Tcells

  Whole genome bisulfate sequencing using tagmentation of Tcells from skin and blood.The method improved TWGBS protocol (Weichenhan et al.), Methods Mol. Biol. 2018, vol. 1708 pp.105-122) was used

  Dataset EGAD50000001022

• WGS data from 20 cholangiocarcinoma cases

  WGS data from biliary tract cancer samples (Holzapfel, et al., J Gastrointest Oncol,14(1):379-389, 2023). The dataset includes tumour and normal BAM files sequenced on Illumina HiSeq/NovaSeq and aligned against GRCh38 (n=20 tumour/normal pairs).

  Dataset EGAD50000002307

• DKFZ-St.Jude Medulloblastoma - 3 XI046 SF controls, WGS data

  3 control whole genomes, SF cohort, sequenced on Illumina machines from the paper "Germline Elongator mutations in Sonic Hedgehog medulloblastoma" (Waszak et al. 2020 Nature).

  Dataset EGAD00001006662

• Exome sequencing of women with familial high-grade serous ovarian carcinoma

  Germline exome sequencing data (paired Fastq files) from 516 BRCA1/2-negative women affected with familial high-grade serous (or similar) ovarian carcinoma, as analysed and described in Subramanian et al (Nature Communications, 2020).

  Dataset EGAD00001006030

• Breast Cancer TNBC Single-Cell RNA-Seq Dataset

  Single-cell RNA-Sequencing of five TNBC primary breast cancers from Wu et al. (2020) EMBO J study. Data was generated using the Chromium controller (10X Genomics) and sequenced on the NextSeq 500 platform.

  Dataset EGAD00001006981

• Martin_Ravenscroft-AUS1

  Whole genome sequencing data on D19-0702 (AUS1), presented in Martin et al. 2020 (AUS1). WGS (Illumina HiSeq) was performed at Kinghorn Centre for Clinical Genetics, Garvan Institute of Medical Research. Data was analyzed using the Seave bioinformatic analysis pipeline (https://www.seave.bio).

  Dataset EGAD00001006276

• Whole exome sequencing data for patients with Bosma arhinia microphthalmia syndrome (BAMS)

  Whole exome sequencing data for patients with Bosma arhinia microphthalmia syndrome (BAMS). The dataset includes 21 samples from 7 families with BAMS; see Gordon et al, Nature Genetics, 2017.

  Dataset EGAD00001003130

• DKFZ-St.Jude Medulloblastoma - 9 SJMB tumor/control pairs, exome

  9 tumor/control exomes, SJMB samples, sequenced on Illumina machines from the paper "Germline Elongator mutations in Sonic Hedgehog medulloblastoma" (Waszak et al. 2020 Nature).

  Dataset EGAD00001006663

• HiSeq Colorectal Cancer Dataset

  Set of 19 patients afflicted with colorectal cancer with matching preoperative and postoperative blood plasma, PBMC, and tumor biopsy sequencing data. Originally referenced by Genome-wide cell-free DNA mutational integration enables ultra-sensitive cancer monitoring. Nat Med. 2020, Zviran et. al.

  Dataset EGAD00001011186

• exercise cfChIP-seq

  cfChIP-seq using an H3K4me3-specific antibody of 9 plasma samples collected after marathon run. cfChIP-seq was performed as described in Sadeh et al. 2021. dataset contains paired-end fastq files and BAM files of raw sequencing data.

  Dataset EGAD00001010284

• Epigenomes and Transcriptomes of Brain Samples of Schizophrenics and Controls

  We examined the effects of antipsychotic medications on the cell-specific epigenomics and transcriptomics in the frontal cortex of schizophrenic subjects. We performed ChIP-seq (H3K4me3 and H3K27ac) and RNA-seq on neuronal and non-neuronal nuclei of postmortem frontal cortex samples from two cohorts of schizophrenics, either previously treated or not with antipsychotic medications, and control subjects individually matched by sex and age. In total, we tested 29 schizophrenics (15 untreated and 14 treated patients) and their matched control samples. Two replicates were produced on each brain sample for each assay. ChIP-seq data were generated using MOWChIP-seq (Zhu et al., PMID: 31666743) and RNA-seq data were produced using the combination of SMART-seq2 (Picelli et al., PMID: 24385147) and a Nextera XT DNA library preparation kit. The filename contains the information on the experimental group, the number of the sample within the group, the type of the brain nuclei, the epigenomic/transcriptomic data set (either H3K27ac or mRNA), and the number of replicate. For example, “SCZ-1-untreated-neuron-H3K4me3-R1” refers untreated schizophrenic sample #1, replicate 1 ChIP-seq data on H3K4me3.

  Study phs002487

• Methotrexate Clearance GWAS in Acute Lymphoblastic Leukemia (ALL) Patients

  We estimated the plasma clearance of methotrexate from 1279 acute lymphoblastic leukemia (ALL) patients treated on the Children Oncology Group 9904 and 9905 trials (http://clinicaltrials.gov/show/NCT00005585 and http://clinicaltrials.gov/show/NCT00005596). Patients received either a 24-hour infusion of a 1 g/m2 dose or 4-hour infusion of a 2 g/m2 dose). Methotrexate clearance was lower in older children (p=7 x 10-7), females (p=2.7 x 10-4), and patients who received a delayed intensification phase (p=0.0022). In a genome-wide analysis, methotrexate clearance was associated with polymorphisms in the SLCO1B1 gene (p=2.1 x 10-11), which encodes for an organic anion transporter. This replicates findings using different schedules of high-dose methotrexate in ALL patients treated on St. Jude protocols. A combined meta-analysis yields a p-value of 5.7 x 10-19 for the association of methotrexate clearance with SLCO1B1 SNP rs4149056 (Trevino et al, PMID 19901119 and Ramsey et al, PMID 23233662). This data set includes the dependent variable of methotrexate clearance and all of the SNP data available from arrays.

  Study phs000637

• Temporal Evolution Reveals Bifurcated Lineages in Aggressive Neuroendocrine Small Cell Prostate Cancer Trans-Differentiation

  Trans-differentiation from adenocarcinoma to small cell neuroendocrine (SCN) cancer is an adverse consequence of treatment escape in various cancers, including prostate, lung, and bladder cancers (Balanis and Sheu et al., PMID 31287989). Expression of dominant negative p53 (TP53DN), myrAkt1, RB1-shRNA, c-Myc, and Bcl2(PARCB forward transformation) using human naïve prostate basal epithelial cells recapitulated both transcriptional and histological characteristics of small cell neuroendocrine prostate cancer (NEPC) (Park et al., PMID 30287662). To study the temporal transcriptional landscape during this trans-differentiation process, we conducted a time course study using a PARCB model by integrating multi-omics sequencing, including bulk RNA-sequencing and ATAC-sequencing on samples taken at different time point, as well as single cell RNA sequencing on serial xenograft tumors. We found a common SCN pathway that resulted in two distinct end states defined by mutually exclusive expression of ASCL1 and ASCL2. Further investigation using CUT&RUN sequencing identified TFAP4 as a potential epigenetic regulator of both proteins. Our study reveals temporal and transcriptional changes from prostate adenocarcinoma to NEPC trans-differentiation.

  Study phs003230

• Yale SPORE in Skin Cancer Project 2

  Understanding the relationship between tumor and peripheral immune environments could allow longitudinal immune monitoring in cancer. Here, we examined whether T cells that share the same TCRab and are found in both tumor and blood can be interrogated to gain insight into the ongoing tumor T cell response. Paired transcriptome and TCRab repertoire of circulating and tumor-infiltrating T cells were analyzed at the single cell level from matched tumor and blood from patients with metastatic melanoma. The data shared here consist of gene expression matrix and fastq files obtained from sequencing single-cell libraries generated with the Chromium (TM) 5' technique. For each of 18 immunotherapy naive and 11 previously treated melanoma patients, T cells (Live/CD45+/TcRab+) were FACS-sorted from PBMCs and freshly resected tumors. Metadata associated with each individual cell consist of Patient I.D., biological sex, immunotherapy status, amino acid and nucleotide CDR3 sequence of the TCR alpha and beta chain (obtained from matched VDJ region enriched libraries), and sequencing batch. Further details about previous treatment history and other clinical parameters can be found in the associated manuscripts (Lucca et al., 2021: PMID: 33651881; Lu et al., PMID: 39609626).

  Study phs002289

• WTCCC2 Visceral Leishmaniasis (VL) samples

  A WTCCC2 project genome-wide association study for visceral leishmaniasis (VL) in individuals from India, Brazil and Sudan, genotyped on the custom Illumina 670k array. The WTCCC2 analysis of the Brazilian and Indian samples is described in Fakiola et al. [Nat Genet. 2013 Feb;45(2):208-13].It should be noted that due to expected family structure in the data, normal analyses of these data should include an estimation of the relatedness between the samples. For more details about sample collection for the project please refer to the Methods section of the paper above. The samples from India were all collected from Bihar state in northeastern India. The Brazilian samples were collected from a wide area of northern Brazil, and managed at two laboratories in Natal and Belem. For more details on the geographic distribution of the Brazil samples please see Jamieson et al. [Genes Immun. 2007 Jan;8(1):84-90]. We have not generated qc_passed or info files for the Sudanese data, a preliminary scan of the data indicated that there may be DNA quality issues with these samples.

  Study EGAS00001000773

• Cross-tissue transcriptomic analysis of human secondary lymphoid organ residing ILC3 reveals a default quiescent state in the absence of inflammation

  A cross tissue transcriptional comparison of human ILC3 isolated from non-inflamed lymph nodes (LN) and spleens, inflamed tonsils and the peripheral blood (PB). Using uniform gating and high-purity cell-sorting, ILC3 were sorted as Lineage(CD3,CD19,CD34,CD14,CD94)-CD45intCD127+CRTH2-CD117+. From LN, spleen, tonsils and PB, NKp44- ILC3 were isolated and, in addition, NKp44+ ILC3 were sorted from spleen and tonsils. RNA quality was measured on a RNA 6000 Pico Kit (Agilent Technologies) using a 2100 Bioanalyzer (Agilent Technologies). 100 pg high-quality RNA was isolated and cDNA was generated using the SMART-Seq (v3) Ultra Low Input RNA Kit (Clontech) with 15 cycles of amplification. Amplified and Covaris-fragmented cDNA was quality-checked on a High Sensitivity DNA chip (Agilent Technologies) and further processed according the TruSeq Nano DNA Library Preparation Kit (Illumina). Data was processed as follows: SMARTer adapters were trimmed with cutadapt and the resulting sequences were aligned to the human RefSeq transcriptome using 14 TopHat2 (Kim et al., 2013). Normalization and quantification was performed using Cufflinks (Trapnell et al., 2012). FPKM counts were determined per gene with HTSeq-count.

  Study EGAS00001002636

• scRNA-seq of HSPC treated with gemcitabine and carbplatin

  The study used cryopreserved hematopoietic stem and progenitor cells (HSPCs) harvested from one patient with chronic myelogenous leukemia (CML). The cells were thawed and acclimatized for 48 hours in StemMACS HSC Expansion Media XF (Miltenyi Biotec) supplemented with StemMACS HSC Expansion Cocktail (Miltenyi Biotec) at 37°C in a humidified atmosphere containing 5% CO2. 4 samples of 2000000 HSPCs in 2 ml media each were then prepared and treated for 24 hours: carboplatin-high: 150 µg/ml carboplatin carboplatin-low: 18.75 µg/ml carboplatin gemcitabine: 25 ng/ml gemcitabine control: no drug Single-cells were subsequently extracted using the ddSEQ™ Single-Cell Isolator (Bio-Rad) and later sequenced using the SureCell™ Whole Transcriptome Analysis 3' Library Prep Kit (Illumina) on the NextSeq 500 System (Illumina) using the NextSeq 500/550 High Output Kit v2.5 150 Cycles (Illumina) all following the manufacturer's instructions. FASTQ-files were then processed following the ddSeeker (Romagnoli, D., et al., BMC Genomics 2018, doi:10.1186/s12864-018-5249-x) and Drop-seq (Macosko, E.Z., et al., Cell 2015, doi:10.1016/j.cell.2015.05.002) protocols for processing scRNA-seq data to yield the final digital gene expression for each cell.

  Study EGAS00001004381

• Institut Curie Neuroblastoma Whole Genome Sequencing Diagnosis Relapse

  Neuroblastoma, a clinically heterogeneous pediatric cancer, is characterized by distinct genomic profiles but few recurrent mutations. As neuroblastoma is expected to have high degree of genetic heterogeneity, study of neuroblastoma's clonal evolution with deep coverage whole-genome sequencing of diagnosis and relapse samples will lead to a better understanding of the molecular events associated with relapse. Samples were included in this study if sufficient DNA from constitutional, diagnosis and relapse tumors was available for WGS. Whole genome sequencing was performed on trios (constitutional, diagnose and relapse DNA) from eight patients using Illumina Hi-seq2500 leading to paired-ends (PE) 90x90 for 6 of them and 100x100 for two. Expected coverage for sample NB0175 100x100bp was 30X for tumor and constitutional samples. For the seven other patients expected coverage was 80X for tumor samples with PE 100x100, 100X in the other tumor samples and 50X for all constitutional samples (see table 1). Following alignment with BWA (Li et al., Oxford J, 2009 Jul) allowing up to 4% of mismatches, bam files were cleaned up according to the Genome Analysis Toolkit (GATK) recommendations (Van der Auwera et al., Current Protocols in Bioinformatics, 2013, picard-1.45, GenomeAnalysisTK-2.2-16). Variant calling was performed in parallel using 3 variant callers: GenomeAnalysisTK-2.2-16, Samtools-0.1.18 and MuTect-1.1.4 (McKenna et al., Genome Res, 2010; Li et al., Oxford J, 2009 Aug; Cibulskis et al., Nature, 2013). Annovar-v2012-10-23 with cosmic-v64 and dbsnp-v137 were used for the annotation and RefSeq for the structural annotation. For GATK and Samtools, single nucleotide variants (SNVs) with a quality under 30, a depth of coverage under 6 or with less than 2 reads supporting the variant were filter out. MuTect with parameters following GATK and Samtools thresholds have been used to filter our irrelevant variants. .SNVs within and around exons of coding genes overlapping splice sites.. Then,variants reported in more than 1% of the population in the 1000 genomes (1000gAprl_2012) or Exome Sequencing Project (ESP6500) have been discarded in order to filter polymorphisms. Finally, synonymous variants were filtered out. MuTect focuses on somatic by filtering with constitutional sample. Mpileup comparison between constitutional and somatic DNAs allowed us to focus also on tumor specific SNVs with GATK and Samtools. Finally, every SNV called by our pipeline and also supported in any constitutional samples were filtered our in order to prevent putative constitutional DNA coverage deficiency. Then we analyzed CNVs (copy number variants) with HMMcopy-v0.1.1 (Gavin et al., Genome Res, 2012) and control-FREEC-v6.7 (Boeva et al., Bioinformatics 2011) with a respective window of 2000bp and 1000 bp, and auto-correction of normal contamination of tumor samples for Control-FREEC. Finally we explored Structural variants (SVs) including deletions, inversions, tandem duplications and translocations using DELLY-v0.5.5 with standard parameters (Rausch et al., Oxford J, 2012). In tumors, at least 10 supporting reads were required to make a call and 5 supporting reads for the sample NB0175 with a coverage of only 40X (see table 2). To predict SVs in constitutional samples for subsequent somatic filtering, only 2 supporting reads were required in order not to miss one. To identify somatic events, all the SVs in each normal sample were first flanked by 500 bp in both directions and any SVs called in a tumor sample which was in the combined flanked regions of respective normal sample was removed (see graph 1). Deletions with more than 5 genes impacted or larger than 1Mb and inversions or tandem duplications covering more than 4 genes, were removed. We focused on exonic and splicing events for deletions, inversions, and tandem duplications. For translocation, we keep all SVs that occurred in intronic, exonic, 5'UTR, upstream or splicing regions. Bioinformatics detection of variations with Deep sequencing approach Once PE reads merged and adaptors trimmed by SeqPrep with default parameters, merged reads were aligned via the BWA (Li H. and Durbin R. 2009 PMID 19451168) allowing up to 1 differences in the 22-base-long seeds and reporting only unique alignments. Only reads having a mapping quality 20 or more have been further analysed. Variant calling software was not used, since we aimed to predict variations at low frequencies, observed in less than 1% of reads. Such variants require a custom approach. Using DepthOfCoverage functions of the Genome Analysis Toolkit (GATK) v2.13.2 (McKenna A, et al., 2010 Genome Research PMID: 20644199), we focused on high quality coverage of bases A, C, G and T at the targeted variant position. Depth of coverage of each base following a mapping quality higher than 20 and a base quality higher than 10 have been taken into account in order to focus only on high quality data. Aiming to determine the background level of variability at the studied regions, 10 control samples were included in the analysis. The same approach and filtering criteria have been applied as introduced above over the entire amplicons. In order to highlight variants, for each sample the frequencies of each bases at each amplicon position were then compared to those observed in the set of controls. Statistical analyses were performed with the R statistical software (http://www.R-project.org). Fisher’s exact two-sided tests with a Bonferroni correction were performed to compare percentages of bases between the data sets, i.e. for a given base between a case and the controls. Finally, significant variations were filtered-in once (i) a significant increase in the percentage of avariant base and (ii) a significant decrease in the percentage of it's reference base following our p.values criteria was observed (p.val < 0.05).

  Dataset EGAD00001001356

• Clonal selection after gene therapy in sickle cell disease

  While gene therapy (GT) provides a potentially curative treatment option for patients with sickle cell disease (SCD), the occurrence of myeloid malignancies in clinical trials has prompted concern. To interrogate potential mechanisms underlying increased cancer risk, we used hematopoietic stem cell (HSC) clonal tracking by whole genome sequencing (WGS) to map the somatic mutation and clonal landscape of 2,592 gene modified as well as unmodified single stem and progenitor cells from six SCD patients undergoing gene therapy (7-26 years old, average 12.7× depth). Pre-GT phylogenetic trees in SCD were highly polyclonal and mutation burdens per cell were elevated in some, but not all, patients. Post-GT, no clonal expansions were identified. However, an increased frequency of driver mutations associated with myeloid neoplasms or clonal hematopoiesis (DNMT3A- and EZH2-mutated clones in particular) were seen in both genetically modified and unmodified cells suggested positive selection of mutant clones during gene therapy. This work sheds light on the mutation landscape and HSC clonal dynamics in gene therapy for SCD and highlights enhanced fitness of some HSCs harboring pre-existing driver mutations following gene therapy. Future studies should define the long-term fate of mutant clones including any contribution to expansions associated with myeloid neoplasms.

  Dataset EGAD00001010913

• International Multi-Center ADHD Gene Project (IMAGE) II Case Sample

  This sample represents a collection of cases across a range of sites. All of these samples were ascertained for ADHD with most meeting criteria for combined type ADHD. The collection sites span Europe and America. Further details on the source and inclusion and exclusion information can be found in Neale et al. "Case-Control Genome-Wide Association of Attention-Deficit / Hyperactivity Disorder" J Am Acad Child Adolesc Psychiatry. 2010 September; 49(9): 906-920 PMID20732627. For online access to this manuscript see: PMC2928577.

  Study phs000407

• Endometrial Cancer Association Consortium - OncoArray Genotypes

  The data come from 9 studies participating in the Endometrial Cancer Association Consortium (ECAC). ECAC data included in this dbGaP submission include OncoArray data from 4486 endometrial cancer cases genotyped at the Center for Inherited Disease Research (CIDR). Samples were genotyped using the Illumina OncoArray beadchip 500K SNP custom array. Details of the genotyping process and sample selection are included in O'Mara et al, Identification of nine new susceptibility loci for endometrial cancer. Nature Communications (PMID:30093612).

  Study phs001885

• Genome-Wide Analysis of Aberrant Position and Sequence of Plasma DNA Fragment Ends in Patients With Cancer

  This study investigated fragmentation characteristics in plasma cell-free DNA and evaluated the relevance of such analysis for cancer diagnostics. Specifically, we performed whole genome sequencing (WGS) of healthy individuals and cancer patients, and evaluated genome-wide coverage and sequences surrounding fragment ends. This was a retrospective study of archived plasma samples. The WGS data are available through this submission. Detailed study design and findings were published in Budhraja et al. Science Translational Medicine 2023.

  Study phs003170