949 results for "moneda mundial 26 26 Visité Buyfc26coins.com. Atención al cliente excepcional. Muy profesionales..GdgI"

in 15.73 milliseconds.

• Next-generation sequencing raw data from metastatic prostate cancer biopsies - observational study Vall d'Hebron Institute of Oncology (Ethics committee code PR-AG-5248)

  Paired tumor-normal whole exome sequencing data from patients with metastatic prostate cancer; dataset from Arce-Gallego et al, Cell Reports Med 2024

  Dac EGAC50000000448

• Khoe-San whole genome sequencing

  High-coverage (>30X) whole genome data (FASTQ) for 25 southern African Khoe-San individuals (from five populations). These are the same individuals as in Schlebusch et al. 2020 (EGAD00001006183), but different data.

  Dataset EGAD50000001559

• DAC_for_study__PDFs_RNA-seq_scRNA/TCR-seq

  Study: Pharmacologic inhibition of nonsense-mediated mRNA decay enhances anti-tumour immunity, R. Vendramin et al (2025), Cell Data details: Patient-derived fragments (PDFs) RNA-seq and scRNA/TCR-seq data; treatment: SMG1 inhibition (small molecule inhibitors) and knockdown of SMG1 (siRNA)

  Dac EGAC50000000725

• WES/WXS data for ependymomas (42 tumor-control pairs)

  Whole exome sequencing data for ependymomas (42 tumor-control pairs). See Mack, Witt et al. Nature 506(7489):445-50, 2014 (PMID: 24553142).

  Dataset EGAD00001000951

• WGS data for ependymomas (5 tumor-control pairs)

  Whole genome sequencing data for ependymomas (5 tumor-control pairs). See Mack, Witt et al. Nature 506(7489):445-50, 2014 (PMID: 24553142).

  Dataset EGAD00001000950

• SNP array data in Massim study

  The compressed file contains plink format file for the Illumina MEGA SNP array data of 255 individuals generated and analyzed in Liu et al study of genom-wide variation of the Massim region.

  Dataset EGAD00001008545

• Very Low Tumor Mutation Burden Identifies Inflamed Recurrent Glioblastomas Responsive to Cancer Immunotherapy

  A subset of available tumors (with matched blood) from patients enrolled in a phase I clinical trial testing a recombinant poliovirus, published by Desjardins et al. NEJM 2018 (PMID:29943666), were analyzed by whole exome sequencing and RNA-seq. It was discovered that low tumor mutation burden correlated with survival outcome, and also discovered that low tumor mutation burden correlated with higher inflammatory gene signatures by RNA-seq.

  Study phs002270

• Genomic study of an AT-AML

  Ataxia Telangiectasia (A-T) is caused by biallelic mutations in ATM and confers predisposition to cancer, with acute myeloid leukaemia (AML) being rarely observed. We investigated an AML arising in an A-T patient for secondary genetic events by performing whole exome sequencing of serial samples over the disease course. AML samples were compared to a reference "germline" sample from a much earlier time point. Goldgraben et al 2020, Pediatric Blood & Cancer

  Study EGAS00001004392

• The Natural History of Human Prostate Cancer

  Whole genome DNA sequencing was used to decrypt the phylogeny of multiple samples from distinct areas of cancer and morphologically normal tissue taken from the prostates of 3 men. For each of three different prostates, multiple tumour samples (4, 5, and 3 depending on the case) and one normal tissue sample were whole genome sequenced with a matched blood sample using the Illumiuna HiSeq platform. Tumour samples were sequenced to a target depth of 50X and normals and blood to a target depth of 30X. As of September 2020, some of the studies using these data include: Cooper et al, Nature Genetics 2015 (PMID: 25730763) Wedge et al, Nature Genetics 2018 (PMID: 29662167) Pan-Cancer Analysis of Whole Genomes, Nature 2020 (PMID: 32025007)

  Dataset EGAD00001000689

• Single-cell whole-genome sequencing reveals convergent evolution in Burkitt lymphoma

  This depository contains the raw BAM/CRAM files used in the manuscript "Single-cell whole-genome sequencing reveals convergent evolution in Burkitt lymphoma" Steemers et al. (2026). This includes: - 332 PTA-based single-cell WGS samples (~15X depth) - 7 bulk tumour samples (~30X depth) with matched germline samples (~30X depth) - 14 bulk tumour samples seqeunced by the Princess Maxima Centre diagnostics department (~105X depth) with matched germline samples (~35X depth)

  Study EGAS50000001681

• Center for Technology Licensing at Cornell University for data associated with Nature Medicine 2024 paper

  For data access to plasma/tumor/normal samples from the manuscript "Ultrasensitive plasma-based monitoring of tumor burden using machine learning-guided signal enrichment" from Widman et al. Nature Medicine 2024

  Dac EGAC50000000207

• WGS data from COMPASS Trial

  WGS data for pancreatic cancer samples (COMPASS Trial; Knox et al., Journal of Clinical Oncology, 2025). The dataset includes tumour and normal BAM files sequenced on Illumina HiSeq/NovaSeq and aligned against GRCh38 (n=263 tumour/normal pairs).

  Dataset EGAD50000001832

• SNPArray_Viet

  The compressed file contains plink format file for the Affymetrix Human Origins SNP array data of 260 individuals generated and analyzed in Liu et al 2020 study of 22 ethnolinguistic groups in Vietnam.

  Dataset EGAD00010002287

• RCC_HTA2.0_Reustle2020

  GeneChip HTA 2.0 data of primary renal cell carcinoma (RCC) related to Reustle et al, Genome Med 12:2020 32. Preprocessing of microarray data was performed using Robust Multi-array Average (RMA).

  Dataset EGAD00010002323

• DKFZ-St.Jude Medulloblastoma - 6 TB control exomes

  6 control exomes, TB cohort, sequenced on Illumina machines from the paper "Germline Elongator mutations in Sonic Hedgehog medulloblastoma" (Waszak et al. 2020 Nature).

  Dataset EGAD00001006664

• McGill EMC Release 4 for assay "ATAC-seq"

  McGill EMC Release 4 for assay "ATAC-seq": Sequencing of transposase-accessible chromatin as described by Buenrostro et al. (Nature Methods 10, 1213?1218 (2013) doi:10.1038/nmeth.2688)

  Dataset EGAD00001001300

• ChIP-seq and Hodgkin lymphoma

  ChIP-seq data (H3K4Me3, H3K27Ac histone modifications) of Hodgkin lymphoma cell line L-428. Samples were processed as previously described (Sud et al., 2018). The files are in bam format, aligned to build 37 of the human genome.

  Dataset EGAD00001004322

• Ewing's Sarcoma Whole Genome Sequencing

  54 WGS Ewing's sarcoma samples sequenced at The Hospital for Sick Children Toronto (Adam Shlien's lab) and published on Science 2018. Reference Anderson et al. "Rearrangement bursts generate canonical gene fusions in bone and soft tissue tumors"

  Dataset EGAD00001004589

• 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).

  Study EGAS00001001184

• egpg-G-1

  Illumina Omni 2.5M SNPchip data (build37) of Ethiopian samples from the Pagani et al. 2015 AJHG paper (doi: http://dx.doi.org/10.1016/j. ajhg.2015.04.019)

  Dataset EGAD00010001221

• egyptgp-G-1

  Illumina Omni 2.5M SNPchip data (build37) of Egyptian samples from the Pagani et al. 2015 AJHG paper (doi: http://dx.doi.org/10.1016/j. ajhg.2015.04.019)

  Dataset EGAD00010001223

• Dataset SNP tumours

  This dataset contains 30 idat files each from 15 SNP array runs on patient colitis-associated colorectal cancer tumours. All phenotypes are cancer. See Baker et al. 2018 Supplementary Table 2 for patient details of 12 tumours used in the analyses in the publication.

  Dataset EGAD00010001587

• SNPArray_Thai

  The compressed file contains plink format file for the Affymetrix Human Origins SNP array data of 452 individuals generated and analyzed in Kutanan, Liu et al 2021 study of 33 ethnolinguistic groups in Thailand and Laos.

  Dataset EGAD00010002285

• 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

• 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

• 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

• 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

• 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

• 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

• PFA ependymoma study -RNA-seq data

  RNA-seq fastq files for the 16 samples used in Michealraj et al. Cell 2020. The samples include PFA and ST ependymoma tissues, normal pediatric brain as control and PFA ependymoma lines.

  Dataset EGAD00001006046

• 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

• 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

• 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

• 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

• 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

• Testicular large B-cell lymphoma is genetically similar to PCNSL and distinct from nodal DLBCL

  Next generation sequencing (NGS) data analyzed in Rivas-Delgado, López, et al. HemaSphere 2024. Single nucleotide variants (SNVs) and short insertions /deletions (indels) were evaluated in 42 samples using a B-cell malignancy-oriented targeted panel covering 115 genes (SureSelectXT, Agilent Technologies and sequenced in a MiSeq instrument (Illumina; 2x150bp; mean coverage of 599x). The NGS data of 39 samples is provided here. The 3 additional samples were previously published. Please, see the original publication for additional details.

  Study EGAS50000000521

• Expression quantitative trait loci influence DNA damage-induced apoptosis in cancer

  The analysis of e2QTL allows for the identification of context-specific eQTL effects (Kim-Hellmuth et al. (2017), PMID: 28814792). To evaluate how inter-individual genetic variability influences the regulation of DNA damage-induced apoptosis, we performed e2QTL analysis of CD8+ T cells from 461 healthy European participants stimulated with high doses of 5 different carcinogens. These include Methyl-methanesulfonate (MMS), tert-butyl-hydroperoxide (TBOOH), benzo(a)pyrene-7,8-diol-9,10-epoxide (BPDE), 4-hydroxycyclophosphamide (HC) and UVC radiation.

  Study EGAS50000000666