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• LBC1936 gvcfs

  1075 members of the LBC1936 were sequenced using the Illumina HiSeq X platform. This dataset contains the gvcfs.

  Dataset EGAD00001006414

• Paired-end RNA-seq analysis of the Hypermutation and Malignant Progression in Low-grade Glioma Patients

  44 DNA samples were derived from the tumors of the low grade glioma patients and sequenced using Illumina RNAseq paired-end technology. Dataset contains 44 BAM files aligned to hg19 using Tophat 2.

  Dataset EGAD00001005387

• Dnase1l3 deletion causes aberrations in length and end-motif frequencies in plasma DNA

  We showed that mice in which Dnase1l3 had been deleted showed aberrations in the fragmentation of plasma DNA. We also observed a change in the ranked frequencies of end motifs of plasma DNA caused by the Dnase1l3 deletion.

  Dataset EGAD00001005071

• Whole genome sequencing of matched esophageal tumor-normal samples

  The dataset includes whole genome sequencing (WGS) data on ten matched esophageal tumor-normal pairs. WGS was performed by the cancer sequencing service of CG with an average read coverage of approximately 50-fold. Illumina HiSeq2000 instrument was used to perform the sequencing.

  Dataset EGAD00001004832

• Maternal plasma data in FetalQuantSD accurate quantification of fetal DNA fraction by shallow-depth sequencing of maternal plasma DNA

  This dataset consist on 70 maternal plasma samples (bam files) used in the FetalQuantSD. The maternal plasma DNA samples were sequenced using the HiSeq 2000 platform (Illumina) with a 50-cycle paired-end mode.

  Dataset EGAD00001004324

• WGS of 4 childhood T-ALL patients - tumor and remission

  Whole genome sequencing of 4 childhood T-ALL patients, which was further used in single-cell analysis in the paper "Single cell sequencing reveals the origin and the order of mutation acquisition in T-cell acute lymphoblastic leukemia".

  Dataset EGAD00001003951

• Papuan Genomes: high depth (30x) whole genome sequence data

  This dataset includes the whole genomes, sequenced to high depth (30x) of 25 individuals from Papua New Guinea. The individuals were chosen from several geographically distinct Papuan groups, focusing on the highland regions: Bundi, Kundiawa, Mendi, Marawaka and Tari.

  Dataset EGAD00001001634

• Characterization of T cell tumor infiltration in brain metastases through the analysis of the cerebrospinal fluid

  Study EGAS00001004751

• Maastricht IBS cohort MIBS

  Gut microbiome data of the Maastricht-IBS study

  Study EGAS00001001914

• Kings_Hepatoblastoma_Behjati_RNA_Managed_Access

  A study of the development of liver based tumours.

  Study EGAS00001006876

• PBAT sequencing of naïve human ESCs

  This project investigates the DNA methylation landscape of naïve human embryonic stem cells (hESCs) derived from human embryos, assessing the impact of ERK inhibition (ERKi). Naïve ESCs were cultured in UXGL or PXGL media. Post bisulphite adaptor tagging protocol was used to profile genome-wide DNA methylation.

  Study EGAS50000001006

• Whole-exome sequencing (WES) analysis of untreated head and neck cancer patient-derived xenografts (PDXs) matched patient tumor tissue and normal mucosa tissue.

  WES analysis was performed on untreated patient tumor/normal mucosa tissue and matched patient-derived xenograft (PDX) models of primary head and neck squamous cell carcinoma (HNSCC) to investigate the preservation of tumor-specific genomic alterations in the in vivo context.

  Study EGAS50000001622

• A single-cell atlas of meningioma.

  In this study, we examined the breadth of cell types and states in meningioma using the largest single-cell cohort of meningioma by generating more than 7.5 million total cells from meningioma and surrounding dura, profiled using single-nuclear RNA sequencing and high-resolution spatial transcriptomics.

  Study EGAS50000001589

• Clinical and genetic analysis of a rare syndrome associated with neoteny

  We describe a novel syndrome in six patients with extreme developmental delay and the defining characteristic of neoteny. In an effort to discover any genetic causes of this syndrome whole genome sequence (WGS) analysis of the patients and their families was performed on Complete Genomics’ nanoarray platform.

  Study EGAS00001002419

• H3K27ac and RNA-seq data of neuroblastoma PDXs and/or primary tumors

  This study includes H3K27ac ChIP-seq and RNA-seq data of 6 neuroblastoma PDXs. It also contains RNA-seq data of 3 pairs of neuroblastoma tumors obtained at diagnosis and relapse. The MAP-GR-B25-NB-1 PDX was derived from the relapse of pair 1.

  Study EGAS00001002505

• High_powered_complex_trait_association_mapping_through_whole_genome_sequencing_of_a_selected_subpopulation_of_the_INGI_Val_Borbera_genetic_isolate

  The VBSEQ project aims to combine available extensive genetic and phenotypic data to the latest high-throughput genome sequencing technology and ad hoc statistical analysis to identify new rare genetic variants underlying complex traits. Up to 100 Val Borbera samples will be sequenced to a 6x depth.

  Study EGAS00001000398

• snRNA-seq of subcortical MS

  Single-nuclei RNA-sequencing (snRNA-seq) from subcortical white matter controls and MS lesions, specifically chronic active (CA) and chronic inactive (CI), that paired with the spatial transcriptomics from the same samples, was used to identify spatial niches and key cell interactions driving inflammation and disease progression.

  Study EGAS50000000354

• Oesophageal_Adenocarcinoma_Organoid_ATAC

  ATAC Sequencing of 5 patient derived organoid cell models. Each model was derived from a piece of patient tumour taken following surgical rescetion of the tumour. All model derivations took place with the CGaP facility in Sanger. This ATAC data will be combined with other sequencing data in order to generate

  Study EGAS00001003890

• Somatic_evolution_in_the_psoriatic_skin

  We will use laser capture microdissection to dissect groups of a few hundred cells, from which we will isolate and sequence nucleic acids. We aim to whole-genome, whole-exome and RNA sequence the samples in effort to understand the accumulation of somatic mutations in psoriasis and their effect on transcription.

  Study EGAS00001004882

• Cellular composition of spheres derived from lymph nodes of lung cancer patients

  To decipher the cell type composition within single spheres we performed RNA-Seq of 25 selected spheres of 4 different categories (including 8 signature-/balanced, 9 signature+/balanced, 1 signature-/aberrant, 7 signature+/aberrant). To track the individual cell types within single spheres we performed deconvolution analysis.

  Study EGAS00001007369

• RNAseq data from 112 samples of benign or malignant ovarian tumours

  This dataset contains ~1.2 TB RNA sequencing data in fastq format from 112 samples of fresh-frozen ovarian tumours from a total of 111 women, two samples being replicates. The samples were collected from the U-CAN collection at Uppsala Biobank and include both benign (n = 18) and malignant (n = 94) tumours. The RNA sequencing samples were sequenced using paired-end sequencing (2x150 bp) on an Illumina NovaSeq 6000 instrument at the SciLifeLab National Genomics Infrastructure (NGI) in Uppsala.

  Dataset EGAD50000001521

• Phase II Trial with Correlative Genomic Analysis of Durvalumab plus Pazopanib Combination in Patients with Advanced Soft Tissue Sarcomas

  This study includes an open-label, phase 2 study to determine the activity of the anti-VEGF receptor tyrosine-kinase inhibitor, pazopanib, combined with the anti-PD-L1 immune checkpoint inhibitor, durvalumab, in unselected advanced sarcomas. We conducted whole exome and transcriptomic sequencing with pre-treatment tissue biopsy to correlate clinical outcomes with molecular and genomic biomarkers to identify patients who would most likely benefit from the combination treatment.

  Dataset EGAD50000000114

• RNA-Seq data from tumor samples collected from 12 UTSW translocation renal cell carcinoma (tRCC) patients.

  This study investigates differentially expressed genes associated with the progression of translocation renal cell carcinoma (tRCC) by analyzing RNA-Seq data from 23 tumor samples obtained from 12 patients. The samples were collected from affiliated hospitals of the University of Texas Southwestern Medical Center (UTSW), including Parkland Hospital and Children’s Medical Center. RNA isolated from tumor samples was sequenced using the Illumina platform.

  Dataset EGAD50000000172

• MassArray1-80

  234 samples genotyped at 40 loci using the MassArrayiPLEX genotyping assay using the iPLEX Gold genotyping kit (Agena Biosciences, cat. 10148-2) Gabriel et al. (2009) [Gabriel, S. , Ziaugra, L. and Tabbaa, D. (2009), SNP Genotyping Using the Sequenom MassARRAY iPLEX Platform. Current Protocols in Human Genetics, 60: 2.12.1-2.12.18. doi:10.1002/0471142905.hg0212s60]). Products were detected on a MassArray mass spectrophotometer and data were acquired in real time with MassArray RT software 4.0.0.2 (Agena Biosciences). SNP clustering and validation was carried out with Typer 4.0.26.75 software (Agena Biosciences).

  Dataset EGAD00010001906

• McGill Sperm Methylome Sequencing Data

  The study includes NGS-based WGBS on one sperm DNA sample pooled from 30 participants, and methylC-capture sequencing (MCC-Seq) on the same pooled sperm sample as well as 45 sperm DNA samples derived from both fertile and infertile individuals in two cohorts (Toronto, a fertile cohort; Montreal, an idiopathic infertility cohort). All the data were generated with 100bp paired-end reads using the Illumina HiSeq2000 or 4000 systems.

  Dataset EGAD00001004978

• Multiple myeloma Total Therapy trial patient sequencing

  DNA was obtained from either CD138+ cells from the bone marrow of multiple myeloma patients (tumor) or from stem cell harvests or peripheral blood cells from the same patient (control). 100 ng of DNA was fragmented, end-repaired, and adapters ligated using the HyperPlus kit (KAPA Biosystems). After PCR amplification the libraries were hybridized with probes against either a targeted panel consisting of 140 genes and chromosomal regions (Nimblegen) using SeqCap reagents (Nimblegen). Hybridized libraries underwent further amplification before being sequenced on a NextSeq500 (Illumina) using 75 bp paired end reads.

  Dataset EGAD00001004373

• DCM-cases

  DCM-cases (149 human DCM samples) human heart biopsies from 149 patients with dilated cardiomyopathy (DCM) were subjected to RNA sequencing in order to assess transcriptome variation. We used Illumina HiSeq2000 technology. Each sample-dataset contains the output from tophat-1.4.1 (one *.bam file with the aligned reads and two *.fq files one with the not aligned forward read and one with the revers unaligned reads). We reveal extensive differences of gene expression and splicing between dilated cardiomyopathy patients and controls.

  Dataset EGAD00001003390

• DCM-controls

  DCM-controls (113 human non-DCM samples) human heart biopsies from 113 non-diseased controls were subjected to RNA sequencing in order to assess transcriptome variation. We used Illumina HiSeq2000 technology. Each sample-dataset contains the output from tophat-1.4.1 (one *.bam file with the aligned reads and two *.fq files one with the not aligned forward read and one with the revers unaligned reads). We reveal extensive differences of gene expression and splicing between dilated cardiomyopathy patients and controls.

  Dataset EGAD00001003391

• Identification of low frequency variants associated with ulcerative colitis using whole-genome sequencing

  2000 ulcerative colitis cases drawn from the UKIBD Genetics Consortium cohort and whole-genome sequenced at 2X depth. A case control association study using control samples whole-genome sequenced by UK10K will be undertaken to identify common, low-frequency and rare variants associated with ulcerative colitis. Data will be combined with similar data across 3000 Crohn's disease cases from the same cohort to identify inflammatory bowel disease (IBD) loci and better understand the genetic differences and similarities of the two common forms of IBD.

  Dataset EGAD00001000409

• NHLBI TOPMed: Genetic Epidemiology Network of Salt Sensitivity (GenSalt)

  The GenSalt study aims to identify genes which interact with dietary sodium and potassium intake to influence blood pressure in Han Chinese participants from rural north China. Whole genome sequencing will be conducted among 1,860 participants of the Genetic Epidemiology Network of Salt Sensitivity (GenSalt) Study. We will work in collaboration with participating TOPMed studies to identify novel common, low-frequency and rare variants associated with an array of cardiometabolic phenotypes. In addition, we will explore the relation of low-frequency and rare variants with salt-sensitivity among GenSalt study participants.

  Study phs001217

• Genomic and Transcriptomic Markers Associated with Response to Immune Checkpoint Blockade

  The neoantigen presentation score (NEOPS) study was designed to better understand molecular determinants of response to immunotherapies, including pembrolizumab and nivolumab in stage III or IV melanoma patients. From this work, we developed a composite approach to predicting neoantigens (NEOPS), which takes into account additional escape mechanisms, including HLA LOH (human leukocyte antigen loss of heterozygosity) and the impact of deleterious mutations to the antigen presentation machinery (APM), yielding an improved prediction of response to immunotherapy compared to TMB (tumor mutational burden) or neoantigen burden alone.

  Study phs002388

• Inherited Defect in ST6GalNAc1 Reveals Roles of Sialylation in Intestinal Homeostasis

  We have conducted a study of patients with unique mutations in the gene ST6GALNAC1 that encodes the protein ST6GALNAC1 (ST6). Exome sequencing was used to screen worldwide inflammatory bowel disease cohorts, which identified human loss of function mutations of ST6 in individual patients. The affected patients had very early onset (< 6 years old) inflammatory bowel disease. A comprehensive investigation of these gene mutations through in vivo and in vitro experiments revealed that decreased sialylation caused defective mucus proteins and congenital inflammatory bowel disease.

  Study phs002598

• Acquired Cross-Resistance in Small Cell Lung Cancer Patient-Derived Xenografts

  Here we present whole genome sequencing and RNA sequencing of patient-derived xenograft (PDX) models of small cell lung cancer (SCLC). These models were derived at a variety of clinical time points from either biopsy/resection samples, malignant effusions, or circulating tumor cells (CTCs), and grown in the subcutaneous flank of NSG (NOD.Cg-Prkdcscid Il2rgtm1Wjl /SzJ) mice. The first analysis published with these data was to determine the genomic and transcriptomic features of PDX models derived after relapse that demonstrated in vivo resistance to multiple chemotherapy regimens (cross-resistance).

  Study phs003486

• AfricanNeo aDNA Study

  With the largest genomic dataset to date of Bantu-speaking populations, including newly generated data of modern-day and ancient DNA from previously unsampled regions in Africa, we shed fresh light on the expansion of peoples speaking Bantu languages that started ~4000 years ago in western Africa. We have generated whole-genome sequences from 12 Late Iron Age individuals. Our comprehensive genetic analysis of the expansion of people speaking Bantu languages reveals a complex history of serial founder events, variable levels of contact with local groups, and spread-over-spread events.

  Study EGAS00001007519

• Smart-seq 2 single cell sequencing of CD4 and CD8 T cells from the blood and synovial fluid of 4 psoriatic arthritis patients.

  This dataset was used to characterise T cell gene expression and clonality at sites of active inflammation within the joints of psoriatic arthritis (PsA) patients, and to compare these results with T cells from the peripheral blood of those same patients. Freshly sorted CD45RA negative CD3+CD4+ and CD3+CD8+ single cells from four patients were individually flow sorted into 96-well full-skirted plates (Eppendorf) containing 10µL of a 2% Dithiothreitol (DTT, 2M Sigma-Aldrich), RTL lysis buffer (Qiagen) solution. Cell lysates were sealed, mixed and spun down before storing at -80 ºC. Paired-end multiplexed sequencing libraries were prepared following the Smart-seq 2 protocol using the Nextera XT DNA library prep kit (Illumina). A pool of barcoded libraries from four different plates were sequenced across two lanes on the Illumina HiSeq 2500.

  Dataset EGAD00001006342

• Myelodysplastic Syndrome Follow Up Series

  Agilent whole exome hybridisation capture was performed on genomic DNA derived from MDS and matched normal DNA from the same patients. Next Generation sequencing performed on the resulting exome libraries and mapped to build 37 of the human reference genome to facilitate the identification of novel cancer genes. Now we aim to discover the prevalence of our findings using bespoke pulldown methods and sequencing the products from a larger set of patient DNA.

  Dataset EGAD00001000283

• Dysregulation of Naive T Cell Quiescence during Aging

  TEA-seq (novel trimodal single-cell analysis of mRNA transcripts, surface protein epitopes, and chromatin accessibility) was performed on CD3+ T cells isolated from peripheral blood of healthy pediatric (11–13 yrs, n = 8) and older adult (55–65 yrs, n = 8) female donors. Single-cell RNA sequencing (scRNA-seq) was additionally performed on peripheral blood mononuclear cells (PBMCs) from a healthy cohort of 16 pediatric, 16 young adult (25–35 yrs), and 16 older adult donors with equal sex distribution. Using these data, we dissected the compositional and molecular alterations within the T cell compartment across the spectrum of healthy age, showing broad transcriptional and epigenetic alterations within the T cell compartment of older adults compared to children, as well as a novel population of pediatric-specific CD8aa T cells.

  Study phs003400

• Investigating Genetics in Suspected Congenital Syndromes

  We employed forward and reverse genetics to identify individuals with heterozygous JAK1 variants associated with autoimmune and inflammatory diseases. We first recruited six unrelated probands and seven affected family members presenting with a range of autoimmune and inflammatory conditions. Whole exome sequencing revealed 4 different variants spanning all domains of the JAK1 protein: FERM domain (chr1: 64873438-G>A; c.415C>T; p.E139K), SH2 domain (chr1: 64855641-G>A; c.1516C>T; p.R506C), pseudokinase domain (chr1: 64845529-C>T; c.2099G>A; p.S700N, and kinase domain (chr1: 64838479-C>T; c.2953G>A; p.V985I). We then performed a systematic review of electronic health records of the BioME BioBank database and identified 43 additional individuals with at least one of these JAK1 variants. Comparing variant-positive and variant-negative individuals, our study revealed increased occurrences of autoimmunity, atopy, colitis, and dermatitis in patients harboring these JAK1 gain-of-function variants. We have deposited in dbGaP the whole exome sequencing files of the 43 patients identified in the BioME biobank, as well as their phenotype information.

  Study phs003453

• The Sys4MS cohort clinical data

  The Sys4MS cohort clinical data - 419 patients

  Dataset EGAD00001010867

• GWAS genotype data of the Japanese population

  GWAS genotype data of the Japanese population (N=2,380).

  Dataset EGAD00001009070

• Nanopore whole-genome sequencing of sample RK067 and variant-call data (SNVs and intermediate-sized deletions) of 174 Japanese samples.

  We performed whole-genome sequencing of a human samples using Oxford Nanopore MinION sequencer, so as to be able to establish methods to detect and identify structural variations in Japanese individuals. Intermediate-sized deletions were identified from 174 Japanese samples��� short-reads whole-genome sequencing data. Variant call information of these intermediate-sized deletions were joined with SNVs identified in a previous study (Fujimoto, A et al., Nat Genet, 2016) to create a reference panel for use in imputation.

  Study JGAS000180

• Circulating tumour DNA abundance and potential utility in de novo metastatic prostate cancer

  Several systemic therapeutic options exist for metastatic castration sensitive prostate cancer (mCSPC). Circulating tumour DNA can profile metastatic castration resistant prostate cancer and influence treatment decisions, but remains untested in mCSPC. We set out to determine ctDNA abundance at de novo mCSPC diagnosis and whether ctDNA provides complementary clinically relevant information to a prostate biopsy. We collected and sequenced 77 plasma cell-free DNA samples from 53 newly diagnosed patients with mCSPC. Targeted sequencing was also performed on DNA from 48 diagnostic prostate tissue samples.

  Study EGAS00001003351

• Pediatric tumor in a single child of three large nuclear families

  We identify three nuclear families in which a pediatric tumor had been diagnosed in a single child and neither parent had been diagnosed with cancer. These families had 4, 5, and 7 children, respectively. Diagnoses included Wilms tumor, low-grade astrocytoma, and Burkitt’s lymphoma, respectively. We used whole-genome sequencing to profile normal cells from each family member and a linked-read technology for genomic phasing, which enabled us to identify compound-heterozygous and de novo variants in addition to simple-heterozygous and homozygous-alternate variants.

  Study EGAS00001005321

• WES data from 165 tumor/germline samples with muscle invasive bladder cancer.

  In this study, a total of 300 patients with MIBC receiving chemotherapy were included; 62 received NAC before cystectomy and 245 received first-line chemotherapy upon detection of locally-advanced (T4b) or metastatic disease. Treatment response, defined as pathological downstaging (< pTa,CIS,N0) after NAC or complete or partial response after first-line treatment (RECIST criteria). WES was performed using DNA from 165 tumors (76x median coverage) and associated germline DNA (46x median coverage). Data provided here consist of 5,828 fastq files for WES.

  Study EGAS00001004507

• Single Cell Genome Sequence for DLP+ library A95621A

  Single Cell Genome Sequence for Immortalized breast epithelium - BRCA2-/-; Tp53-/- cell line 184-hTERT-22 L9 112.109 cell line SA1055 on DLP+ library A95621A

  Dataset EGAD00001009488

• Single Cell Genome Sequence for DLP+ library A95621B

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA501 passage 2 on DLP+ library A95621B

  Dataset EGAD00001009487

• Single Cell Genome Sequence for DLP+ library A98279A

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1050A passage 1, patient SA1234 on DLP+ library A98279A

  Dataset EGAD00001009486

• Single Cell Genome Sequence for DLP+ library A98181A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA919 passage 7, patient-derived xenograft SA1050A passage 1 on DLP+ library A98181A

  Dataset EGAD00001009479

• Single Cell Genome Sequence for DLP+ library A96213A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA501 passage 6, cell line SA1090 on DLP+ library A96213A

  Dataset EGAD00001009477

• Single Cell Genome Sequence for DLP+ library A96192A

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1093C passage 1, patient SA1147 on DLP+ library A96192A

  Dataset EGAD00001009467

• Single Cell Genome Sequence for DLP+ library A96189A

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1049C passage 1 on DLP+ library A96189A

  Dataset EGAD00001009465

• Single Cell Genome Sequence for DLP+ library A96187A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA501 passage 11, patient-derived xenograft SA609 passage 7 on DLP+ library A96187A

  Dataset EGAD00001009464

• Single Cell Genome Sequence for DLP+ library A96177C

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA604 passage 6 on DLP+ library A96177C

  Dataset EGAD00001009462

• Single Cell Genome Sequence for DLP+ library A96174A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA501 passage 11 on DLP+ library A96174A

  Dataset EGAD00001009460

• Single Cell Genome Sequence for DLP+ library A96173A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA501 passage 15 on DLP+ library A96173A

  Dataset EGAD00001009459

• Single Cell Genome Sequence for DLP+ library A96171A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA501 passage 2, patient-derived xenograft SA611 passage 3 on DLP+ library A96171A

  Dataset EGAD00001009458

• Single Cell Genome Sequence for DLP+ library A96142B

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient SA1162SA on DLP+ library A96142B

  Dataset EGAD00001009455

• 10x Genomics Single Cell Gene Expression for SA610X3XB03802

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA610 passage 3

  Dataset EGAD00001009160

• 10x Genomics Single Cell Gene Expression for SA604X9XB02425

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA604 passage 9

  Dataset EGAD00001009159

• 10x Genomics Single Cell Gene Expression for TOV2295(R)

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma cell line TOV2295(R)

  Dataset EGAD00001009158

• 10x Genomics Single Cell Gene Expression for SA535X9XB03617

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 9

  Dataset EGAD00001009157

• 10x Genomics Single Cell Gene Expression for SA535X5XB02895

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 5

  Dataset EGAD00001009156

• 10x Genomics Single Cell Gene Expression for SA535X5XB02891

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 5

  Dataset EGAD00001009155

• 10x Genomics Single Cell Gene Expression for SA1035X6XB03216

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA1035 passage 6

  Dataset EGAD00001009154

• 10x Genomics Single Cell Gene Expression for SA1035X5XB03021

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA1035 passage 5

  Dataset EGAD00001009153

• 10x Genomics Single Cell Gene Expression for SA535X4XB02498

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 4

  Dataset EGAD00001009152

• 10x Genomics Single Cell Gene Expression for SA1035X7XB03502

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA1035 passage 7

  Dataset EGAD00001009151

• 10x Genomics Single Cell Gene Expression for SA1035X6XB03211

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA1035 passage 6

  Dataset EGAD00001009150

• 10x Genomics Single Cell Gene Expression for SA535X6XB03101

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 6

  Dataset EGAD00001009149

• 10x Genomics Single Cell Gene Expression for SA1035X8XB03425

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA1035 passage 8

  Dataset EGAD00001009148

• 10x Genomics Single Cell Gene Expression for SA1047BX1XB01022

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1047 passage 1

  Dataset EGAD00001009146

• 10x Genomics Single Cell Gene Expression for SA1184SA

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient SA1184

  Dataset EGAD00001009145

• 10x Genomics Single Cell Gene Expression for OV2295(R2)

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma cell line OV2295(R2)

  Dataset EGAD00001009144

• 10x Genomics Single Cell Gene Expression for OV2295

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma cell line OV2295

  Dataset EGAD00001009143

• 10x Genomics Single Cell Gene Expression for SA1035X4XB02879

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA1035 passage 4

  Dataset EGAD00001009147

• 10x Genomics Single Cell Gene Expression for SA605X3XB01966

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA605 passage 3

  Dataset EGAD00001009142

• A95662A

  Whole genome sequencing for single cells for library A95662A 637 samples; filetype=bam

  Dataset EGAD00001008226

• A95654A

  Whole genome sequencing for single cells for library A95654A 901 samples; filetype=bam

  Dataset EGAD00001008225

• A95635D

  Whole genome sequencing for single cells for library A95635D 367 samples; filetype=bam

  Dataset EGAD00001008224

• A95635B

  Whole genome sequencing for single cells for library A95635B 593 samples; filetype=bam

  Dataset EGAD00001008223

• A95632D

  Whole genome sequencing for single cells for library A95632D 644 samples; filetype=bam

  Dataset EGAD00001008222

• A90679

  Whole genome sequencing for single cells for library A90679 1034 samples; filetype=bam

  Dataset EGAD00001008219

• A98168B

  Whole genome sequencing for single cells for library A98168B 1290 cells; filetype=bam

  Dataset EGAD00001007625

• A98168A

  Whole genome sequencing for single cells for library A98168A 1294 cells; filetype=bam

  Dataset EGAD00001007624

• A96233B

  Whole genome sequencing for single cells for library A96233B 1601 cells; filetype=bam

  Dataset EGAD00001007623

• A96226B

  Whole genome sequencing for single cells for library A96226B 1274 cells; filetype=bam

  Dataset EGAD00001007621

• A96183B

  Whole genome sequencing for single cells for library A96183B 1191 cells; filetype=bam

  Dataset EGAD00001007619

• A96162A

  Whole genome sequencing for single cells for library A96162A 1505 cells; filetype=bam

  Dataset EGAD00001007618

• A96155C

  Whole genome sequencing for single cells for library A96155C 1316 cells; filetype=bam

  Dataset EGAD00001007617

• A96124B

  Whole genome sequencing for single cells for library A96124B 1241 cells; filetype=bam

  Dataset EGAD00001007616

• A95720A

  Whole genome sequencing for single cells for library A95720A 1467 cells; filetype=bam

  Dataset EGAD00001007613

• A95703A

  Whole genome sequencing for single cells for library A95703A 740 cells; filetype=bam

  Dataset EGAD00001007612

• A95700A

  Whole genome sequencing for single cells for library A95700A 1172 cells; filetype=bam

  Dataset EGAD00001007611

• A95697B

  Whole genome sequencing for single cells for library A95697B 1233 cells; filetype=bam

  Dataset EGAD00001007610

• A95623B

  Whole genome sequencing for single cells for library A95623B 1363 cells; filetype=bam

  Dataset EGAD00001007608

• A118862B

  Whole genome sequencing for single cells for library A118862B 1152 cells; filetype=bam

  Dataset EGAD00001007606

• A118862A

  Whole genome sequencing for single cells for library A118862A 1070 cells; filetype=bam

  Dataset EGAD00001007605

• A118830A

  Whole genome sequencing for single cells for library A118830A 912 cells; filetype=bam

  Dataset EGAD00001007604

• A110673B

  Whole genome sequencing for single cells for library A110673B 1153 cells; filetype=bam

  Dataset EGAD00001007603

• A110673A

  Whole genome sequencing for single cells for library A110673A 1089 cells; filetype=bam

  Dataset EGAD00001007602