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• Single-cell genome-wide concurrent haplotyping and copy-number profiling through genotyping-by-sequencing

  scGBS is a single-cell sequencing-based methodology to haplotype and copy-number profile single cells. Genomic size and complexity is reduced through restriction enzyme digestion and DNA is genotyped through sequencing of the restriction fragments. scGBS data serves as the input for haplarithmisis, an algorithm we previously developed for SNP array-based single-cell haplotyping (Zamani Esteki et al., 2015). We established technical parameters and developed an analysis pipeline enabling accurate concurrent haplotyping and copy-number profiling of single cells with the use of a HapMap cell line pedigree (7 single cells). A clinical validation of the methodology with a total of 14 single blastomeres and 3 trophectoderm samples biopsies from human preimplantation embryos for 6 PGT-M families were processed with scGBS and were previously haploptyped via SNP array.

  Dataset EGAD00001007794

• Oesophageal adenocarcinoma WGS from LUD2015-005 study (NCT02735239, EudraCT 2015-005298-19)

  This dataset contains whole genome sequencing (WGS) generated from the inoperable cohorts of the LUD2015-005 study (NCT02735239, EudraCT 2015-005298-19). WGS data were generated with the aim to assess previously reported (e.g. tumour mutational burden) and novel predictive genomic markers for immunochemotherapy treatment in this setting. Using these data, mutation and copy number profiles were generated for the LUD2015-005 study, which were assessed for correlation with patient outcomes from this trial.

  Dataset EGAD00001009400

• African Partnership for Chronic Disease Research (APCDR) DAC

  Dac EGAC00001000237

• DAC for the Study EGAS00001006374: Pathogenic variants damage cell composition and single cell transcription in cardiomyopathies. snRNAseq of 79 (61 CM patients + 18 controls).

  Dac EGAC00001002804

• Tumor Profiler DAC

  The Tumor Profiler DAC is responsible for the access to data generated in the Tumor Profiler Study.

  Dac EGAC50000000199

• BREAKFAST trial DAC

  Data Access Committee for BREAKFAST trial datasets, IFOM-ETS The AIRC Institute of Molecular Oncology and INT Istituto nazionale Tumori Milano, Italy.

  Dac EGAC50000000401

• RCIDIBAPS001

  DAC created by the Research Computing platform of IDIBAPS for the submission of 2 WES (Whole exome sequencing) samples provided by Sergi Castellví-Bel

  Dac EGAC50000000466

• cfDNA whole-genome TAPS data for cancer detection DAC

  80x whole-genome sequencing data from circulating tumour DNA pre-treated with TAPS from 91 patients

  Dac EGAC50000000440

• ITER-FIISC Data Access Committee (FPF)

  DAC for Whole-exome variant calling of individuals from the study of familial pulmonary fibrosis in the Canary Islands.

  Dac EGAC50000000473

• DAC for "Cross-species Comparison Reveals Therapeutic Vulnerabilities Halting Glioblastoma Progression" with Prof. Dr. Ana Martin-Villalba(a.martin-villalba@dkfz-heidelberg.de), Santiago Cerrizuela(s.cerrizuela@dkfz-heidelberg.de)

  Dac EGAC00001003564

• SpainUDP Data Access Committee

  Data access committee handling data access requests for RNAseq data related to the Spanish Undiagnosed Rare Diseases Program SpainUDP cohort.

  Dac EGAC50000000932

• Clonotype Analysis Data

  This dataset includes: amplicon sequencing data for a total of 3 BCP-ALL patients - fastq files

  Dataset EGAD50000001518

• RNAseq for 190 AML patients

  This dataset contains 190 fastq files sequenced with Illumina HiSeq500.

  Dataset EGAD50000000490

• HPAH Genotyping data

  Genotyping data for 32 individuals from a family affected by HPAH

  Dataset EGAD00010001633

• WTCCC2 Pre-eclampsia

  Genotype data from the Affymetrix 6.0 platform for 4,375 Colombian pre-eclampsia cases and controls

  Dataset EGAD00010001647

• dataset_raw_NativesAmericans_LDGH_august2020

  Genome-wide data for 98 Native American individuals from Andes and Amazon

  Dataset EGAD00010001958

• README-for-EGAS00001004349-linking-HIPO-K09R-WGS-files

  Dataset EGAD00001006739

• Whole exome data for AEL paper

  Dataset EGAD00001003413

• RNA-Seq data for AEL paper

  Dataset EGAD00001003412

• Longitudinal analysis of treatment induced genomic alterations

  Variant call set (vcf) for three (primary and two recurrent) tumors

  Dataset EGAD00001003164

• TSACP TruSeq Amplicon Panel dataset

  TSACP TruSeq Amplicon Panel dataset for the TraIT cell line use case

  Dataset EGAD00001002109

• GATCI whole genome sequencing fastqs

  Raw whole genome sequencing data (fastq) for the GATCI project

  Dataset EGAD00001005926

• Cancer Alliance RNA-Seq PolyA

  PolyA selection transcriptome profiling by high-throughput for individualized cancer interpretation

  Dataset EGAD00001006234

• RNA-seq of human astroblastomas

  RNA-seq for fusion gene discovery of human astroblastomas

  Dataset EGAD00001009664

• Genome Sequencing of Circulating Tumor Cells for Minimally Invasive Molecular Characterization of Multiple Myeloma Pathology

  ​​Multiple Myeloma (MM) develops from well-defined precursor stages known as Monoclonal Gammopathy of Undetermined Significance (MGUS) and Smoldering MM (SMM); however, invasive bone marrow (BM) biopsy limits screening and monitoring strategies for patients. In this study, we enumerated circulating tumor cells (CTCs) from patients with precursor and overt diseases, and developed a novel approach, MinimuMM-seq, which enables detection of translocations and copy number abnormalities through whole-genome sequencing of highly pure CTCs. In a cohort of 51 patients, 24 with paired BM, we applied our method MinimuMM-seq and detected 100% of clinically reported BM biopsy events. The next-generation sequencing (NGS) assay could replace molecular cytogenetics by Fluorescence In Situ Hybridization (FISH) for diagnostic yield and risk classification. In 8 patients we could apply MinimuMM-seq on serially collected samples to track and monitor major clones in peripheral blood. We describe clonal evolution and shifting dynamics of subclones over time, as well as in between blood and marrow compartments. All whole-genome sequencing (WGS) data generated in this study will be made accessible through dbGaP.

  Study phs003084

• VA APOLLO Project - Research for Precision Oncology (RePOP)

  The Research for Precision Oncology Program (RePOP) is a research activity that establishes a cohort of Veterans diagnosed with cancer and who have had genomic analyses performed on their tumor tissue as part of standard of care. All data relevant to a patient's cancer and cancer care will be collected under RePOP, including patient demographics, co-morbidities, genomic analysis, treatments, medications, lab values, imaging studies, and outcomes. All RePOP participants will have signed/verbal informed consent and signed HIPAA authorization to have their data stored and shared from RePOP's Precision Oncology Program Data Repository (PODR). The Applied Proteogenomics OrganizationaL Learning and Outcomes (APOLLO) network is a collaboration between NCI, the Department of Defense (DoD), and the Department of Veterans Affairs (VA) to incorporate proteogenomics into patient care as a way of looking beyond the genome, to the activity and expression of the proteins that the genome encodes. The emerging field of proteogenomics aims to better predict how patients will respond to therapy by screening their tumors for both genetic abnormalities and protein information, an approach that has been made possible in recent years due to advances in proteomic technology.

  Study phs001374

• CSER: Evaluating Utility and Improving Implementation of Genomic Sequencing for Pediatric Cancer Patients in the Diverse Population and Healthcare Settings of Texas: The KidsCanSeq Study

  Through this Clinical Sequencing Evidence-Generating Research (CSER) with Enhanced Diversity project we are completing a clinical study (The Texas KidsCanSeq Study) comparing the results of targeted cancer panel sequencing versus genome-scale testing in pediatric cancer patients across diverse clinical settings. We will compare the targeted pediatric cancer panel to germline whole exome sequencing (WES) of unselected childhood cancer patients. We will also compare an RNA/DNA targeted pediatric cancer panel versus WES, transcriptome sequencing and copy number array of FFPE tumor samples for the subset of patients with high-risk tumors. Exome sequencing is performed from a blood or saliva sample of the pediatric age (0-18) patient. We are building on our success completing the CSER program BASIC3 exome sequencing trial (which included 60% Hispanic and African-American patients from a single large academic center) in this large multi-institutional study of an even more diverse patient population from six pediatric oncology healthcare settings across Texas. Data from the Texas KidsCanSeq study will be submitted through the CSER Data Coordinating Center for access in NIH-supported databases.

  Study phs002378

• Reference Exome Data for a Northern Brazilian population

  Whole exome sequencing (WES) is a popular technology widely used in the diagnosis of rare genetic diseases and provides useful variant data for analysis of complex diseases. However, there is not always adequate population-specific reference data available to assist clinical geneticists in assigning a diagnostic variant to a specific clinical condition. Here we provide a catalogue of variants called after sequencing the exomes of 45 babies from the State of Rio Grande do Nord in Brazil. Sequence data were processed using an ‘intersect-then-combine’ (ITC) approach, using GATK and SAMtools to call variants. A total of 612,761 variants were identified in at least one individual in this Brazilian Cohort, including 559,448 single nucleotide variants (SNVs) and 53,313 insertion/deletions. Of these, 58,111 overlapped with nonsynonymous (nsSNVs) or splice site (ssSNVs) SNVs in dbNSFP. As an aid to clinical diagnosis of rare diseases, we used the American College of Medicine Genetics and Genomics (ACMG) guidelines to assign pathogenic/likely pathogenic status to 439 (0.755%) of the 58,111 nsSNVs and ssSNVs. Our data set provides a useful reference point for diagnosis of rare diseases in Brazil.

  Study EGAS00001004112

• Patient-tailored design for selective co-inhibition of leukemic cell subpopulations

  The extensive primary and secondary drug resistance in many cancer types requires rational approaches to design personalized and selective combinatorial therapies that do not only show synergistic effect in overall cancer cell killing but also result in minimal toxic side effects on non-malignant cells. To address the combinatorial explosion in the number of relevant combinations, we implemented a machine learning approach that prioritizes patient-customized drug combinations with a desired synergy-efficacy-toxicity balance by combining single-cell RNA-sequencing with ex vivo single-agent testing in scarce patient-derived primary cells. When applied to two diagnostic and two refractory AML patient cases, each with a different genetic background, our integrated approach accurately predicted patient-specific combinations that were shown to result not only in synergistic cancer cell co-inhibition but were also capable of targeting specific AML cell subpopulations that emerge in differing stages of disease pathogenesis or treatment regimens. Our data-driven approach provides an unbiased means for systematic identification of personalized combinatorial regimens that selectively co-inhibit leukemic cells while avoiding inhibition of non-malignant cells, and highlight the relevance of considering cell heterogeneity for personalized cancer therapy.

  Study EGAS00001004614

• SmMIP-tools:a computational toolset for processing and analysis of single-molecule molecular inversion probes derived data

  Single-molecule molecular inversion probes (smMIPs) provides a modular and cost-effective platform for high-multiplex targeted next-generation sequencing (NGS). Nevertheless, translating the raw smMIP-derived sequencing data into accurate and meaningful information currently requires proficient computational skills and a large amount of computational work, prohibiting wide-scale adoption of smMIP-based technologies. To enable easy, efficient, and accurate interrogation of smMIP-derived data, we developed SmMIP-tools, a computational toolset that combines the critical analytic steps for smMIP data interpretation into a single computational pipeline. Here, we describe in detail two major components of the software. The first is a read processing tool that performs quality control steps, generates read-smMIP linkages and retrieves molecular tags. The second is an error-aware variant caller capable of detecting single nucleotide variants (SNVs) and short insertions and deletions (indels). Using a cell-line DNA dilution series and a cohort of blood cancer patients, we benchmarked SmMIP-tools and evaluated its performance against clinical sequencing reports. We anticipate that SmMIP-tools will increase accessibility to smMIP-technology, enabling cost-effective genetic research to push personalized medicine forward.

  Study EGAS00001005359

• Data Access Committee for the Nichols Group at LHSC

  Dac EGAC00001002164

• DAC for data acquired during the Down Syndrome acute lymphoblastic leukemia project. The project was performed with clinical samples of the AIEOP-BFM trial.

  Dac EGAC00001000644

• DAC Johns Hopkins University - AML scRNAseq study

  This is the Data Access Committee (DAC) from Johns Hopkins University for the study scRNAseq of acute myeloid leukemia

  Dac EGAC50000000242

• MELIS-UPF - Endocrine Regulatory Genomics Lab

  DAC for accessing human data produced by the Endocrine Regulatory Genomics Lab at MELIS-UPF (Barcelona, Spain).

  Dac EGAC50000000224

• ITER-FIISC Data Access Committee (Sepsis)

  DAC for Whole-exome variant calling of individuals from the study of sepsis and acute distress respiratory syndrome in Spain.

  Dac EGAC50000000641

• Inserm U1231 GAD TEAM

  This Data Access Committee reviews requests for access to the data submitted by the Inserm U1231 – Team GAD research team.

  Dac EGAC50000000707

• Mater Research Translational Bioinformatics DAC

  Data Access Committee for controlled-access sequence data published by the Translational Bioinformatics Group at the Mater Research Institute (Brisbane, Australia).

  Dac EGAC50000000755

• CLUSTER DAC

  The DAC for CLUSTER Consortium datasets relating to JIA and healthy controls RNAseq data and their associated clinical data.

  Dac EGAC50000000426

• Adrenal scRNA sequencing data access

  Raw sequencing datasets for manuscript "Hybrid untargeted and targeted RNA sequencing facilitates genotype-phenotype associations at single-cell resolution".

  Dac EGAC50000000869

• CNCD Recall by Genotypes

  This is a DAC to handle recall by genotype studies by the conducted by the Center for Non-Communicable Disease Karachi, Pakistan.

  Dac EGAC50000000937

• LOGGIC Core BioClinical Data Bank: Added clinical value of RNA-Seq in an international molecular diagnostic registry for pediatric low-grade glioma patients

  Study EGAS00001007072

• G3-MB ATAC Sequencing

  ATAC sequencing for two MYC-amplified G3-MB cell models.

  Dataset EGAD50000002305

• G3-MB WGS

  Whole genome sequencing BAM files for four samples of MYC-amplified Group 3 medulloblastoma cell models.

  Dataset EGAD50000002302

• SMPaeds tumour tissue lcWGS

  Low coverage whole genome sequencing (0.5-4X) for both relapse and archival diagnostic tumour tissue.

  Dataset EGAD50000000784

• Oncoscan CNV FFPE SNP-arrays (Affymetrix, Thermo Fisher Scientific) for the study "Molecular profiling of EBV associated diffuse large B-cell lymphoma"

  Dataset EGAD00010002363

• Hessequa-descendants Genome-wide SNP data

  Genome-wide data for population genetics analyses

  Dataset EGAD00010002113

• GWAS results from Danjou et al, Nature Genetics 2015

  GWAS results in epacts format for Danjou et al, Nature Genetics 2015

  Dataset EGAD00010001722

• ADME_gene_expression

  Gene expression for 303 ADME and ADME related genes (averaged log2 signal intensities using Human-WG6v2 Expression BeadChip)

  Dataset EGAD00010001709

• EGAD00010000498

  Affymetrix SNP6.0 genotype data for prostate cancer patients

  Dataset EGAD00010000498

• ChIPseq data

  ChIPseq data for H3K4me1 and H3K9me3 in HT29; H3K4me1, H3K27me3, H3K9me3, H3K36me3 in LoVo.

  Dataset EGAD00001003258