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• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A96156A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 648 samples; filetype=bam

  Dataset EGAD00001004765

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90553A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 612 samples; filetype=bam

  Dataset EGAD00001004728

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90553C

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 1700 samples; filetype=bam

  Dataset EGAD00001004729

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A95670A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 615 samples; filetype=bam

  Dataset EGAD00001004753

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A95624A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 604 samples; filetype=bam

  Dataset EGAD00001004747

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A95724A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 480 samples; filetype=bam

  Dataset EGAD00001004756

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A96187A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 1222 samples; filetype=bam

  Dataset EGAD00001004770

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90694B

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 1222 samples; filetype=bam

  Dataset EGAD00001004743

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A95732A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 928 samples; filetype=bam

  Dataset EGAD00001004759

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90685

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 481 samples; filetype=bam

  Dataset EGAD00001004738

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A96156B

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 375 samples; filetype=bam

  Dataset EGAD00001004766

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A96165A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 755 samples; filetype=bam

  Dataset EGAD00001004767

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90560B

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 766 samples; filetype=bam

  Dataset EGAD00001004734

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90648B

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 620 samples; filetype=bam

  Dataset EGAD00001004736

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90689A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 378 samples; filetype=bam

  Dataset EGAD00001004739

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90694A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 493 samples; filetype=bam

  Dataset EGAD00001004742

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A95736A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 1072 samples

  Dataset EGAD00001004761

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A96171A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 492 samples; filetype=bam

  Dataset EGAD00001004768

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A75616A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 230 samples; filetype=bam

  Dataset EGAD00001004724

• Clinal phenotype dataset

  Clinical data from IMvigor010: Clinical data include race, sex, baseline ecog, tumor stage, node status, prior neoadjuvant, PD-L1 status, number lymph nodes resected, pridis, arm, overall survival and disease free survival for 809 patients across IMvigor010.

  Dataset EGAD00001007576

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90554B

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 596 samples; filetype=bam

  Dataset EGAD00001004731

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A75616C

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 239 samples; filetype=bam

  Dataset EGAD00001004726

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90682

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 624 samples; filetype=bam

  Dataset EGAD00001004737

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90689C

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 718 samples; filetype=bam

  Dataset EGAD00001004741

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90689B

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 735 samples; filetype=bam

  Dataset EGAD00001004740

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A95664B

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 606 samples; filetype=bam

  Dataset EGAD00001004752

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A95664A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 465 samples; filetype=bam

  Dataset EGAD00001004751

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A73044A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 1340 samples; filetype=bam

  Dataset EGAD00001004719

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A95621B

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 488 samples; filetype=bam

  Dataset EGAD00001004745

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A95632D

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 635 samples; filetype=bam

  Dataset EGAD00001004749

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90554C

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 1735 samples; filetype=bam

  Dataset EGAD00001004732

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A95622A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 509 samples; filetype=bam

  Dataset EGAD00001004746

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A95732B

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 635 samples

  Dataset EGAD00001004760

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A73046B

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 1970 samples; filetype=bam

  Dataset EGAD00001004721

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A73047D

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 2091 samples; filetype=bam

  Dataset EGAD00001004722

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A73056B

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 1267 samples; filetype=bam

  Dataset EGAD00001004723

• MicroRNA profiling by next-generation sequencing for colorectal cancer screening

  The dataset contains raw miRNA sequencing data of plasma samples from 20 newly diagnosed colorectal cancer cases and 20 controls free of colorectal neoplasms matched by age and sex. It includes files in the FASTQ compressed (.gz) format.

  Dataset EGAD00001006966

• Methylome sequencing of cell-free DNA and RRBS of solid tissue

  Early cancer detection by cell-free DNA (cfDNA) faces multiple challenges: low fraction of tumor cfDNA, molecular heterogeneity of cancer, and sample sizes not sufficient to reflect diverse patient population. We develop a cancer detection approach to address these challenges. It consists of an assay, cfMethyl-Seq, for cost-effective sequencing of cfDNA methylome (with >12-fold enrichment over whole genome bisulfite sequencing in CpG islands), and a computational method to extract methylation information and diagnose patients. Applying our system to 408 colon, liver, lung, and stomach cancer patients and controls, at 97.9% specificity we achieve 80.7% and 74.5% sensitivity in detecting all-stage and early-stage cancer, and 89.1% and 85.0% accuracy for locating tissue-of-origin of all-stage and early-stage cancer, respectively. Our approach cost-effectively retains methylome profiles of cancer abnormalities, thereby permitting the classification models to learn new features as training cohorts grow, and expanding their scope to other cancer types.

  Study EGAS00001006020

• The effect of pre-analytical and physiological variables on cell-free DNA fragmentation

  Background: assays that account for the biological properties and fragmentation of cell-free DNA (cfDNA) can improve the performance of liquid biopsy. However, pre-analytic and physiological differences between individuals on fragmentomic analysis are poorly defined. Methods: we analyzed the impact of collection tube, plasma processing time and physiology on the size distribution of cfDNA, their genome-wide representation and sequence diversity at the cfDNA fragment-ends using shallow Whole Genome Sequencing. Results: we observed that using different stabilizing collection tubes, or processing times does not affect the cfDNA fragment sizes, but can impact the genome-wide fragmentation patterns and fragment-end sequences of cfDNA. In addition, beyond differences depending on the gender, the physiological conditions tested between 63 individuals (age, body mass index, use of medication and chronic conditions) minimally influenced the outcome of fragmentomic methods. Conclusions: our results highlight that fragmentomic approaches have potential for implementation in the clinic, pending clear traceability of analytical and physiological factors.

  Study EGAS00001005748

• cfRRBS on cfDNA from pediatric cancer

  In the clinical management of pediatric solid tumors, histological examination of tumor tissue obtained by a biopsy remains the gold standard to establish a conclusive pathological diagnosis. The DNA methylation pattern of a tumor is known to correlate with the histopathological diagnosis across cancer types and is showing promise in the diagnostic workup of tumor samples. This methylation pattern can be detected in the cell-free DNA. Here, we provide proof-of-concept of histopathologic classification of pediatric tumors using cell-free reduced representation bisulfite sequencing (cf-RRBS) from retrospectively collected plasma and cerebrospinal fluid samples. We determined the correct tumor type in 49 out of 60 (81.6%) samples starting from minute amounts (less than 10 ng) of cell-free DNA. We demonstrate that the majority of misclassifications were associated with sample quality and not with the extent of disease. Our approach has the potential to help tackle some of the remaining diagnostic challenges in pediatric oncology in a cost-effective and minimally invasive manner.

  Study EGAS00001004194

• Healthy control cfMeDIP-seq

  This dataset comprises 46 raw fastq files derived from 23 plasma cfDNA samples from 23 healthy (CTL) cancer-free donors. For each sample, 10ng of cfDNA was used for library preparation utilizing the cfMeDIP-seq technology based on the 5mC antibody-immunoprecipitation strategy. Libraries were validated via Bioanalyzer trace analysis and sequenced on the Illumina NovaSeq 6000 platform with paired-end 150-bp model for ~100 million reads per sample.

  Dataset EGAD50000000652

• Whole genome sequencing data for five Japanese subjects

  WGS was performed for five Japanese subjects. DNA samples isolated from whole blood were sequenced at Macrogen Japan Corporation. All libraries were constructed using the TruSeq DNA PCR-Free Library Preparation Kit according to the manufacturer’s protocols. Libraries were sequenced on HiSeqX (Illumina, San Diego, CA, USA) or Novaseq6000 (Illumina, San Diego, CA, USA).

  Dataset EGAD00001010075

• Gluten-free microbiome study UMCG

  Study EGAS00001005225

• Single-cell T-cell receptor sequencing of intraepithelial gammadelta T-cells in celiac disease

  Single-cell paired TCR sequencing of intraepithelial γδ T-cells in gluten-free diet treated and untreated celiac disease patients and in controls. (Paired end, amplicon sequencing).

  Study EGAS00001003897

• 106 mouse cases and 10 human cases

  Consists of 76 mouse plasma cell-free DNA, 30 mouse Liver DNA, 10 human plasma cell-free DNA

  Dataset EGAD00001008838

• Center for Common Disease Genomics [CCDG] - Cardiovascular: Genetic and Phenotypic Determinants of Blood Pressure and Other Cardiovascular Risk Factors

  This study is a part of NHGRI's Center for Common Disease Genomics, which is a collaborative large-scale genome sequencing effort to comprehensively identify rare risk and protective variants contributing to multiple common disease phenotypes. Current estimates anticipate that the CCDG program will sequence approximately 140K whole genomes and 225K whole exomes during the life of the project. The Cardiovascular Disease working group of the CCDG considered five diseases: early-onset coronary artery disease (EOCAD), stroke, atrial fibrillation, congestive heart failure and type 2 diabetes. Atrial fibrillation will affect between 6-12 million individuals in the US by 2050. AF also is associated with increased risks of stroke, dementia, heart failure, death, and high health care costs. Many risk factors for AF have been identified, including advancing age, cardiovascular disease (CVD), and CVD risk factors. However, there is little knowledge how to prevent AF. Furthermore, therapies for AF are only partially effective, and are themselves associated with substantial morbidity. Previously, heritable forms of AF have been considered rare; yet in the last decade, it has been established that AF, and in particular early-onset forms of AF, are heritable. Genome-wide association studies (GWAS) provide a powerful tool to identify common variants underlying disease risk. The AFGen Consortium currently consists of investigators from more than 25 studies with >20,000 individuals with AF and >100,000 without AF. In the latest analyses, 14 loci have been identified for AF1. Broadly, the loci implicate genes related to cardiopulmonary development, cardiac-expressed ion channels, and cell signaling molecules. Source: ccdg.rutgers.edu The GAPP study is an ongoing national population-based prospective cohort study conducted in the Principality of Liechtenstein. Between 2010 and 2014, all inhabitants of Liechtenstein aged 25-41 years old were asked to participate in the study, and 2170 could be enrolled. The aim of the study is to identify the determinants of blood pressure and other cardiovascular risk factors and their progression over time. A large number of baseline characteristics and health information as well as several blood, urinary and genetic markers were collected. The samples were sent to The Broad Institute of MIT and Harvard to generate genetic data using whole exome sequencing and whole genome genotyping (Infinium Global Screening Array); all samples serving as controls.

  Study phs002236

• Clinical utility of combined low-pass whole genome and targeted sequencing in liquid biopsies for diagnosis and monitoring of pediatric solid tumors

  We designed a liquid biopsy (LB) platform employing Low-Pass Whole Genome Sequencing (LP-WGS) and targeted sequencing of cell-free (cf) DNA isolated from plasma for detecting genome-wide copy number alterations and targeted gene fusions in pediatric solid tumors. A total of 143 plasma samples were analyzed from 19 controls (patients with non-oncologic diagnoses) and 73 patients, including 44 with bone or soft tissue sarcomas, 12 with renal tumors, 10 with germ cell tumors, 5 with hepatic tumors, and 2 with thyroid tumors.

  Study EGAS00001006913

• Copy number variations and fragmentation features in cell-free DNA predict response in advanced non-small cell lung cancer patients under anti-PD-(L)1 therapy

  Anti-PD-(L)1 therapy is routinely used in metastatic NSCLC. However, response rates vary and biomarkers predicting treatment efficacy are lacking. Liquid biopsies allow monitoring of residual disease during therapy. Residual circulating tumor DNA (ctDNA) after therapy represents a predictive marker for adverse outcome. Here, we assessed copy number variations (CNVs) in serial plasma samples of NSCLC patients receiving PD-(L)1 blockade. We profiled CNV and fragmentation features in 118 plasma samples of advanced NSCLC patients by low-coverage whole genome sequencing taken at baseline, after four therapy cycles and at disease progression. CNVs and fragment features (i.e., fragment length, end motifs, position) were significantly altered in patients compared to healthy donors. Furthermore, fragment length changes at baseline and residual CNVs after four therapy cycles correlated with short progression-free survival. Our results highlight the suitability of plasma-based CNV and fragment analysis for disease monitoring and as a predictive marker for response duration in NSCLC patients under anti-PD-(L)1 therapy.

  Study EGAS50000000441

• Error-corrected flow-based sequencing at whole genome scale and its application to circulating cell-free DNA profiling

  Differentiating sequencing errors from true variants is a central genomics challenge calling for error suppression strategies that balance costs when sparse material or ultra-rare variants require high-accuracy methods. For example, circulating cell-free DNA (ccfDNA) sequencing for cancer monitoring is limited by sparsity of circulating tumor DNA (ctDNA), abundance of genomic material in samples, library preparation error rates and sequencing errors. Whole-genome sequencing (WGS) can overcome the low abundance of ccfDNA by integrating signal across the mutation landscape, but higher cost limits adoption compared with targeted panels, where ccfDNA abundance limits maximal coverage depths. Here we applied deep (~120x) lower-cost WGS (Ultima Genomics) with analytic error correction for tumor-informed ctDNA detection within the part-per-million range. We further developed high-coverage duplex error-corrected WGS of ccfDNA, achieving 7.7x10-8 error rates, to assess disease burden in melanoma and urothelial cancer patients without matched tumor sequencing. Together, we show that deeply sequenced error-corrected WGS accurately calls somatic variants, demonstrating the feasibility of WGS for tumor-informed and tumor-agnostic ctDNA detection.

  Study EGAS50000000844

• Cell-free DNA Sequencing Data Access Committee

  Dac EGAC00001003304