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• 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

• WGS

  Whole Genome sequencing. 1 ug of genomic DNA from each lymph node sample was used for the construction of a TruSeq DNA PCR Free (350) library before sequencing in a Illumina HiSeq X Ten (2 × 151 bp). Mean coverage 30x.

  Dataset EGAD00001005423

• Transrenal DNA Analysis

  Urinary cell-free DNA (ucfDNA) is an emerging marker in liquid biopsy. However, the characteristics of transrenal DNA and urinary-tract DNA are not fully understood. Therefore, we investigated the fragmentomic features and demonstrated the potential clinical utilities of ucfDNA in the current study.

  Study EGAS50000000766

• 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

• 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

• Charles R. Bronfman Institute for Personalized Medicine (IPM) BioBank Genome Wide Association Study of Cardiovascular, Renal and Metabolic Phenotypes

  The Institute for Personalized Medicine (IPM) Biobank Project is a consented, EMR-linked medical care setting biorepository of the Mount Sinai Medical Center (MSMC) drawing from a population of over 70,000 inpatients and 800,000 outpatient visits annually. MSMC serves diverse local communities of upper Manhattan, including Central Harlem (86% African American), East Harlem (88% Hispanic Latino), and Upper East Side (88% Caucasian/white) with broad health disparities. IPM Biobank populations include 28% African American (AA), 38% Hispanic Latino (HL) predominantly of Caribbean origin, 23% Caucasian/White (CW). IPM Biobank disease burden is reflective of health disparities with broad public health impact: average body mass index of 28.9 and frequencies of hypertension (55%), hypercholesterolemia (32%), diabetes (30%), coronary artery disease (25%), chronic kidney disease (23%), among others. Biobank operations are fully integrated in clinical care processes, including direct recruitment from clinical sites, waiting areas and phlebotomy stations by dedicated Biobank recruiters independent of clinical care providers, prior to or following a clinician standard of care visit. Recruitment currently occurs at a broad spectrum of over 30 clinical care sites. Minorities are strikingly underrepresented in GWAS, including Coronary Artery Disease (CAD) and Chronic Kidney Disease; multigenic genetic risk scores for CAD have been recently validated in European ancestry populations, but not in AA or HL populations. Several important opportunities exist for extending additional GWAS to minority populations with a shared risk spectrum of CAD and CKD. For example, progressive CKD is a major and independent risk factor for CVD with an inverse relationship between estimated GFR (eGFR), and risk for mortality and cardiovascular events. This increased risk is only partially explained by the prevalence of cardiovascular risk factors among these patients. We conducted a GWAS of CAD and CKD related phenotypes in IPM Biobank with the primary objective to explore the genetics of overlapping CAD and CKD predominantly in minority populations characterized by increased risk.

  Study phs000388

• Genetic Basis of Early Onset Bicuspid Aortic Valve Disease

  The study protocol was approved by the Committee for the Protection of Human Subjects at the University of Texas Health Science Center at Houston (HSC-MS-11-0185). Study recruitment began on July 1, 2017, and concluded on March 30, 2022. After written informed consent, we enrolled probands with early onset BAV disease (EBAV), which we defined as individuals with BAV who were under the age of 30 at the time of first clinical event. Clinical events were defined as aortic replacement, aortic valve surgery, aortic dissection, moderate or severe aortic stenosis or aortic regurgitation, large aneurysm (Z > 4.5), or intervention for BAV-related conditions. Those with hypoplastic left heart, known genetic mutations, genetic syndromes, or complex congenital heart disease were excluded. Samples were collected and genotyped as previously reported. For comparison, we analyzed a cohort of older individuals of European ancestry with sporadic BAV disease selected from the International BAV Consortium (BAVGWAS). Phenotypes were derived from record review with confirmation of image data whenever possible [25-26]. The computational pipeline for CNV analysis of Illumina single nucleotide polymorphism (SNP) array data included three independent CNV detection algorithms.GenomeStudio was used to exclude samples with indeterminate sex or more than 5% missing genotypes, and single nucleotide polymorphisms (SNPs) with GenTrain = 0. Principal component analysis was used to remove outliers that did not cluster with European ancestry. Prior to CNV analysis, each dataset was trimmed by selecting a common set of 650,000 SNPs that were genotyped on each of the microarrays used in this study.Three independent algorithms (PennCNV, cnvPartition, and QuantiSNP) were used to generate CNV calls and sample-level quality statistics from SNP intensity data. PennCNV and QuantiSNP were run on Unix clusters and cnvPartition data were exported from GenomeStudio. The analysis was run using default configurations. PennCNV was used to generate QC data and remove CNV calls that intersect with polymorphic genomic regions. Samples that met any of the following criteria were excluded, standard deviation of the LogR ratio (obtained from PennCNV) > 0.35 or number of CNVs > 2 standard deviations above the mean for each data set. CNV calls less than 20 Kilobases in length and/or spanned by fewer than 6 probes were excluded. The overlap function for rare CNVs in PLINK was used to construct CNV regions (CNVRs) after adjacent regions were merged using PennCNV. LogR ratio (LRR) and B allele frequency (BAF) data at CNVRs and calls of interest were visualized in GenomeStudio for validation. For segregation analysis, GenomeStudio was used to determine the presence of CNVs in relatives.A total of 22,014 unselected control Illumina Genotypes obtained from the Database of Genotypes and Phenotypes were analyzed using identical methods (S1 Table). The Wisconsin Longitudinal Study (WLS) includes data on a cohort of 10,300 individuals who graduated from Wisconsin high schools in 1957. The Health and Retirement Study (HRS) includes data on 37,000 individuals aged 50 above from 23,000 households across the United States. Principal component analysis was used to select European ancestry genotypes from these datasets for analysis. Datasets were paired for case-control analysis based on the concordance of log-transformed sample-level quality control statistics (number of CNV calls and standard deviation of logR ratios). Chi-squared or Fisher exact tests were used to compare CNV frequencies in cases and controls.Rare CNV functions in PLINK (v1.7) were used to perform permutation-based burden tests or gene set-based enrichment tests. Case control burden tests were restricted to CNVs that were longer than 110 Kb and less than 0.1% in frequency. CNV overlap functions in PLINK were used to identify rare CNVs that intersect between datasets or involve specific BAV or CHD genes . The list of candidate genes included 190 CHD genes that have strong cumulative evidence to cause BAV or related congenital malformations from human or animal model data. Genome Reference Consortium Human Build 37 was used for CNV annotation [34].

  Study phs003705

• The data access committee for Single molecule genome-wide mutation profiles of cell-free DNA for non-invasive detection of cancer

  Dac EGAC00001003253

• A fast, affordable and minimally-invasive diagnostic test for Cancer of Unknown Primary (CUP) using DNA methylation profiling

  CUP diagnosis on FFPE tissue and liquid biopsies (blood, pleural and peritoneal effusions) using cfRRBS (cell-free reduced representation bisulfite sequencing), a technique that allows for identification of the methylation profile starting from minimal amounts of highly fragmented DNA.

  Study EGAS50000000257

• Assessment of RNA-Seq Sample Preparation Methodology

  The goal of this study was two-fold, to determine if common purification techniques have any effect on downstream differential expression analysis and to evaluate combinations of alignment and differential expression software for reliability. To this end, blood was collected from three individuals and pooled during and after extraction. After pooling and mixing was completed, samples were divided into three aliquots for testing. The first aliquot was a control and not purified or concentrated. It was diluted to produce samples of varying concentrations for testing. The second aliquot was diluted to 20 ng/μL and used to test six different variations on the AMPure XP bead purification procedure. The last aliquot was also diluted to varying concentrations of 60, 30, and 9.6 ng/μL and purified using MinElute columns. Samples were submitted for total RNA-Seq library preparation and sequencing. Library preparation was performed using the TruSeq Stranded Total RNA with Ribo-Zero Globin kit (20020612, Illumina Inc.), and 2x150 bp PE sequencing was done on an Illumina NovaSeq 6000 with the S4 reagent kit. Comparisons were made between methods (AMPure vs. unpurified, AMPure vs. MinElute, MinElute vs. unpurified) to assess the effects of purification method on downstream differential expression. Comparisons were also made within methods using the varying concentrations tested for unpurified samples and for MinElute purified samples to assess the effects of concentration on differential expression. Variations of the AMPure procedure were also compared to assess the effectiveness of the variations tested in comparison to the unmodified procedure. A subset of samples was selected for use with alignment and differential expression package comparison. Unpurified high concentration samples eluted in RNAse-free water were compared to unpurified high concentration samples eluted in BR5, a buffer from the PAXgene blood miRNA kit, with the expectation that there should be no or very few differentially expressed genes. Unpurified high concentration samples eluted in RNAse-free water were also compared to unpurified low concentration samples also eluted in RNAse-free water, with possibly a small number of differentially expressed genes anticipated. A third dataset of simulated RNA-seq data was created with a known rate of differential expression. These files were aligned using Bowtie2, HISAT2, kallisto, RSEM, Rsubread, Salmon, and STAR. Results were then analyzed for differential expression using ALDEx2, baySeq, DEGseq, DESeq2, edgeR, limma, NOISeq, PoissonSeq, and SAMseq. Differential expression results of all three comparisons were evaluated to determine which combinations provided the most reliable results for both real and simulated data.

  Study phs003001

• Atezolizumab Plus Personalized Neoantigen Vaccination in Patients with Urothelial Cancer: a Phase 1 Trial

  This is a phase 1 single center clinical trial evaluating a multi-peptide personalized neoantigen vaccine, PGV001, in combination with atezolizumab for patients with urothelial cancer. Patients were enrolled in either the adjuvant or metastatic setting. Those in the adjuvant setting included patients considered high risk of recurrence (pathological stage ypT2-T4 or ypN+ for those treated with prior neoadjuvant chemotherapy or pT3-T4 or pN+ for those not treated with prior neoadjuvant chemotherapy). Those in the metastatic setting included patients progressing after prior chemotherapy or those with at least stable disease after chemotherapy treated as “switch maintenance” therapy. The primary endpoint was feasibility (including the number of neoantigens identified per patient, vaccine production time, the proportion of patients consenting to the vaccine development phase for whom a vaccine product was prepared, and the proportion of patients eligible for the treatment phase who completed the priming cycle 1 of PGV001 plus atezolizumab) and safety. Secondary endpoints included the objective response rate, progression-free survival or disease-free survival, overall survival, and immunomodulatory effects of treatment. Urothelial tumor tissue and normal tissue or peripheral blood specimens were utilized for DNA sequencing. HLA typing was performed utilizing genomic DNA isolated from whole blood, and RNA sequencing was performed utilizing tumor tissue specimens. Neoantigen prediction and final selection for incorporation into the vaccine was determined by the OpenVax computational pipeline. 12 patients were initially enrolled into the vaccine development phase and 10 patients initiated the treatment phase. PGV001 was administered with poly-ICLC and tetanus peptide on subsequent days for up to 10 doses, including an initial priming cycle 1. Atezolizumab was administered every 3 weeks for up to 1 year in the adjuvant setting or 2 years in the metastatic setting. The vaccine was feasible and safe, the median vaccine production time was 20.3 weeks, and 100% of patients that initiated the treatment phase completed the priming cycle. The most common treatment-related adverse events were local grade 1 injection site reactions, fever, and fatigue. Three of the 4 patients in the adjuvant setting remained disease-free at median follow-up of 39.5 months. There was a 40% objective response rate among patients with measurable disease in the metastatic setting. The single patient treated as “switch maintenance” therapy remained with no evidence of disease at 39.4 month follow-up. All patients demonstrated on-treatment neoantigen-specific T cell reactivity, as measured by ELISPOT and flow cytometry assays. Overall, PGV001 plus atezolizumab was feasible, safe, and warrants further investigation. Whole exome sequencing from tumor tissue and normal tissue or whole blood specimens and RNA sequencing from tumor tissue specimens will be available through dbGaP.

  Study phs003922

• The data access committee for Detecting Liver Cancer Using Cell-Free DNA Fragmentomes

  Dac EGAC00001003279

• Differential Presence of Exons in Cell-Free DNA (NIMGenetics/IIS-FJD)

  Dac EGAC00001002885

• Differential Presence of Exons in Cell-Free DNA Reveals Different Patterns in Colorectal Cancer Between Metastatic and Non-Metastatic Patients

  Dac EGAC00001000749

• Framingham Heart Study-Cohort (FHS-Cohort) - Imaging

  The Framingham Heart Study (FHS) is a population-based, observational cohort study initiated in 1948 to prospectively investigate the determinants of cardiovascular disease to guide public health prevention. The FHS began by recruiting an Original Cohort of 5,209 men and women between the ages of 30 and 62 from the town of Framingham, Massachusetts, who had not yet developed overt symptoms of cardiovascular disease or suffered a heart attack or stroke. The Original cohort Exam 1 took place between 1948 and 1953. Since that time the cohort has had a total of 32 biennial exams (ending in 2014) and event follow-up through 2022. In 1971, the FHS added the Offspring cohort, comprising 5124 children whose parents were enrolled in the Original cohort and the spouses of the children. This cohort on average has been examined every three to four years. However, there was an eight year gap between Exam 1 and Exam 2 and a seven year gap between Exam 7 and Exam 8. The latest exam (10) was completed in 2022. In 2002, the transgenerational FHS design was facilitated with the recruitment of 4095 children of the Offspring cohort (Third Generation), and 103 spouses of the Offspring who were not previously enrolled in the study (New Offspring Spouses, NOS). These cohorts have completed 3 exams through 2019. To reflect the changing demographic characteristics of the greater Framingham community, the FHS additionally recruited and enrolled 2 cohorts comprising racial and ethnic minority groups, termed Omni-1 and Omni-2, (n = 506 and 410, respectively) in 1995 and 2002, respectively. These cohorts included individuals of African American, Hispanic, Asian, Indian, Native American, and Pacific Islander descent. The OMNI-1 cohort have completed 5 exams through 2022. The OMNI-2 cohort have completed 3 exams through 2019. Data available for request include Echocardiogram images, available from the following exams in each cohort. Original Cohort: exams 18-32; Offspring cohort: exams 3-10; Third Generation, NOS and OMNI-2 cohorts: exams 1-3; OMNI-1 cohort: exams 1-5.CT image data at one or two timepoints were added for 4427 participants in the offspring, third generation, and OMNI1 and OMNI2 cohorts.Summary level phenotypes for the Framingham Cohort study participants can be viewed at the top-level study page phs000007 Framingham Cohort. Individual level phenotype data and molecular data for all Framingham top-level study and substudies are available by requesting Authorized Access to the Framingham Cohort study phs000007.

  Study phs003593

• Enhanced Detection of Landmark Minimal Residual Disease in Lung Cancer using Cell-Free DNA Fragmentomics

  The circulating tumor DNA (ctDNA) mutation-based approach shows limited performance in minimal residual disease (MRD) detection, especially for landmark MRD detection at an early-stage cancer after surgery. Here, A total of 87 NSCLC patients, who received curative surgical resections (23 patients relapsed during follow-up), enrolled in this study. A total of 163 plasma samples, collected at 7 days and 6 months postsurgical, were used for high-throughput sequencing.

  Dataset EGAD00001010300

• Genome-wide cell-free DNA mutational integration enables ultra-sensitive cancer monitoring

  In many areas of oncology, we lack sensitive tools to track low burden disease. While cell-free DNA (cfDNA) shows promise in detecting cancer mutations, we found that the combination of low tumor fraction (TF) and limited number of DNA fragments restricts low disease burden monitoring through the prevailing deep targeted sequencing paradigm. We reasoned that breadth may supplant depth of sequencing to overcome the barrier of cfDNA abundance. Whole genome sequencing (WGS) of cfDNA allowed ultra-sensitive detection, capitalizing on the cumulative signal of thousands of somatic mutations observed in solid malignancies, with TF detection sensitivity as low as 10-5. The WGS approach enabled dynamic tumor burden tracking and post-operative residual disease detection, associated with adverse outcome. Thus, we present an orthogonal framework for cfDNA cancer monitoring via genome wide mutational integration, enabling ultra-sensitive detection, overcoming the limitation of cfDNA abundance, and empowering treatment optimization in low-disease burden oncology care

  Study EGAS00001004406

• Center for Common Disease Genomics [CCDG] - Cardiovascular: Pharmacokinetic Polymorphisms in Japanese General Population

  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: https://ccdg.rutgers.edu/sites/default/files/CCDG_CVD_EOAF_FINAL_w_link.pdfAnalysis of 165 pharmacokinetic-related gene polymorphisms (the number of polymorphisms may increase) using DNA derived from the blood of approximately 1,000 Japanese general populations. Tokai University School of Medicine Molecular Life Sciences 2 Isogo Lab has been trying to find new genes which contribute to the development of multiple diseases and to develop new methods for human genome diversity analysis that will lead to the development of technology for disease screening by finding genes related to diseases and incorporating gene analysis technology. Using this method, we are trying to clarify the relationship between the characteristics of rheumatoid arthritis and psoriasis vulgaris and genes to seek effective treatments and prevention methods. Raw sequencing data, metadata, vcf and phenotype data at individual level are available at https://anvilproject.org/data. For questions about availability contact help@lists.anvilproject.org.

  Study phs002985

• Cell-free DNA methylation profiling of sepsis patients

  This data set contains paired-end NGS files (in FASTQ format) generated from a methylation profiling experiment of cell-free DNA derived from the plasma of sepsis patients and healthy controls. Methylation profiling was performed using the TET-assisted pyridine borane sequencing (TAPS) protocol.

  Dataset EGAD50000001505

• Center for Common Disease Genomics [CCDG] - Cardiovascular: The Genetics of Atrial Fibrillation

  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: https://ccdg.rutgers.edu/sites/default/files/CCDG_CVD_EOAF_FINAL_w_link.pdfFor this study, The Broad Institute of MIT and Harvard generated genetic data for UWO Genetic Investigation of Supraventricular Tachycardias.Patients confirmed to possess a supraventricular tachycardia and/or an accessory pathway at the time of invasive electrophysiology study at the London Health Sciences Centre, London, Ontario, Canada were eligible for enrollment. Potential supraventricular tachycardias included atrioventricular nodal reentrant tachycardia (AVNRT), atrioventricular reentrant tachycardia (AVRT), atrial tachycardia, junctional tachycardia, atriofascicular tachycardia, nodofascicular tachycardia, and nodoventricular tachycardia. Patients that did not have inducible supraventricular tachycardia, but had an accessory pathway that conducted antegrade, retrograde, or both, were also eligible for enrollment. All patients additionally underwent, at minimum, a clinical history, physical examination, 12-lead ECG, and echocardiogram. Study participants provided informed written consent under a protocol approved by the research ethics board of Western University.Raw sequencing data, metadata, vcf and phenotype data at individual level are available at https://anvilproject.org/data For questions about availability contact help@lists.anvilproject.org.

  Study phs002811

• End structure of DNA in plasma: detection, characterizationand diagnostic applications

  The physical properties of cell-free DNA fragments in plasma, such as fragment sizes and ends, have attracted much recent interest, leading to the emerging field of fragmentomics. This study attempted to further characterize the end structure of plasma DNA.

  Study EGAS00001004080

• DAC - A standardised framework for robust fragmentomic feature extraction from cell-free DNA sequencing data

  Dac EGAC00001003530

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90560A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 585 samples; filetype=bam

  Dataset EGAD00001004733

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A95660A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 1522 samples; filetype=bam

  Dataset EGAD00001004750

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90706

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 522 samples; filetype=bam

  Dataset EGAD00001004744

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A95632B

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 626 samples; filetype=bam

  Dataset EGAD00001004748

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A95670B

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 636 samples; filetype=bam

  Dataset EGAD00001004754

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A95724B

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 561 samples; filetype=bam

  Dataset EGAD00001004757

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A73044B

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 2057 samples; filetype=bam

  Dataset EGAD00001004720

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90600C

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 2055 samples; filetype=bam

  Dataset EGAD00001004735

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90554A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 628 samples; filetype=bam

  Dataset EGAD00001004730

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A96150A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 656 samples; filetype=bam

  Dataset EGAD00001004764

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A75617A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 692 samples; filetype=bam

  Dataset EGAD00001004727

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A75616B

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 232 samples; filetype=bam

  Dataset EGAD00001004725

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A95722A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 968 samples; filetype=bam

  Dataset EGAD00001004755

• 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 A96139A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 1436 samples; filetype=bam

  Dataset EGAD00001004762

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A96145A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 589 samples; filetype=bam

  Dataset EGAD00001004763

• 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 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 A96156B

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

  Dataset EGAD00001004766