Search Results - EGA European Genome-Phenome Archive

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 forlibrary A96176A

Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 531 samples; filetype=bam

Dataset EGAD00001004769

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

Comparison with genomic measurement

Heatrich-BS was performed on 5 healthy volunteers and 15 CRC patient cell-free DNA. The Heatrich-BS predicted tumor fractions were compared with tumor burden values obtained by genomic methods such as targeted amplicon sequencing and low pass sequencing.

Dataset EGAD00001008715

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

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 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 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 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 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 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 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 sequence data from Illumina HiSeqX instruments

This dataset includes bam files from 120 samples. These samples were sequenced using 2x150bp reads on an Illumina HiSeqX sequencer and aligned using the Isaac aligner. All samples were processed with TruSeq DNA PCR-free sample preparation.

Dataset EGAD00001003562

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

WGS

Purified DNA from PDX samples were subjected to WGS. Libraries were performed using the TruSeq DNA PCR-Free kit (Illumina) starting with 1μg of input DNA and performed following manufacturer's instructions. Libraries were sequenced on a NovaSeq 6000 (2x151 bp) instrument (Illumina). A mean coverage of 30.4x was obtained.

Dataset EGAD00001010309

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 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 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 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

Androgen receptor blockade promotes response to BRAF/MEK-targeted therapy

We studied a group of melanoma patients treated with neoadjuvant BRAF/MEK-targeted therapy (NCT02231775, n=51), and observed significantly higher rates of major pathologic response (MPR= <10% viable tumor at resection) and improved recurrence-free survival (RFS) in females versus males (MPR-66% versus 14%, p=0.001; RFS-64% versus 32% at 2 years, p=0.021). Findings were validated in a several additional cohorts 2-4 patients with unresectable metastatic melanoma treated with BRAF and/or MEK-targeted therapy (n=664 patients in total), demonstrating improved progression-free survival (PFS) and overall survival (OS) in females versus males in several of these studies. Studies in pre-clinical models demonstrated significantly impaired anti-tumor activity in male versus female BRAF/MEK-treated mice (p=0.006), with significantly higher expression of androgen receptor (AR) in tumors of male and female BRAF/MEK-treated mice versus control (p=0.0006 and 0.0025). Pharmacologic inhibition of AR signaling improved responses to BRAF/MEK-targeted therapy in male and female mice (p=0.018 and p=0.003), whereas induction of AR signaling (via testosterone administration) was associated with significantly impaired response to BRAF/MEK-targeted therapy in males and females (p=0.021 and p<0.0001).

Study EGAS00001006196

Privacy Notice for EGA Web user

Privacy Notice for EGA Public Website This Privacy Notice explains what personal data is collected by the specific service you are requesting, for what purposes, how it is processed, and how we keep it secure. 1. Who controls your personal data and how to contact us? European Genome- Phenome Archive - EGA offers a service for permanent archiving and sharing of all types of personally identifiable genetic and phenotypic data resulting from biomedical research projects, jointly managed by European Molecular Biology Laboratory – European Bioinformatics Institute (EMBL-EBI) and Fundació Centre de Regulació Genòmica - Centre for Genomic Regulation (CRG). EMBL-EBI and CRG represent joint Data Controllers’ of processing of your personal data. They and their Data protection officers may be contacted for data protection queries and for exercising your rights under Section 8. You may contact EMBL-EBI, represented by Mallory Freeberg, by: email at: mfreeberg@ebi.ac.uk , or post at EMBL-EBI, Wellcome Genome Campus, CB10 1SD Hinxton, Cambridgeshire, UK. EMBL’s Data Protection Officer may be contacted by: telephone at +49 6221 387-8590, email at dpo@embl.org , or post at EMBL Heidelberg, Data protection officer, Meyerhofstraße 1, 69117 Heidelberg, Germany. You may contact CRG, whose EGA team is represented by dr. Jordi Rambla de Argila, by: email at jordi.rambla@crg.eu, or post at Fundació Centre de Regulació Genòmica - Centre for Genomic Regulation (CRG), Dr.Aiguader 88, PRBB Building, 08003 Barcelona, Spain. CRG Data protection officer may be contacted by: email at dpo@crg.eu post at Fundació Centre de Regulació Genòmica - Centre for Genomic Regulation (CRG), C/ Dr. Aiguader, 88, PRBB Building, 08003 Barcelona, Spain. 2. Which is the lawful basis for processing personal data? For providing you access to the website and for offering support whilst using the website, we need your consent under Article 5 (2) of the IP 68 (equivalent to Article 6 (1)(a) of the GDPR). For monitoring your activities on the website, we process your personal data on the grounds of important public interest. Such legal basis is found in Article 5(1)(a) of EMBL Internal Policy No 68 on General Data Protection (hereinafter IP 68), which is equivalent to Article 6 (1)(e) of the EU General Data Protection Regulation (hereinafter GDPR) and upon which personal data are processed for the achievement of the aims laid down in 1973 agreement establishing EMBL, such as the promotion of the cooperation in the fundamental research, in the development of advanced instrumentation and in advanced teaching in molecular biology and dissemination of information. 3. What personal data is collected from users of the service? How do we use this personal data? We collect the following personal data from you: IP addressesDate and time of a visit to the service website Operating systemAmount of data transmittedBrowserEmail address (only when support is requested by the user) The data controller will use your personal data for the following purposes: To provide the user access to the serviceTo better understand the needs of the users and guide future improvements of the serviceTo monitor website activities according to and in compliance with the Terms of UseTo better understand the needs of the website visitors and guide future improvements of the serviceTo conduct and monitor website security activitiesTo create anonymous usage statistics 4. Who will have access to your personal data? The personal data will be disclosed to: Authorised staff in the data controller’s institutions acting on data controller`s behalf and instructions. 5. Will your personal data be transferred to third countries (i.e. countries not part of EU/EEA) and/or international organisations? There are no personal data transfers to third countries or international organisations. 6. How long do we keep your personal data? Any personal data directly obtained from you will be retained as long as the service is live. Such duration serves the purpose of enabling scientific research and ensures legal compliance and facilitates internal and external audits if they arise. By contrast, the log files for the data categories related to anonymous usage statistics (raw web service logs) are processed only for 30 days and thereafter erased. 7. The joint Data Controllers provide these rights regarding your personal data You have the right to: Not be subject to decisions based solely on an automated processing of data (i.e. without human intervention) without you having your views taken into consideration.Request at reasonable intervals and without excessive delay or expense, information about the personal data processed about you. Under your request we will inform you in writing about, for example, the origin of the personal data or the preservation period.Request information to understand data processing activities when the results of these activities are applied to you.Object at any time to the processing of your personal data unless we can demonstrate that we have legitimate reasons to process your personal data.Request free of charge and without excessive delay rectification or erasure of your personal data if we have not been processing it respecting the data protection policies of the respective controllers. It must be clarified that rights under points 4 and 5 are only available whenever you need support whilst using our website. For other processing based on the grounds of important public interest you cannot exercise your rights to object, rectify or erase your personal data according to the Article 13(2)(a)(b) of IP 68 (equivalent to Article 17(3)(b)(d) and Article 21(6) of the GDPR). 8. Supervisory authority If you wish to complain against the processing of your personal data, you may do so by post at: EMBL Heidelberg, Data Protection Committee, Meyerhofstraße 1, 69117 Heidelberg, Germany, or Autoritat Catalana de Protecció de Dades (Catalan Data Protection Authority), C/Rosselló 214, Esc A, 1r 1a, Barcelona 08008, Spain. 9. CookiesWe use cookies and similar technologies that are strictly necessary for the correct functioning and security of the EGA website (for example, to enable core site features and to help protect the service). These cookies do not store information that directly identifies you.You can set your browser to block or delete cookies. Please note that if you disable strictly necessary cookies, parts of the website may not function properly.Published on February 6, 2019

Documentation data-protection/privacy-notice/ega-website

Federated discovery and sharing of genomic data using Beacons

The Beacon Project is a Global Alliance for Genomics & Health (GA4GH) initiative that enables genomic and clinical data sharing across federated networks. The project is working toward developing regulatory, ethics and security guidance to ensure proportionate safeguards for distribution of data according to the GA4GH-developed “Framework for Responsible Sharing of Genomic and Health-Related Data”. Within this Nature Biotechnology Correspondence a description of the Beacon protocol and how it can be used as a model for the federated discovery and sharing of genomic data is offered to the community. A Beacon is defined as a web-accessible service that can be queried for information about a specific allele. A user of a Beacon can pose queries of the form “Have you observed this nucleotide (e.g., C) at this genomic location (e.g., position 32,936,732 on chromosome 13)?” to which the Beacon responds with either “yes” or “no.” In this way, a Beacon allows allelic information of interest to be discovered by a remote searcher with no reference to a specific sample or patient, thereby mitigating privacy risks. The Beacon API (represented as a RESTful web application) provides a technical specification that a Beacon server must implement. The specification is open source and available online here. To simplify the process of lighting a Beacon, a free, open-source reference implementation of the latest specification has been developed. GA4GH is promoting different levels of data access (open, registered, and controlled) for convenience and for compatibility across its projects. Each so-called access tier has distinct visibility and requirements for authorization. For example, ‘open access’ Beacons are accessible to anonymous users of the internet, whereas ‘registered access’ Beacons are accessible to registered users (for example, bona fide researchers and clinicians) who have agreed to a set of conditions of data use. Many of the largest genomic archives, such as dbGaP, the European Genome-phenome Archive and the European Variation Archive, have provided access to variation data through Beacons for some or all of their datasets. Beacons can be interconnected through the Beacon Network, a directory and search engine for Beacons. Although individual Beacons answer the question “Have you observed this allele?”, the Beacon Network answers the question “Who has observed this allele?”. Beacons can be freely registered to the Beacon Network and can be searched independently or in aggregate with other connected Beacons.

Blog federated-discovery-using-beacons

A Somatic Reference Standard for Cancer Genome Sequencing

In this study, we separately performed PCR-free whole genome sequencing on the paired tumor/normal melanoma COLO-829 and lymphoblastoid COLO-829BL cell lines across three separate institutions (TGen, Illumina, Genome Sciences Centre at the British Columbia Cancer Agency). We performed a meta-analysis of all data, in combination with the originally reported analysis of these cell lines by Pleasance et al., and report a somatic reference standard based on consensus events. This standard can be used as a reference for tumor/normal whole genome analyses.

Study phs000932

CALGB/SWOG 80405 ct DNA Biomarker Evaluation

To investigate treatment-specific mutations that underlie resistance to treatment with chemotherapy +/- bevacizumab or cetuximab in first line metastatic Colorectal Cancer (mCRC) patients, we performed targeted sequencing of circulating free DNA (cfDNA) from longitudinal plasma samples. These samples were collected at baseline (PRE, n=354), during treatment (OTH, n=254), and/or at progression or end of the protocol treatment (EOT, n=345) from CALGB/SWOG 80405, a randomized phase III trial that evaluated the efficacy of chemotherapy plus bevacizumab or cetuximab treatment in first line colorectal cancer patients (NCT00265850).

Study phs002941

Molecular Biomarkers of Obesity and Metformin Response in Endometrial Cancer: Analysis of GOG-0286B

The primary endpoint for the phase 2 study was progression free survival (PFS), and the primary endpoint for the phase 3 study was overall survival (OS). Data from phase 2 were included in the phase 3 analysis. Tissue was collected from patients with endometrial cancer who participated in the study (NCT02065687). The tissue will be analyzed for genetic data and combined with the clinical data to see if some gene variants are associated with longer PFS or OS, particularly on the metformin arm of the study.

Study phs002934

Genomic Translation for ALS Care (GTAC) - WGS

To look for genetic correlates of disease progression, the GTAC study recruited patients with motor neuron disease from 13 academic medical center clinics and followed them longitudinally for up to 5 years. Participants were regularly assessed for changes in respiratory function, the ALS Functional Rating Scale, and the ALS Cognitive Behavioral Scale. Participants underwent PCR-free whole genome sequencing on the Illumina NovaSeq, as well as repeat-prime PCR assessment of the C9ORF72 repeat expansion. Participants had PBMCs and whole blood RNA banked for future correlative studies.

Study phs002973

Single Nucleus Transcriptomes from the Ventral Midbrain of Opioid Overdose Cases and Controls

We generated ventral midbrain single nuclei RNA-seq libraries for 95 brain donors, including 45 subjects with documented histories of opioid abuse and overdose, and 50 demographically-matched, opioid-free control subjects, collected from two geographically distinct regions within the U.S. (greater Detroit area, Michigan and Miami, Florida). Each sample included both substantia nigra and the adjacent ventral tegmental area. Nuclei were processed in pools of 3-4 brains using the 10X Chromium system followed by Illumina sequencing, read alignment and processing by 10X Cellranger.

Study phs003260

NIBIT-EPI-MESO study samples

Multi-omics analysis of pre-immuno checkpoint inhibitors (ICI) therapy lesions from 91 pleural mesothelioma (PM) patients enrolled in the multicenter NIBIT-EPI-MESO study. Neoplastic lesions were characterized using multiple molecular platforms, including RNA sequencing (n=82), Whole Exome Sequencing (WES) (n=87), Reduced Representation Bisulfite Sequencing (RRBS) (n=83) and cell-free methylated immunoprecipitation sequencing (cfMeDIP-seq) (n=18). Tumor samples were collected during the standard diagnostic pleuroscopy or thoracoscopy before ICI therapy. Matched Peripheral Blood Mononuclear Cells (PBMC) were collected from PM patients before ICI therapy.

Study EGAS50000001478

A prospective multicenter study of plasma ctDNA versus archival tumor tissue to guide FGFR-targeted therapy in metastatic urothelial cancer - Targeted

Targeted sequencing data related to the publication titled "A prospective multicenter study of plasma ctDNA versus archival tumor tissue to guide FGFR-targeted therapy in metastatic urothelial cancer." FGFR alterations, seen in up to 25% of metastatic urothelial carcinoma, can be exploited with targeted therapy. Here, we perform targeted sequencing of cell-free DNA, archival FFPE, and white blood cell DNA to compare ctDNA versus tissue for FGFR biomarker status and monitor temporal changes to FGFR over the disease course, specifically after progressing on targeted therapy.

Study EGAS50000001450

ctDNA Quantification in Blood Plasma Using Deep Learning Based Fragle Tool

The project aims to develop an easy to use software named Fragle that can quantify ctDNA in blood plasma. This can help in cancer screening and progress monitoring from simple blood test without any need for tumor biopsy. We aim to make Fragle usable for both low pass whole genome sequencing and targeted sequencing data of cell free DNA. Fragle can work directly on the raw bam files mapped to hg19/GRCh37. We are uploading the bam files used in this study for Fragle evaluation.

Study EGAS50000000122

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

Transcriptome analysis of samples derived from GBM tumors, and relevant cell lines established and maintained at 5% or 20% oxygen.

The goal of the study was to compare the transcriptome of patient-derived serum-free GBM cell lines to that of parental GBM tumors. For each patient, three samples were analyzed: GBM tumor, cell line growing at 5% oxygen, and cell line growing at 20% oxygen. Analysis demonstrated the enrichement in cell cycle and DNA repair gene expression in cell lines when compared to parental tumor sample. Moreover, cells cultured at 5% oxygen maintained hypoxia, carbon metabolism and tumor progression transcriptional pattern characteristic for GBM.

Study EGAS00001006267

Multi-institutional collaboration to characterize 5hmC in prostate cancer, both tumor biopsies and cfDNA.

This study used pull-down sequencing to measure 5-hydroxymethylcytosine (5hmC) levels in tissue biopsy specimens from patients with localized and metastatic castration-resistant (mCRPC) prostate cancer. For a subset of the cohort, the same technique was used to sequence cell-free DNA (cfDNA) isolated from patient plasma. To assess 5hmC levels in cfDNA from a larger number of specimens, plasma samples from another mCRPC cohort were also assayed with this method. The larger cohort of cfDNA samples was further anlayzed with a targeted sequencing gene panel.

Study EGAS00001004942

Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library for library A95728A

Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 844 samples; filetype=bam

Dataset EGAD00001004758

cfDNA methylation profiling in Metastatic Cancer and Cancer of Unknown Primary

This study contains methyl-binding domain sequencing and shallow whole genome sequencing from circulating cell-free DNA (cfDNA) for 143 patients with metastatic cancer of known type, 41 patients with Cancer of Unknown Primary (CUP) and 27 non-cancer controls.

Dataset EGAD00001011178

Copy number profiling of tissue and plasma samples from high grade serous epithelial ovarian cancer patients

This dataset includes 289 samples from 46 high grade serous epithelial ovarian cancer patients. Data are from both tissue samples (either primary tumor, or synchronous metastases) and circulating cell-free DNA (cfDNA) of plasma samples taken during therapy and follow-up.

Dataset EGAD00001006422

Cell and Circuit-Specific Exploration of HIV Neurogenomics in Context of Opiate and Cocaine Misuse

Utilizing frozen postmortem brain tissue from the Manhattan HIV Brain Bank, we performed cell-type-specific, genome-wide sequencing of three different types: 1) single-nucleus RNA-sequencing (snRNA-seq), 2) in situ Hi-C, and 3) HIV integration site sequencing (IS-seq). We assayed tissue from three different donor types: 1) HIV-uninfected control (HIV-), HIV-infected (HIV+), and HIV-infected with HIV encephalitis (HIVE), who served as a positive control for active CNS viral infection. The diagnosis of HIVE was made by a board-certified neuropathologist on routine histopathology, based on the presence of a microglial nodule encephalitis with characteristic multinucleated syncytial cells and staining for HIV p24 antigen. Other HIV-infected individuals (the HIV+ group) did not have this histologic evidence of active HIV brain replication, and were variable with regard to cART status and virologic control prior to death. Donor characteristics, including ante-mortem plasma HIV loads, CD4 counts, and cART status, were documented. Quality controls were used for each assay: For snRNA-seq we applied filters to remove low quality nuclei from analysis and performed experiments in which mouse tissue was mixed with human tissue to determine a reliable HIV read count threshold. For Hi-C, we used paired HIV- and HIVE samples and processed all samples in parallel, at the same time. Correlation analysis was used to ensure biological replicates were aligned. For IS-seq, each time the assay was run, a positive and negative control were included. This allowed us to ensure a lack of contamination in IS-seq libraries and that, when a particular sample failed to yield library, this was due to low levels of HIV rather than a failure of the assay. Altogether, we built an integrative dataset from 79 sequencing files, including snRNA-seq, and cell type specific Hi-C, IS-seq, and ChIP-seq datasets. Human autopsy brain samples were collected by the Manhattan HIV Brain Bank (MHBB, U24MH100931), using protocols under the supervision of the Icahn School of Medicine at Mount Sinai (ISMMS) Institutional Review Board. Written informed consent was obtained either from decedents or their primary next of kin. Major Findings: Reorganization of open/repressive (A/B) compartment structures in HIVE microglia encompassing 6.4% of the genome was linked to transcriptional activation of interferon (IFN) signaling and cell migratory pathways and was partially recapitulated by IFN-gamma stimulation of cultured microglia. In contrast, decreased expression and repressive compartmentalization of genes regulating neuronal health and signaling was seen in both HIVE and HIV+ microglia. IS-seq recovered 1,221 integration sites in the brain that displayed distinct genomic patterns as compared to peripheral lymphocyte integration and were enriched for chromosomal domains newly mobilized into a permissive chromatin environment in HIVE microglia. Viral transcription occurred in a subset of highly activated microglia comprising 0.003% of all nuclei in HIVE brain. Our findings point to a disruption of microglia-neuronal interactions in the HIV infected brain and an interrelation of retroviral integration and expression with interferon-associated remodeling of the microglial 3D genome during progression to HIVE. Search terms: HIV, postmortem brain, microglia, substance abuse, viral integration, chromosomal conformation capture, Hi-C, chromatin, single nuclei RNA-seq, Irf8, NeuN, HIV encephalitis, neuron-microglia interaction, cytokine

Study phs003080

Genomic Profiling of Pediatric Tumors by Cell Free DNA Sequencing

BACKGROUND Analysis of cell-free DNA (cfDNA) from peripheral blood has been studied extensively for detection and monitoring therapy response in adult cancers, but utility in pediatric tumors is unclear. METHODS We studied 233 children with hematologic, solid and brain tumors. cfDNA was extracted from 1 mL of plasma collected at diagnosis. Circulating tumor DNA (ctDNA) in cfDNA was quantified and diverse classes of somatic genomic variants were detected using PeCan-Seq. PeCan-Seq findings were compared with matched tumor whole-genome and whole-exome sequencing data. Minimal residual disease (MRD) status was evaluated by cfDNA PeCan-Seq in 14 patients with B-cell acute lymphoblastic leukemia. RESULTS Plasma cfDNA yields at diagnosis were higher for hematologic malignancies than for solid and brain tumors. Patients with hematologic malignancy were 100% ctDNA-positive, the median ctDNA fraction in cfDNA was 0.77. We detected 97% variants that were present in tumors and covered by the capture panel, and 50 subclonal variants that were present in cfDNA only. Our approach enabled detection of sequence and structural variants in B-ALL ctDNA at varying levels of MRD. Nineteen of 38 patients with solid tumors were ctDNA-positive, with high prevalence in Ewing Sarcoma, liver tumors and osteosarcoma. One of 19 brain tumor patients was ct-DNA positive, indicating lack of utility for plasma-derived cfDNA in this disease. Notably, cfDNA analysis of a single plasma sample identified genomic features in ctDNA derived from B-ALL, concurrent primary neuroblastoma, and a later neuroblastoma relapse, indicating that genomic variations from multiple tumors could be detected simultaneously. CONCLUSIONS Pediatric patients with hematologic malignancy had an abundance of plasma ctDNA at diagnosis. Plasma ctDNA levels varied between different solid tumor types, more studies are needed to determine utility. Panel-based cfDNA sequencing could serve as a non-invasive approach for pediatric cancer diagnosis and disease monitoring.

Study EGAS50000000072

Illumina HumanOmniExpress genotyping data from the TEENAGE (TEENs of Attica: Genes and Environment) study.

The TEENAGE study is a cross-sectional study comprising healthy Greek adolescents. The study was conducted according to the guidelines specified in the Declaration of Helsinki and all procedures involving human subjects were approved by the Institutional Review Board of Harokopio University and the Greek Ministry of Education, Lifelong Learning and Religious Affairs. The TEENAGE study target population comprised adolescent students aged 13 to 15 years attending the first three classes of public secondary schools located in the wider Athens area of Attica. The regions across Attica from which students were recruited were physically similar and all urban in nature. Sampling of regions and schools selected to participate was random. Students were enrolled from the wider urban region to ensure that they were coming from families with varying educational background and socio-economic status. Moreover, in order to exclude any vast economic disparities, all participating schools were exclusively public schools, which are free of charge. A total of twenty-four schools participated in the study out of 285 junior secondary public schools in Attica. Anthropometric, medical and dietary information was obtained. DNA samples were genotyped using Illumina HumanOmniExpress BeadChips (Illumina, San Diego, CA, USA) at the Wellcome Trust Sanger Institute, Hinxton, UK. Genotype calling algorithm used was Illuminus (Teo et al., 2007).

Study EGAS00001000996

deep-learning-powered tissue deconvolution for cfDNA

Plasma cell-free DNA (cfDNA) is a noninvasive biomarker for cell death of all organs. Deciphering the tissue origin of cfDNA can reveal abnormal cell death because of diseases, which has great clinical potential in disease detection and monitoring. Despite the great promise, the sensitive and accurate quantification of tissue-derived cfDNA remains challenging to existing methods due to the limited characterization of tissue methylation and the reliance on unsupervised methods. To fully exploit the clinical potential of tissue-derived cfDNA, here we present one of the largest comprehensive and high-resolution methylation atlas based on 521 noncancer tissue samples spanning 29 major types of human tissues. We systematically identified fragment-level tissue-specific methylation patterns and extensively validated them in orthogonal datasets. Based on the rich tissue methylation atlas, we develop the first supervised tissue deconvolution approach, a deep-learning-powered model, cfSort, for sensitive and accurate tissue deconvolution in cfDNA. On the benchmarking data, cfSort showed superior sensitivity and accuracy compared to the existing methods. We further demonstrated the clinical utilities of cfSort with two potential applications: aiding disease diagnosis and monitoring treatment side effects. The tissue-derived cfDNA fraction estimated from cfSort reflected the clinical outcomes of the patients. In summary, the tissue methylation atlas and cfSort enhanced the performance of tissue deconvolution in cfDNA, thus facilitating cfDNA-based disease detection and longitudinal treatment monitoring.

Study EGAS00001007213

National Cancer Institute (NCI) Genome Wide Association Study (GWAS) of Lung Cancer in Never Smokers

A genomewide study of lung cancer in never smokers Abstract and specific aims In the United States, lung cancer incidence and mortality rates have been steadily declining over the past decade, following decline in the prevalence of tobacco smoking. However, lung cancer remains the leading cause of cancer death, killing more patients than breast, colon, and prostate cancers combined. Although tobacco smoke is the predominant risk factor for development of lung cancer, some patients develop the disease without a history of tobacco smoking. About 10 - 15% of all lung cancers occur in lilfetime never smokers. This figure will increase as the proportion of never smokers increases in the population. Even at present rates, lung cancer in never smokers, if considered a separate disease, is 6th to 8th top cause of cancer death. The growing number of never smokers in the USA and other countries emphasizes the importance of understanding the epidemiology and biology underlying lung cancer in this group. Genetic polymorphisms associated with the risk of lung cancer in never smokers are expected to overlap with those associated with the risk of lung cancer in ever smokers only partially. Epidemiological, molecular and clinical data suggest that molecular mechanisms of LC may differ in smokers and non-smokers, implying that lung cancer in never smokers is a different disease compared to the lung cancer in smokers. One can expect that there should be stronger genetic component in the control lung cancer in never smokers because effects of the genetic factors in never smokers are unmasked by the lack of tobacco smoke exposure. The genetic epidemiology of lung cancer in never smokers has not been well explored, largely because of difficulties in accruing the needed sample size for association studies. We propose a multicenter (total 14 sites from the US and Europe) genomewide association study of lung cancer in never smokers with the following specific aims: Aim 1: To identify candidate SNPs influencing risk for lung cancer in never smokers using Discovery sample. In the Discovery phase we will genotype 1256 Caucasian cases and 1365 age- and gender-matched never smoker controls using the Illumina Human660W-Quad platform. In addition, we will include in the analysis 284 cases and 175 matched controls already genotyped on the 610Quad platform. In this phase we will only include the study sites that have collected blood specimens (MDACC, Mayo Clinic, Karmanos Cancer Institute, The University of Liverpool Cancer research Centre, Institute of Cancer Research in Sutton, and Lunenfeld Research Institute in Toronto, Canada). All the samples will be sent to the independent lab for genotyping, to reduce site-specific technical artifacts. The final sample will consist of 1540 cases and 1540 controls matched by study site. Aim 2: To perform the second phase (validation) analysis of significant SNPs identified in aim 1 using an independent set of cases and controls. SNPs associated with risk at the significance level of 0.01 or below in the discovery set will be included in the replication phase. The proposed threshold guarantees an adequate power to retain SNPs with the typical effect size of 1.3. We plan to carry 6000-7000 SNPs for validation. The independent replication set will include 800 cases and 800 controls, mostly from sites that collected tissue (Mayo Clinic, Karmanos Cancer Institute, UT Southwestern) or buccal specimens (UCLA), but also blood samples (Imperial College London, University of Pennsylvana, German Cancer Research Center, Heidelberg, National Research Center for Environment and Health, Neuherberg, Carmel Medical Center, Haifa). We will then perform a joint analysis to test the significance of the SNPs identified in the first stage using a stringent critical p-value of 10-7. There will be 2340 cases and 2340 controls in the joint set. Based on our experience with GWAS in smokers and assuming that genetic component in lung cancer risk in never smokers can be higher than genetic component in smokers, we expect to identify about 5-10 candidate regions associated with lung cancer risk in never smokers. Aim 3: To identify and explore pathways associated with the risk of lung cancer in never smokers. Results of the number of studies on the molecular mechanisms and drug response suggest that lung cancer in never smokers is a different disease and different pathways will be associated with lung cancer risk in non-smokers and smokers. To identify pathways and molecular functions associated with lung cancer risk in never smokers we will apply Ingenuity and DAVID bioinformatics tools. We will use at least 300 top candidate genes identified in joint and discovery analysis. The reason why we select rather large number of candidate genes for functional annotation is two-fold: 1. Both algorithms are looking for enrichment of pathways and function by most significant genes and they produce statistically robust results only when number of genes is relatively high. 2. Despite the fact that this study will be largest possible for never smokers we still are underpowered to detect SNPs with relatively small effect size. But though those SNPs will not reach genome wide level of significance they will tend to be on the top of the list. In other words genes from the gray zone (significant on individual level and non-significant for genome wide level) are expected to be enriched by true discoveries. True discoveries are likely to be associated with limited number of pathways / functions while false positives are expected to be uniformly distributed across functions and pathways. Therefore significant clustering of the gene to a given function will suggest that that those genes are true discoveries. This is the first GWAS aiming at identifying the genetic control of susceptibility to lung cancer in Caucasian never smokers. We will combine the available resources from the multiple sites to achieve the sample size sufficient for this study. The study will identify genetic architecture of the predisposition to the lung cancer in never smokers.

Study phs000634

T2D-GENES Project 2: San Antonio Mexican American Family Studies

The Type 2 Diabetes (T2D) Genetic Exploration by Next-generation sequencing in Ethnic Samples (T2D-GENES) Consortium is a collaborative international effort to identify genes influencing susceptibility to T2D in multiple ethnic groups using next generation sequencing. T2D-GENES Project 2 is a complex pedigree-based study designed to identify low frequency or rare variants influencing susceptibility to T2D, using whole genome sequence (WGS) information from 1,043 individuals in 20 Mexican American T2D-enriched pedigrees from San Antonio, Texas. The major objectives of this study are to identify low frequency or rare variants in and around known common variant signals for T2D, as well as to find novel low frequency or rare variants influencing susceptibility to T2D. The sampled individuals are obtained from two studies: the San Antonio Family Heart Study (SAFHS) and the San Antonio Family Diabetes/Gallbladder Study (SAFDGS), collectively referred to as the San Antonio Mexican American Family Studies (SAMAFS). The strategy is to sequence approximately 600 individuals at an average of 50x coverage across the entire genome, then impute genome wide genotypes for about 440 additional family members. The 600 sequenced individuals are specifically chosen for their value in imputing sequence information into other family members. By studying large pedigrees, we expect to find multiple individuals carrying each genetic variant, even if this variant is very rare in the population at large. Thus, a pedigree-based approach provides an excellent opportunity for identifying rare novel variants influencing risk of T2D and quantitative variation in T2D-related phenotypes. The whole genome sequencing has been done commercially by Complete Genomics, Inc. (CGI). The final data set includes whole genome sequence data for 607 individuals. After quality control, 585 sequenced individuals provide data for family based imputation, using Merlin linkage analysis software, into approximately 440 additional family members for whom chip based genotypes are available to indicate which parental haplotype is transmitted. Extensive phenotype data is provided for 1048 individuals. These include 5 sequenced individuals who do not belong to any of the 20 large pedigrees. Phenotype information was collected between 1991 and 2011 in the two contributing longitudinal studies. SAFHS participants may have information from up to 5 visits, and SAFDGS participants may have up to 4 visits. The clinical variables reported are coordinated with T2D-GENES Project 1 (multi-ethnic exome sequencing) and include T2D status and age at diagnosis, glycemic traits (fasting and 2 hour glucose and insulin), blood pressure, blood lipids (total cholesterol, HDL cholesterol, calculated LDL cholesterol and triglycerides), clinical chemistry (cystatin c, glutamic acid decarboxylase antibody titer (GadAb), creatinine, adiponectin and leptin). Glycated hemoglobin (HbA1c) was not measured for these individuals and insulin C-peptide is not included in this data set. Additional phenotype data include the medication status at each visit, classified in four categories as any current use of diabetes, hypertension or lipid-lowering medications, and, for females, current use of female hormones. Anthropometric measurements include age, sex, height, weight, hip circumference, waist circumference and derived ratios. Each phenotype variable has an initial summary column containing the most recent non-missing measurement for each individual, followed by the five potential time points for each individual, the number of non-missing measurements, and the age and year for the most recent non-missing measurement. For historical reasons, the order in which variables are presented on the dbGaP web site differs from their order in the data download file. When reading the comment fields for each variable, please note that commas are omitted to support data exchange in .csv format.

Study phs000462

Scalable Whole-Exome Sequencing of Cell-Free DNA Reveals High Concordance with Metastatic Tumors

We performed whole genome sequencing and whole-exome sequencing of cell-free DNA (cfDNA) and whole-exome sequencing of matched tumor biopsies and germline DNA from patients with metastatic cancer. Using ichorCNA, a software tool that quantifies tumor content in ultra-low pass whole genome sequencing (~0.1x) of cfDNA without prior genomic characterization of the tumor, we show genome-wide concordance between cfDNA and tumor biopsies and detectability of high tumor fractions (>0.1) in the cfDNA of many patients with metastatic cancer. We then established that whole-exome sequencing of cfDNA can enable comprehensive profiling of tumors from blood, with high tumor-content cfDNA samples demonstrating concordance of clonal somatic mutations, copy number alterations, mutational signatures, and neoantigens with matched tumor biopsies. This study introduces a new method to identify patients that could be eligible for tumor profiling from cfDNA and sheds light on the concordance between metastatic tissue and blood biopsies.

Study phs001417

T cell receptor repertoire in cell-free DNA as a proxy for tumor infiltrates in patients treated with pembrolizumab

Profiling tumour and systemic T-cell receptor (TCR) repertoires can identify features associated with clinical benefit of cancer patients on immune checkpoint blockade therapy. This study reports on diversity and specificity analysis of TCR sequences from 59 tumor, 306 peripheral blood (PBMC), and 73 cell-free DNA (cfDNA) samples from 81 patients with solid tumors treated with pembrolizumab. This analysis reveals that head and neck carcinomas have significantly lower PBMC diversity and shorter persistence of pembrolizumab-induced diversification compared to other cancers. Using GLIPHII (Grouping Lymphocyte Interactions with Paratope Hotspots), we find that tumor-derived clonotypes form non-microbial specificity signatures that are shared across patients and cancer types. Tumor-derived clonotypes are enriched in cfDNA compared to PBMCs, despite the diversity of cfDNA TCR repertories being two orders of magnitude lower than PBMCs. TCRs shared between tumors and cfDNAs persist in PBMCs beyond 50 weeks of treatment, suggesting cfDNA captures clonotypes contributing to anti-tumor immune surveillance.

Study EGAS50000001306

White blood cell and cell-free DNA analyses for detection of residual disease in gastric cancer

Liquid biopsies are providing new opportunities for detection of residual disease in cell-free DNA (cfDNA) after surgery but may be confounded through identification of alterations arising from clonal hematopoiesis. Here, we identify circulating tumor-derived DNA alterations (ctDNA) through ultrasensitive targeted sequencing analyses of matched cfDNA and white blood cells from the same patient. We apply this approach to analyze samples from patients in the CRITICS trial, a phase III randomized controlled study of perioperative chemotherapy in patients with operable gastric cancer. After filtering alterations derived from matched white-blood cells, the presence of ctDNA predicts recurrence when analyzed within nine weeks after preoperative treatment and after surgery in patients eligible for multimodal treatment. These analyses provide a facile method for distinguishing ctDNA from other cfDNA alterations and highlight the utility of ctDNA as a predictive biomarker of patient outcome to perioperative cancer therapy and surgical resection in patients with gastric cancer.

Study EGAS00001004114

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

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

Single molecule genome-wide mutation profiles of cell-free DNA for non-invasive detection of cancer

Somatic mutations are a hallmark of tumorigenesis and may be useful for non-invasive diagnosis of cancer. We analyzed whole-genome sequencing (WGS) data from 2,511 individuals in the Pan-Cancer Analysis of Whole Genomes (PCAWG) study as well as 489 individuals from four prospective cohorts and found distinct regional and mutation type specific frequencies in tissue and cell-free DNA (cfDNA) of cancer patients that were associated with replication timing and other chromatin features. A machine learning model using genome-wide mutational profiles combined with other features and followed by CT imaging detected >90% of lung cancer patients, including those with stage I and II disease. The fixed model was validated in an independent cohort, detected patients with cancer earlier than standard approaches, and could be used to monitor response to therapy. This approach lays the groundwork for non-invasive cancer detection using genome-wide mutation features that may facilitate cancer screening and monitoring.

Study EGAS00001007248

Joint-Specific TF Regulation in RA

The mechanisms responsible for the distribution and severity of joint involvement in rheumatoid arthritis (RA) are not known. To explore whether site-specific fibroblast-like synoviocyte (FLS) biology might be associated with location-specific synovitis and explain the predilection for hand (wrist/metacarpal phalangeal joints) involvement in RA, we generated bulk, whole-genome transcriptomic and chromatin accessibility data from FLS. Samples were matched in age, sex, and were homogeneous in that all had significant joint damage with longstanding disease requiring surgical intervention. Analysis revealed joint-specific patterns of FLS phenotypes, with proliferative, migratory, proinflammatory, and matrix-degrading characteristics observed in resting FLS derived from the hand joints compared with hip or knee. TNF-stimulation amplified these differences, with greater enrichment of proinflammatory and proliferative genes in hand FLS compared with hip and knee FLS. Hand FLS also had the greatest expression of markers associated with an ‘activated' state relative to the ‘resting' state, with the greatest cytokine and MMP expression in TNF-stimulated hand FLS. Predicted differences in proliferation and migration were biologically validated with hand FLS exhibiting greater migration and cell growth than hip or knee FLS. Distinctive joint-specific FLS biology associated with a more aggressive inflammatory response might contribute to the distribution and severity of joint involvement in RA.

Study phs003633

DNA methylation-based classification of sinonasal tumors [Proteomics data]

This dataset includes raw label-free mass spectrometry proteomics data of different sinonasal tumor entities as well as normal sinonasal tissue. 72 samples were processed on a Q Exactive HF-X instrument coupled to an easy nanoLC 1200 system using one microgram of peptides and an 110 minutes gradient.

Dataset EGAD00010002381

Paired FastQ files from deep targeted DNA sequencing

This dataset comprises paired-end and time-matched FastQ files from 356 cell-free DNA samples and 356 white blood cell DNA samples, derived from 299 patients with metastatic renal cell carcinoma or urothelial carcinoma. A baseline sample was available for all patients. Sequencing was performed using Illumina technology.

Dataset EGAD50000001267

WGS of thymic epithelial tumors and paired normal

Among the 20 newly sequenced cases, we conducted additional whole-genome sequencing (WGS) on five out of eight CN-type thymomas, excluding three with low tumor cell fractions (< 0.1). Libraries for WGS were prepared using the TrueSeq DNA PCR-free Prep Kit following standard Illumina protocols.

Dataset EGAD50000001160

A mathematical model of cell-free DNA fragment size reveals cancer-specific fragmentomic patterns

This study aim to test a mathematical model of cfDNA length distribution across multiple biodiy flulids. It try to investigate biological mechanisms of cfDNA fragmentation and release.

Study EGAS50000001560

Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A96213A

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

Dataset EGAD00001004771

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EGA Webin EGA Webin serves as a platform for registering metadata for array based submissions, large scale sequence submission as well as for legacy EGA submission accounts (ega-box-XXXX). For large scale submitters of sequence data you have also the option to submit metadata via XMLS programmatic submission, while new submitters are advised to utilise the Submitter Portal for their submissions. You can request a legacy EGA submission account (ega-box-XXXX) by populating this form. Please, allow two business days for our Helpdesk team to contact you after populating this form. WEBIN actions: Register metadata for a sequence submission Register study, samples, experiments, runs, DAC, policy and dataset/s after file upload. Register components for your array-based metadata submission Register study, samples, DAC or policy before uploading files. Edit existing submission metadata Change or update previously submitted metadata. 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Documentation submission/metadata/submission/EGA_webin

National Institutes of Health The Cancer Genome Atlas (TCGA)

The Cancer Genome Atlas (TCGA) is a comprehensive and coordinated effort to accelerate our understanding of the molecular basis of cancer through the application of genome analysis technologies, including large-scale genome sequencing. TCGA is a joint effort of the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI), which are both part of the National Institutes of Health, U.S. Department of Health and Human Services. TCGA projects are organized by cancer type or subtype. Click here for a current list of cancer types selected for study in TCGA. Data from TCGA (e.g., gene expression, copy number variation and clinical information), are available via the Genomic Data Commons (GDC). Data from TCGA projects are organized into two tiers: Open Access and Controlled Access. Open Access data tier contains data that cannot be attributed to an individual research participant. The Open Access data tier does not require user certification. Data in Open Access tier are available in the TCGA Data Portal. Controlled Access data tier contains individual-level genotype data that are unique to an individual. Access to data in the Controlled Access data tier requires user certification through dbGaP Authorized Access. Controlled Access data types consist of the following: Individual germline variant data (SNP .cel files) Primary sequence data (.bam files), which are available at GDC Clinical free text fields Exon Array files (for Glioblastoma and Ovarian projects only) NOTE: TCGA strives to release most data in the open access tier. Individual genotype or sequence files are prominent exceptions. Commonly requested files such as descriptions of somatic mutations or clinical data are open access. Please go to this page: https://tcga-data.nci.nih.gov/docs/publications/ to access all data associated with TCGA tumor specific publications. The TCGA study is utilized in the following dbGaP substudies. To view genotypes and other molecular data collected in these substudies, please click on the following substudies below or in the "Substudies" section of this top-level study page phs000178 TCGA study. phs000854 Genome-wide Analysis of Noncoding Regulatory Mutations in Cancer

Study phs000178

Circulating cell-free DNA analysis in Small Cell Lung Cancer

Small cell lung cancer (SCLC) accounts for ~250,000 deaths worldwide each year. Acquisition of adequate tumour biopsies is challenging and liquid biopsies present the best option for patient stratification and response monitoring. We present genome-wide and targeted plasma DNA sequencing data that identifies tumour related changes in 91% of limited (LS-SCLC) and 100% of extensive stage (ES-SCLC) patients. Parallel analysis of circulating tumour cells (CTCs, by CellSearch) based on ≥ 1 CTC/7.5ml blood increased tumour detection frequencies to 98% patients (95% for LS-SCLC) where CTC count and plasma DNA readouts correlated with disease stage and overall survival (OS). Targeted plasma DNA sequencing identified potential therapeutic targets in >50% of patients. This approach will support trials of targeted therapies where improved patients outcomes are urgently required.

Study EGAS00001003110

Plasma cell‐Free transcriptome profiling in chronic liver disease

Chronic liver disease (CLD) encompasses a wide range of hepatic disorders that progressively compromise liver function. In order to support the development of non-invasive diagnostic tools and improve patient stratification, we generated a high-throughput extracellular RNA sequencing dataset from blood plasma samples collected from patients at various stages of liver disease. Samples were obtained from two independent cohorts of 112 (cohort 1) and 489 (cohort 2) patients using standardized collection and processing protocols, and profiled using total RNA sequencing (SMARTer Stranded Total RNA-Seq Kit v3 - Pico Input Mammalian), resulting in 112 and 820 extracellular transcriptomic profiles for cohort 1 and 2, respectively. This dataset is provided with full documentation of the preprocessing analysis pipeline and accompanying metadata, enabling its use as an independent validation cohort for biomarker discovery and as a complementary resource for integrative multi-omics studies, even in cases with incomplete data modalities. The publicly available resource is intended to facilitate reproducibility and foster further research in the field of liver disease diagnostics

Study EGAS50000000545

Profiles of extracellular miRNA in cerebrospinal fluid and serum from patients with Alzheimer's or Parkinson's disease correlate with disease status and features of pathology

We profiled the miRNA contents from cerebrospinal fluid and serum (using next generation sequencing) from postmortem patients diagnosed with Alzheimer's or Parkinson's disease, and neurologically normal controls. All biofluids were cell-free and patient matched, there was one cerebrospinal fluid and one serum sample from each of 70 patients with Alzheimer's disease, 67 patients with Parkinson's disease and 78 age-similar controls. We correlated the miRNA changes with measurements of neuropathology such as plaques, tangles and Lewy bodies.

Study phs000727

Enabling sensitive and precise detection of ctDNA through somatic copy number aberrations in breast cancer

Cell-free DNA (cfDNA) extracted from peripheral blood has emerged as a crucial biomarker source in oncology research. To enhance the detection of somatic copy number alterations (SCNAs) and circulating tumor DNA (ctDNA), we developed eSENSES, a 2Mb breast cancer-targeted NGS panel. It includes 15,000 genome-wide SNPs, 500 focal SNPs in breast cancer driver regions, and exons from 81 commonly altered genes, alongside a custom computational approach. eSENSES provides a reliable, powerful and cost-effective tool for monitoring disease progression and guiding therapeutic decisions in breast cancer patients.

Study EGAS50000000793

ERBB2/HER2 alterations in ctDNA and metachronous tissues of patients with metastatic urothelial cancer - WGS

Whole genome sequencing data related to the publication "ERBB2/HER2 alterations in ctDNA and metachronous tissues of patients with metastatic urothelial cancer." ERBB2 alterations, usually amplifications or activating mutations, are seen in up to 20% of urothelial carcinoma and can be targeted by monoclonal antibodies, small molecule inhibitors, and most recently, antibody-drug conjugates. Here, we perform targeted and whole-genome sequencing of cell-free DNA and archival FFPE of mUC with and without ERBB2 alterations to identify unique genomic and clinical features of ERBB2-altered cancers, accompanied by IHC, FISH, and detailed clinical outcomes.

Study EGAS50000001082

ERBB2/HER2 alterations in ctDNA and metachronous tissues of patients with metastatic urothelial cancer - Targeted

Targeted sequencing data related to the publication titled "ERBB2/HER2 alterations in ctDNA and metachronous tissues of patients with metastatic urothelial cancer." ERBB2 alterations, usually amplifications or activating mutations, are seen in up to 20% of urothelial carcinoma and can be targeted by monoclonal antibodies, small molecule inhibitors, and most recently, antibody-drug conjugates. Here, we perform targeted and whole-genome sequencing of cell-free DNA and archival FFPE of mUC with and without ERBB2 alterations to identify unique genomic and clinical features of ERBB2-altered cancers, accompanied by IHC, FISH, and detailed clinical outcomes.

Study EGAS50000001081

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A96139A

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

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

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

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

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

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

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

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

Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires forlibrary A96176A

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

Comparison with genomic measurement

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

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

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

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

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

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

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

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

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

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

Clinal phenotype dataset

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

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

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

Whole genome sequence data from Illumina HiSeqX instruments

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

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

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

WGS

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

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

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

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

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

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

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

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

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

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

MicroRNA profiling by next-generation sequencing for colorectal cancer screening

Androgen receptor blockade promotes response to BRAF/MEK-targeted therapy

Privacy Notice for EGA Web user

Federated discovery and sharing of genomic data using Beacons

A Somatic Reference Standard for Cancer Genome Sequencing

CALGB/SWOG 80405 ct DNA Biomarker Evaluation

Molecular Biomarkers of Obesity and Metformin Response in Endometrial Cancer: Analysis of GOG-0286B

Genomic Translation for ALS Care (GTAC) - WGS

Single Nucleus Transcriptomes from the Ventral Midbrain of Opioid Overdose Cases and Controls

NIBIT-EPI-MESO study samples

A prospective multicenter study of plasma ctDNA versus archival tumor tissue to guide FGFR-targeted therapy in metastatic urothelial cancer - Targeted

ctDNA Quantification in Blood Plasma Using Deep Learning Based Fragle Tool

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

Transcriptome analysis of samples derived from GBM tumors, and relevant cell lines established and maintained at 5% or 20% oxygen.

Multi-institutional collaboration to characterize 5hmC in prostate cancer, both tumor biopsies and cfDNA.

Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library for library A95728A

cfDNA methylation profiling in Metastatic Cancer and Cancer of Unknown Primary

Copy number profiling of tissue and plasma samples from high grade serous epithelial ovarian cancer patients

Cell and Circuit-Specific Exploration of HIV Neurogenomics in Context of Opiate and Cocaine Misuse

Genomic Profiling of Pediatric Tumors by Cell Free DNA Sequencing

Illumina HumanOmniExpress genotyping data from the TEENAGE (TEENs of Attica: Genes and Environment) study.

deep-learning-powered tissue deconvolution for cfDNA

National Cancer Institute (NCI) Genome Wide Association Study (GWAS) of Lung Cancer in Never Smokers