14465 results for "PLOS+ONE+Pennsylvania+German+ACS+language+code+pdc+open+access+study"

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• PELICAN33 Phenomic Dataset

  To better understand variation in metastatic prostate cancer behaviour, we assembled and analyzed longitudinal clinical and autopsy records in 33 men. The dataset is contained in a self-explanatory Excel Workbook, with each patient identified as A1, A2, etc. as listed in the "Combined longitudinal clinical and autopsy phenomic assessment in lethal metastatic prostate cancer: recommendations for advancing precision medicine" publication in European Urology Open Science. Please see Jasu J, Tolonen T, Antonarakis ES, Beltran H, Halabi S, Eisenberger MA, Carducci MA, Loriot Y, Van der Eecken K, Lolkema M, Ryan CJ, Taavitsainen S, Gillessen S, Högnäs G, Talvitie T, Taylor RJ, Koskenalho A, Ost P, Murtola TJ, Rinta-Kiikka I, Tammela T, Auvinen A, Kujala P, Smith TJ, Kellokumpu-Lehtinen PL, Isaacs WB, Nykter M, Kesseli J, Bova GS. Combined Longitudinal Clinical and Autopsy Phenomic Assessment in Lethal Metastatic Prostate Cancer: Recommendations for Advancing Precision Medicine. Eur Urol Open Sci. 2021 Jul 2;30:47-62. doi: 10.1016/j.euros.2021.05.011. PMID: 34337548; PMCID: PMC8317817. for more details.

  Dataset EGAD00001007800

• FEGA FAQs: a summary of the Q+A session from the FEGA workshop at ELIXIR AHM 2024

  The ELIXIR All Hands Meeting (AHM) was held in Uppsala, Sweden this June. We at EGA always look forward to this meeting as a chance to see many of our partners and colleagues face-to-face. The Federated EGA (FEGA) is one initiative that always benefits from the AHM, and this year FEGA was in the spotlight, with several questions related to the advances we've made recently. We held this year's FEGA workshop a little differently, with most time for discussions on various “hot topics”. Organised in different breakouts, over 2 rounds, participants could choose which session they wanted to attend. One breakout was a general Q+A about FEGA and the path to join. We had a diverse range of participants, including experts from Central EGA (CEGA) and FEGA inaugural nodes, people from up and coming nodes at various stages of readiness, as well as participants not directly a part of FEGA but curious to learn more. It made for a great discussion! In this blog post, we highlight some of the questions for those who didn’t have a chance to be there. What is the FEGA Collaboration Agreement? It is a legal agreement between the node and Central EGA (CEGA) where both parties agree to certain responsibilities, like what services will be provided, sharing of public metadata with CEGA, interoperable technologies and more. The agreement is for 4 years, with the expectation of long-term sustainability. If a node is made up of multiple institutes, one institute must be the signatory. This could require a legal agreement within the node. CEGA is composed of CRG and EMBL-EBI (EGA itself has its own legal agreement between these institutes) Check the FEGA Collaboration Agreement What about purely medical data? Is this accepted in FEGA? CEGA only accepts data approved for secondary research use. We do accept medical data collected in this context, as do some nodes. Nodes are able to accept any data they like, as long as it is sensitive, thus in need of controlled access. What about other data types other than genomics? Like proteomics or images? They can be accepted, although CEGA is currently not designed with those formats in mind, so they might not fit with precision in the data model. The FEGA nodes are welcome to accept any data they want. Proteomic studies generating sensitive data are increasing, and specific work will likely be developed in the near future to adress this need. Does CEGA require a certain amount or type of data to be shared by the node? No, we do not and it's up to each node to decide what specific data types they accept and in what format. Nonetheless, a FEGA node has the duty to be open to data submission from any institution of their country, and can't be limited by design to a single or limited data source. Does CEGA and FEGA require a certain amount or type of details and description on the data to be submitted to the node? Data description (technical and biological), usually called metadata in this field, is crucial for the findability of the submitted data. Thus, we highly recommend including as much information as available to maximise the discoverability of the data. Specifical minimal metadata is defined in the submission schema, and thresholds are specified by the nodes. We are currently working towards a unified FEGA metadata model. What are the geographical borders of FEGA? FEGA should make the E for Everyone, because any institute worldwide could join and we indeed do have countries and use-cases worldwide. Some of them are very close to officially joining the federation. What are the main differences between FEGA and GDI? The two networks have a lot in common and also some critical differences. You can read all about this in one of our latest blog post. If you have more questions, please consider having a look at the FEGA onboarding webpage and, of course, don't miss our workshop at the ELIXIR AHM next year!

  Blog fega-faqs-elixir-ahm-2024

• Transcriptomic profiles of early and late passage metastatic colorectal cancer (mCRC) tumoroids

  The breadth and depth at which cancer models are interrogated contribute to successful translation of drug discovery efforts to the clinic. In colorectal cancer (CRC), model availability is limited by a dearth of large-scale collections of patient-derived xenografts (PDXs) and paired tumoroids from metastatic disease, the setting where experimental therapies are typically tested. XENTURION is a unique open-science resource that combines a platform of 129 PDX models and a sister platform of 129 matched PDX-derived tumoroids (PDXTs) from patients with metastatic CRC, with accompanying multidimensional molecular and therapeutic characterization. This specific dataset is focused on a comparison of early and late passage tumoroids transcriptomes.

  Study EGAS50000000108

• Subtype-associated epigenomic landscape and 3D genome structure in bladder cancer

  We determine the genome-wide transcriptome, enhancer landscape and transcription factor binding profiles of FOXA1 and GATA3 in luminal and basal subtypes of bladder cancer. Furthermore, we report the first-ever mapping of genome-wide chromatin interactions by Hi-C in both bladder cancer cell lines and primary patient tumors. We show that subtype-specific transcription is accompanied by specific open chromatin and epigenomic marks, at least partially driven by distinct transcription factor binding at distal-enhancers of luminal and basal bladder cancers. Finally, we identify a novel clinically relevant transcription factor, Neuronal PAS Domain Protein 2 (NPAS2), in luminal bladder cancers that regulates other subtype-specific genes and influences cancer cell proliferation and migration.

  Study EGAS00001005071

• Starting Treatment with Agonist Replacement Therapies (START): A Randomized Trial of Methadone vs Buprenorphine/Naloxone for the Treatment of Opioid Dependence

  The Starting Treatment with Agonist Replacement Therapy (START) study was a 24-week, open-label, randomized trial of methadone versus Suboxone (buprenorphine/naloxone) for the treatment of opioid dependence. Patients were over the age of 18 and met DSM-IV qualifications for opioid dependence. The primary goal of the study was to determine if either medicine was associated with liver toxicity issues in this vulnerable patient population. Liver enzymes were measured at four time points. Urinalysis for opioids, cocaine, cannabis, benzodiazepines, and methamphetamine were performed on a weekly basis. A flexible dosing scheme was used during which physicians could alter medication dose based on withdrawal symptoms and urinalysis results. Neither medication was found to be associated with liver enzyme levels. In version 2 of this study, a text file containing 7704 SNPs with large allele frequency discrepancy as compared to 1000 Genomes is included. Users have the option to exlude these 7704 SNPs.

  Study phs001135

• Exome sequencing of early and late passage Patient Derived Xenogratf Tumoroids with matched Patient Derived Xenogratfs and matched normal liver

  The breadth and depth at which cancer models are interrogated contribute to successful translation of drug discovery efforts to the clinic. In colorectal cancer (CRC), model availability is limited by a dearth of large-scale collections of patient-derived xenografts (PDXs) and paired tumoroids from metastatic disease, the setting where experimental therapies are typically tested. XENTURION is a unique open-science resource that combines a platform of 129 PDX models and a sister platform of 129 matched PDX-derived tumoroids (PDXTs) from patients with metastatic CRC, with accompanying multidimensional molecular and therapeutic characterization. This specific dataset is focused on a comparison of early and late passage tumoroids transcriptomes. In this study we focused our attention on early (passage 3) and late (passage 8-12) PDXTs with their matched PDXs and normal liver

  Study EGAS50000000185

• Detection of brain cancer using genome-wide cell-free DNA fragmentomes

  Diagnostic delays in patients with brain cancer are common and can impact patient outcome. Development of a blood-based assay for detection of brain cancers could accelerate brain cancer detection. In this study, we analyzed genome-wide cell-free (cfDNA) fragmentomes, including fragmentation profiles and repeat landscapes, from the plasma of individuals with (n=148) and without (n=357) brain cancer. Machine learning analyses of cfDNA fragmentome features detected brain cancer across all grade gliomas (AUC=0.90, 95% CI: 0.87-0.93) and these results were validated in an independent prospectively collected cohort. cfDNA fragmentome changes in patients with gliomas represented a combination of fragmentation profiles from glioma cells and altered white blood cell populations in the circulation. These analyses reveal the properties of cfDNA in patients with brain cancer and open new avenues for noninvasive detection of these individuals.

  Study EGAS50000000986

• UNITO_Molpheno_Closed

  This dataset contains Whole Genome Sequencing, RNA-sequencing and ATAC-sequencing data obtained from PBMCs derived from blood samples of one patient with complex genomic rearrangements and the biological parents. The patient has multiple congenital anomalies and delayed development. Data access is closed.

  Dataset EGAD00001004863

• Rapid Acceleration of Diagnostics - Underserved Populations (RADx-UP) Collection of General Research Use Datasets

  This individual-level collection of datasets contains all the RADx Underserved Populations (RADx-UP) studies in dbGaP for General Research Use (GRU) and have no further limitations beyond those outlined in the model RADx Data Use Certification Agreement. Access to this study collection will include additional authorized individual-level GRU datasets that become available. Renewal of this study collection is required annually. Access to studies in this collection is on the RADx® Data Hub. To request access to this study collection, select phs003832 in the dbGaP when submitting a data access request.

  Study phs003832

• Rapid Acceleration of Diagnostics - Tech Program (RADx-Tech) Collection of General Research Use Datasets

  This individual-level collection of datasets contains all the RADx® Tech Programs (RADx-Tech) studies in dbGaP for General Research Use (GRU) and have no further limitations beyond those outlined in the model RADx Data Use Certification Agreement. Access to this study collection will include additional authorized individual-level GRU datasets that become available. Renewal of this study collection is required annually. Access to studies in this collection is on the RADx® Data Hub. To request access to this study collection, select phs003831 in the dbGaP when submitting a data access request.

  Study phs003831