APOLLO Network Project - Applied Proteogenomics OrganizationaL Learning and Outcomes Network
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PROVIDER: PRJNA388725 | ENA |
REPOSITORIES: ENA
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Project description:APOLLO Network Project - Applied Proteogenomics OrganizationaL Learning and Outcomes Network
| PRJNA388726 | ENA
Project description:<p>The Research for Precision Oncology Program (RePOP) is a research activity that establishes a cohort of Veterans diagnosed with cancer and who have had genomic analyses performed on their tumor tissue as part of standard of care. All data relevant to a patient's cancer and cancer care will be collected under RePOP, including patient demographics, co-morbidities, genomic analysis, treatments, medications, lab values, imaging studies, and outcomes. All RePOP participants will have signed/verbal informed consent and signed HIPAA authorization to have their data stored and shared from RePOP's Precision Oncology Program Data Repository (PODR). </p> <p>The Applied Proteogenomics OrganizationaL Learning and Outcomes (APOLLO) network is a collaboration between NCI, the Department of Defense (DoD), and the Department of Veterans Affairs (VA) to incorporate proteogenomics into patient care as a way of looking beyond the genome, to the activity and expression of the proteins that the genome encodes. The emerging field of proteogenomics aims to better predict how patients will respond to therapy by screening their tumors for both genetic abnormalities and protein information, an approach that has been made possible in recent years due to advances in proteomic technology. </p>
| phs001374 | dbGaP
Project description:Myocardial Applied Genomics Network (MAGNet) Study
| PRJNA433593 | ENA
Project description:Myocardial Applied Genomics Network (MAGNet) Study
| PRJNA433594 | ENA
Project description:Proteogenomics methods have identified many non-annotated protein-coding genes in the human genome. Many of the newly discovered protein-coding genes encode peptides and small proteins, referred to collectively as microproteins. Microproteins are produced through ribosome translation of small open reading frames (smORFs). The discovery of many smORFs reveals a blind spot in traditional gene-finding algorithms for these genes. Biological studies have found roles for microproteins in cell biology and physiology, and the potential that there exists additional bioactive microproteins drives the interest in detection and discovery of these molecules. A key step in any proteogenomics workflow is the assembly of RNA-Seq data into likely mRNA transcrips that are then used to create a searchable protein databases. Here we demonstrate that specific features of the assembled transcriptome impact microprotein detection by shotgun proteomics. By tailoring transcript assembly for downstream mass spectrometry searching, we show that we can detect more than double the number of high-quality microprotein candidates and introduce a novel open-source mRNA assembler for proteogenomics (MAPS) that incorporates all of these features. By integrating our specialized assembler, MAPS, and a popular generalized assembler into our proteogenomics pipeline, we detect 45 novel human microproteins from a high quality proteogenomics dataset of a human cell line. We then characterize the features of the novel microproteins, identifying two classes of microproteins. Our work highlights the importance of specialized transcriptome assembly upstream of proteomics validation when searching for short and potentially rare and poorly conserved proteins.
2017-12-20 | GSE92659 | GEO
Project description:We have develop a proteogenomics-based approach for identification of human MHC class I-associated peptides, including those deriving from polymorphisms, mutations and non-canonical reading frames
2016-12-22 | GSE67174 | GEO
Project description:Pilocytic astrocytoma (PA) is the most common pediatric brain tumor and driven by aberrant MAPK signaling, typically mediated by BRAF alterations. While five-year overall survival rates exceed 95%, tumor recurrence constitutes a major clinical challenge in incompletely resected tumors despite chemotherapeutic or radiation based therapies. Therefore, we used proteogenomics to discern the biological heterogeneity of PA to improve classification of this tumor entity and identify novel therapeutic targets. Our proteogenomics approach integrates RNA sequencing and LC/MS-based proteomic profiling data from a cohort of 58 confirmed, primary PA samples. An integrative genomics approach was conducted to discern the biological heterogeneity of PA and to identify aberrant pathway activation in these biological subgroups. In summary, pilocytic astrocytomas segregate into two groups where younger patients are significantly associated with Group 1. Importantly, we validate the two distinct biological subgroups in two non-overlapping cohorts. The biological heterogeneity seen here may improve biological classification and reveal novel therapeutic targets specifically useful for non-resectable tumors with high risk of recurrent or progressive disease.
2023-09-20 | GSE210353 | GEO
Project description:Despite their enormous importance, the molecular circuits that control the differentiation of Th17 cells remain largely unknown. Recent studies have reconstructed regulatory networks in mammalian cells, but have focused on short-term responses and relied on perturbation approaches that cannot be applied to primary T cells. Here, we develop a systematic strategy – combining transcriptional profiling at high temporal resolution, novel computational algorithms, and innovative nanowire-based tools for performing gene perturbations in primary T cells – to derive and experimentally validate a temporal model of the dynamic regulatory network that controls Th17 differentiation. The network is arranged into two self-reinforcing and mutually antagonistic modules that either suppress or promote Th17 differentiation. The two modules contain 12 novel regulators with no previous implication in Th17 differentiation, which may be essential to maintain the appropriate balance of Th17 and other CD4+ T cell subsets. Overall, our study identifies and validates 39 regulatory factors that are embedded within a comprehensive temporal network and identifies novel drug targets and organizational principles for the differentiation of Th17 cells. DNA binding of TSC22D3 in Th17 cells compared to WCE
2013-03-07 | E-GEOD-43949 | biostudies-arrayexpress
Project description:Despite their enormous importance, the molecular circuits that control the differentiation of Th17 cells remain largely unknown. Recent studies have reconstructed regulatory networks in mammalian cells, but have focused on short-term responses and relied on perturbation approaches that cannot be applied to primary T cells. Here, we develop a systematic strategy – combining transcriptional profiling at high temporal resolution, novel computational algorithms, and innovative nanowire-based tools for performing gene perturbations in primary T cells – to derive and experimentally validate a temporal model of the dynamic regulatory network that controls Th17 differentiation. The network is arranged into two self-reinforcing and mutually antagonistic modules that either suppress or promote Th17 differentiation. The two modules contain 12 novel regulators with no previous implication in Th17 differentiation, which may be essential to maintain the appropriate balance of Th17 and other CD4+ T cell subsets. Overall, our study identifies and validates 39 regulatory factors that are embedded within a comprehensive temporal network and identifies novel drug targets and organizational principles for the differentiation of Th17 cells. Time course microarray data for Th17 differentiation, including Th0 control
2013-03-07 | E-GEOD-43955 | biostudies-arrayexpress