Homo sapiens
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ABSTRACT:
PROVIDER: PRJNA350185 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Clinical and Genetic Evaluations of Individuals with Undiagnosed Disorders Through the Undiagnosed Diseases Network
| PRJNA350184 | ENA
Project description:The deposited data were collected from 148 patients and 133 family members accepted into the Undiagnosed Diseases Network (https://undiagnosed.hms.harvard.edu/). The NIH Common Fund Undiagnosed Diseases Network (UDN) seeks to provide diagnoses for individuals with undiagnosed disease. Here, we report and provide the mass spectrometry-based metabolomics (GC-MS) and lipidomics (LC-MS/MS) analyses of urine from 148 patients and 133 family members. We have deposited mass spectrometry-based metabolomics and lipidomics files including instrument files, normalized data processed files to allow for statistical analysis, and metabolomics and lipidomics results for each patient and associated relatives. In addition, as part of the mass spectrometry data made available, we have included mass spectrometry analyses and results from a reference population of individuals with no known metabolic diseases. UDN patients suffer from undiagnosed diseases and thus are typically represented as a sample size of one; therefore, understanding normal variation within a proband's condition needs to be measured against of dataset of normal individuals, which is included here.
2019-12-17 | MSV000084717 | MassIVE
Project description:The deposited data were collected from 148 patients and 133 family members accepted into the Undiagnosed Diseases Network (https://undiagnosed.hms.harvard.edu/). The NIH Common Fund Undiagnosed Diseases Network (UDN) seeks to provide diagnoses for individuals with undiagnosed disease. Here, we report and provide the mass spectrometry-based metabolomics (GC-MS) and lipidomics (LC-MS/MS) analyses of blood plasma from 148 patients and 133 family members. We have deposited mass spectrometry-based metabolomics and lipidomics files including instrument files, normalized data processed files to allow for statistical analysis, and metabolomics and lipidomics results for each patient and associated relatives. In addition, as part of the mass spectrometry data made available, we have included mass spectrometry analyses and results from a reference population of individuals with no known metabolic diseases. UDN patients suffer from undiagnosed diseases and thus are typically represented as a sample size of one; therefore, understanding normal variation within a proband's condition needs to be measured against of dataset of normal individuals, which is included here.
2019-12-17 | MSV000084716 | MassIVE
Project description:The deposited data were collected from 148 patients and 133 family members accepted into the Undiagnosed Diseases Network (https://undiagnosed.hms.harvard.edu/). The NIH Common Fund Undiagnosed Diseases Network (UDN) seeks to provide diagnoses for individuals with undiagnosed disease. Here, we report and provide the mass spectrometry-based metabolomics (GC-MS) and lipidomics (LC-MS/MS) analyses of cerebrospinal fluid from 148 patients and 133 family members. We have deposited mass spectrometry-based metabolomics and lipidomics files including instrument files, normalized data processed files to allow for statistical analysis, and metabolomics and lipidomics results for each patient and associated relatives. In addition, as part of the mass spectrometry data made available, we have included mass spectrometry analyses and results from a reference population of individuals with no known metabolic diseases. UDN patients suffer from undiagnosed diseases and thus are typically represented as a sample size of one; therefore, understanding normal variation within a proband's condition needs to be measured against of dataset of normal individuals, which is included here.
2019-12-17 | MSV000084715 | MassIVE
Project description:Purpose: Evaluation of XCI status in a cohot of female patients with suspected rare genetic diseases using exome and RNA sequencing Results: We developed a method for estimating X inactivation status, using exome and transcriptome sequencing data from 112 female samples. We built a reference model for evaluation of XCI in 135 females from the GTEx consortium. We tested and validated the model on 14 female individuals with different types of undiagnosed rare genetic disorders who were clinically tested for X-skew using the AR gene assay and compared results to our outlier-based analysis technique. In comparison to the AR clinical test for identification of X inactivation, our method was concordant with AR method in 9 samples, discordant in 3, and provided measures of X inactivation in 2 samples with uninformative clinical results. We applied this method on an additional 98 females presenting to the clinic with phenotypes consistent with different hereditary disorders without a known genetic diagnosis. Here we show the use of transcriptome sequencing data to provide an accurate and complete estimation of X-inactivation and skew status in female patients.
2024-04-24 | GSE234607 | GEO
Project description:<p>The Undiagnosed Diseases Network (UDN) is an initiative to facilitate the diagnosis of conditions that have eluded diagnosis through the coordinated action of leading clinical and research centers. The purpose of this cooperative research network is to establish a national network added to and building upon the NIH Undiagnosed Diseases Program (NIH UDP). The objectives of this program are to: </p> <ol> <li>Improve the level of diagnosis and care for patients with undiagnosed diseases through the development of common protocols designed by a community of investigators; </li> <li>Facilitate research into the etiology of undiagnosed diseases, by collecting and sharing standardized, high-quality clinical and laboratory data including genotyping, phenotyping, and documentation of environmental exposures; and </li> <li>Create an integrated and collaborative research community across multiple clinical sites and among laboratory and clinical investigators prepared to investigate the pathophysiology of these new and rare diseases and share this understanding to identify improved options for optimal patient management.</li> </ol>
| phs001232 | dbGaP
Project description:The deposited data were collected from 148 patients and 133 family members accepted into the Undiagnosed Diseases Network (https://undiagnosed.hms.harvard.edu/). The NIH Common Fund Undiagnosed Diseases Network (UDN) seeks to provide diagnoses for individuals with undiagnosed disease. Here, we report and provide the mass spectrometry-based metabolomics (GC-MS) and lipidomics (LC-MS/MS) analyses of urine from 148 patients and 133 family members. We have deposited mass spectrometry-based metabolomics and lipidomics files including instrument files, normalized data processed files to allow for statistical analysis, and metabolomics and lipidomics results for each patient and associated relatives. In addition, as part of the mass spectrometry data made available, we have included mass spectrometry analyses and results from a reference population of individuals with no known metabolic diseases. UDN patients suffer from undiagnosed diseases and thus are typically represented as a sample size of one; therefore, understanding normal variation within a proband's condition needs to be measured against of dataset of normal individuals, which is included here.
2019-12-17 | MSV000084717 | GNPS
Project description:The deposited data were collected from 148 patients and 133 family members accepted into the Undiagnosed Diseases Network (https://undiagnosed.hms.harvard.edu/). The NIH Common Fund Undiagnosed Diseases Network (UDN) seeks to provide diagnoses for individuals with undiagnosed disease. Here, we report and provide the mass spectrometry-based metabolomics (GC-MS) and lipidomics (LC-MS/MS) analyses of blood plasma from 148 patients and 133 family members. We have deposited mass spectrometry-based metabolomics and lipidomics files including instrument files, normalized data processed files to allow for statistical analysis, and metabolomics and lipidomics results for each patient and associated relatives. In addition, as part of the mass spectrometry data made available, we have included mass spectrometry analyses and results from a reference population of individuals with no known metabolic diseases. UDN patients suffer from undiagnosed diseases and thus are typically represented as a sample size of one; therefore, understanding normal variation within a proband's condition needs to be measured against of dataset of normal individuals, which is included here.
2019-12-17 | MSV000084716 | GNPS
Project description:The deposited data were collected from 148 patients and 133 family members accepted into the Undiagnosed Diseases Network (https://undiagnosed.hms.harvard.edu/). The NIH Common Fund Undiagnosed Diseases Network (UDN) seeks to provide diagnoses for individuals with undiagnosed disease. Here, we report and provide the mass spectrometry-based metabolomics (GC-MS) and lipidomics (LC-MS/MS) analyses of cerebrospinal fluid from 148 patients and 133 family members. We have deposited mass spectrometry-based metabolomics and lipidomics files including instrument files, normalized data processed files to allow for statistical analysis, and metabolomics and lipidomics results for each patient and associated relatives. In addition, as part of the mass spectrometry data made available, we have included mass spectrometry analyses and results from a reference population of individuals with no known metabolic diseases. UDN patients suffer from undiagnosed diseases and thus are typically represented as a sample size of one; therefore, understanding normal variation within a proband's condition needs to be measured against of dataset of normal individuals, which is included here.
2019-12-17 | MSV000084715 | GNPS
Project description:The deposited data were collected from 148 patients and 133 family members accepted into the Undiagnosed Diseases Network (https://undiagnosed.hms.harvard.edu/). The NIH Common Fund Undiagnosed Diseases Network (UDN) seeks to provide diagnoses for individuals with undiagnosed disease. Here, we report and provide the mass spectrometry-based metabolomics (GC-MS) and lipidomics (LC-MS/MS) analyses of cerebrospinal fluid from 148 patients and 133 family members. We have deposited mass spectrometry-based metabolomics and lipidomics files including instrument files, normalized data processed files to allow for statistical analysis, and metabolomics and lipidomics results for each patient and associated relatives. In addition, as part of the mass spectrometry data made available, we have included mass spectrometry analyses and results from a reference population of individuals with no known metabolic diseases. UDN patients suffer from undiagnosed diseases and thus are typically represented as a sample size of one; therefore, understanding normal variation within a proband's condition needs to be measured against of dataset of normal individuals, which is included here.
2020-05-29 | MSV000085506 | GNPS