14512 results for "PLOS+ONE+Pennsylvania+German+ACS+language+code+pdc+open+access+study"

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• Admixture histories of São Tomé e Príncipe.

  In this study, we aim to reconstruct the complex genetic admixture histories of the population of São Tomé e Príncipe, an archipelago in the Gulf of Guinea that experienced successive waves of forced and deliberate migration from Africa since the 15th century, during and after the Trans-Atlantic Slave Trade.

  Study EGAS50000000920

• Dataset of DNA methylation profiles of 189 pediatric central nervous system, soft tissue, and bone tumors

  The research of CNS tumors brought knowledge about the role of epigenetics in these tumors. Certain subgroups of tumors are defined solely by their epigenetic profiles. In this study we carried on epigenetic profiling using targeted methylation sequencing of aproximately 180 pediatric CNS and Sarcoma tumor samples.

  Study EGAS50000000051

• Development of actionable molecular targets and/or biomarkers for prognostication and patient stratification in gynecological cancer based on whole-exome sequencing and epigenomic analysis

  We collected 92 endometrial hyperplasia lesions (48 non-atypical from 30 cases and 44 atypical from 24 cases) and matched normal tissues/whole blood. The purpose of this study is to develop actionable molecular targets and/or biomarkers for prognostication and patient stratification.

  Study JGAS000642

• LongVar low-coverage data

  This dataset includes sequencing data from participants of the LongVar study. Specifically, it comprises low-coverage whole genome sequencing (WGS) data which was derived from cell-free DNA from blood samples. These data were generated to compare imputation accuracy. 133 individuals agreed to share their data, and are healthy volunteers.

  Study EGAS50000001114

• Whole genome sequence: cardiomyopathy, 1 HCM patient

  In this study, we analyzed the genomic information by whole genome sequencing using peripheral blood samples from patients with various cardiovascular diseases including severe heart failure to identify genetic variants related to the pathology of cardiovascular diseases. The purpose is to elucidate the underlying mechanism and develop new therapeutic interventions for cardiovascular disease.

  Study JGAS000704

• Whole genome sequence: cardiomyopathy, 1 ARVC patient

  In this study, we analyzed the genomic information by whole genome sequencing using peripheral blood samples from patients with various cardiovascular diseases including severe heart failure to identify genetic variants related to the pathology of cardiovascular diseases. The purpose is to elucidate the underlying mechanism and develop new therapeutic interventions for cardiovascular disease.

  Study JGAS000705

• Germline WES-data of pediatric cancer patients with variants in HBOC-related genes

  This study focused on the identification of (likely) pathogenic germline variants (LP/PV) in 25 adult-onset HBOC-related genes in a cohort of 372 pediatric cancer patients. The cohort was sequenced on Illumina platforms. The files uploaded here belong to the 27 individuals with such a LP/PV.

  Study EGAS50000001073

• T-cell receptor repertoire profiling (PBMC)

  This study assessed the TCR repertoire in peripheral blood from patients before, during and after neoadjuvant chemoimmunotherapy. TCR beta chain sequences in RNA from peripheral blood were amplified with the OncomineTM long read assay, templated and sequenced. TCR metrics such as clonality, evenness and diversity were assessed in relation with response metrics.

  Study EGAS50000001137

• T-cell receptor repertoire profiling (FFPE tissue)

  This study assessed the TCR repertoire in tumor tissue from NSCLC patients before, during and after neoadjuvant chemoimmunotherapy. TCR beta chain sequences in RNA from FFPE tissue were amplified with the OncomineTM short read assay, templated and sequenced. TCR metrics such as clonality, evenness and diversity were assessed in relation with response metrics.

  Study EGAS50000001138

• GTestimate: Improving relative gene expression estimation in scRNA-seq using the Good-Turing estimator

  This study introduces a novel scRNA-seq normalization method, which takes unobserved genes into account. To validate GTestimate we designed a cell targeted amplification strategy, enabling us to sequence a small set of cells twice, once at a "typical" sequencing depth and once at a "ultra-deep" sequencing depth.

  Study EGAS50000000915