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• Dataset for neuroendocrine_adrenal_tumor-RNA

  Rare cancer sequencing data of 145 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008862

• Methylation Profiles of Cell-Free DNA Using Nanopore Sequencing

  Epigenetic characterization of cell-free DNA (cfDNA) is an emerging approach for detecting and characterizing diseases such as cancer. We developed a strategy using nanopore-based single-molecule sequencing to measure cfDNA methylomes. This approach generated up to hundreds of millions of reads for a single cfDNA sample from cancer patients, an order of magnitude improvement over existing nanopore sequencing methods. We developed a single-molecule classifier to determine whether individual reads originated from a tumor or immune cells. Leveraging methylomes of matched tumors and immune cells, we characterized cfDNA methylomes of cancer patients for longitudinal monitoring during treatment.Control subjects consist of healthy individuals without a positive diagnosis of cancer. cfDNA was obtained through purification of plasma from blood draws. Genomic DNA (gDNA) was derived from extraction of primary tumor tissue or from leukocytes from blood draws.

  Study phs002950

• Integrated Exome and RNA Sequencing of Dedifferentiated Liposarcoma

  Multiomics analysis of rare cancers to identify genetic alterations related with pathogenesis and prognosis. To identification of genetics alterations of Dedifferentiated liposarcoma, DNAs and RNAs were extracted from dedifferentiated liposarcoma and paired non-cancer (normal) tissues. NGS libaraies were prepared by using Agilent SureSelect XT Human All Exon V5 + IncRNA kit for whole exome sequencing (WES) and TruSeq Stranded mRNA Prep kit or TruSeq RNA Access Library Prep kit (RNA seq). Sequencing was perfomred by Illumina HiSeq 2500.

  Study JGAS000177

• Cachexia - Non-Cachexia Metagenome Analysis

  Cancer cachexia has been linked to gut bacterial alterations, but alterations of gut viruses, mostly bacteriophages, have not yet been explored. We performed shotgun metagenomic sequencing of DNA from stool samples of 78 cachectic and 42 non-cachectic cancer patients. K-mer-based matching to reference databases revealed abundance variations of bacteria and viruses. Beyond bacterial alterations, cachectic patients exhibited significantly lower bacteriophage abundance, predominantly affecting Caudovirales and Siphoviridae species (double-stranded DNA) but also Inoviridae and Microviridae families (single-stranded DNA).

  Study EGAS00001007156

• Development and Validation of a Prognostic and Predictive 32-Gene Signature for Gastric Cancer

  We applied this signature to a 567-patient GC cohort to establish genomic-based molecular subtypes and then used a support vector machine to build a molecular subtype-based risk-scoring model. Both source code and supplementary datasets for risk score prediction are available at https://github.com/hwanglab/Yonsei_gastric_cancer_32genes.

  Dataset EGAD00001008091

• Microdissected Pancreatic Cancer Whole Exome Sequencing

  109 micro-dissected pancreatic cancer cases and normal control tissue were subjected to whole-exome sequencing. Microdissection enriches tumor cellularity and enhances mutation calling.

  Study phs000953

• Clinical and neuroimaging study on preclinical Alzheimer's disease.

  This clinical study aims to discriminate MCI individuals at risk for development of Alzheimer dementia, as well as preclinical AD without clinical manifestations.

  Study JGAS000272

• scRNA-seq of monochorionic dizygotic twin chimera PBMC

  Single cell RNA-seq of PBMC of monochorionic dizygotic twin chimeras. 10x chromium v3.1 was used to generate barcoded scRNA library.

  Study EGAS50000000097

• WES of Adenoid Cystic Carcinoma Treated with Axitinib and Avelumab

  To determine potential mutational biomarkers of response to combination axitinib and avelumab, whole exome sequencing was performed on 24 adenoid cystic carcinomas treated with combination axitinib and avelumab with paired normal samples.

  Dataset EGAD50000002467

• Single-cell genotype-to-phenotype (scG2P) of normal esophageal epithelium

  Single-cell genotype-to-phenotype (scG2P) of normal esophageal epithelium, based on targeted DNA genotyping of mutation hot spots and targeted RNA capture.

  Dataset EGAD50000002066