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• Genome-wide cell-free DNA termini in patients with cancer

  The structure, fragmentation pattern, length and terminal sequence of cell-free DNA (cfDNA) is under the influence of nucleases present in the blood. We hypothesized that differences in the diversity of bases at the end of cfDNA fragments can be leveraged on a genome-wide scale to enhance the sensitivity for detecting the presence of tumor signals in plasma. We surveyed the cfDNA termini in 72 plasma samples from 319 patients with 18 different cancer types using low-coverage whole genome sequencing. The fragment-end sequence and diversity were altered in all cancer types in comparison to 76 healthy controls. We converted the fragment end sequences into a quantitative metric and observed that this correlates with circulating tumor DNA tumor fraction (R = 0.58, p < 0.001, Spearman). Using these metrics, we were able to classify cancer samples from control at a low tumor content (AUROC of 91% at 1% tumor fraction) and shallow sequencing coverage (mean AUROC = 0.99 at >1M fragments). Combining fragment-end sequences and diversity using machine learning, we classified cancer from healthy controls (mean AUROC = 0.99, SD = 0.01). Using unsupervised clustering we showed that early-stage lung cancer can be classified from control or later stages based on fragment-end sequences. We observed that fragment-end sequences can be used for prognostication (hazard ratio: 0.49) and residual disease detection inresectable esophageal adenocarcinoma patients, moving fragmentomics toward a greater clinical implementation.

  Study EGAS00001005738

• Genome-wide cell-free DNA termini in patients with cancer

  The structure, fragmentation pattern, length and terminal sequence of cell-free DNA (cfDNA) is under the influence of nucleases present in the blood. We hypothesized that differences in the diversity of bases at the end of cfDNA fragments can be leveraged on a genome-wide scale to enhance the sensitivity for detecting the presence of tumor signals in plasma. We surveyed the cfDNA termini in 72 plasma samples from 319 patients with 18 different cancer types using low-coverage whole genome sequencing. The fragment-end sequence and diversity were altered in all cancer types in comparison to 76 healthy controls. We converted the fragment end sequences into a quantitative metric and observed that this correlates with circulating tumor DNA tumor fraction (R = 0.58, p < 0.001, Spearman). Using these metrics, we were able to classify cancer samples from control at a low tumor content (AUROC of 91% at 1% tumor fraction) and shallow sequencing coverage (mean AUROC = 0.99 at >1M fragments). Combining fragment-end sequences and diversity using machine learning, we classified cancer from healthy controls (mean AUROC = 0.99, SD = 0.01). Using unsupervised clustering we showed that early-stage lung cancer can be classified from control or later stages based on fragment-end sequences. We observed that fragment-end sequences can be used for prognostication (hazard ratio: 0.49) and residual disease detection inresectable esophageal adenocarcinoma patients, moving fragmentomics toward a greater clinical implementation.

  Study EGAS00001005747

• Targeted gene fusion sequencing (Fus-seq) in mesothelioma

  This is a small pilot data set to test the feasibility of cDNA exomes across 1200 cancer cell line panel. cDNA exomes or Fus-seq is further explained in this studies Abstract.

  Dataset EGAD00001000361

• Mucosal Melanoma transcriptomes

  This is an RNA sequencing dataset of 101 mucosal melanoma tumors. The original corresponding study is titled "Diversity of the immune microenvironment and response to checkpoint inhibitor immunotherapy in mucosal melanoma" by J.L. Vos et al., published in JCI Insight.

  Dataset EGAD50000000892

• Genetic architecture of disease in Greenland is shaped by demographic history, fine-structure, and selection - WGS data

  This file set has 448 Greenlandic individuals sequenced using Illumina 150 Paired end sequencing and has an average sequencing depth of 35X. The data is unfiltered, in VCF format, and covers 19.751.308 variants.

  Dataset EGAD50000000933

• Next-generation sequencing-based comprehensive genetic analysis of undiagnosed disease

  The purpose of this study is to perform comprehensive genetic analysis using next-generation sequencers to make genetic diagnosis of "undiagnosed diseases".

  Study JGAS000522

• Department of Human Genetics at Yokohama City University (YCU) — Data Access Committee

  The Data Access Committee at Yokohama City University (Department of Human Genetics) is overseeing controlled access to data for our research manuscripts. Access is granted to bona fide researchers in line with the Data Access Agreement and DUO restrictions.

  Dac EGAC50000000770

• Helleday_HRAS_Project

  This experiment is looking at the mutational signatures generated by engineered HRAS mutations by using whole genome sequence generated on massively parallel next generation sequencers.

  Study EGAS00001000332

• Landscape and significance of multiple mutations in oncogenes.

  This study is a comprehensive pan-cancer study of oncogenic multiple mutations (MMs) which includes long-read whole-genome sequencing data (n = 3).

  Study EGAS00001003763

• Illumina genome sequencing data for HICF2 craniosynostosis families (Genome Medicine)

  BAM files for two families recruited to the HICF2 genome sequencing project due to craniosynostosis. One family is a singleton and the other is an affected mother-daughter duo.

  Dataset EGAD00001011373

• ChIP seq samples

  Dataset contains 12 ChIP-Seq samples sequenced on the Illumina NextSeq500 sequencer. 3 samples are control samples (HcAct-Chip 1,HcAct-Chip 2,HcAct-Imput 2) 9 samples are case samples (HcAct2DG-Chip 1,HcAct2DG-Chip 2,HcAct2DG-Imput 1,JIAAct-Chip1,JIAAct-Chip2,JIAAct-Imput2,JIAAct2DG-Chip1,JIAAct2DG-Chip2,JIAAct2DG-Imput2)

  Dataset EGAD50000001188

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Resveratrol from donor D3, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008059

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Oligomycin from donor D2, replicate 1, glucose concentration 1mM

  Dataset EGAD00001008055

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Rotenone from donor D3, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008061

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Oligomycin from donor D3, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008063

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Oligomycin from donor D3, replicate 1, glucose concentration 1mM

  Dataset EGAD00001008065

• Transcriptome of human CD4+ T-lymphyocyte (naive-T-cells) treated with Resveratrol from donor D1, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008067

• Transcriptome of human CD4+ T-lymphyocyte (regulatory-T-cells) treated with Resveratrol from donor D1, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008069

• Transcriptome of human CD4+ T-lymphyocyte (naive-T-cells) treated with Resveratrol from donor D2, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008071

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Resveratrol from donor D1, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008039

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Rotenone from donor D1, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008041

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Oligomycin from donor D1, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008043

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Oligomycin from donor D1, replicate 1, glucose concentration 1mM

  Dataset EGAD00001008045

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Resveratrol from donor D2, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008049

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Rotenone from donor D2, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008051