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• Genomic sequencing data for PNG15 and PNG16

  This study includes both long- and short-read sequencing data for PNG15 and PNG16 as well as their phased haplotype assemblies.

  Study EGAS50000001105

• Semibulk RNA-seq analysis as a convenient method for measuring gene expression statuses in a local cellular environment

  For two cases of breast cancers, semibulk RNA-seq and single cell RNA-seq were performed.

  Study JGAS000387

• Whole genome sequencing and transcriptome sequencing data for Burkitt lyphomas

  Whole genome sequencing data for 101 BL patients and transcriptome sequencing for 82 (out of 101) BL patients.

  Dataset EGAD00001005781

• DATA FILES FOR SJAMLM7

  DATA FILES FOR SJAMLM7

  Dataset EGAD00001000259

• Medulloblastoma WES

  WES for tumor and normal samples

  Study EGAS50000000261

• The data access committee for "Validation of cfDNA fragmentome analyses for early detection of liver cancer"

  Dac EGAC00001003543

• Deep_sequencing_of_melanoma_for_driver_mutations

  Deep sequencing of melanoma for driver mutations

  Study EGAS00001000857

• RNA-seq of longitudinal human blood and nose swab samples from a controlled human infection experiment with SARS-CoV-2 virus

  Controlled human infection experiments enable longitudinal profiling of immune responses to a pathogen. 36 healthy volunteers aged 18-29 years, with no evidence of previous infection or vaccination, were inoculated with SARS-CoV-2 virus and quarantined for 14 days. Blood samples (n=374) for RNA sequencing were collected into PAXgene tubes before virus challenge, 6 hours after challenge, daily thereafter for 14 days and on day 28. Mid-turbinate nose swabs (n=96) for RNA sequencing were collected before virus challenge, and on days 1, 3, 5, 7, 10 and 14 after challenge, preserved in RNAprotect. 18 of 36 participants developed a replicative SARS-CoV-2 infection as evidenced by consecutive PCR-positive swabs for the virus. For every participant, blood RNA from selected days were extracted and depleted for genomic DNA and globin mRNA, before cDNA libraries were constructed using KAPA RNA HyperPrep with RiboErase kits. Libraries were sequenced on the Illumina NovaSeq 6000 platform using NovaSeq 6000 S4 Reagent Kits (200 cycles). Nose swab RNA samples were extracted and depleted for genomic DNA before cDNA libraries were constructed using KAPA mRNA HyperPrep Kits. Libraries were sequenced on the Illumina NextSeq platform the using the NextSeq 500/550 High Output Kit (75 cycles).

  Dataset EGAD50000000942

• Deep amplicon sequencing to infer malignant clonal populations for The interface of malignant and immunologic clonal dynamics in high-grade serous ovarian cancer

  A total of 192 positions per patient were deeply sequenced in each corresponding tumor sample (including 4 experimental controls and SNVs predicted to originate at each node of the sample phylogeny, see Zhang et al. for details). Genomic DNA templates were used as starting material to generate PCR products. PCR was set up using Phusion DNA polymerase according to the manufacturer’s specifications. The standard PCR conditions used were an initial denaturation at 98C for 30 seconds, followed by 35 cycles of 98C for 10 seconds, 60C for 15 seconds and 72C for 8 seconds, and a final extension at 72C for 10 minutes. PCR products were cleaned up using PCRClean DX beads. Amplicons were pooled by template for sequencing sample preparation. Sample preparation involved a second round of amplification using Phusion DNA polymerase with 6 PCR cycles, with primers specified in Zhang et al. DNA quality was assessed using the Caliper LabChip GX HighSensitivity Assay and DNA quantity was measured using a Qubit dsDNA HS assay kit on a Qubit fluorometer. The indexed libraries were pooled together and sequenced on the Illumina NextSeq500 platform with paired-end 150bp reads using v2 chemistry reagents.

  Dataset EGAD00001003986

• Dataset developed for use with EOSC4Cancer of synthetic colorectal cancer tumor/normal pairs.

  This dataset contains 10 tumor and normal pairs synthetic WGS data of colorectal cancer that were simulated in a standard format of Illumina paired-end reads. The NEAT read simulator (version 3.0, https://github.com/zstephens/neat-genreads) was utilized to synthetize these 10 pairs of tumor and normal WGS data. In the procedure of data generation, simulated parameters (i.e., sequencing error statistics, read fragment length distribution and GC% coverage bias) were learned from data models provided by NEAT. The average sequencing depth for tumor and normal samples aimed to reach around 110X and 60X, respectively. For generation of synthetic normal WGS data per each sample, a germline variant profile from a real patient was down-sampled randomly, representing 50% germline variants of a given patient. These were mixed with the other 50% in silico germline variants that were modelled randomly using an average mutation rate (0.001), finally constituting a full germline profile for normal synthetic WGS data. For generation of synthetic tumor WGS data per each sample, a pre-defined somatic short variant profile (SNVs+Indels) learnt from a real CRC patient was added to the germline variant profile used for creating the normal synthetic WGS data of the same patient, consisting of the variants for tumor sample. Neither copy number profile nor structural variation profile was introduced into the tumor synthetic WGS data. Tumor content and ploidy were assumed to be 100% and 2, respectively. For mapping/variant detection, the Sarek pipeline v3.1.2 (https://nf-co.re/sarek/3.1.2) was used, specifically: 1. BWA v0.7.17-r1188 for read mapping 2. GATK v4.3.0.0 for pre-processing BAM file (including markduplicates and recalibration). 2. Mutect2 (GATK v4.3.0.0) for somatic variant calling 3. Strelka2 v2.9.10 for germline and somatic variant calling Metadata information of 10 CRC patients used for the generation of synthetic normal and tumor WGS data: Patient_id Tumor_barcode Normal_barcode Age Sex Tissue Cancer SIM007 SIM007_T SIM007_N 71 F Rectal Primary CRC SIM008 SIM008_T SIM008_N 45 F Colon Neuroendocrine Metastasis CRC SIM010 SIM010_T SIM010_N 62 M Colon Metastasis CRC SIM011 SIM011_T SIM011_N 55 M Colon Neuroendocrine Metastasis CRC SIM012 SIM012_T SIM012_N 57 M Rectal Metastasis CRC SIM013 SIM013_T SIM013_N 69 M Colon Metastasis CRC SIM014 SIM014_T SIM014_N 68 M Colon Neuroendocrine primary CRC SIM015 SIM015_T SIM015_N 58 F Colon Primary CRC SIM016 SIM016_T SIM016_N 49 M Colon/Rectal Primary CRC SIM017 SIM017_T SIM017_N 78 M Colon Neuroendocrine primary CRC

  Dataset EGAD50000000564