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• Targeted sequencing

  Paired-end BAM files with associated index files of 15 AML samples were generated using Agilent SureSelect XT library capture system using a custom panel as described before Takahashi et al Blood 2018. Libraries were sequenced using Illumina HiSeq 2500.

  Dataset EGAD00001007671

• RNA-seq for identification of neotranscripts

  This dataset contains RNA-seq data (Fastq files of paired-end data) of 18 patient tumors used for identification of neotranscripts in 18 different types of fusion-driven sarcomas and other cancers as described in Vibert et al., Mol Cell 2022 (PMID: 35550257)

  Dataset EGAD00001008832

• sQTL summary statistics

  This dataset contains the sQTL summary statistics (nominal pass, significant sQTLs). sQTL mapping was performed with QTLtools. This dataset was generated as part of the following study: Panousis et al (2019). Combined genetic and transcriptome analysis of patients with SLE: Distinct, targetable signatures for susceptibility and severity.

  Dataset EGAD00001005042

• DKFZ-St.Jude Medulloblastoma - 225 clinical cases, control exomes with some paired tumor.

  225 clinical cases, control exomes with some paired tumor data, sequenced on Illumina machines from the paper "Germline Elongator mutations in Sonic Hedgehog medulloblastoma" (Waszak et al. 2020 Nature).

  Dataset EGAD00001006665

• Carcinoid study - WGS dataset

  Fernandez-Cuesta et al., 2014, Nature Communication, Whole genome sequencing was performed using a read length of 2x100 bp for all samples. On average, 110 Gb of sequence were produced per sample, aiming a mean coverage of 30x for both tumour and matched normal.

  Dataset EGAD00001000813

• DKFZ-St.Jude Medulloblastoma - 41 MB germline cases, exome data

  Germline exome tumor/control pairs for 41 medulloblastoma cases, MBG cohort sequenced on Illumina machines from the paper "Germline Elongator mutations in Sonic Hedgehog medulloblastoma" (Waszak et al. 2020 Nature).

  Dataset EGAD00001006658

• NIDDK IBD Genetics Consortium Repository Immunochip

  The Immunochip is a custom Illumina Infinium chip comprising 196,524 SNPs and small indels selected primarily based on GWAS analysis of 12 autoimmune and inflammatory diseases. The chip has two purposes: fine mapping of 289 established associations corresponding to 187 distinct loci, and deep replication of suggestive, but not yet proven, associations. Fine-mapping regions were defined as 0.2cM centered on GWAS hit SNPs, and all SNPs and short indels in these regions from the 1000 Genomes Project low coverage pilot CEU samples, as well as variants discovered in resequencing experiments conducted by groups collaborating in the chip design were selected for inclusion. Replication of autoimmune and inflammatory GWAS (including Crohn's disease and ulcerative colitis) contributed the bulk of the remaining SNP lists. Approximately 25,000 SNPs were included as replication of unrelated diseases as part of the WTCCC2 project, which serve as useful null SNPs for these analyses. In total, approximately 240,000 SNPs were selected for inclusion, with an assay design success rate of ~80%. Reprinted from Jostins et al. (2012), with permission from Springer Nature (see https://www.nature.com/reprints/permission-requests.html). Jostins, L., et al. (2012) Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature, 491, 119-24. PMID: 23128233.

  Study phs001721

• Profiling RNA Translation in Pediatric Medulloblastoma

  This study provides RNA-sequencing and ribosome profiling data for patient-derived cell lines and patient tissue samples for children with medulloblastoma. Ribosome profiling is a variant protocol of RNA-sequencing that directly sequences ribosome-bound RNA fragments only. Associated RNA-seq and Ribo-seq data obtained separately for some cancer cell lines can be found on the NCBI SRA as PRJNA957428. Samples were processed for poly-A mRNA sequencing using the Roche Kapa Kit. Ribosome profiling was performed as described in the manuscript referenced below (Hofman et al.) and based on the article by McGlincy et al., Methods (2017). Ribo-seq data were analyzed for sample quality using RiboseQC (Calviello, Nat Struct Mol Biol, 2020). These data were used to quantify P-sites of open reading frames. RNAseq and ribo-seq data were integrated and compared to determine translational efficiency values using TPM (Ribo-seq) over TPM (RNA-seq) as the metric. Ribo-seq data on 14 samples and RNAseq data on 21 samples derived from 16 fresh-frozen surgical samples for medulloblastoma and 5 autopsy samples are available through dbGaP. A second cohort of 4 medulloblastoma samples (RNAseq, Ribo-seq) related to this study can be found on the EGA at EGAS00001007426.

  Study phs003446

• A Somatic Reference Standard for Cancer Genome Sequencing

  In this study, we separately performed PCR-free whole genome sequencing on the paired tumor/normal melanoma COLO-829 and lymphoblastoid COLO-829BL cell lines across three separate institutions (TGen, Illumina, Genome Sciences Centre at the British Columbia Cancer Agency). We performed a meta-analysis of all data, in combination with the originally reported analysis of these cell lines by Pleasance et al., and report a somatic reference standard based on consensus events. This standard can be used as a reference for tumor/normal whole genome analyses.

  Study phs000932

• Resistance to Latent Mycobacterium tuberculosis Infection in Uganda: Immunologic Profiling

  Despite significant exposure to Mycobacterium tuberculosis (Mtb), some humans remain uninfected, as defined by persistently negative immune sensitization tests (tuberculin skin test, TST; interferon gamma release assays, IGRA). To explore mechanisms of natural resistance to Mtb infection, we profiled tissues from highly-exposed Mtb contacts from Uganda. The cohort from this submission were household contacts of pulmonary tuberculosis cases who were followed longitudinally over 8 years and maintained negative TST and IGRA testing on serial evaluations. This cohort is well-described (Stein et al. 2019, PMID: 30165605).

  Study phs002445