12302 results for "cancer rna-seq"

in 28.51 milliseconds.

• Genome and transcriptome sequence data from a metastatic choroidal melanoma patient

  Genome and transcriptome sequence data from a metastatic choroidal melanoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004605

• Genome and transcriptome sequence data from a non-small cell lung carcinoma patient

  Genome and transcriptome sequence data from a non-small cell lung carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004596

• Genome and transcriptome sequence data from a metastatic basal cell carcinoma patient

  Genome and transcriptome sequence data from a metastatic basal cell carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004630

• Genome and transcriptome sequence data from a metastatic breast carcinoma patient

  Genome and transcriptome sequence data from a metastatic breast carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004632

• Genome and transcriptome sequence data from a metastatic renal carcinoma patient

  Genome and transcriptome sequence data from a metastatic renal carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004633

• Genome and transcriptome sequence data from a metastatic adenocarcinoma of the lung patient

  Genome and transcriptome sequence data from a metastatic adenocarcinoma of the lung patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004634

• sWGS of HGSOC samples for fixative comparison study

  Shallow whole-genome sequencing of samples from the study "Methanol-based fixation is superior to buffered formalin for next-generation sequencing of DNA from clinical cancer samples". DNA from each sample (100ng) was sheared on Covaris S220 (Covaris): duty cycle - 10%, intensity -5.0, bursts per sec - 200, duration - 300 sec, mode - frequency sweeping, power - 23V, temperature -5:5 C to 6 C, water level - 13. Libraries were prepared with the TruSeq Nano DNA LT Sample Prep Kit (Illumina) using a modi?ed protocol - Sample Puri?cation Beads were replaced by Agencourt AMPure XP beads (Beckman Coultier) and size selection after the End Repair was done to remove only the short fragments. Quality and quantity for contructed libraries were assessed with DNA 7500 kit on Agilent 2100 Bioanalyzer and with Kapa Quanti?cation kit (KAPA Biosystems) on 7900HT Fast Real-Time PCR System (Applied Biosystems) according to the supplier's recommendations, respectively. Libraries from 18 barcoded samples were pooled together in equimolar amounts and each pool was loaded on a single lane of a HiSeq Single End Flowcell (Illumina), followed by cluster generation on a cBot (Illumina) and sequencing on a HiSeq 2500 (Illumina) in a single-read 50bp mode. Reads were aligned using bwa-mem v0.7.12-r1039 to the 1000 genomes version of human genome build GRCh37. Picard (http://picard.sourceforge.net) was used to remove duplicate reads.

  Dataset EGAD00001001938

• Use of Deep Sequencing to Dectect Clonal Mutations In Sun Exposed Skin Epidermis PART2

  This study aims to define the landscape of somatic mutations in sun exposed human skin by deep sequencing, analyse their frequency and use the data to infer the effect of mutations on proliferating cell behaviour. The frequency of each mutation will reflect the size of the clone of cells in the tissue sample. By analyzing small samples, clones with as few as 100 cells will be detectable. Allele frequency distributions for each mutation will be used to infer cell fate using published methods (Klein et al. 2010). This study will shed unprecedented light on the early clonal events that lead to the emergence of cancer.

  Dataset EGAD00001000825

• Genome and transcriptome sequence data from a metastatic adenoid cystic carcinoma of the breast patient

  Genome and transcriptome sequence data from a metastatic adenoid cystic carcinoma of the breast patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004921

• Genome and transcriptome sequence data from a squamous cell carcinoma of the alveolar ridge patient

  Genome and transcriptome sequence data from a squamous cell carcinoma of the alveolar ridge patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004934

• Tobacco exposure and somatic mutations in normal human bronchial epithelium

  That tobacco smoking causes lung cancer is well-established, but we lack quantitative understanding of its effects on genomes of normal bronchial epithelium. We sequenced whole genomes of 632 colonies derived from single bronchial epithelial cells across 16 subjects. Tobacco smoking is the major influence on mutation burden, adding 1000-10,000+ mutations/cell, massively increasing both between-subject and within-subject variance, and generating several distinct signatures of substitutions and indels. A population of cells in subjects with smoking history had mutation burdens equivalent to that expected for never-smokers: these cells lacked tobacco-specific mutational signatures, were four-fold more frequent in ex-smokers than current smokers, and had significantly longer telomeres than their more mutated counterparts. Driver mutations increased in frequency with age, affecting 4-14% of cells in middle-aged never-smokers. In current smokers, ≥25% of cells carried driver mutations and 0-6% cells had 2 or even 3 drivers. Thus, tobacco smoking increases mutation burden, cell-to-cell heterogeneity and driver mutations, but quitting promotes replenishment of bronchial epithelium from mitotically quiescent cells that have avoided tobacco mutagenesis.

  Dataset EGAD00001005193

• Dataset for soft_tissue_tumor-WHOLE_GENOME

  Rare cancer sequencing data of 142 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008900

• Targeted Myeloid DNA-Panelsequencing, MLL

  Sequencing data from a targeted myeloid DNA-Panelsequnencing at the MLL Dx, Munich lab. Targeted sequencing was performed using the Nextera DNA Flex library preparation kit, starting with 100ng of genomic DNA (Illumina, San Diego, CA, USA). The target regions were enriched by a custom xGen Lockdown panel using a hybridization capture workflow (IDT Integrated DNA Technologies, Coralville, IA, USA). All libraries were sequenced with 100bp paired-end reads on a NovaSeq6000 (Illumina) with a mean coverage of 3206x. Somatic variant calling was performed with Pisces and a sensitivity cut off of 2%. Large deletions and medium-sized insertions, as they are for example found in CALR and FLT3, were called with Pindel. Variant annotation considered the publicly available data bases Cosmic (v91), ClinVar (2020-03), gnomAd (non-cancer, v2.1.1), dbNSFP (v3.5) and UMD TP53 (2017_R2). Variants that are described as somatic, protein truncating or affecting splice sites were considered as mutations while variants with no or discrepant data base information were considered as variant of uncertain significance.

  Dataset EGAD00001008485

• Tumour sample for patient SA667

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA667

  Dataset EGAD00001009210

• Tumour sample for patient SA668

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA668

  Dataset EGAD00001009211

• Tumour sample for patient SA669

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA669

  Dataset EGAD00001009212

• Tumour sample for patient SA671

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA671

  Dataset EGAD00001009213

• Tumour sample for patient SA672

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA672

  Dataset EGAD00001009214

• Tumour sample for patient SA535

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA535

  Dataset EGAD00001009215

• Normal sample for patient SA1035

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1035

  Dataset EGAD00001009216

• Normal sample for patient SA604

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA604

  Dataset EGAD00001009217

• Normal sample for patient SA605

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA605

  Dataset EGAD00001009218

• Normal sample for patient SA609

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA609

  Dataset EGAD00001009219

• Normal sample for patient SA218

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA218

  Dataset EGAD00001009220

• Normal sample for patient SA219

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA219

  Dataset EGAD00001009221