15167 results for "cancer studies using rna-seq"

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• Next Generation Sequencing Characterization of Tregs in Human Peripheral Blood during Autoimmunity

  Tregs were sorted as CD4+CD25+CD127- cells from peripheral blood of 14 healthy individuals, 8 patients with systemic lupus erythematosus, 9 patients with rheumatoid arthritis, and 11 patients with multiple sclerosis. RNA was extracted and polyA libraries were prepared using the Illumina Truseq sample preparation kit v.2. Single-end sequencing was performed on NextSeq500.

  Dataset EGAD00001006193

• UK10K NEURO IMGSAC

  In the UK10K project we propose a series of complementary genetic approaches to find new low frequency/rare variants contributing to disease phenotypes. These will be based on obtaining the genome wide sequence of 4000 samples from the TwinsUK and ALSPAC cohorts (at 6x sequence coverage), and the exome sequence (protein coding regions and related conserved sequence) of 6000 samples selected for extreme phenotypes. Our studies will focus primarily on cardiovascular-related quantitative traits, obesity and related metabolic traits, neurodevelopmental disorders and a limited number of extreme clinical phenotypes that will provide proof-of-concept for future familial trait sequencing. We will analyse directly quantitative traits in the cohorts and the selected traits in the extreme samples, and also use imputation down to 0.1% allele frequency to extend the analyses to further sample sets with genome wide genotype data. In each case we will investigate indels and larger structural variants as well as SNPs, and use statistical methods that combine rare variants in a locus or pathway as well as single-variant approaches.The IMGSAC cohort is an international collection of families containing children ascertained for ASDs (autism spectrum disorders). The affected individuals are have been phenotyped, including using the ADI-R and ADOS instruments. Individuals with a past or current medical disorder of probable etiological significance or TSC have been excluded. Where possible, karyotyping has been performed on one affected individual per family to exclude Fragile X syndrome. Many of the samples have been genotyped, using the Affymetrix 10k and Illumina 1M platforms. All samples to be included in the current study are of UK origin.For further information on this cohort please contact Jeremy Parr (jeremy.parr@newcastle.ac.uk).

  Study EGAS00001000120

• IBD-dysplasia

  This data set concerns DNA copy number alterations and mutation data from 30 IBD-associated dysplastic lesions and 13 IBD-associated cancers. DNA was isolated from formalin-fixed, paraffin-embedded material. Whole-genome shallow seq and Truseq amplicon cancer panel (Illumina) were used for detection of DNA copy number alterations and gene mutations, respectively.

  Dataset EGAD00001005196

• Shallow whole genome sequencing from plasma DNA of BC, CRC and NSCLC patients

  This is the raw data obtained from shallow whole-genome sequencing of plasma DNA (plasma-Seq) for calling of somatic copy number alterations as well as focal amplifications and deletions from patients with breast, colorectal and non-small cell lung cancer.

  Dataset EGAD00001006288

• Genomic Landscape of High-Grade Neuroendocrine Neoplasms

  This project is a multi-omics study of high grade (grade 3) neuroendocrine neoplasms across diverse organs. Genomic analysis using low-pass whole genome sequencing for copy number analysis, whole exome sequencing and whole transcriptomic sequencing using RNAseq were performed. As high grade neuroendocrine neoplasms are highly lethal with a median survival of approximately 7 months, new insights are urgently needed for better prognosis, diagnosis and management. This study focuses on gene and pathway discoveries in this cancer, and includes samples from a diverse set of organs, including the lungs, gastroenteropancreatic tract, breast, prostate, head and neck and bladder. This study was made possible by a generous donation from The Helen B. Rodde Fund.

  Study phs002070

• Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data

  We present Epiclomal, a probabilistic clustering method arising from a hierarchical mixture model to simultaneously cluster sparse single-cell DNA methylation data and infer their corresponding hidden methylation profiles. Using synthetic and published single-cell CpG datasets we show that Epiclomal outperforms non-probabilistic methods and is able to handle the inherent missing data feature which dominates single-cell CpG genome sequences. Using a recently published single-cell 5mCpG sequencing method (PBAL), we show that Epiclomal discovers sub-clonal patterns of methylation in aneuploid tumour genomes, thus defining epiclones. We show that epiclones may transcend copy number determined clonal lineages, thus opening this important form of clonal analysis in cancer.

  Study EGAS00001003504

• Upper respiratory microbiome of COVID-19 patients

  Understanding the pathology of COVID-19 is a global research priority. Early evidence suggests that the microbiome may be playing a role in disease progression, yet current studies report contradictory results. Here, we examine potential confounders in COVID-19 microbiome studies by analyzing the upper respiratory tract microbiome in well-phenotyped COVID-19 patients and controls combining microbiome sequencing, viral load determination, and immunoprofiling. We found that time in the intensive care unit and the type of oxygen support explained the most variation within the upper respiratory tract microbiome, dwarfing (non-significant) effects from viral load, disease severity, and immune status. Specifically, mechanical ventilation was linked to altered community structure, lower species- and higher strain-level diversity, and significant shifts in oral taxa previously associated with COVID-19.

  Study EGAS00001004951

• Nurminen et al ("GP2Men") Study Primary and Metastatic Prostate Cancer Whole Genome Sequence Data

  We used novel processing techniques to obtain whole genome data together with 3D anatomic and histomorphologic analysis in two men (GP5 and GP12) with high risk PrCa undergoing radical prostatectomy. A total of 22 whole genome-sequenced sites (16 primary cancer foci and 6 lymph node metastatic) were analyzed using evolutionary reconstruction tools and spatio-evolutionary models. Probability models were used to trace spatial and chronological origins of the primary tumor and metastases, chart their genetic drivers, and distinguish metastatic and non-metastatic subclones.

  Study EGAS50000000005

• Deep Sequencing of 3 Cancer Cell Lines on 2 Sequencing Platforms (Illumina HiSeqX and NovaSeq)

  To test the performance of a new sequencing platform, develop an updated somatic calling pipeline and establish a reference for future benchmarking experiments, we performed whole-genome sequencing of 3 common cancer cell lines (COLO-829, HCC-1143 and HCC-1187) along with their matched normal cell lines to great sequencing depths (up to 278X coverage) on both Illumina HiSeqX and NovaSeq sequencing instruments. This dataset can be used for benchmarking purposes by the genomic community and as a reference call set for scientists using these 3 cell lines.

  Study phs001839

• ProstOmics - DNA methylation v1

  Bulk methylation array data from 64 prostate tissue samples from 16 patients (5 with post-surgery relapse). Methylation data were acquired using the microarray kit Illumina Infinium MethylationEPIC BeadChip (n=64 ). The individual samples have information on patient origin and sample type (cancer, cancer-adjacent field-effect normal or normal sample far from cancer). Patient metadata include information of age at surgery, time (months) until reported relapse, pre-surgery PSA and post-surgery T-stage.

  Dataset EGAD50000000605