690 results for ""PhD+2001"+"Professor+of+Bioinformatics"+2012+18000+citations+profile"

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• Tam-Seq of HGSOC samples for fixative comparison study

  Targeted amplicon sequencing of samples as part of the study "Methanol-based fixation is superior to buffered formalin for next-generation sequencing of DNA from clinical cancer samples. The amplicon panel consists of 48 amplicons in TP53, PTEN, EGFR, PIK3CA, KRAS and BRAF genes as described previously [Forshew, STM 2012]. All libraries were pooled and quantify using DNA 1000 kit on Agilent 2100 Bioanalyzer and KAPA SYBR FAST ABI Prism qPCR Kit (KAPA Biosystems) on 7900HT Fast Real-Time PCR System (Applied Biosystems) according to the supplier's recommendations. Reads were aligned using bwa-mem v0.7.12-r1039 to the 1000 genomes version of human genome build GRCh37, retaining duplicate reads.

  Dataset EGAD00001001937

• Genetic Studies in the Hutterites

  We conducted genetic studies of disease-associated quantitative phenotypes, including microbiome and gene expression studies, and common diseases, such as asthma and Alzheimer's disease, by genotyping and/or sequencing in the Hutterites, a founder population of European descent. The Hutterites who have participated in our studies live on communal farms in South Dakota and are related to each other through multiple lines of descent in a 3,657-person, 13-generation pedigree with 64 founders. The small number of founding genomes reduces genetic heterogeneity whereas their communal lifestyle ensures that non-genetic factors are remarkably uniform between individuals. Our sample includes >1,600 subjects who have participated in our studies of fertility (since 1982), asthma, cardiovascular disease and other complex phenotypes (since 1993), microbiome studies (since 2011), and/or immune response (since 2012). The variable 'Total number of consented subjects' reflects the count of subjects with individual level phenotype and genotype data in dbGaP.

  Study phs000185

• Chordoma_Sequencing_Project_Whole_Genome

  This is a continuation of the Chordoma Sequencing Project, prelim #G0175-122-CF. All cancers arise due to somatically acquired abnormalities in DNA sequence. Systematic sequencing of cancer genomes allows acquisition of complete catalogues of all classes of somatic mutation present in cancer. These mutation catalogues will allow identification of the somatically mutated cancer genes that are operative and characterise patterns of somatic mutation that may reflect previous exogenous and endogenous mutagenic exposures. In this application, we aim to perform whole genome sequencing on 10 chordoma matched genome pairs. The Chordoma Foundation will fund the RNA Sequencing/Methylation and SNP6 costs for this project while the whole genome and library sequencing will be covered under the CGP, 2012-2013 core WTSI internal funding. An additional sequencing of three cancer cell lines will be added to this work, financed by the Chordoma Foundation. In this project, the Chordoma foundation will pay for the additional costs of methylation, RNA Seq,

  Study EGAS00001000409

• Genomic data from analysis of the human placenta, part of the Pregnancy Outcome Prediction study (POPs)

  The Pregnancy Outcome Prediction (POP) study is a prospective cohort study of nulliparous women attending the Rosie Hospital (Cambridge, UK) for their dating ultrasound scan. The study included 4512 women with a viable singleton pregnancy; study participants provided written informed consent and were recruited between January 2008 and July 2012. The POP study aimed both at evaluating performance of known biomarkers and serial ultrasonography in assessing maternal and fetal well-being, as well as identifying novel biomarkers. The study is sufficiently large to be powered for relatively uncommon adverse pregnancy outcomes. Women attended four study visits scheduled every 8 weeks, starting with the first trimester. Participants had blood taken during the dating/recruitment visit (at approximately 12 weeks gestation (wkGA)), as well as at three subsequent visits (at ∼20wkGA, ∼28wkGA and ∼36wkGA). For more detail, please visit: https://www.obgyn.cam.ac.uk/research/pops-2/.

  Study EGAS00001002205

• Northern Ireland COhort for the Longitudinal study of Ageing

  The Northern Ireland COhort for the Longitudinal study of Ageing (NICOLA) is a representative sample of ~8,500 people from across Northern Ireland. The study, which was set up in 2012, aims to understand what it is like to grow older in Northern Ireland. • NICOLA has a strong focus on molecular biomarkers so there is complementary genetic, epigenetic and transcriptomic data available for a subset of individuals. • We inherit much of our DNA from our parents while a small amount of this material changes as we get older. In NICOLA we have 551,839 directly genotyped and 18,148,478 imputed Single Nucleotide Polymorphisms (SNPs) currently available for 2969 participants. • Summary statistics for the association of these gene polymorphisms with ~30 phenotypes were generated. • Epigenetics provides a link between our inherited DNA and environmental influences from a person’s diet, medication and lifestyle. NICOLA has the epigenetic quality controlled profiles of 1984 individuals arising from variations in DNA methylation at 862,927 genetic sites.

  Study EGAS00001007915

• UK10K_RARE_NEUROMUSCULAR REL-2012-07-05

  UK10K_RARE_NEUROMUSCULAR REL-2012-07-05

  Dataset EGAD00001000219

• UK10K_RARE_SIR REL-2012-07-05

  UK10K_RARE_SIR REL-2012-07-05

  Dataset EGAD00001000218

• UK10K_RARE_CILIOPATHIES REL-2012-07-05

  UK10K_RARE_CILIOPATHIES REL-2012-07-05

  Dataset EGAD00001000217

• UK10K_RARE_CHD REL-2012-07-05

  UK10K_RARE_CHD REL-2012-07-05

  Dataset EGAD00001000210

• UK10K_RARE_FIND REL-2012-07-05

  UK10K_RARE_FIND REL-2012-07-05

  Dataset EGAD00001000209

• UK10K_RARE_THYROID REL-2012-07-05

  UK10K_RARE_THYROID REL-2012-07-05

  Dataset EGAD00001000208

• UK10K_RARE_HYPERCHOL REL-2012-07-05

  UK10K_RARE_HYPERCHOL REL-2012-07-05

  Dataset EGAD00001000207

• UK10K_RARE_COLOBOMA REL-2012-07-05

  UK10K_RARE_COLOBOMA REL-2012-07-05

  Dataset EGAD00001000206

• UK10K_OBESITY_SCOOP REL-2012-02-22

  UK10K_OBESITY_SCOOP REL-2012-02-22

  Dataset EGAD00001000193

• UK10K_RARE_CHD REL-2012-02-22

  UK10K_RARE_CHD REL-2012-02-22

  Dataset EGAD00001000192

• UK10K_RARE_CILIOPATHIES REL-2012-02-22

  UK10K_RARE_CILIOPATHIES REL-2012-02-22

  Dataset EGAD00001000191

• UK10K_RARE_FIND REL-2012-02-22

  UK10K_RARE_FIND REL-2012-02-22

  Dataset EGAD00001000190

• UK10K_RARE_NEUROMUSCULAR REL-2012-02-22

  UK10K_RARE_NEUROMUSCULAR REL-2012-02-22

  Dataset EGAD00001000189

• UK10K_RARE_SIR REL-2012-02-22

  UK10K_RARE_SIR REL-2012-02-22

  Dataset EGAD00001000188

• UK10K_RARE_THYROID REL-2012-02-22

  UK10K_RARE_THYROID REL-2012-02-22

  Dataset EGAD00001000187

• UK10K_NEURO_FSZNK REL-2012-01-13

  UK10K_NEURO_FSZNK REL-2012-01-13

  Dataset EGAD00001000183

• UK10K_NEURO_UKSCZ REL-2012-01-13

  UK10K_NEURO_UKSCZ REL-2012-01-13

  Dataset EGAD00001000182

• UK10K_RARE_NEUROMUSCULAR REL-2012-01-13

  UK10K_RARE_NEUROMUSCULAR REL-2012-01-13

  Dataset EGAD00001000180

• UK10K_RARE_COLOBOMA REL-2012-01-13

  UK10K_RARE_COLOBOMA REL-2012-01-13

  Dataset EGAD00001000179

• UK10K_RARE_CHD REL-2012-01-13

  UK10K_RARE_CHD REL-2012-01-13

  Dataset EGAD00001000178