Institution
Wellcome Trust Sanger Institute
Nonprofit•Cambridge, United Kingdom•
About: Wellcome Trust Sanger Institute is a nonprofit organization based out in Cambridge, United Kingdom. It is known for research contribution in the topics: Population & Genome. The organization has 4009 authors who have published 9671 publications receiving 1224479 citations.
Topics: Population, Genome, Gene, Genome-wide association study, Genomics
Papers published on a yearly basis
Papers
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TL;DR: An extensive study of variability in genes encoding proteins that are believed to be involved in the action and biotransformation of warfarin finds that weaker associations observed for other genes could explain up to ∼10% additional dose variance, but require testing and validation in an independent and larger data set.
Abstract: We report an extensive study of variability in genes encoding proteins that are believed to be involved in the action and biotransformation of warfarin. Warfarin is a commonly prescribed anticoagulant that is difficult to use because of the wide interindividual variation in dose requirements, the narrow therapeutic range and the risk of serious bleeding. We genotyped 201 patients for polymorphisms in 29 genes in the warfarin interactive pathways and tested them for association with dose requirement. In our study, polymorphisms in or flanking the genes VKORC1, CYP2C9, CYP2C18, CYP2C19, PROC, APOE, EPHX1, CALU, GGCX and ORM1-ORM2 and haplotypes of VKORC1, CYP2C9, CYP2C8, CYP2C19, PROC, F7, GGCX, PROZ, F9, NR1I2 and ORM1-ORM2 were associated with dose (P < 0.05). VKORC1, CYP2C9, CYP2C18 and CYP2C19 were significant after experiment-wise correction for multiple testing (P < 0.000175), however, the association of CYP2C18 and CYP2C19 was fully explained by linkage disequilibrium with CYP2C9*2 and/or *3. PROC and APOE were both significantly associated with dose after correction within each gene. A multiple regression model with VKORC1, CYP2C9, PROC and the non-genetic predictors age, bodyweight, drug interactions and indication for treatment jointly accounted for 62% of variance in warfarin dose. Weaker associations observed for other genes could explain up to approximately 10% additional dose variance, but require testing and validation in an independent and larger data set. Translation of this knowledge into clinical guidelines for warfarin prescription will be likely to have a major impact on the safety and efficacy of warfarin.
383 citations
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TL;DR: Analysis of blood from a healthy human show that haematopoietic stem cells increase rapidly in numbers through early life, reaching a stable plateau in adulthood, and contribute to myeloid and B lymphocyte populations throughout life.
Abstract: Haematopoietic stem cells drive blood production, but their population size and lifetime dynamics have not been quantified directly in humans Here we identified 129,582 spontaneous, genome-wide somatic mutations in 140 single-cell-derived haematopoietic stem and progenitor colonies from a healthy 59-year-old man and applied population-genetics approaches to reconstruct clonal dynamics Cell divisions from early embryogenesis were evident in the phylogenetic tree; all blood cells were derived from a common ancestor that preceded gastrulation The size of the stem cell population grew steadily in early life, reaching a stable plateau by adolescence We estimate the numbers of haematopoietic stem cells that are actively making white blood cells at any one time to be in the range of 50,000-200,000 We observed adult haematopoietic stem cell clones that generate multilineage outputs, including granulocytes and B lymphocytes Harnessing naturally occurring mutations to report the clonal architecture of an organ enables the high-resolution reconstruction of somatic cell dynamics in humans
383 citations
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Cambridge University Hospitals NHS Foundation Trust1, Wellcome Trust Sanger Institute2, University of London3, University College London4, University of Oxford5, Liverpool School of Tropical Medicine6, Quadram Institute7, Pasteur Institute8, University of Melbourne9, University of Birmingham10, Kenya Medical Research Institute11, Centers for Disease Control and Prevention12, Emory University13, University of Liverpool14, University of Malawi15, Katholieke Universiteit Leuven16, Institute of Tropical Medicine Antwerp17, University of the Republic18, International Vaccine Institute19, Novartis20, University of the Witwatersrand21, Nagasaki University22, Patan Academy of Health Sciences23, World Health Organization24, Hasanuddin University25, Mahosot Hospital26, Mahidol University27, University of Otago28, Public Health England29, Angkor Hospital for Children30
TL;DR: This whole-genome sequence analysis of Salmonella enterica serovar Typhi identifies a single dominant MDR lineage, H58, that has emerged and spread throughout Asia and Africa over the last 30 years, and identifies numerous transmissions of H58.
Abstract: The emergence of multidrug-resistant (MDR) typhoid is a major global health threat affecting many countries where the disease is endemic. Here whole-genome sequence analysis of 1,832 Salmonella enterica serovar Typhi (S. Typhi) identifies a single dominant MDR lineage, H58, that has emerged and spread throughout Asia and Africa over the last 30 years. Our analysis identifies numerous transmissions of H58, including multiple transfers from Asia to Africa and an ongoing, unrecognized MDR epidemic within Africa itself. Notably, our analysis indicates that H58 lineages are displacing antibiotic-sensitive isolates, transforming the global population structure of this pathogen. H58 isolates can harbor a complex MDR element residing either on transmissible IncHI1 plasmids or within multiple chromosomal integration sites. We also identify new mutations that define the H58 lineage. This phylogeographical analysis provides a framework to facilitate global management of MDR typhoid and is applicable to similar MDR lineages emerging in other bacterial species.
383 citations
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TL;DR: In this article, the suitability of these methods for single-cell transcriptomics has not been assessed and the authors discuss commonly used normalization approaches and illustrate how these can produce misleading results.
Abstract: Single-cell transcriptomics is becoming an important component of the molecular biologist's toolkit. A critical step when analyzing data generated using this technology is normalization. However, normalization is typically performed using methods developed for bulk RNA sequencing or even microarray data, and the suitability of these methods for single-cell transcriptomics has not been assessed. We here discuss commonly used normalization approaches and illustrate how these can produce misleading results. Finally, we present alternative approaches and provide recommendations for single-cell RNA sequencing users.
381 citations
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TL;DR: A software package that interfaces Mascot with Percolator, a well performing machine learning method for rescoring database search results, is presented and it is demonstrated to be amenable for both low and high accuracy mass spectrometry data.
Abstract: Sound scoring methods for sequence database search algorithms such as Mascot and Sequest are essential for sensitive and accurate peptide and protein identifications from proteomic tandem mass spectrometry data. In this paper, we present a software package that interfaces Mascot with Percolator, a well performing machine learning method for rescoring database search results, and demonstrate it to be amenable for both low and high accuracy mass spectrometry data, outperforming all available Mascot scoring schemes as well as providing reliable significance measures. Mascot Percolator can be readily used as a stand alone tool or integrated into existing data analysis pipelines.
381 citations
Authors
Showing all 4058 results
Name | H-index | Papers | Citations |
---|---|---|---|
Nicholas J. Wareham | 212 | 1657 | 204896 |
Gonçalo R. Abecasis | 179 | 595 | 230323 |
Panos Deloukas | 162 | 410 | 154018 |
Michael R. Stratton | 161 | 443 | 142586 |
David W. Johnson | 160 | 2714 | 140778 |
Michael John Owen | 160 | 1110 | 135795 |
Naveed Sattar | 155 | 1326 | 116368 |
Robert E. W. Hancock | 152 | 775 | 88481 |
Julian Parkhill | 149 | 759 | 104736 |
Nilesh J. Samani | 149 | 779 | 113545 |
Michael Conlon O'Donovan | 142 | 736 | 118857 |
Jian Yang | 142 | 1818 | 111166 |
Christof Koch | 141 | 712 | 105221 |
Andrew G. Clark | 140 | 823 | 123333 |
Stylianos E. Antonarakis | 138 | 746 | 93605 |