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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Princess Margaret Cancer Centre1, Wellcome Trust Sanger Institute2, University of Cambridge3, University of Toronto4, Weizmann Institute of Science5, Ontario Institute for Cancer Research6, International Agency for Research on Cancer7, European Bioinformatics Institute8, University Health Network9, Norwich University10, University of East Anglia11, National and Kapodistrian University of Athens12, University of Milan13, University of Granada14, Cancer Epidemiology Unit15, Prevention Institute16, University of Naples Federico II17, German Cancer Research Center18, Imperial College London19, Utrecht University20, Memorial Sloan Kettering Cancer Center21
TL;DR: Deep sequencing is used to analyse genes that are recurrently mutated in AML to distinguish between individuals who have a high risk of developing AML and those with benign ARCH, providing proof-of-concept that it is possible to discriminate ARCH from pre-AML many years before malignant transformation.
Abstract: The incidence of acute myeloid leukaemia (AML) increases with age and mortality exceeds 90% when diagnosed after age 65. Most cases arise without any detectable early symptoms and patients usually present with the acute complications of bone marrow failure1. The onset of such de novo AML cases is typically preceded by the accumulation of somatic mutations in preleukaemic haematopoietic stem and progenitor cells (HSPCs) that undergo clonal expansion2,3. However, recurrent AML mutations also accumulate in HSPCs during ageing of healthy individuals who do not develop AML, a phenomenon referred to as age-related clonal haematopoiesis (ARCH)4–8. Here we use deep sequencing to analyse genes that are recurrently mutated in AML to distinguish between individuals who have a high risk of developing AML and those with benign ARCH. We analysed peripheral blood cells from 95 individuals that were obtained on average 6.3 years before AML diagnosis (pre-AML group), together with 414 unselected age- and gender-matched individuals (control group). Pre-AML cases were distinct from controls and had more mutations per sample, higher variant allele frequencies, indicating greater clonal expansion, and showed enrichment of mutations in specific genes. Genetic parameters were used to derive a model that accurately predicted AML-free survival; this model was validated in an independent cohort of 29 pre-AML cases and 262 controls. Because AML is rare, we also developed an AML predictive model using a large electronic health record database that identified individuals at greater risk. Collectively our findings provide proof-of-concept that it is possible to discriminate ARCH from pre-AML many years before malignant transformation. This could in future enable earlier detection and monitoring, and may help to inform intervention.
567 citations
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TL;DR: A new version of Artemis has been developed, which reads from and writes to a relational database schema, and allows users to annotate more complex, often large and fragmented, genome sequences.
Abstract: Motivation: Artemis and Artemis Comparison Tool (ACT) have become mainstream tools for viewing and annotating sequence data, particularly for microbial genomes. Since its first release, Artemis has been continuously developed and supported with additional functionality for editing and analysing sequences based on feedback from an active user community of laboratory biologists and professional annotators. Nevertheless, its utility has been somewhat restricted by its limitation to reading and writing from flat files. Therefore, a new version of Artemis has been developed, which reads from and writes to a relational database schema, and allows users to annotate more complex, often large and fragmented, genome sequences.
Results: Artemis and ACT have now been extended to read and write directly to the Generic Model Organism Database (GMOD, http://www.gmod.org) Chado relational database schema. In addition, a Gene Builder tool has been developed to provide structured forms and tables to edit coordinates of gene models and edit functional annotation, based on standard ontologies, controlled vocabularies and free text.
Availability: Artemis and ACT are freely available (under a GPL licence) for download (for MacOSX, UNIX and Windows) at the Wellcome Trust Sanger Institute web sites: http://www.sanger.ac.uk/Software/Artemis/http://www.sanger.ac.uk/Software/ACT/
Contact: artemis@sanger.ac.uk
Supplementary information:Supplementary data are available at Bioinformatics online.
567 citations
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TL;DR: A GAL4 knock-in approach as well as the chromosome conformation capture technique are used to show that the differentially methylated regions in the imprinted genes Igf2 and H19 interact in mice and partition maternal and paternal chromatin into distinct loops.
Abstract: Imprinted genes are expressed from only one of the parental alleles and are marked epigenetically by DNA methylation and histone modifications. The paternally expressed gene insulin-like growth-factor 2 (Igf2) is separated by approximately 100 kb from the maternally expressed noncoding gene H19 on mouse distal chromosome 7. Differentially methylated regions in Igf2 and H19 contain chromatin boundaries, silencers and activators and regulate the reciprocal expression of the two genes in a methylation-sensitive manner by allowing them exclusive access to a shared set of enhancers. Various chromatin models have been proposed that separate Igf2 and H19 into active and silent domains. Here we used a GAL4 knock-in approach as well as the chromosome conformation capture technique to show that the differentially methylated regions in the imprinted genes Igf2 and H19 interact in mice. These interactions are epigenetically regulated and partition maternal and paternal chromatin into distinct loops. This generates a simple epigenetic switch for Igf2 through which it moves between an active and a silent chromatin domain.
567 citations
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Wellcome Trust Sanger Institute1, European Bioinformatics Institute2, Francis Crick Institute3, Broad Institute4, University of Oxford5, University of Cambridge6, University of Toronto7, Oregon Health & Science University8, University of Texas MD Anderson Cancer Center9, German Cancer Research Center10, Heidelberg University11, University of Ljubljana12, NorthShore University HealthSystem13, Vancouver Prostate Centre14, Simon Fraser University15, Walter and Eliza Hall Institute of Medical Research16, University of Melbourne17, Katholieke Universiteit Leuven18, Cornell University19, University of California, Santa Cruz20, Ontario Institute for Cancer Research21, University of California, Los Angeles22, Peter MacCallum Cancer Centre23, Harvard University24, Indiana University25, University of Chicago26, University of Cologne27, University of Helsinki28, University of Glasgow29
TL;DR: Whole-genome sequencing data for 2,778 cancer samples from 2,658 unique donors is used to reconstruct the evolutionary history of cancer, revealing that driver mutations can precede diagnosis by several years to decades.
Abstract: Cancer develops through a process of somatic evolution1,2. Sequencing data from a single biopsy represent a snapshot of this process that can reveal the timing of specific genomic aberrations and the changing influence of mutational processes3. Here, by whole-genome sequencing analysis of 2,658 cancers as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA)4, we reconstruct the life history and evolution of mutational processes and driver mutation sequences of 38 types of cancer. Early oncogenesis is characterized by mutations in a constrained set of driver genes, and specific copy number gains, such as trisomy 7 in glioblastoma and isochromosome 17q in medulloblastoma. The mutational spectrum changes significantly throughout tumour evolution in 40% of samples. A nearly fourfold diversification of driver genes and increased genomic instability are features of later stages. Copy number alterations often occur in mitotic crises, and lead to simultaneous gains of chromosomal segments. Timing analyses suggest that driver mutations often precede diagnosis by many years, if not decades. Together, these results determine the evolutionary trajectories of cancer, and highlight opportunities for early cancer detection.
565 citations
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Wellcome Trust Sanger Institute1, Katholieke Universiteit Leuven2, Montreal Heart Institute3, Wellcome Trust Centre for Human Genetics4, Canterbury Christ Church University5, University of Chicago6, University of Kiel7, Peninsula College of Medicine and Dentistry8, University of Adelaide9, Royal Adelaide Hospital10, Casa Sollievo della Sofferenza11, Ludwig Maximilian University of Munich12, Johns Hopkins University13, Yale University14, Broad Institute15, Cedars-Sinai Medical Center16, University of Pittsburgh17, University of Auckland18, University of Otago19, Christchurch Hospital20, Children's Hospital of Philadelphia21, Örebro University22, Oslo University Hospital23, University of Edinburgh24, Lithuanian University of Health Sciences25, University of Western Australia26, University of Cambridge27, University of Liège28, Leiden University Medical Center29, University Medical Center Groningen30, Royal Hospital for Sick Children31, Newcastle University32, University of Toronto33, Royal Brisbane and Women's Hospital34, QIMR Berghofer Medical Research Institute35
TL;DR: The largest genotype association study, to date, in widely used clinical subphenotypes of inflammatory bowel disease with the goal of further understanding the biological relations between diseases.
563 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 |