scispace - formally typeset
Search or ask a question
Institution

Wellcome Trust Sanger Institute

NonprofitCambridge, 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.


Papers
More filters
Journal ArticleDOI
Sagi Abelson1, Grace Collord2, Grace Collord3, Stanley W.K. Ng4, Omer Weissbrod5, Netta Mendelson Cohen5, Elisabeth Niemeyer5, Noam Barda, Philip C. Zuzarte6, Lawrence E. Heisler6, Yogi Sundaravadanam6, Robert Luben3, Shabina Hayat3, Ting Ting Wang1, Ting Ting Wang4, Zhen Zhao1, Iulia Cirlan1, Trevor J. Pugh6, Trevor J. Pugh4, Trevor J. Pugh1, David Soave6, Karen Ng6, Calli Latimer2, Claire Hardy2, Keiran Raine2, David T. Jones2, Diana Hoult3, Abigail Britten3, John Douglas Mcpherson6, Mattias Johansson7, Faridah Mbabaali6, Jenna Eagles6, Jessica Miller6, Danielle Pasternack6, Lee Timms6, Paul M. Krzyzanowski6, Philip Awadalla6, Rui Costa8, Eran Segal5, Scott V. Bratman6, Scott V. Bratman1, Scott V. Bratman4, Philip A. Beer2, Sam Behjati2, Sam Behjati3, Inigo Martincorena2, Jean C.Y. Wang4, Jean C.Y. Wang9, Jean C.Y. Wang1, Kristian M. Bowles10, Kristian M. Bowles11, J. Ramón Quirós, Anna Karakatsani12, Carlo La Vecchia13, Antonia Trichopoulou, Elena Salamanca-Fernández14, José María Huerta, Aurelio Barricarte, Ruth C. Travis15, Rosario Tumino, Giovanna Masala16, Heiner Boeing, Salvatore Panico17, Rudolf Kaaks18, Alwin Krämer18, Sabina Sieri, Elio Riboli19, Paolo Vineis19, Matthieu Foll7, James McKay7, Silvia Polidoro, Núria Sala, Kay-Tee Khaw3, Roel Vermeulen20, Peter J. Campbell2, Peter J. Campbell3, Elli Papaemmanuil21, Elli Papaemmanuil2, Mark D. Minden, Amos Tanay5, Ran D. Balicer, Nicholas J. Wareham3, Moritz Gerstung2, Moritz Gerstung8, John E. Dick1, John E. Dick4, Paul Brennan7, George S. Vassiliou3, George S. Vassiliou2, Liran I. Shlush1, Liran I. Shlush5 
09 Jul 2018-Nature
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

Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
06 Feb 2020-Nature
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

Journal ArticleDOI
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

NameH-indexPapersCitations
Nicholas J. Wareham2121657204896
Gonçalo R. Abecasis179595230323
Panos Deloukas162410154018
Michael R. Stratton161443142586
David W. Johnson1602714140778
Michael John Owen1601110135795
Naveed Sattar1551326116368
Robert E. W. Hancock15277588481
Julian Parkhill149759104736
Nilesh J. Samani149779113545
Michael Conlon O'Donovan142736118857
Jian Yang1421818111166
Christof Koch141712105221
Andrew G. Clark140823123333
Stylianos E. Antonarakis13874693605
Network Information
Related Institutions (5)
Broad Institute
11.6K papers, 1.5M citations

96% related

Howard Hughes Medical Institute
34.6K papers, 5.2M citations

95% related

Laboratory of Molecular Biology
24.2K papers, 2.1M citations

94% related

Salk Institute for Biological Studies
13.1K papers, 1.6M citations

93% related

National Institutes of Health
297.8K papers, 21.3M citations

93% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202317
202270
2021836
2020810
2019854
2018764