The common information model for climate modelling digital repositories: The metafor project

A poster highlighting the common information model for climate modelling digital repositorie

The potential of a new larviciding method for the control of malaria vectors

Malaria pathogens are transmitted to humans by the bite of female Anopheles mosquitoes. The juvenile stages of these mosquitoes develop in a variety of water bodies and are key targets for vector control campaigns involving the application of larvicides. The effective operational implementation of these campaigns is difficult, time consuming, and expensive. New evidence however, suggests that adult mosquitoes can be co-opted into disseminating larvicides in a far more targeted and efficient manner than can be achieved using conventional methods

Ecology: a prerequisite for malaria elimination and eradication

* Existing front-line vector control measures, such as insecticide-treated nets and residual sprays, cannot break the transmission cycle of Plasmodium falciparum in the most intensely endemic parts of Africa and the Pacific * The goal of malaria eradication will require urgent strategic investment into understanding the ecology and evolution of the mosquito vectors that transmit malaria * Priority areas will include understanding aspects of the mosquito life cycle beyond the blood feeding processes which directly mediate malaria transmission * Global commitment to malaria eradication necessitates a corresponding long-term commitment to vector ecolog

Integrating sequence and array data to create an improved 1000 Genomes Project haplotype reference panel

A major use of the 1000 Genomes Project (1000GP) data is genotype imputation in genome-wide association studies (GWAS). Here we develop a method to estimate haplotypes from low-coverage sequencing data that can take advantage of single-nucleotide polymorphism (SNP) microarray genotypes on the same samples. First the SNP array data are phased to build a backbone (or 'scaffold') of haplotypes across each chromosome. We then phase the sequence data 'onto' this haplotype scaffold. This approach can take advantage of relatedness between sequenced and non-sequenced samples to improve accuracy. We use this method to create a new 1000GP haplotype reference set for use by the human genetic community. Using a set of validation genotypes at SNP and bi-allelic indels we show that these haplotypes have lower genotype discordance and improved imputation performance into downstream GWAS samples, especially at low-frequency variants. © 2014 Macmillan Publishers Limited. All rights reserved

A global reference for human genetic variation

The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.We thank the many people who were generous with contributing their samples to the project: the African Caribbean in Barbados; Bengali in Bangladesh; British in England and Scotland; Chinese Dai in Xishuangbanna, China; Colombians in Medellin, Colombia; Esan in Nigeria; Finnish in Finland; Gambian in Western Division – Mandinka; Gujarati Indians in Houston, Texas, USA; Han Chinese in Beijing, China; Iberian populations in Spain; Indian Telugu in the UK; Japanese in Tokyo, Japan; Kinh in Ho Chi Minh City, Vietnam; Luhya in Webuye, Kenya; Mende in Sierra Leone; people with African ancestry in the southwest USA; people with Mexican ancestry in Los Angeles, California, USA; Peruvians in Lima, Peru; Puerto Ricans in Puerto Rico; Punjabi in Lahore, Pakistan; southern Han Chinese; Sri Lankan Tamil in the UK; Toscani in Italia; Utah residents (CEPH) with northern and western European ancestry; and Yoruba in Ibadan, Nigeria. Many thanks to the people who contributed to this project: P. Maul, T. Maul, and C. Foster; Z. Chong, X. Fan, W. Zhou, and T. Chen; N. Sengamalay, S. Ott, L. Sadzewicz, J. Liu, and L. Tallon; L. Merson; O. Folarin, D. Asogun, O. Ikpwonmosa, E. Philomena, G. Akpede, S. Okhobgenin, and O. Omoniwa; the staff of the Institute of Lassa Fever Research and Control (ILFRC), Irrua Specialist Teaching Hospital, Irrua, Edo State, Nigeria; A. Schlattl and T. Zichner; S. Lewis, E. Appelbaum, and L. Fulton; A. Yurovsky and I. Padioleau; N. Kaelin and F. Laplace; E. Drury and H. Arbery; A. Naranjo, M. Victoria Parra, and C. Duque; S. Däkel, B. Lenz, and S. Schrinner; S. Bumpstead; and C. Fletcher-Hoppe. Funding for this work was from the Wellcome Trust Core Award 090532/Z/09/Z and Senior Investigator Award 095552/Z/11/Z (P.D.), and grants WT098051 (R.D.), WT095908 and WT109497 (P.F.), WT086084/Z/08/Z and WT100956/Z/13/Z (G.M.), WT097307 (W.K.), WT0855322/Z/08/Z (R.L.), WT090770/Z/09/Z (D.K.), the Wellcome Trust Major Overseas program in Vietnam grant 089276/Z.09/Z (S.D.), the Medical Research Council UK grant G0801823 (J.L.M.), the UK Biotechnology and Biological Sciences Research Council grants BB/I02593X/1 (G.M.) and BB/I021213/1 (A.R.L.), the British Heart Foundation (C.A.A.), the Monument Trust (J.H.), the European Molecular Biology Laboratory (P.F.), the European Research Council grant 617306 (J.L.M.), the Chinese 863 Program 2012AA02A201, the National Basic Research program of China 973 program no. 2011CB809201, 2011CB809202 and 2011CB809203, Natural Science Foundation of China 31161130357, the Shenzhen Municipal Government of China grant ZYC201105170397A (J.W.), the Canadian Institutes of Health Research Operating grant 136855 and Canada Research Chair (S.G.), Banting Postdoctoral Fellowship from the Canadian Institutes of Health Research (M.K.D.), a Le Fonds de Recherche duQuébec-Santé (FRQS) research fellowship (A.H.), Genome Quebec (P.A.), the Ontario Ministry of Research and Innovation – Ontario Institute for Cancer Research Investigator Award (P.A., J.S.), the Quebec Ministry of Economic Development, Innovation, and Exports grant PSR-SIIRI-195 (P.A.), the German Federal Ministry of Education and Research (BMBF) grants 0315428A and 01GS08201 (R.H.), the Max Planck Society (H.L., G.M., R.S.), BMBF-EPITREAT grant 0316190A (R.H., M.L.), the German Research Foundation (Deutsche Forschungsgemeinschaft) Emmy Noether Grant KO4037/1-1 (J.O.K.), the Beatriu de Pinos Program grants 2006 BP-A 10144 and 2009 BP-B 00274 (M.V.), the Spanish National Institute for Health Research grant PRB2 IPT13/0001-ISCIII-SGEFI/FEDER (A.O.), Ewha Womans University (C.L.), the Japan Society for the Promotion of Science Fellowship number PE13075 (N.P.), the Louis Jeantet Foundation (E.T.D.), the Marie Curie Actions Career Integration grant 303772 (C.A.), the Swiss National Science Foundation 31003A_130342 and NCCR “Frontiers in Genetics” (E.T.D.), the University of Geneva (E.T.D., T.L., G.M.), the US National Institutes of Health National Center for Biotechnology Information (S.S.) and grants U54HG3067 (E.S.L.), U54HG3273 and U01HG5211 (R.A.G.), U54HG3079 (R.K.W., E.R.M.), R01HG2898 (S.E.D.), R01HG2385 (E.E.E.), RC2HG5552 and U01HG6513 (G.T.M., G.R.A.), U01HG5214 (A.C.), U01HG5715 (C.D.B.), U01HG5718 (M.G.), U01HG5728 (Y.X.F.), U41HG7635 (R.K.W., E.E.E., P.H.S.), U41HG7497 (C.L., M.A.B., K.C., L.D., E.E.E., M.G., J.O.K., G.T.M., S.A.M., R.E.M., J.L.S., K.Y.), R01HG4960 and R01HG5701 (B.L.B.), R01HG5214 (G.A.), R01HG6855 (S.M.), R01HG7068 (R.E.M.), R01HG7644 (R.D.H.), DP2OD6514 (P.S.), DP5OD9154 (J.K.), R01CA166661 (S.E.D.), R01CA172652 (K.C.), P01GM99568 (S.R.B.), R01GM59290 (L.B.J., M.A.B.), R01GM104390 (L.B.J., M.Y.Y.), T32GM7790 (C.D.B., A.R.M.), P01GM99568 (S.R.B.), R01HL87699 and R01HL104608 (K.C.B.), T32HL94284 (J.L.R.F.), and contracts HHSN268201100040C (A.M.R.) and HHSN272201000025C (P.S.), Harvard Medical School Eleanor and Miles Shore Fellowship (K.L.), Lundbeck Foundation Grant R170-2014-1039 (K.L.), NIJ Grant 2014-DN-BX-K089 (Y.E.), the Mary Beryl Patch Turnbull Scholar Program (K.C.B.), NSF Graduate Research Fellowship DGE-1147470 (G.D.P.), the Simons Foundation SFARI award SF51 (M.W.), and a Sloan Foundation Fellowship (R.D.H.). E.E.E. is an investigator of the Howard Hughes Medical Institute

Portable Infrastructure for the Metafor Metadata System

PIMMS (Portable Infrastructure for the Metafor Metadata System) provides institutions with tools to capture information about the workflow of running simulations from the design of experiments to the implementation of experiments via simulations running models. PIMMS uses the Metafor methodology for simulation documentation which consists of a common information model (CIM), a set of controlled vocabularies (CV) and software tools. PIMMS software tools provide for the creation and consumption of CIM content via a web infrastructure and portal.PIMMS will refactor the “CMIP5 questionnaire” metadata management tool, that is collecting climate model metadata for the CMIP5 model inter-comparison project, so that it can be more easily portable into stand alone installations within the university environment and customised to address the specific requirements of individual research groups. Initial model descriptions may take time to complete but once they have been cre ated the PIMMS infrastructure can be used to document subsequent variations by describing only those elements that are changed. An established PIMMS infrastructure will fit seamlessly into the research metadata workflow and significantly reduce subsequent documentation effort. The key to the customisation of PIMMS is in the modularity of its tools and the clear separation of structure (CIM) from content (CV). The PIMMS project will extend the CMIP5 controlled vocabulary to encompass descriptions of paleoclimate models and will also demonstrate how the CIM can be used to document an Integrated Assessment Model (IAM). This proof of concept prototype will create a new controlled vocabulary in collaboration with Ermitage and use it to reconfigure PIMMS to collect metadata in a different discipline. PIMMS will further explore how the CV that is used to configure PIMMS may be of further use to our stake holders and the wider JISC community through the development of the Uni versity of Cambridge chemicaltagger tool. PIMMS will provide a local portal so that research groups can view and search their own content, as well as publish their metadata content to institutional, national and international services. In addition PIMMS will also include data node software so that data documented with PIMMS can also be published to the web, both locally, and to national and international services

The CMIP5 Model Documentation Questionnaire: Development of a Metadata Retrieval System for the Metafor Common Information Model

The EU METAFOR Project (http://metaforclimate.eu) has created a web-based model documentation questionnaire to collect metadata from the modelling groups that are running simulations in support of the Coupled Model Intercomparison Project - 5 (CMIP5). The CMIP5 model documentation questionnaire will retrieve information about the details of the models used, how the simulations were carried out, how the simulations conformed to the CMIP5 experiment requirements and details of the hardware used to perform the simulations. The metadata collected by the CMIP5 questionnaire will allow CMIP5 data to be compared in a scientifically meaningful way. This paper describes the life-cycle of the CMIP5 questionnaire development which starts with relatively unstructured input from domain specialists and ends with formal XML documents that comply with the METAFOR Common Information Model (CIM). Each development step is associated with a specific tool. (1) Mind maps are used to capture information requirements from domain experts and build a controlled vocabulary, (2) a python parser processes the XML files generated by the mind maps, (3) Django (python) is used to generate the dynamic structure and content of the web based questionnaire from processed xml and the METAFOR CIM, (4) Python parsers ensure that information entered into the CMIP5 questionnaire is output as CIM compliant xml, (5) CIM compliant output allows automatic information capture tools to harvest questionnaire content into databases such as the Earth System Grid (ESG) metadata catalogue. This paper will focus on how Django (python) and XML input files are used to generate the structure and content of the CMIP5 questionnaire. It will also address how the choice of development tools listed above provided a framework that enabled working scientists (who we would never ordinarily get to interact with UML and XML) to be part the iterative development process and ensure that the CMIP5 model documentation questionnaire reflects what scientists want to know about the models

Forage soybeans (Glycine max (L.) Merr.) in the United Kingdom: test of new cultivars

Traditionally the soybean was used as a forage crop. Recently several cultivars and experimental lines have been bred for forage production. This coincides with the banning of meat and bone meal as a source of protein in ruminant diets in the European Union, which has led to a greater demand for high protein crops. Two USA bred cultivars, Derry and Donegal, were grown in a trial at the Royal Agricultural College in 2000 and 2001 to assess the viability of soybeans as a forage crop in the UK. In 2001 six experimental lines were added to the study. In 2000 dry matter yields ranged from 5.91 to 6.09 t$\cdot$ha$^{-1}$ (Derry and Donegal, respectively) for the early harvest and 7.68 to 7.95 t$\cdot$ha$^{-1}$ DM (Derry and Donegal, respectively) for the late harvest. In the second season Donegal was the highest yielding at the early harvest with 12.1 t$\cdot$ha$^{-1}$ and SG13#169 had 12.1 t$\cdot$ha$^{-1}$ at the late harvest. The proportion of leaf was not consistently associated with protein content. Across the two years the pod component did not exceed 110 g$\cdot$kg$^{-1}$. The experimental line SG13#169 had the highest CP, 153 g$\cdot$kg$^{-1}$, and a yield of 8.51 t$\cdot$ha$^{-1}$ (at the first cut), and at the second harvest the CP fell to 146 g$\cdot$kg$^{-1}$, while the yield rose to 12.1 t$\cdot$ha$^{-1}$. This more than offsetting the dilution of nutritional quality, by offering more CP$\cdot$ha$^{-1}$ from the late harvest. Additional testing is required for more conclusive evaluation of these experimental lines.Soja fourrage (Glycine max (L.) Merr.) au Royaume-Uni. Traditionnellement le soja était utilisé comme culture fourragère. Récemment, plusieurs espèces ont été cultivées pour leur production fourragère dans le cadre de programmes de recherche. Ils coïncident avec l'interdiction des farines animales comme source de protéine dans l'alimentation des ruminants dans l'Union Européenne, qui implique une demande accrue de cultures protéagineuses. Un essai de culture de deux variétés des États-Unis, Derry et Donegal, a été mené au Royal Agricultural College en 2000 et 2001 pour évaluer la viabilité du soja comme culture fourragère au Royaume-Uni. En 2001, 6 expérimentations ont été ajoutées à l'étude. En 2000, les rendements de matière sèche étaient de 5,91 à 6,09 t$\cdot$ha$^{-1}$ (Derry et Donegal, respectivement) pour la récolte précoce et de 7,68 à 7,95 t$\cdot$ha$^{-1}$ (matière sèche, Derry et Donegal respectivement) pour la récolte tardive. En seconde saison, Donegal était la variété à plus fort rendement pour la récolte précoce avec 12,1 t$\cdot$ha$^{-1}$ et SG13#169 avec 12,1 t$\cdot$ha$^{-1}$ à la récolte tardive. La proportion de feuilles n'était pas corrélée de façon nette au contenu protéique. Au cours des 2 années, les composants de la gousse (CP) n'ont pas dépassé 110 g$\cdot$kg$^{-1}$. La ligne expérimentale sur SG13#169 a obtenu la plus forte CP : 153 g$\cdot$kg$^{-1}$, et un rendement de 8,51 t$\cdot$ha$^{-1}$ (à la première coupe), et à la seconde récolte, la CP est tombé à 146 g$\cdot$kg$^{-1}$, alors que le rendement atteignait 12,1 t$\cdot$ha$^{-1}$. Cela fait plus que compenser la dilution de la qualité nutritionnelle par une offre de CP plus importante pour la récolte tardive. Des expériences complémentaires sont nécessaires pour une évaluation plus probante de ces recherches