1
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Dangerfield CE, David Abrahams I, Budd C, Butchers M, Cates ME, Champneys AR, Currie CS, Enright J, Gog JR, Goriely A, Déirdre Hollingsworth T, Hoyle RB, INI Professional Services, Isham V, Jordan J, Kaouri MH, Kavoussanakis K, Leeks J, Maini PK, Marr C, Merritt C, Mollison D, Ray S, Thompson RN, Wakefield A, Wasley D. Getting the most out of maths: How to coordinate mathematical modelling research to support a pandemic, lessons learnt from three initiatives that were part of the COVID-19 response in the UK. J Theor Biol 2023; 557:111332. [PMID: 36323393 PMCID: PMC9618296 DOI: 10.1016/j.jtbi.2022.111332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 10/14/2022] [Accepted: 10/17/2022] [Indexed: 11/16/2022]
Abstract
In March 2020 mathematics became a key part of the scientific advice to the UK government on the pandemic response to COVID-19. Mathematical and statistical modelling provided critical information on the spread of the virus and the potential impact of different interventions. The unprecedented scale of the challenge led the epidemiological modelling community in the UK to be pushed to its limits. At the same time, mathematical modellers across the country were keen to use their knowledge and skills to support the COVID-19 modelling effort. However, this sudden great interest in epidemiological modelling needed to be coordinated to provide much-needed support, and to limit the burden on epidemiological modellers already very stretched for time. In this paper we describe three initiatives set up in the UK in spring 2020 to coordinate the mathematical sciences research community in supporting mathematical modelling of COVID-19. Each initiative had different primary aims and worked to maximise synergies between the various projects. We reflect on the lessons learnt, highlighting the key roles of pre-existing research collaborations and focal centres of coordination in contributing to the success of these initiatives. We conclude with recommendations about important ways in which the scientific research community could be better prepared for future pandemics. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".
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Affiliation(s)
- Ciara E. Dangerfield
- Isaac Newton Institute to Mathematical Sciences, University of Cambridge, United Kingdom,Joint UNIversities Pandemic and Epidemiological Research (JUNIPER) Consortium, United Kingdom1,Corresponding author
| | - I. David Abrahams
- Department for Applied Mathematics and Theoretical Physics, University of Cambridge, United Kingdom
| | - Chris Budd
- Department of Mathematics, University of Bath, United Kingdom
| | - Matt Butchers
- Department of Mathematics, University of Bath, United Kingdom
| | - Michael E. Cates
- Department for Applied Mathematics and Theoretical Physics, University of Cambridge, United Kingdom
| | - Alan R. Champneys
- Department of Engineering Mathematics, University of Bristol, United Kingdom
| | | | - Jessica Enright
- School of Computing Science, University of Glasgow, United Kingdom
| | - Julia R. Gog
- Joint UNIversities Pandemic and Epidemiological Research (JUNIPER) Consortium, United Kingdom1,Department for Applied Mathematics and Theoretical Physics, University of Cambridge, United Kingdom
| | - Alain Goriely
- Mathematical Institute, University of Oxford, United Kingdom
| | - T. Déirdre Hollingsworth
- Joint UNIversities Pandemic and Epidemiological Research (JUNIPER) Consortium, United Kingdom1,Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, United Kingdom
| | - Rebecca B. Hoyle
- School of Mathematical Sciences, University of Southampton, United Kingdom
| | | | - Valerie Isham
- Department of Statistical Science, University College London, United Kingdom
| | | | - Maha H. Kaouri
- Isaac Newton Institute to Mathematical Sciences, University of Cambridge, United Kingdom
| | | | - Jane Leeks
- Isaac Newton Institute to Mathematical Sciences, University of Cambridge, United Kingdom
| | - Philip K. Maini
- Mathematical Institute, University of Oxford, United Kingdom
| | - Christie Marr
- Isaac Newton Institute to Mathematical Sciences, University of Cambridge, United Kingdom
| | - Clare Merritt
- Isaac Newton Institute to Mathematical Sciences, University of Cambridge, United Kingdom
| | - Denis Mollison
- Department of Actuarial Mathematics and Statistics, Heriot-Watt University, United Kingdom
| | - Surajit Ray
- School of Mathematics and Statistics, University of Glasgow, United Kingdom
| | - Robin N. Thompson
- Mathematics Institute, University of Warwick, United Kingdom,Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, United Kingdom
| | | | - Dawn Wasley
- International Centre for Mathematical Sciences, University of Edinburgh & Heriot-Watt University, United Kingdom
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2
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Cope AL, Anderson F, Favate J, Jackson M, Mok A, Kurowska A, Liu J, MacKenzie E, Shivakumar V, Tilton P, Winterbourne SM, Xue S, Kavoussanakis K, Lareau LF, Shah P, Wallace EWJ. riboviz 2: a flexible and robust ribosome profiling data analysis and visualization workflow. Bioinformatics 2022; 38:2358-2360. [PMID: 35157051 PMCID: PMC9004635 DOI: 10.1093/bioinformatics/btac093] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 09/28/2021] [Accepted: 02/09/2022] [Indexed: 02/04/2023] Open
Abstract
MOTIVATION Ribosome profiling, or Ribo-seq, is the state-of-the-art method for quantifying protein synthesis in living cells. Computational analysis of Ribo-seq data remains challenging due to the complexity of the procedure, as well as variations introduced for specific organisms or specialized analyses. RESULTS We present riboviz 2, an updated riboviz package, for the comprehensive transcript-centric analysis and visualization of Ribo-seq data. riboviz 2 includes an analysis workflow built on the Nextflow workflow management system for end-to-end processing of Ribo-seq data. riboviz 2 has been extensively tested on diverse species and library preparation strategies, including multiplexed samples. riboviz 2 is flexible and uses open, documented file formats, allowing users to integrate new analyses with the pipeline. AVAILABILITY AND IMPLEMENTATION riboviz 2 is freely available at github.com/riboviz/riboviz.
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Affiliation(s)
- Alexander L Cope
- Department of Genetics, Rutgers University, Piscataway, NJ 08854-8082, USA
| | - Felicity Anderson
- Institute for Cell Biology and SynthSys, School of Biological Sciences, The University of Edinburgh, Edinburgh EH9 3BF, UK
| | - John Favate
- Department of Genetics, Rutgers University, Piscataway, NJ 08854-8082, USA
| | | | - Amanda Mok
- Center for Computational Biology, University of California, Berkeley, CA 94720, USA
| | - Anna Kurowska
- Institute for Cell Biology and SynthSys, School of Biological Sciences, The University of Edinburgh, Edinburgh EH9 3BF, UK
| | - Junchen Liu
- EPCC, The University of Edinburgh, Edinburgh EH8 9BT, UK
| | - Emma MacKenzie
- Institute for Cell Biology and SynthSys, School of Biological Sciences, The University of Edinburgh, Edinburgh EH9 3BF, UK
| | - Vikram Shivakumar
- Department of Bioengineering, University of California, Berkeley, CA 94720, USA
| | - Peter Tilton
- Department of Genetics, Rutgers University, Piscataway, NJ 08854-8082, USA
| | - Sophie M Winterbourne
- Institute for Cell Biology and SynthSys, School of Biological Sciences, The University of Edinburgh, Edinburgh EH9 3BF, UK
| | - Siyin Xue
- Institute for Cell Biology and SynthSys, School of Biological Sciences, The University of Edinburgh, Edinburgh EH9 3BF, UK
| | | | - Liana F Lareau
- Center for Computational Biology, University of California, Berkeley, CA 94720, USA
- Department of Bioengineering, University of California, Berkeley, CA 94720, USA
| | - Premal Shah
- Department of Genetics, Rutgers University, Piscataway, NJ 08854-8082, USA
| | - Edward W J Wallace
- Institute for Cell Biology and SynthSys, School of Biological Sciences, The University of Edinburgh, Edinburgh EH9 3BF, UK
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3
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Abstract
Workflow management systems represent, manage, and execute multistep computational analyses and offer many benefits to bioinformaticians. They provide a common language for describing analysis workflows, contributing to reproducibility and to building libraries of reusable components. They can support both incremental build and re-entrancy—the ability to selectively re-execute parts of a workflow in the presence of additional inputs or changes in configuration and to resume execution from where a workflow previously stopped. Many workflow management systems enhance portability by supporting the use of containers, high-performance computing (HPC) systems, and clouds. Most importantly, workflow management systems allow bioinformaticians to delegate how their workflows are run to the workflow management system and its developers. This frees the bioinformaticians to focus on what these workflows should do, on their data analyses, and on their science. RiboViz is a package to extract biological insight from ribosome profiling data to help advance understanding of protein synthesis. At the heart of RiboViz is an analysis workflow, implemented in a Python script. To conform to best practices for scientific computing which recommend the use of build tools to automate workflows and to reuse code instead of rewriting it, the authors reimplemented this workflow within a workflow management system. To select a workflow management system, a rapid survey of available systems was undertaken, and candidates were shortlisted: Snakemake, cwltool, Toil, and Nextflow. Each candidate was evaluated by quickly prototyping a subset of the RiboViz workflow, and Nextflow was chosen. The selection process took 10 person-days, a small cost for the assurance that Nextflow satisfied the authors’ requirements. The use of prototyping can offer a low-cost way of making a more informed selection of software to use within projects, rather than relying solely upon reviews and recommendations by others. Data analysis involves many steps, as data are wrangled, processed, and analysed using a succession of unrelated software packages. Running the right steps, in the right order, and putting the right outputs in the right places, is a major source of frustration. Workflow management systems require that each data analysis step be “wrapped” in a structured way, describing its inputs, parameters, and outputs. By writing these wrappers, the scientist can focus on the meaning of each step, and how they fit together, which is the interesting part. The system uses these wrappers to decide what steps to run and how to run these and takes charge of running the steps, including reporting on errors. This makes it much easier to repeatedly run the analysis and to run it transparently upon different computers. To select a workflow management system, we surveyed available tools and chose 4 in which we developed prototype implementations to evaluate their suitability for our project. We conclude that many similar multistep data analysis workflows can be rewritten in a workflow management system, and we advocate prototyping as a low-cost (both time and effort) way of making an informed selection of software for use within a research project.
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Affiliation(s)
- Michael Jackson
- EPCC, The University of Edinburgh, Edinburgh, United Kingdom
- * E-mail: (MJ); (EWJW)
| | | | - Edward W. J. Wallace
- Institute for Cell Biology and SynthSys, School of Biological Sciences, The University of Edinburgh, Edinburgh, United Kingdom
- * E-mail: (MJ); (EWJW)
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Nowell J, Kavoussanakis K, Palansuriya C, Piotrowski M, Scharinger F, Graham P, Dobrzelecki B, Trew A. Standards-based network monitoring for the grid. Philos Trans A Math Phys Eng Sci 2009; 367:2495-2505. [PMID: 19451105 DOI: 10.1098/rsta.2009.0033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
As large grid infrastructures, such as Enabling Grids for E-sciencE, mature, they are being used by scientists around the world in their daily work, running thousands of concurrent computational jobs and transferring large amounts of data. The successful and sustainable operation of such grid infrastructures is only possible through the use of monitoring tools. The underlying networks upon which grid infrastructures are built are critical to their operation; therefore, network monitoring becomes an important part of the overall grid monitoring strategy. In this paper, the design and implementation of a set of tools for providing access to federated network monitoring data are presented, based on standards developed within the Open Grid Forum Network Measurements Working Group (NM-WG). These tools give access to data collected by heterogeneous, NM-WG compliant network monitoring tools.
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Affiliation(s)
- Jeremy Nowell
- EPCC, University of Edinburgh, Edinburgh EH9 3JZ, UK.
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5
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Tenesa A, Farrington SM, Prendergast JGD, Porteous ME, Walker M, Haq N, Barnetson RA, Theodoratou E, Cetnarskyj R, Cartwright N, Semple C, Clark AJ, Reid FJL, Smith LA, Kavoussanakis K, Koessler T, Pharoah PDP, Buch S, Schafmayer C, Tepel J, Schreiber S, Völzke H, Schmidt CO, Hampe J, Chang-Claude J, Hoffmeister M, Brenner H, Wilkening S, Canzian F, Capella G, Moreno V, Deary IJ, Starr JM, Tomlinson IPM, Kemp Z, Howarth K, Carvajal-Carmona L, Webb E, Broderick P, Vijayakrishnan J, Houlston RS, Rennert G, Ballinger D, Rozek L, Gruber SB, Matsuda K, Kidokoro T, Nakamura Y, Zanke BW, Greenwood CMT, Rangrej J, Kustra R, Montpetit A, Hudson TJ, Gallinger S, Campbell H, Dunlop MG. Genome-wide association scan identifies a colorectal cancer susceptibility locus on 11q23 and replicates risk loci at 8q24 and 18q21. Nat Genet 2008; 40:631-7. [PMID: 18372901 DOI: 10.1038/ng.133] [Citation(s) in RCA: 456] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2007] [Accepted: 02/29/2008] [Indexed: 12/12/2022]
Abstract
In a genome-wide association study to identify loci associated with colorectal cancer (CRC) risk, we genotyped 555,510 SNPs in 1,012 early-onset Scottish CRC cases and 1,012 controls (phase 1). In phase 2, we genotyped the 15,008 highest-ranked SNPs in 2,057 Scottish cases and 2,111 controls. We then genotyped the five highest-ranked SNPs from the joint phase 1 and 2 analysis in 14,500 cases and 13,294 controls from seven populations, and identified a previously unreported association, rs3802842 on 11q23 (OR = 1.1; P = 5.8 x 10(-10)), showing population differences in risk. We also replicated and fine-mapped associations at 8q24 (rs7014346; OR = 1.19; P = 8.6 x 10(-26)) and 18q21 (rs4939827; OR = 1.2; P = 7.8 x 10(-28)). Risk was greater for rectal than for colon cancer for rs3802842 (P < 0.008) and rs4939827 (P < 0.009). Carrying all six possible risk alleles yielded OR = 2.6 (95% CI = 1.75-3.89) for CRC. These findings extend our understanding of the role of common genetic variation in CRC etiology.
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Affiliation(s)
- Albert Tenesa
- Colon Cancer Genetics Group, Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Edinburgh EH4 2XU, UK
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