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P'ng C, Green J, Chong LC, Waggott D, Prokopec SD, Shamsi M, Nguyen F, Mak DYF, Lam F, Albuquerque MA, Wu Y, Jung EH, Starmans MHW, Chan-Seng-Yue MA, Yao CQ, Liang B, Lalonde E, Haider S, Simone NA, Sendorek D, Chu KC, Moon NC, Fox NS, Grzadkowski MR, Harding NJ, Fung C, Murdoch AR, Houlahan KE, Wang J, Garcia DR, de Borja R, Sun RX, Lin X, Chen GM, Lu A, Shiah YJ, Zia A, Kearns R, Boutros PC. BPG: Seamless, automated and interactive visualization of scientific data. BMC Bioinformatics 2019; 20:42. [PMID: 30665349 PMCID: PMC6341661 DOI: 10.1186/s12859-019-2610-2] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Accepted: 01/04/2019] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND We introduce BPG, a framework for generating publication-quality, highly-customizable plots in the R statistical environment. RESULTS This open-source package includes multiple methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it suitable for complex datasets. A web-based interactive tool allows online figure customization, from which R code can be downloaded for integration with computational pipelines. CONCLUSION BPG provides a new approach for linking interactive and scripted data visualization and is available at http://labs.oicr.on.ca/boutros-lab/software/bpg or via CRAN at https://cran.r-project.org/web/packages/BoutrosLab.plotting.general.
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Affiliation(s)
| | - Jeffrey Green
- Ontario Institute for Cancer Research, Toronto, Canada
| | | | - Daryl Waggott
- Ontario Institute for Cancer Research, Toronto, Canada
| | | | | | | | | | - Felix Lam
- Ontario Institute for Cancer Research, Toronto, Canada
| | | | - Ying Wu
- Ontario Institute for Cancer Research, Toronto, Canada
| | - Esther H Jung
- Ontario Institute for Cancer Research, Toronto, Canada
| | | | | | - Cindy Q Yao
- Ontario Institute for Cancer Research, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Bianca Liang
- Ontario Institute for Cancer Research, Toronto, Canada
| | - Emilie Lalonde
- Ontario Institute for Cancer Research, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Syed Haider
- Ontario Institute for Cancer Research, Toronto, Canada
| | | | | | - Kenneth C Chu
- Ontario Institute for Cancer Research, Toronto, Canada
| | | | - Natalie S Fox
- Ontario Institute for Cancer Research, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | | | | | - Clement Fung
- Ontario Institute for Cancer Research, Toronto, Canada
| | | | - Kathleen E Houlahan
- Ontario Institute for Cancer Research, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Jianxin Wang
- Ontario Institute for Cancer Research, Toronto, Canada.,Present address: Center for Computational Research, Buffalo Institute for Genomics and Data Analytics, NYS Center for Excellence in Bioinformatics & Life Science, University at Buffalo, Buffalo, USA
| | | | | | - Ren X Sun
- Ontario Institute for Cancer Research, Toronto, Canada.,Department of Pharmacology and Toxicology, University of Toronto, Toronto, Canada
| | - Xihui Lin
- Ontario Institute for Cancer Research, Toronto, Canada
| | | | - Aileen Lu
- Ontario Institute for Cancer Research, Toronto, Canada.,Department of Pharmacology and Toxicology, University of Toronto, Toronto, Canada
| | - Yu-Jia Shiah
- Ontario Institute for Cancer Research, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Amin Zia
- Ontario Institute for Cancer Research, Toronto, Canada
| | - Ryan Kearns
- Ontario Institute for Cancer Research, Toronto, Canada
| | - Paul C Boutros
- Ontario Institute for Cancer Research, Toronto, Canada. .,Department of Medical Biophysics, University of Toronto, Toronto, Canada. .,Department of Pharmacology and Toxicology, University of Toronto, Toronto, Canada. .,Department of Human Genetics, University of California, Los Angeles, USA. .,Department of Urology, University of California, Los Angeles, USA. .,Institute for Precision Health, University of California, Los Angeles, USA. .,Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA.
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Albuquerque MA, Grande BM, Ritch EJ, Pararajalingam P, Jessa S, Krzywinski M, Grewal JK, Shah SP, Boutros PC, Morin RD. Enhancing knowledge discovery from cancer genomics data with Galaxy. Gigascience 2018; 6:1-13. [PMID: 28327945 PMCID: PMC5437943 DOI: 10.1093/gigascience/gix015] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2016] [Accepted: 03/06/2017] [Indexed: 01/15/2023] Open
Abstract
The field of cancer genomics has demonstrated the power of massively parallel sequencing techniques to inform on the genes and specific alterations that drive tumor onset and progression. Although large comprehensive sequence data sets continue to be made increasingly available, data analysis remains an ongoing challenge, particularly for laboratories lacking dedicated resources and bioinformatics expertise. To address this, we have produced a collection of Galaxy tools that represent many popular algorithms for detecting somatic genetic alterations from cancer genome and exome data. We developed new methods for parallelization of these tools within Galaxy to accelerate runtime and have demonstrated their usability and summarized their runtimes on multiple cloud service providers. Some tools represent extensions or refinement of existing toolkits to yield visualizations suited to cohort-wide cancer genomic analysis. For example, we present Oncocircos and Oncoprintplus, which generate data-rich summaries of exome-derived somatic mutation. Workflows that integrate these to achieve data integration and visualizations are demonstrated on a cohort of 96 diffuse large B-cell lymphomas and enabled the discovery of multiple candidate lymphoma-related genes. Our toolkit is available from our GitHub repository as Galaxy tool and dependency definitions and has been deployed using virtualization on multiple platforms including Docker.
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Affiliation(s)
- Marco A Albuquerque
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
| | - Bruno M Grande
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
| | - Elie J Ritch
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
| | - Prasath Pararajalingam
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
| | - Selin Jessa
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
| | - Martin Krzywinski
- Canada's Michael Smith Genome Sciences Center, BC Cancer Agency, Vancouver, BC, Canada
| | - Jasleen K Grewal
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
| | - Sohrab P Shah
- Department of Pathology, University of British Columbia, Vancouver, BC, Canada
| | - Paul C Boutros
- Ontario Institute for Cancer Research, Toronto, ON, Canada
| | - Ryan D Morin
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada.,Canada's Michael Smith Genome Sciences Center, BC Cancer Agency, Vancouver, BC, Canada
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Chong LC, Albuquerque MA, Harding NJ, Caloian C, Chan-Seng-Yue M, de Borja R, Fraser M, Denroche RE, Beck TA, van der Kwast T, Bristow RG, McPherson JD, Boutros PC. SeqControl: process control for DNA sequencing. Nat Methods 2014; 11:1071-5. [PMID: 25173705 DOI: 10.1038/nmeth.3094] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2014] [Accepted: 07/27/2014] [Indexed: 12/15/2022]
Abstract
As high-throughput sequencing continues to increase in speed and throughput, routine clinical and industrial application draws closer. These 'production' settings will require enhanced quality monitoring and quality control to optimize output and reduce costs. We developed SeqControl, a framework for predicting sequencing quality and coverage using a set of 15 metrics describing overall coverage, coverage distribution, basewise coverage and basewise quality. Using whole-genome sequences of 27 prostate cancers and 26 normal references, we derived multivariate models that predict sequencing quality and depth. SeqControl robustly predicted how much sequencing was required to reach a given coverage depth (area under the curve (AUC) = 0.993), accurately classified clinically relevant formalin-fixed, paraffin-embedded samples, and made predictions from as little as one-eighth of a sequencing lane (AUC = 0.967). These techniques can be immediately incorporated into existing sequencing pipelines to monitor data quality in real time. SeqControl is available at http://labs.oicr.on.ca/Boutros-lab/software/SeqControl/.
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Affiliation(s)
- Lauren C Chong
- Informatics &Biocomputing Platform, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Marco A Albuquerque
- Informatics &Biocomputing Platform, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Nicholas J Harding
- Informatics &Biocomputing Platform, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Cristian Caloian
- Informatics &Biocomputing Platform, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Michelle Chan-Seng-Yue
- Informatics &Biocomputing Platform, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Richard de Borja
- Informatics &Biocomputing Platform, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Michael Fraser
- Department of Pathology, University Health Network, Toronto, Ontario, Canada
| | - Robert E Denroche
- Informatics &Biocomputing Platform, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Timothy A Beck
- Informatics &Biocomputing Platform, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | | | - Robert G Bristow
- 1] Ontario Cancer Institute, University Health Network, Toronto, Ontario, Canada. [2] Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - John D McPherson
- 1] Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada. [2] Genomics Platform, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Paul C Boutros
- 1] Informatics &Biocomputing Platform, Ontario Institute for Cancer Research, Toronto, Ontario, Canada. [2] Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada. [3] Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada
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Albuquerque MA. [Drug addiction and freedom]. Acta Psiquiatr Psicol Am Lat 1982; 28:53-62. [PMID: 7136825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
The author, in a historical and philosophical approach, analyses the concept of freedom as opposed to slavery. He also refers to the legal and social restrictions and studies the determinism and free will as the causes of human behaviour. Quoting Spinoza, the author states that man accepts the idea of freedom because he realizes the "how" of his options but ignores "why". Without the hypothesis of causality and determinism, there seems to have no science. Without freedom, there seems to be no anthropos man (Jimeno Valdez). The principles of anticausality, of nonreproducibility and of differentiation characterize the human freedom, but are contrary to the way science works. According to the social and political point of view, it was established that the State has the right to oblige and to violently limit freedom. Practically speaking, though, the State is violent just for being the State; the dominant groups are the government because they are and they have been violent. There is a need to limit and to discipline this right of the State of being violent within the dilemma of safety and freedom. By working, the slave avoided the whip. And by doing this, he encouraged the behaviour of the one who whipped him. The non-aversive attitudes limit the freedom in the modern world more and more for they also enchain our will, a rebellion becoming impossible. One is not granted the freedom; it shall be conquered and kept. Freedom, either as a concept or a phenomenon, is always relative. The concept of toxicomania or pharmacodependance is analysed according to the same perspective. The conclusion is that this is always more a problem of the society than of the individual, and this is how it has to be understood and treated. The present world is described as a millenial human culture specifically characterized by eight groups of phenomena: 1. Transport increased human mobility, reduced the relative dimensions of the earth, mixed peoples, compared cultures and created opportunities for conflicts; 2. The means of communication spread information surpassing their own capacity of being formed, they becoming traumatic; 3. The natural energies and electronic computing disturbed the work and comsumption market, man becoming insecure and disposable; 4. The industrial development produced a mass-culture and old time establishment became unreliable; 5. The demographic boom altered the age groups ratio which now presents an excess of children and grandparents with a relative lack of parents; 6. The diffusion of knowledge on human nature; 7. The religions emerged again, even the most primitive, persuading youngsters and governments; 8. The law, institutions and governments did not keep up with the rhythm of the changings and progress. The result was a generalized dissatisfaction.
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