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Moos P, Cheminant J, Adhikari U, Venosa A. Transcriptomic-based roadmap to the healthy and ozone-exposed lung. CURRENT OPINION IN TOXICOLOGY 2024; 37:100445. [PMID: 38187954 PMCID: PMC10769160 DOI: 10.1016/j.cotox.2023.100445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
The lung is constantly exposed to a myriad of exogenous stressors. Ground-level ozone represents a ubiquitous and extremely reactive anthropogenic toxicant, impacting the health of millions across the globe. While abundant, epidemiological, in vivo, and in vitro data focuses the ozone toxicity in individual cell types (e.g. epithelial type II, alveolar macrophages) or signaling pathways involved in the injury (e.g., akt, glutathione). When appropriately used, bulk and single cell RNA sequencing techniques have the potential to provide complete, and in certain cases unbiased, information of the molecular events taking place in the steady state and injured lung, and even capture the phenotypic diversity of neighboring cells. To this end, this review compiles information pertaining to the latest understanding of lung cell identity and activation in the steady state and ozone exposed lung. In addition, it discusses the value and benefits of multi-omics approaches and other tools developed to predict cell-cell communication and dissect spatial heterogeneity.
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Affiliation(s)
- Philip Moos
- Department of Pharmacology and Toxicology, University of Utah College of Pharmacy, Salt Lake City, Utah
| | - Jenna Cheminant
- Department of Pharmacology and Toxicology, University of Utah College of Pharmacy, Salt Lake City, Utah
| | - Ujjwal Adhikari
- Department of Pharmacology and Toxicology, University of Utah College of Pharmacy, Salt Lake City, Utah
| | - Alessandro Venosa
- Department of Pharmacology and Toxicology, University of Utah College of Pharmacy, Salt Lake City, Utah
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2
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Ayoob JC, Kangas JD. 10 simple rules for teaching wet-lab experimentation to computational biology students, i.e., turning computer mice into lab rats. PLoS Comput Biol 2020; 16:e1007911. [PMID: 32497035 PMCID: PMC7271982 DOI: 10.1371/journal.pcbi.1007911] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Affiliation(s)
- Joseph C. Ayoob
- Joint Carnegie Mellon–University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, Pennsylvania, United States of America
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
- * E-mail:
| | - Joshua D. Kangas
- Joint Carnegie Mellon–University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, Pennsylvania, United States of America
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
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3
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Takko H, Pajanoja C, Kurtzeborn K, Hsin J, Kuure S, Kerosuo L. ShapeMetrics: A userfriendly pipeline for 3D cell segmentation and spatial tissue analysis. Dev Biol 2020; 462:7-19. [PMID: 32061886 DOI: 10.1016/j.ydbio.2020.02.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 01/29/2020] [Accepted: 02/01/2020] [Indexed: 12/20/2022]
Abstract
The demand for single-cell level data is constantly increasing within life sciences. In order to meet this demand, robust cell segmentation methods that can tackle challenging in vivo tissues with complex morphology are required. However, currently available cell segmentation and volumetric analysis methods perform poorly on 3D images. Here, we generated ShapeMetrics, a MATLAB-based script that segments cells in 3D and, by performing unbiased clustering using a heatmap, separates the cells into subgroups according to their volumetric and morphological differences. The cells can be accurately segregated according to different biologically meaningful features such as cell ellipticity, longest axis, cell elongation, or the ratio between cell volume and surface area. Our machine learning based script enables dissection of a large amount of novel data from microscope images in addition to the traditional information based on fluorescent biomarkers. Furthermore, the cells in different subgroups can be spatially mapped back to their original locations in the tissue image to help elucidate their roles in their respective morphological contexts. In order to facilitate the transition from bulk analysis to single-cell level accuracy, we emphasize the user-friendliness of our method by providing detailed step-by-step instructions through the pipeline hence aiming to reach users with less experience in computational biology.
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Affiliation(s)
- Heli Takko
- Department of Biochemistry and Developmental Biology, Biomedicum, University of Helsinki, Finland
| | - Ceren Pajanoja
- Department of Biochemistry and Developmental Biology, Biomedicum, University of Helsinki, Finland; National Institute of Dental and Craniofacial Research, National Institutes of Health, Neural Crest Development and Disease Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA
| | - Kristen Kurtzeborn
- Department of Biochemistry and Developmental Biology, Biomedicum, University of Helsinki, Finland; Stem Cells and Metabolism Research Program, Faculty of Medicine, University of Helsinki, Finland
| | - Jenny Hsin
- National Institute of Dental and Craniofacial Research, National Institutes of Health, Neural Crest Development and Disease Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA
| | - Satu Kuure
- Department of Biochemistry and Developmental Biology, Biomedicum, University of Helsinki, Finland; Stem Cells and Metabolism Research Program, Faculty of Medicine, University of Helsinki, Finland; GM-unit, Laboratory Animal Centre, Helsinki Institute of Life Science, University of Helsinki, Finland
| | - Laura Kerosuo
- Department of Biochemistry and Developmental Biology, Biomedicum, University of Helsinki, Finland; National Institute of Dental and Craniofacial Research, National Institutes of Health, Neural Crest Development and Disease Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA.
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4
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Signatures of medical student applicants and academic success. PLoS One 2020; 15:e0227108. [PMID: 31940377 PMCID: PMC6961867 DOI: 10.1371/journal.pone.0227108] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2019] [Accepted: 12/12/2019] [Indexed: 01/06/2023] Open
Abstract
The acceptance of students to a medical school places a considerable emphasis on performance in standardized tests and undergraduate grade point average (uGPA). Traditionally, applicants may be judged as a homogeneous population according to simple quantitative thresholds that implicitly assume a linear relationship between scores and academic success. This ‘one-size-fits-all’ approach ignores the notion that individuals may show distinct patterns of achievement and follow diverse paths to success. In this study, we examined a dataset composed of 53 variables extracted from the admissions application records of 1,088 students matriculating to NYU School of Medicine between the years 2006–2014. We defined training and test groups and applied K-means clustering to search for distinct groups of applicants. Building an optimized logistic regression model, we then tested the predictive value of this clustering for estimating the success of applicants in medical school, aggregating eight performance measures during the subsequent medical school training as a success factor. We found evidence for four distinct clusters of students—we termed ‘signatures’—which differ most substantially according to the absolute level of the applicant’s uGPA and its trajectory over the course of undergraduate education. The ‘risers’ signature showed a relatively higher uGPA and also steeper trajectory; the other signatures showed each remaining combination of these two main factors: ‘improvers’ relatively lower uGPA, steeper trajectory; ‘solids’ higher uGPA, flatter trajectory; ‘statics’ both lower uGPA and flatter trajectory. Examining the success index across signatures, we found that the risers and the statics have significantly higher and lower likelihood of quantifiable success in medical school, respectively. We also found that each signature has a unique set of features that correlate with its success in medical school. The big data approach presented here can more sensitively uncover success potential since it takes into account the inherent heterogeneity within the student population.
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5
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Shome S, Parra RG, Fatima N, Monzon AM, Cuypers B, Moosa Y, Coimbra NDR, Assis J, Giner-Delgado C, Dönertaş HM, Cuesta-Astroz Y, Saarunya G, Allali I, Gupta S, Srivastava A, Kalsan M, Valdivia C, J. Olguin-Orellana G, Papadimitriou S, Parisi D, Kristensen NP, Rib L, Guebila MB, Bauer E, Zaffaroni G, Bekkar A, Ashano E, Paladin L, Necci M, Moreyra NN, Rydén M, Villalobos-Solís J, Papadopoulos N, Rafael C, Karakulak T, Kaya Y, Gladbach Y, Dhanda SK, Šoštarić N, Alex A, DeBlasio D, Rahman F. Global network of computational biology communities: ISCB's Regional Student Groups breaking barriers. F1000Res 2019; 8:ISCB Comm J-1574. [PMID: 31508204 PMCID: PMC6720036 DOI: 10.12688/f1000research.20408.1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/26/2019] [Indexed: 11/20/2022] Open
Abstract
Regional Student Groups (RSGs) of the International Society for Computational Biology Student Council (ISCB-SC) have been instrumental to connect computational biologists globally and to create more awareness about bioinformatics education. This article highlights the initiatives carried out by the RSGs both nationally and internationally to strengthen the present and future of the bioinformatics community. Moreover, we discuss the future directions the organization will take and the challenges to advance further in the ISCB-SC main mission: "Nurture the new generation of computational biologists".
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Affiliation(s)
- Sayane Shome
- Bioinformatics and Computational Biology Program, Iowa State University, Iowa, USA
| | - R. Gonzalo Parra
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Nazeefa Fatima
- Science for Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Upsala, Sweden
| | | | - Bart Cuypers
- Department of Biomedical Sciences, Institute of Tropical Medicine, Antwerp, Belgium
- Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium
| | - Yumna Moosa
- KZN Research and Innovation Sequencing Platform, University of KwaZulu Natal, Durban, South Africa
| | - Nilson Da Rocha Coimbra
- Graduate Program in Bioinformatics, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Juliana Assis
- Graduate Program in Bioinformatics, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Carla Giner-Delgado
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Handan Melike Dönertaş
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Yesid Cuesta-Astroz
- School of Microbiology, Universidad de Antioquía, Medellín, Colombia
- Colombian Tropical Medicine Institute (ICMT), Universidad CES, Medellín, Colombia
| | - Geetha Saarunya
- Department of Biological Sciences, University of South Carolina, South Caroli a, USA
| | - Imane Allali
- Department of Biology, Faculty of Sciences, Mohammed V University in Rabat, Rabat, Morocco
- Division of Computational Biology, Department of Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Shruti Gupta
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India
| | - Ambuj Srivastava
- Department of Biotechnology, Indian Institute of Technology Madras, Chennai, India
| | - Manisha Kalsan
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India
| | - Catalina Valdivia
- Ecosystem’s Health Laboratory, Universidad Andres, Bello Santiago, Chile
| | | | - Sofia Papadimitriou
- Interuniversity Institute of Bioinformatics in Brussels, Université libre de Bruxelles-Vrije Universiteit Brussel, Brussels, Belgium
| | | | | | - Leonor Rib
- The Bioinformatics Center, Biology and Biotech Research and Innovation Center, University of Copenhagen, Copenhagen, Denmark
| | - Marouen Ben Guebila
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Eugen Bauer
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Gaia Zaffaroni
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Amel Bekkar
- Swiss Institute of Bioinformatics (SIB), University of Lausanne, Lausanne, Switzerland
| | - Efejiro Ashano
- Molecular Diagnostics, Laboratory Services, APIN Public Health Initiatives, Abuja, Nigeria
| | - Lisanna Paladin
- Department of Biomedical Sciences, University of Padova, Padova, Italy
| | - Marco Necci
- Department of Biomedical Sciences, University of Padova, Padova, Italy
| | - Nicolás N. Moreyra
- Genetics and Evolution of Buenos Aires (IEGEBA), CONICET-UBA, Institute of Ecology, Buenos Aires, Argentina
| | - Martin Rydén
- Biomedical Centre, Faculty of Medicine, Lund University, Lund, Sweden
| | - Jordan Villalobos-Solís
- Laboratorio de Biotenología de Plantas, Universidad Nacional de Costa Rica (UNA), Heredia, Costa Rica
| | - Nikolaos Papadopoulos
- Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Candice Rafael
- Research Unit for Bioinformatics, Rhodes University, Grahamstown, South Africa
| | - Tülay Karakulak
- Izmir Biomedicine and Genome Center, Dokuz Eylül University, Izmir, Turkey
| | - Yasin Kaya
- Hacettepe University, Faculty of Science, Department of Biology, Ankara, Turkey
| | - Yvonne Gladbach
- University Medical Center Rostock, University Heidelberg, Heidelberg, Germany
| | - Sandeep Kumar Dhanda
- La Jolla Institute for Allergy and Immunology, La Jolla Institute for Immunology, California, USA
| | | | - Aishwarya Alex
- Roche Diagnostics Automation Solutions GmbH, Roche, Waiblingen, Germany
| | - Dan DeBlasio
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, USA
| | - Farzana Rahman
- Genomics and Computational Biology Research Group, University of South Wales, Pontypridd, UK
- School of Human and Life Sciences, Canterbury Christ Church University, Kent, UK
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6
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Ahmed AE, Mpangase PT, Panji S, Baichoo S, Souilmi Y, Fadlelmola FM, Alghali M, Aron S, Bendou H, De Beste E, Mbiyavanga M, Souiai O, Yi L, Zermeno J, Armstrong D, O'Connor BD, Mainzer LS, Crusoe MR, Meintjes A, Van Heusden P, Botha G, Joubert F, Jongeneel CV, Hazelhurst S, Mulder N. Organizing and running bioinformatics hackathons within Africa: The H3ABioNet cloud computing experience. AAS Open Res 2019; 1:9. [PMID: 32382696 PMCID: PMC7194140 DOI: 10.12688/aasopenres.12847.1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/18/2019] [Indexed: 08/20/2023] Open
Abstract
The need for portable and reproducible genomics analysis pipelines is growing globally as well as in Africa, especially with the growth of collaborative projects like the Human Health and Heredity in Africa Consortium (H3Africa). The Pan-African H3Africa Bioinformatics Network (H3ABioNet) recognized the need for portable, reproducible pipelines adapted to heterogeneous computing environments, and for the nurturing of technical expertise in workflow languages and containerization technologies. Building on the network's Standard Operating Procedures (SOPs) for common genomic analyses, H3ABioNet arranged its first Cloud Computing and Reproducible Workflows Hackathon in 2016, with the purpose of translating those SOPs into analysis pipelines able to run on heterogeneous computing environments and meeting the needs of H3Africa research projects. This paper describes the preparations for this hackathon and reflects upon the lessons learned about its impact on building the technical and scientific expertise of African researchers. The workflows developed were made publicly available in GitHub repositories and deposited as container images on Quay.io.
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Affiliation(s)
- Azza E. Ahmed
- Centre for Bioinformatics and Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan
- Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Khartoum, Khartoum, Sudan
| | - Phelelani T. Mpangase
- Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Sumir Panji
- Computational Biology Division, Integrative Medical Biosciences, University of Cape Town, Cape Town, South Africa
| | - Shakuntala Baichoo
- Department of Digital Technologies, University of Mauritius, Reduit, Mauritius
| | - Yassine Souilmi
- Australian Centre for Ancient DNA, University of Adelaide, Adelaide, Australia
| | - Faisal M. Fadlelmola
- Centre for Bioinformatics and Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan
| | - Mustafa Alghali
- Centre for Bioinformatics and Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan
| | - Shaun Aron
- Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Hocine Bendou
- South African National Bioinformatics Institute, University of the Western Cape, Cape Town, South Africa
| | - Eugene De Beste
- South African National Bioinformatics Institute, University of the Western Cape, Cape Town, South Africa
| | - Mamana Mbiyavanga
- Computational Biology Division, Integrative Medical Biosciences, University of Cape Town, Cape Town, South Africa
| | - Oussema Souiai
- Institut Pasteur De Tunis and Institut Superieur des Technologies Médicales de Tunis, University Tunis Al Manar, Tunis, Tunisia
| | - Long Yi
- South African National Bioinformatics Institute, University of the Western Cape, Cape Town, South Africa
| | - Jennie Zermeno
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Don Armstrong
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Brian D. O'Connor
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Liudmila Sergeevna Mainzer
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | | | - Ayton Meintjes
- Computational Biology Division, Integrative Medical Biosciences, University of Cape Town, Cape Town, South Africa
| | - Peter Van Heusden
- South African National Bioinformatics Institute, University of the Western Cape, Cape Town, South Africa
| | - Gerrit Botha
- Computational Biology Division, Integrative Medical Biosciences, University of Cape Town, Cape Town, South Africa
| | - Fourie Joubert
- Centre for Bioinformatics and Computational Biology, Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Pretoria, South Africa
| | - C. Victor Jongeneel
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Scott Hazelhurst
- Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
- School of Electrical & Information Engineering, University of the Witwatersrand, Johannesburg, South Africa
| | - Nicola Mulder
- Computational Biology Division, Integrative Medical Biosciences, University of Cape Town, Cape Town, South Africa
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7
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Ahmed AE, Mpangase PT, Panji S, Baichoo S, Souilmi Y, Fadlelmola FM, Alghali M, Aron S, Bendou H, De Beste E, Mbiyavanga M, Souiai O, Yi L, Zermeno J, Armstrong D, O'Connor BD, Mainzer LS, Crusoe MR, Meintjes A, Van Heusden P, Botha G, Joubert F, Jongeneel CV, Hazelhurst S, Mulder N. Organizing and running bioinformatics hackathons within Africa: The H3ABioNet cloud computing experience. AAS Open Res 2019; 1:9. [PMID: 32382696 PMCID: PMC7194140 DOI: 10.12688/aasopenres.12847.2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/18/2019] [Indexed: 11/20/2022] Open
Abstract
The need for portable and reproducible genomics analysis pipelines is growing globally as well as in Africa, especially with the growth of collaborative projects like the Human Health and Heredity in Africa Consortium (H3Africa). The Pan-African H3Africa Bioinformatics Network (H3ABioNet) recognized the need for portable, reproducible pipelines adapted to heterogeneous computing environments, and for the nurturing of technical expertise in workflow languages and containerization technologies. Building on the network’s Standard Operating Procedures (SOPs) for common genomic analyses, H3ABioNet arranged its first Cloud Computing and Reproducible Workflows Hackathon in 2016, with the purpose of translating those SOPs into analysis pipelines able to run on heterogeneous computing environments and meeting the needs of H3Africa research projects. This paper describes the preparations for this hackathon and reflects upon the lessons learned about its impact on building the technical and scientific expertise of African researchers. The workflows developed were made publicly available in GitHub repositories and deposited as container images on Quay.io.
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Affiliation(s)
- Azza E Ahmed
- Centre for Bioinformatics and Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan.,Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Khartoum, Khartoum, Sudan
| | - Phelelani T Mpangase
- Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Sumir Panji
- Computational Biology Division, Integrative Medical Biosciences, University of Cape Town, Cape Town, South Africa
| | - Shakuntala Baichoo
- Department of Digital Technologies, University of Mauritius, Reduit, Mauritius
| | - Yassine Souilmi
- Australian Centre for Ancient DNA, University of Adelaide, Adelaide, Australia
| | - Faisal M Fadlelmola
- Centre for Bioinformatics and Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan
| | - Mustafa Alghali
- Centre for Bioinformatics and Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan
| | - Shaun Aron
- Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Hocine Bendou
- South African National Bioinformatics Institute, University of the Western Cape, Cape Town, South Africa
| | - Eugene De Beste
- South African National Bioinformatics Institute, University of the Western Cape, Cape Town, South Africa
| | - Mamana Mbiyavanga
- Computational Biology Division, Integrative Medical Biosciences, University of Cape Town, Cape Town, South Africa
| | - Oussema Souiai
- Institut Pasteur De Tunis and Institut Superieur des Technologies Médicales de Tunis, University Tunis Al Manar, Tunis, Tunisia
| | - Long Yi
- South African National Bioinformatics Institute, University of the Western Cape, Cape Town, South Africa
| | - Jennie Zermeno
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Don Armstrong
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Brian D O'Connor
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Liudmila Sergeevna Mainzer
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.,National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | | | - Ayton Meintjes
- Computational Biology Division, Integrative Medical Biosciences, University of Cape Town, Cape Town, South Africa
| | - Peter Van Heusden
- South African National Bioinformatics Institute, University of the Western Cape, Cape Town, South Africa
| | - Gerrit Botha
- Computational Biology Division, Integrative Medical Biosciences, University of Cape Town, Cape Town, South Africa
| | - Fourie Joubert
- Centre for Bioinformatics and Computational Biology, Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Pretoria, South Africa
| | - C Victor Jongeneel
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Scott Hazelhurst
- Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa.,School of Electrical & Information Engineering, University of the Witwatersrand, Johannesburg, South Africa
| | - Nicola Mulder
- Computational Biology Division, Integrative Medical Biosciences, University of Cape Town, Cape Town, South Africa
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8
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Anton Feenstra K, Abeln S, Westerhuis JA, Brancos dos Santos F, Molenaar D, Teusink B, Hoefsloot HCJ, Heringa J. Training for translation between disciplines: a philosophy for life and data sciences curricula. Bioinformatics 2018; 34:i4-i12. [PMID: 29950011 PMCID: PMC6022589 DOI: 10.1093/bioinformatics/bty233] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
Motivation Our society has become data-rich to the extent that research in many areas has become impossible without computational approaches. Educational programmes seem to be lagging behind this development. At the same time, there is a growing need not only for strong data science skills, but foremost for the ability to both translate between tools and methods on the one hand, and application and problems on the other. Results Here we present our experiences with shaping and running a masters' programme in bioinformatics and systems biology in Amsterdam. From this, we have developed a comprehensive philosophy on how translation in training may be achieved in a dynamic and multidisciplinary research area, which is described here. We furthermore describe two requirements that enable translation, which we have found to be crucial: sufficient depth and focus on multidisciplinary topic areas, coupled with a balanced breadth from adjacent disciplines. Finally, we present concrete suggestions on how this may be implemented in practice, which may be relevant for the effectiveness of life science and data science curricula in general, and of particular interest to those who are in the process of setting up such curricula. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- K Anton Feenstra
- Department of Computer Science, IBIVU Centre for Integrative Bioinformatics Vrije Universiteit Amsterdam, HV Amsterdam, Netherlands
- AIMMS Amsterdam Institute for Molecules, Medicines and Systems, Vrije Universiteit Amsterdam, MC Amsterdam, The Netherlands
| | - Sanne Abeln
- Department of Computer Science, IBIVU Centre for Integrative Bioinformatics Vrije Universiteit Amsterdam, HV Amsterdam, Netherlands
- Amsterdam Data Science, GH Amsterdam, The Netherlands
| | - Johan A Westerhuis
- Swammerdam Institute for Life Sciences, Universiteit van Amsterdam, GE Amsterdam, The Netherlands
| | | | - Douwe Molenaar
- AIMMS Amsterdam Institute for Molecules, Medicines and Systems, Vrije Universiteit Amsterdam, MC Amsterdam, The Netherlands
| | - Bas Teusink
- AIMMS Amsterdam Institute for Molecules, Medicines and Systems, Vrije Universiteit Amsterdam, MC Amsterdam, The Netherlands
- Amsterdam Data Science, GH Amsterdam, The Netherlands
| | - Huub C J Hoefsloot
- Swammerdam Institute for Life Sciences, Universiteit van Amsterdam, GE Amsterdam, The Netherlands
| | - Jaap Heringa
- Department of Computer Science, IBIVU Centre for Integrative Bioinformatics Vrije Universiteit Amsterdam, HV Amsterdam, Netherlands
- AIMMS Amsterdam Institute for Molecules, Medicines and Systems, Vrije Universiteit Amsterdam, MC Amsterdam, The Netherlands
- Amsterdam Data Science, GH Amsterdam, The Netherlands
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