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Mervin L, Voronov A, Kabeshov M, Engkvist O. QSARtuna: An Automated QSAR Modeling Platform for Molecular Property Prediction in Drug Design. J Chem Inf Model 2024; 64:5365-5374. [PMID: 38950185 DOI: 10.1021/acs.jcim.4c00457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/03/2024]
Abstract
Machine-learning (ML) and deep-learning (DL) approaches to predict the molecular properties of small molecules are increasingly deployed within the design-make-test-analyze (DMTA) drug design cycle to predict molecular properties of interest. Despite this uptake, there are only a few automated packages to aid their development and deployment that also support uncertainty estimation, model explainability, and other key aspects of model usage. This represents a key unmet need within the field, and the large number of molecular representations and algorithms (and associated parameters) means it is nontrivial to robustly optimize, evaluate, reproduce, and deploy models. Here, we present QSARtuna, a molecule property prediction modeling pipeline, written in Python and utilizing the Optuna, Scikit-learn, RDKit, and ChemProp packages, which enables the efficient and automated comparison between molecular representations and machine learning models. The platform was developed by considering the increasingly important aspect of model uncertainty quantification and explainability by design. We provide details for our framework and provide illustrative examples to demonstrate the capability of the software when applied to simple molecular property, reaction/reactivity prediction, and DNA encoded library enrichment classification. We hope that the release of QSARtuna will further spur innovation in automatic ML modeling and provide a platform for education of best practices in molecular property modeling. The code for the QSARtuna framework is made freely available via GitHub.
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Affiliation(s)
- Lewis Mervin
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Cambridge CB2 0AA, United Kingdom
| | - Alexey Voronov
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg 412 96, Sweden
| | - Mikhail Kabeshov
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg 412 96, Sweden
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg 412 96, Sweden
- Department of Computer Science and Engineering, University of Gothenburg, Chalmers University of Technology, Gothenburg 412 96, Sweden
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2
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Hicks CB, Martinez TJ. Massively scalable workflows for quantum chemistry: BigChem and ChemCloud. J Chem Phys 2024; 160:142501. [PMID: 38591672 DOI: 10.1063/5.0190834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 03/14/2024] [Indexed: 04/10/2024] Open
Abstract
Electronic structure theory, i.e., quantum chemistry, is the fundamental building block for many problems in computational chemistry. We present a new distributed computing framework (BigChem), which allows for an efficient solution of many quantum chemistry problems in parallel. BigChem is designed to be easily composable and leverages industry-standard middleware (e.g., Celery, RabbitMQ, and Redis) for distributed approaches to large scale problems. BigChem can harness any collection of worker nodes, including ones on cloud providers (such as AWS or Azure), local clusters, or supercomputer centers (and any mixture of these). BigChem builds upon MolSSI packages, such as QCEngine to standardize the operation of numerous computational chemistry programs, demonstrated here with Psi4, xtb, geomeTRIC, and TeraChem. BigChem delivers full utilization of compute resources at scale, offers a programable canvas for designing sophisticated quantum chemistry workflows, and is fault tolerant to node failures and network disruptions. We demonstrate linear scalability of BigChem running computational chemistry workloads on up to 125 GPUs. Finally, we present ChemCloud, a web API to BigChem and successor to TeraChem Cloud. ChemCloud delivers scalable and secure access to BigChem over the Internet.
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Affiliation(s)
- Colton B Hicks
- Department of Chemistry and The PULSE Institute, Stanford University, Stanford, California 94305, USA and SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - Todd J Martinez
- Department of Chemistry and The PULSE Institute, Stanford University, Stanford, California 94305, USA and SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
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3
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Zhan C, Tang T, Wu E, Zhang Y, He M, Wu R, Bi C, Wang J, Zhang Y, Shen B. From multi-omics approaches to personalized medicine in myocardial infarction. Front Cardiovasc Med 2023; 10:1250340. [PMID: 37965091 PMCID: PMC10642346 DOI: 10.3389/fcvm.2023.1250340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 10/17/2023] [Indexed: 11/16/2023] Open
Abstract
Myocardial infarction (MI) is a prevalent cardiovascular disease characterized by myocardial necrosis resulting from coronary artery ischemia and hypoxia, which can lead to severe complications such as arrhythmia, cardiac rupture, heart failure, and sudden death. Despite being a research hotspot, the etiological mechanism of MI remains unclear. The emergence and widespread use of omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and other omics, have provided new opportunities for exploring the molecular mechanism of MI and identifying a large number of disease biomarkers. However, a single-omics approach has limitations in understanding the complex biological pathways of diseases. The multi-omics approach can reveal the interaction network among molecules at various levels and overcome the limitations of the single-omics approaches. This review focuses on the omics studies of MI, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and other omics. The exploration extended into the domain of multi-omics integrative analysis, accompanied by a compilation of diverse online resources, databases, and tools conducive to these investigations. Additionally, we discussed the role and prospects of multi-omics approaches in personalized medicine, highlighting the potential for improving diagnosis, treatment, and prognosis of MI.
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Affiliation(s)
- Chaoying Zhan
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Tong Tang
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Erman Wu
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Yuxin Zhang
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- KeyLaboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China
| | - Mengqiao He
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Rongrong Wu
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Cheng Bi
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- KeyLaboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China
| | - Jiao Wang
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Yingbo Zhang
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- Tropical Crops Genetic Resources Institute, Chinese Academy of Tropical Agricultural Sciences, Haikou, China
| | - Bairong Shen
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
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4
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Gou C, Li J, Li Y, Liu J, Zhao S, Xiao Y, Duan B. Construction of a specialized integrated simulation platform for molecule screening based on scientific computing workflow engine. Sci Rep 2023; 13:15549. [PMID: 37730936 PMCID: PMC10511626 DOI: 10.1038/s41598-023-42913-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 09/16/2023] [Indexed: 09/22/2023] Open
Abstract
Numerical simulation is an efficient tool for evaluation and prediction of material properties and behavior in many industrial domains such as the development of novel materials and medicines. For numerical studies of complex processes or systems with high fidelity, various data processing tools, modeling and simulation programs are typically involved, desiring an integrated platform that can effectively manage the collaboration of such software resources and the execution of the underlying simulation workflow for efficiency purpose. Such a platform could be practically built with a scientific computing workflow engine that focuses on the automatic scheduling and execution of a batch of interrelated computing tasks. In this work, the main procedures on construction of a specialized integrated simulation platform for material research based on a general purpose scientific computing workflow engine named HSWAP is introduced in detail, and its application to molecule screening process of energetic materials is demonstrated. Due to the flexibility and the extensibility of the platform, the work could be handily extended to the screening of other materials such as protein to find optimized protein structures or high entropy alloys to find the best configuration of component contents, as well as other application scenarios such as geometry optimizations of complex structures.
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Affiliation(s)
- Chengqiu Gou
- Institute of Computer Application, China Academy of Engineering Physics, Sichuan, 621000, China
| | - Jifeng Li
- Institute of Computer Application, China Academy of Engineering Physics, Sichuan, 621000, China
| | - Yufeng Li
- Institute of Computer Application, China Academy of Engineering Physics, Sichuan, 621000, China
| | - Jian Liu
- Institute of Chemical Materials, China Academy of Engineering Physics, Sichuan, 621000, China
| | - Shicao Zhao
- Institute of Computer Application, China Academy of Engineering Physics, Sichuan, 621000, China
| | - Yonghao Xiao
- Institute of Computer Application, China Academy of Engineering Physics, Sichuan, 621000, China
| | - Bowen Duan
- Institute of Computer Application, China Academy of Engineering Physics, Sichuan, 621000, China.
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5
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Measuring the Energy and Performance of Scientific Workflows on Low-Power Clusters. ELECTRONICS 2022. [DOI: 10.3390/electronics11111801] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Scientific problems can be formulated as workflows to allow them to take advantage of cluster computing resources. Generally, the assumption is that the greater the resources dedicated to completing these tasks the better. This assumption does not take into account the energy cost of performing the computation and the specific characteristics of each workflow. In this paper, we present a unique approach to evaluating the energy consumption of scientific workflows on compute clusters. Two workflows from different domains, Astronomy and Bioinformatics, are presented and their execution is analyzed on a cluster of low powered small board computers. The paper presents a theoretical analysis of an energy-aware execution of workflows that can reduce the energy consumption of workflows by up to 68% compared to normal execution. We demonstrate that there are limitations to the benefits of increasing cluster sizes and there are trade-offs when considering energy vs. performance of the workflows and that the performance and energy consumption of any scientific workflow is heavily dependent on its underlying structure. The study concludes that the energy consumption of workflows can be optimized to improve both aspects of the workflow and motivates the development of an energy-aware scheduler.
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6
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Visionary: a framework for analysis and visualization of provenance data. Knowl Inf Syst 2022. [DOI: 10.1007/s10115-021-01645-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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7
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Krämer M, Würz HM, Altenhofen C. Executing cyclic scientific workflows in the cloud. JOURNAL OF CLOUD COMPUTING: ADVANCES, SYSTEMS AND APPLICATIONS 2021. [DOI: 10.1186/s13677-021-00229-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
AbstractWe present an algorithm and a software architecture for a cloud-based system that executes cyclic scientific workflows whose structure may change during run time. Existing approaches either rely on workflow definitions based on directed acyclic graphs (DAGs) or require workarounds to implement cyclic structures. In contrast, our system supports cycles natively, avoids workarounds, and as such reduces the complexity of workflow modelling and maintenance. Our algorithm traverses workflow graphs and transforms them iteratively into linear sequences of executable actions. We call these sequences process chains. Our software architecture distributes the process chains to multiple compute nodes in the cloud and oversees their execution. We evaluate our approach by applying it to two practical use cases from the domains of astronomy and engineering. We also compare it with two existing workflow management systems. The evaluation demonstrates that our algorithm is able to execute dynamically changing workflows with cycles and that design and maintenance of complex workflows is easier than with existing solutions. It also shows that our software architecture can run process chains on multiple compute nodes in parallel to significantly speed up the workflow execution. An implementation of our algorithm and the software architecture is available with the Steep Workflow Management System that we released under an open-source license. The resources for the first practical use case are also available as open source for reproduction.
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8
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Kathiravelu P, Sarikhani P, Gu P, Mahmoudi B. Software-Defined Workflows for Distributed Interoperable Closed-Loop Neuromodulation Control Systems. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:131733-131745. [PMID: 34631327 PMCID: PMC8500400 DOI: 10.1109/access.2021.3113892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Closed-loop neuromodulation control systems facilitate regulating abnormal physiological processes by recording neurophysiological activities and modifying those activities through feedback loops. Designing such systems requires interoperable service composition, consisting of cycles. Workflow frameworks enable standard modular architectures, offering reproducible automated pipelines. However, those frameworks limit their support to executions represented by directed acyclic graphs (DAGs). DAGs need a pre-defined start and end execution step with no cycles, thus preventing the researchers from using the standard workflow languages as-is for closed-loop workflows and pipelines. In this paper, we present NEXUS, a workflow orchestration framework for distributed analytics systems. NEXUS proposes a Software-Defined Workflows approach, inspired by Software-Defined Networking (SDN), which separates the data flows across the service instances from the control flows. NEXUS enables creating interoperable workflows with closed loops by defining the workflows in a logically centralized approach, from microservices representing each execution step. The centralized NEXUS orchestrator facilitates dynamically composing and managing scientific workflows from the services and existing workflows, with minimal restrictions. NEXUS represents complex workflows as directed hypergraphs (DHGs) rather than DAGs. We illustrate a seamless execution of neuromodulation control systems by supporting loops in a workflow as the use case of NEXUS. Our evaluations highlight the feasibility, flexibility, performance, and scalability of NEXUS in modeling and executing closed-loop workflows.
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Affiliation(s)
| | - Parisa Sarikhani
- Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, USA
| | - Ping Gu
- Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, USA
| | - Babak Mahmoudi
- Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, USA
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
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9
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Leipzig J, Nüst D, Hoyt CT, Ram K, Greenberg J. The role of metadata in reproducible computational research. PATTERNS (NEW YORK, N.Y.) 2021; 2:100322. [PMID: 34553169 PMCID: PMC8441584 DOI: 10.1016/j.patter.2021.100322] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Reproducible computational research (RCR) is the keystone of the scientific method for in silico analyses, packaging the transformation of raw data to published results. In addition to its role in research integrity, improving the reproducibility of scientific studies can accelerate evaluation and reuse. This potential and wide support for the FAIR principles have motivated interest in metadata standards supporting reproducibility. Metadata provide context and provenance to raw data and methods and are essential to both discovery and validation. Despite this shared connection with scientific data, few studies have explicitly described how metadata enable reproducible computational research. This review employs a functional content analysis to identify metadata standards that support reproducibility across an analytic stack consisting of input data, tools, notebooks, pipelines, and publications. Our review provides background context, explores gaps, and discovers component trends of embeddedness and methodology weight from which we derive recommendations for future work.
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Affiliation(s)
- Jeremy Leipzig
- Metadata Research Center, College of Computing and Informatics, Drexel University, Philadelphia, PA, USA
| | - Daniel Nüst
- Institute for Geoinformatics, University of Münster, Münster, Germany
| | | | - Karthik Ram
- Berkeley Institute for Data Science, University of California, Berkeley, Berkeley, CA, USA
| | - Jane Greenberg
- Metadata Research Center, College of Computing and Informatics, Drexel University, Philadelphia, PA, USA
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10
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Grzesik P, Augustyn DR, Wyciślik Ł, Mrozek D. Serverless computing in omics data analysis and integration. Brief Bioinform 2021; 23:6367629. [PMID: 34505137 PMCID: PMC8499876 DOI: 10.1093/bib/bbab349] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 06/28/2021] [Accepted: 08/06/2021] [Indexed: 11/30/2022] Open
Abstract
A comprehensive analysis of omics data can require vast computational resources and access to varied data sources that must be integrated into complex, multi-step analysis pipelines. Execution of many such analyses can be accelerated by applying the cloud computing paradigm, which provides scalable resources for storing data of different types and parallelizing data analysis computations. Moreover, these resources can be reused for different multi-omics analysis scenarios. Traditionally, developers are required to manage a cloud platform’s underlying infrastructure, configuration, maintenance and capacity planning. The serverless computing paradigm simplifies these operations by automatically allocating and maintaining both servers and virtual machines, as required for analysis tasks. This paradigm offers highly parallel execution and high scalability without manual management of the underlying infrastructure, freeing developers to focus on operational logic. This paper reviews serverless solutions in bioinformatics and evaluates their usage in omics data analysis and integration. We start by reviewing the application of the cloud computing model to a multi-omics data analysis and exposing some shortcomings of the early approaches. We then introduce the serverless computing paradigm and show its applicability for performing an integrative analysis of multiple omics data sources in the context of the COVID-19 pandemic.
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Affiliation(s)
- Piotr Grzesik
- Silesian University of Technology, Department of Applied Informatics, Gliwice 44-100, Poland
| | - Dariusz R Augustyn
- Silesian University of Technology, Department of Applied Informatics, Gliwice 44-100, Poland
| | - Łukasz Wyciślik
- Silesian University of Technology, Department of Applied Informatics, Gliwice 44-100, Poland
| | - Dariusz Mrozek
- Corresponding author: Dariusz Mrozek, Department of Applied Informatics, Silesian University of Technology, Gliwice 44-100, Poland. E-mail:
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11
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Lamprecht AL, Palmblad M, Ison J, Schwämmle V, Al Manir MS, Altintas I, Baker CJO, Ben Hadj Amor A, Capella-Gutierrez S, Charonyktakis P, Crusoe MR, Gil Y, Goble C, Griffin TJ, Groth P, Ienasescu H, Jagtap P, Kalaš M, Kasalica V, Khanteymoori A, Kuhn T, Mei H, Ménager H, Möller S, Richardson RA, Robert V, Soiland-Reyes S, Stevens R, Szaniszlo S, Verberne S, Verhoeven A, Wolstencroft K. Perspectives on automated composition of workflows in the life sciences. F1000Res 2021; 10:897. [PMID: 34804501 PMCID: PMC8573700 DOI: 10.12688/f1000research.54159.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/27/2021] [Indexed: 12/29/2022] Open
Abstract
Scientific data analyses often combine several computational tools in automated pipelines, or workflows. Thousands of such workflows have been used in the life sciences, though their composition has remained a cumbersome manual process due to a lack of standards for annotation, assembly, and implementation. Recent technological advances have returned the long-standing vision of automated workflow composition into focus. This article summarizes a recent Lorentz Center workshop dedicated to automated composition of workflows in the life sciences. We survey previous initiatives to automate the composition process, and discuss the current state of the art and future perspectives. We start by drawing the "big picture" of the scientific workflow development life cycle, before surveying and discussing current methods, technologies and practices for semantic domain modelling, automation in workflow development, and workflow assessment. Finally, we derive a roadmap of individual and community-based actions to work toward the vision of automated workflow development in the forthcoming years. A central outcome of the workshop is a general description of the workflow life cycle in six stages: 1) scientific question or hypothesis, 2) conceptual workflow, 3) abstract workflow, 4) concrete workflow, 5) production workflow, and 6) scientific results. The transitions between stages are facilitated by diverse tools and methods, usually incorporating domain knowledge in some form. Formal semantic domain modelling is hard and often a bottleneck for the application of semantic technologies. However, life science communities have made considerable progress here in recent years and are continuously improving, renewing interest in the application of semantic technologies for workflow exploration, composition and instantiation. Combined with systematic benchmarking with reference data and large-scale deployment of production-stage workflows, such technologies enable a more systematic process of workflow development than we know today. We believe that this can lead to more robust, reusable, and sustainable workflows in the future.
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Affiliation(s)
| | - Magnus Palmblad
- Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Jon Ison
- French Institute of Bioinformatics, 91057 Évry, France
| | | | | | - Ilkay Altintas
- University of California San Diego, La Jolla, CA, 92093, USA
| | - Christopher J. O. Baker
- University of New Brunswick, Saint John, E2L 4L5, Canada
- IPSNP Computing Inc., Saint John, E2L 4S6, Canada
| | | | | | | | | | - Yolanda Gil
- University of Southern California, Marina Del Rey, CA, 90292, USA
| | - Carole Goble
- Department of Computer Science, The University of Manchester, Manchester, M13 9PL, UK
| | - Timothy J. Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Paul Groth
- University of Amsterdam, 1090 GH Amsterdam, The Netherlands
| | - Hans Ienasescu
- Technical University of Denmark, 2800 Kongens Lyngby, Denmark
| | - Pratik Jagtap
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, 55455, USA
| | | | | | | | - Tobias Kuhn
- VU Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Hailiang Mei
- Sequencing Analysis Support Core, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands
| | | | - Steffen Möller
- IBIMA, Rostock University Medical Center, 18057 Rostock, Germany
| | | | | | - Stian Soiland-Reyes
- Department of Computer Science, The University of Manchester, Manchester, M13 9PL, UK
- Informatics Institute, University of Amsterdam, 1090 GH Amsterdam, The Netherlands
| | - Robert Stevens
- Department of Computer Science, The University of Manchester, Manchester, M13 9PL, UK
| | | | - Suzan Verberne
- Leiden Institute of Advanced Computer Science, Leiden University, 2333 BE Leiden, The Netherlands
| | - Aswin Verhoeven
- Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Katherine Wolstencroft
- Leiden Institute of Advanced Computer Science, Leiden University, 2333 BE Leiden, The Netherlands
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12
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Chao KH, Hsiao YW, Lee YF, Lee CY, Lai LC, Tsai MH, Lu TP, Chuang EY. RNASeqR: An R Package for Automated Two-Group RNA-Seq Analysis Workflow. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2023-2031. [PMID: 31796413 DOI: 10.1109/tcbb.2019.2956708] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
RNA-Seq analysis has revolutionized researchers' understanding of the transcriptome in biological research. Assessing the differences in transcriptomic profiles between tissue samples or patient groups enables researchers to explore the underlying biological impact of transcription. RNA-Seq analysis requires multiple processing steps and huge computational capabilities. There are many well-developed R packages for individual steps; however, there are few R/Bioconductor packages that integrate existing software tools into a comprehensive RNA-Seq analysis and provide fundamental end-to-end results in pure R environment so that researchers can quickly and easily get fundamental information in big sequencing data. To address this need, we have developed the open source R/Bioconductor package, RNASeqR. It allows users to run an automated RNA-Seq analysis with only six steps, producing essential tabular and graphical results for further biological interpretation. The features of RNASeqR include: six-step analysis, comprehensive visualization, background execution version, and the integration of both R and command-line software. RNASeqR provides fast, light-weight, and easy-to-run RNA-Seq analysis pipeline in pure R environment. It allows users to efficiently utilize popular software tools, including both R/Bioconductor and command-line tools, without predefining the resources or environments. RNASeqR is freely available for Linux and macOS operating systems from Bioconductor (https://bioconductor.org/packages/release/bioc/html/RNASeqR.html).
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13
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Samota EK, Davey RP. Knowledge and Attitudes Among Life Scientists Toward Reproducibility Within Journal Articles: A Research Survey. Front Res Metr Anal 2021; 6:678554. [PMID: 34268467 PMCID: PMC8276979 DOI: 10.3389/frma.2021.678554] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 05/18/2021] [Indexed: 12/22/2022] Open
Abstract
We constructed a survey to understand how authors and scientists view the issues around reproducibility, focusing on interactive elements such as interactive figures embedded within online publications, as a solution for enabling the reproducibility of experiments. We report the views of 251 researchers, comprising authors who have published in eLIFE Sciences, and those who work at the Norwich Biosciences Institutes (NBI). The survey also outlines to what extent researchers are occupied with reproducing experiments themselves. Currently, there is an increasing range of tools that attempt to address the production of reproducible research by making code, data, and analyses available to the community for reuse. We wanted to collect information about attitudes around the consumer end of the spectrum, where life scientists interact with research outputs to interpret scientific results. Static plots and figures within articles are a central part of this interpretation, and therefore we asked respondents to consider various features for an interactive figure within a research article that would allow them to better understand and reproduce a published analysis. The majority (91%) of respondents reported that when authors describe their research methodology (methods and analyses) in detail, published research can become more reproducible. The respondents believe that having interactive figures in published papers is a beneficial element to themselves, the papers they read as well as to their readers. Whilst interactive figures are one potential solution for consuming the results of research more effectively to enable reproducibility, we also review the equally pressing technical and cultural demands on researchers that need to be addressed to achieve greater success in reproducibility in the life sciences.
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Affiliation(s)
- Evanthia Kaimaklioti Samota
- Earlham Institute, Norwich, United Kingdom
- School of Biological Sciences, University of East Anglia, Norwich, United Kingdom
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14
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Abstract
Brain scientists are now capable of collecting more data in a single experiment than researchers a generation ago might have collected over an entire career. Indeed, the brain itself seems to thirst for more and more data. Such digital information not only comprises individual studies but is also increasingly shared and made openly available for secondary, confirmatory, and/or combined analyses. Numerous web resources now exist containing data across spatiotemporal scales. Data processing workflow technologies running via cloud-enabled computing infrastructures allow for large-scale processing. Such a move toward greater openness is fundamentally changing how brain science results are communicated and linked to available raw data and processed results. Ethical, professional, and motivational issues challenge the whole-scale commitment to data-driven neuroscience. Nevertheless, fueled by government investments into primary brain data collection coupled with increased sharing and community pressure challenging the dominant publishing model, large-scale brain and data science is here to stay.
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Affiliation(s)
- John Darrell Van Horn
- Department of Psychology, University of Virginia, Charlottesville, Virginia, USA
- School of Data Science, University of Virginia, Charlottesville, Virginia, USA
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15
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Chapman M, Rasmussen LV, Pacheco JA, Curcin V. Phenoflow: A Microservice Architecture for Portable Workflow-based Phenotype Definitions. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2021; 2021:142-151. [PMID: 34457128 PMCID: PMC8378606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Phenotyping is an effective way to identify cohorts of patients with particular characteristics within a population. In order to enhance the portability of a phenotype definition across institutions, it is often defined abstractly, with implementers expected to realise the phenotype computationally before executing it against a dataset. However, un-clear definitions, with little information about how best to implement the definition in practice, hinder this process. To address this issue, we propose a new multi-layer, workflow-based model for defining phenotypes, and a novel authoring architecture, Phenoflow, that supports the development of these structured definitions and their realisation as computable phenotypes. To evaluate our model, we determine its impact on the portability of both code-based (COVID-19) and logic-based (diabetes) definitions, in the context of key datasets, including 26,406 patients at North-western University. Our approach is shown to ensure the portability of phenotype definitions and thus contributes to the transparency of resulting studies.
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Affiliation(s)
| | | | | | - Vasa Curcin
- King's College London, London, United Kingdom
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16
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Kruiger JF, Kasalica V, Meerlo R, Lamprecht A, Nyamsuren E, Scheider S. Loose programming of GIS workflows with geo-analytical concepts. TRANSACTIONS IN GIS : TG 2021; 25:424-449. [PMID: 33776542 PMCID: PMC7983927 DOI: 10.1111/tgis.12692] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Loose programming enables analysts to program with concepts instead of procedural code. Data transformations are left underspecified, leaving out procedural details and exploiting knowledge about the applicability of functions to data types. To synthesize workflows of high quality for a geo-analytical task, the semantic type system needs to reflect knowledge of geographic information systems (GIS) at a level that is deep enough to capture geo-analytical concepts and intentions, yet shallow enough to generalize over GIS implementations. Recently, core concepts of spatial information and related geo-analytical concepts were proposed as a way to add the required abstraction level to current geodata models. The core concept data types (CCD) ontology is a semantic type system that can be used to constrain GIS functions for workflow synthesis. However, to date, it is unknown what gain in precision and workflow quality can be expected. In this article we synthesize workflows by annotating GIS tools with these types, specifying a range of common analytical tasks taken from an urban livability scenario. We measure the quality of automatically synthesized workflows against a benchmark generated from common data types. Results show that CCD concepts significantly improve the precision of workflow synthesis.
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Affiliation(s)
- Johannes F. Kruiger
- Department of Human Geography and Spatial PlanningUtrecht UniversityUtrechtthe Netherlands
| | - Vedran Kasalica
- Department of Information and Computing SciencesUtrecht UniversityUtrechtthe Netherlands
| | - Rogier Meerlo
- Department of Human Geography and Spatial PlanningUtrecht UniversityUtrechtthe Netherlands
| | - Anna‐Lena Lamprecht
- Department of Information and Computing SciencesUtrecht UniversityUtrechtthe Netherlands
| | - Enkhbold Nyamsuren
- Department of Human Geography and Spatial PlanningUtrecht UniversityUtrechtthe Netherlands
| | - Simon Scheider
- Department of Human Geography and Spatial PlanningUtrecht UniversityUtrechtthe Netherlands
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17
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18
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Pal S, Mondal S, Das G, Khatua S, Ghosh Z. Big data in biology: The hope and present-day challenges in it. GENE REPORTS 2020. [DOI: 10.1016/j.genrep.2020.100869] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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19
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Mou X, Jamil HM. Visual Life Sciences Workflow Design Using Distributed and Heterogeneous Resources. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:1459-1473. [PMID: 30561349 DOI: 10.1109/tcbb.2018.2886185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Programming or querying usually presupposes some degree of technical familiarity with the syntax of a language and the peculiarity of the objects it manipulates to produce useful information. The degree of abstractions supported in a language helps lessen the depth of such familiarity needed, and aids in improving access to and usability of these resources. To help biologists concentrate more on their science questions and not on how to compute it, several successful workflow orchestration languages and systems have been proposed. Despite their popularity, significant limitations reduce their usability and limit applicability in novel applications. In this paper, we present a visual language, called VisFlow, for workflow orchestration using heterogeneous and distributed resources. We advance the idea that once resources are minimally described and abstracted, arbitrary workflows can be designed solely using query primitives supported in VisFlow. Its capabilities can be augmented by including computational artifacts in the form of library functions written in R, Python, and Java, or even in SQL and XQuery, making it a truly extensible system. We discuss its salient features and illustrate its capabilities using a substantial set of examples.
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20
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Echegaray S, Bakr S, Rubin DL, Napel S. Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images. J Digit Imaging 2019; 31:403-414. [PMID: 28993897 DOI: 10.1007/s10278-017-0019-x] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
The aim of this study was to develop an open-source, modular, locally run or server-based system for 3D radiomics feature computation that can be used on any computer system and included in existing workflows for understanding associations and building predictive models between image features and clinical data, such as survival. The QIFE exploits various levels of parallelization for use on multiprocessor systems. It consists of a managing framework and four stages: input, pre-processing, feature computation, and output. Each stage contains one or more swappable components, allowing run-time customization. We benchmarked the engine using various levels of parallelization on a cohort of CT scans presenting 108 lung tumors. Two versions of the QIFE have been released: (1) the open-source MATLAB code posted to Github, (2) a compiled version loaded in a Docker container, posted to DockerHub, which can be easily deployed on any computer. The QIFE processed 108 objects (tumors) in 2:12 (h/mm) using 1 core, and 1:04 (h/mm) hours using four cores with object-level parallelization. We developed the Quantitative Image Feature Engine (QIFE), an open-source feature-extraction framework that focuses on modularity, standards, parallelism, provenance, and integration. Researchers can easily integrate it with their existing segmentation and imaging workflows by creating input and output components that implement their existing interfaces. Computational efficiency can be improved by parallelizing execution at the cost of memory usage. Different parallelization levels provide different trade-offs, and the optimal setting will depend on the size and composition of the dataset to be processed.
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Affiliation(s)
- Sebastian Echegaray
- Department of Radiology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305, USA.
| | - Shaimaa Bakr
- Department of Electrical Engineering, Stanford University, 650 Serra Mall, Stanford, CA, 94305, USA
| | - Daniel L Rubin
- Department of Radiology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305, USA.,Department of Medicine (Biomedical Informatics Research), Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305, USA
| | - Sandy Napel
- Department of Radiology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305, USA
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21
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Morandi E, Cereda M, Incarnato D, Parlato C, Basile G, Anselmi F, Lauria A, Simon LM, Laurence Polignano I, Arruga F, Deaglio S, Tirtei E, Fagioli F, Oliviero S. HaTSPiL: A modular pipeline for high-throughput sequencing data analysis. PLoS One 2019; 14:e0222512. [PMID: 31613890 PMCID: PMC6793853 DOI: 10.1371/journal.pone.0222512] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Accepted: 08/30/2019] [Indexed: 11/25/2022] Open
Abstract
Background Next generation sequencing methods are widely adopted for a large amount of scientific purposes, from pure research to health-related studies. The decreasing costs per analysis led to big amounts of generated data and to the subsequent improvement of software for the respective analyses. As a consequence, many approaches have been developed to chain different software in order to obtain reliable and reproducible workflows. However, the large range of applications for NGS approaches entails the challenge to manage many different workflows without losing reliability. Methods We here present a high-throughput sequencing pipeline (HaTSPiL), a Python-powered CLI tool designed to handle different approaches for data analysis with a high level of reliability. The software relies on the barcoding of filenames using a human readable naming convention that contains any information regarding the sample needed by the software to automatically choose different workflows and parameters. HaTSPiL is highly modular and customisable, allowing the users to extend its features for any specific need. Conclusions HaTSPiL is licensed as Free Software under the MIT license and it is available at https://github.com/dodomorandi/hatspil.
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Affiliation(s)
- Edoardo Morandi
- Department of Life Sciences and System Biology, University of Turin, Turin, Italy
- Italian Institute for Genomic Medicine (IIGM), Turin, Italy
| | - Matteo Cereda
- Italian Institute for Genomic Medicine (IIGM), Turin, Italy
| | - Danny Incarnato
- Department of Life Sciences and System Biology, University of Turin, Turin, Italy
- Italian Institute for Genomic Medicine (IIGM), Turin, Italy
| | | | - Giulia Basile
- Italian Institute for Genomic Medicine (IIGM), Turin, Italy
| | - Francesca Anselmi
- Department of Life Sciences and System Biology, University of Turin, Turin, Italy
- Italian Institute for Genomic Medicine (IIGM), Turin, Italy
| | - Andrea Lauria
- Department of Life Sciences and System Biology, University of Turin, Turin, Italy
- Italian Institute for Genomic Medicine (IIGM), Turin, Italy
| | - Lisa Marie Simon
- Department of Life Sciences and System Biology, University of Turin, Turin, Italy
- Italian Institute for Genomic Medicine (IIGM), Turin, Italy
| | | | | | - Silvia Deaglio
- Italian Institute for Genomic Medicine (IIGM), Turin, Italy
- Department of Medical Sciences, University of Turin, Turin, Italy
| | - Elisa Tirtei
- Paediatric Onco-Haematology, Stem Cell Transplantation and Cellular Therapy Division, City of Science and Health of Turin, Regina Margherita Children’s Hospital, Turin, Italy
| | - Franca Fagioli
- Paediatric Onco-Haematology, Stem Cell Transplantation and Cellular Therapy Division, City of Science and Health of Turin, Regina Margherita Children’s Hospital, Turin, Italy
| | - Salvatore Oliviero
- Department of Life Sciences and System Biology, University of Turin, Turin, Italy
- Italian Institute for Genomic Medicine (IIGM), Turin, Italy
- * E-mail: ,
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22
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van der Spek A, Broer L, Draisma HHM, Pool R, Albrecht E, Beekman M, Mangino M, Raag M, Nyholt DR, Dharuri HK, Codd V, Amin N, de Geus EJC, Deelen J, Demirkan A, Yet I, Fischer K, Haller T, Henders AK, Isaacs A, Medland SE, Montgomery GW, Mooijaart SP, Strauch K, Suchiman HED, Vaarhorst AAM, van Heemst D, Wang-Sattler R, Whitfield JB, Willemsen G, Wright MJ, Martin NG, Samani NJ, Metspalu A, Eline Slagboom P, Spector TD, Boomsma DI, van Duijn CM, Gieger C. Metabolomics reveals a link between homocysteine and lipid metabolism and leukocyte telomere length: the ENGAGE consortium. Sci Rep 2019; 9:11623. [PMID: 31406173 PMCID: PMC6690953 DOI: 10.1038/s41598-019-47282-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Accepted: 06/26/2019] [Indexed: 01/03/2023] Open
Abstract
Telomere shortening has been associated with multiple age-related diseases such as cardiovascular disease, diabetes, and dementia. However, the biological mechanisms responsible for these associations remain largely unknown. In order to gain insight into the metabolic processes driving the association of leukocyte telomere length (LTL) with age-related diseases, we investigated the association between LTL and serum metabolite levels in 7,853 individuals from seven independent cohorts. LTL was determined by quantitative polymerase chain reaction and the levels of 131 serum metabolites were measured with mass spectrometry in biological samples from the same blood draw. With partial correlation analysis, we identified six metabolites that were significantly associated with LTL after adjustment for multiple testing: lysophosphatidylcholine acyl C17:0 (lysoPC a C17:0, p-value = 7.1 × 10−6), methionine (p-value = 9.2 × 10−5), tyrosine (p-value = 2.1 × 10−4), phosphatidylcholine diacyl C32:1 (PC aa C32:1, p-value = 2.4 × 10−4), hydroxypropionylcarnitine (C3-OH, p-value = 2.6 × 10−4), and phosphatidylcholine acyl-alkyl C38:4 (PC ae C38:4, p-value = 9.0 × 10−4). Pathway analysis showed that the three phosphatidylcholines and methionine are involved in homocysteine metabolism and we found supporting evidence for an association of lipid metabolism with LTL. In conclusion, we found longer LTL associated with higher levels of lysoPC a C17:0 and PC ae C38:4, and with lower levels of methionine, tyrosine, PC aa C32:1, and C3-OH. These metabolites have been implicated in inflammation, oxidative stress, homocysteine metabolism, and in cardiovascular disease and diabetes, two major drivers of morbidity and mortality.
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Affiliation(s)
- Ashley van der Spek
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Linda Broer
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands.,Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Harmen H M Draisma
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands.,Amsterdam Public Health research institute, Amsterdam University Medical Centers, Amsterdam, The Netherlands.,Section of Genomics of Common Disease, Imperial College London, Burlington Danes Building Room E301, Du Cane Road, London, W12 0NN, UK
| | - René Pool
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands.,Amsterdam Public Health research institute, Amsterdam University Medical Centers, Amsterdam, The Netherlands.,BBMRI-NL: Infrastructure for the Application of Metabolomics Technology in Epidemiology (RP4), Utrecht, The Netherlands
| | - Eva Albrecht
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Marian Beekman
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.,NIHR Biomedical Research Centre at Guy's and St Thomas' Foundation Trust, London, SE1 9RT, UK
| | - Mait Raag
- Institute of Family Medicine and Public Health, University of Tartu, Tartu, Estonia
| | - Dale R Nyholt
- School of Biomedical Sciences, Faculty of Health, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia
| | - Harish K Dharuri
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Veryan Codd
- Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Najaf Amin
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Eco J C de Geus
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands.,Amsterdam Public Health research institute, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Joris Deelen
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.,Max Planck Institute for Biology of Ageing, Cologne, Germany
| | - Ayse Demirkan
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands.,Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Idil Yet
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.,Department of Bioinformatics, Institute of Health Sciences, Hacettepe University, 06100, Ankara, Turkey
| | - Krista Fischer
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia.,Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia
| | - Toomas Haller
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Anjali K Henders
- The Institute for Molecular Biosciences, The University of Queensland, Brisbane, Australia
| | - Aaron Isaacs
- CARIM School for Cardiovascular Diseases, Maastricht Centre for Systems Biology (MaCSBio), and Department of Biochemistry, Maastricht University, Maastricht, The Netherlands
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | | | - Simon P Mooijaart
- Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.,Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU Munich, Germany
| | - H Eka D Suchiman
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Anika A M Vaarhorst
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Diana van Heemst
- Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Rui Wang-Sattler
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | | | - Gonneke Willemsen
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands.,Amsterdam Public Health research institute, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Margaret J Wright
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | | | - Nilesh J Samani
- Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Andres Metspalu
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - P Eline Slagboom
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Dorret I Boomsma
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands.,Amsterdam Public Health research institute, Amsterdam University Medical Centers, Amsterdam, The Netherlands.,BBMRI-NL: Infrastructure for the Application of Metabolomics Technology in Epidemiology (RP4), Utrecht, The Netherlands
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands. .,Leiden Academic Centre for Drug Research, Leiden University, Leiden, Netherlands. .,Nuffield Department of Population Health, University of Oxford, Oxford, UK.
| | - Christian Gieger
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
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Yildiz O, Ejarque J, Chan H, Sankaranarayanan S, Badia RM, Peterka T. Heterogeneous Hierarchical Workflow Composition. Comput Sci Eng 2019. [DOI: 10.1109/mcse.2019.2918766] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
| | | | | | | | - Rosa M. Badia
- Barcelona Supercomputing Center Spanish National Research Council
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24
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Banegas-Luna AJ, Imbernón B, Llanes Castro A, Pérez-Garrido A, Cerón-Carrasco JP, Gesing S, Merelli I, D'Agostino D, Pérez-Sánchez H. Advances in distributed computing with modern drug discovery. Expert Opin Drug Discov 2018; 14:9-22. [PMID: 30484337 DOI: 10.1080/17460441.2019.1552936] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
INTRODUCTION Computational chemistry dramatically accelerates the drug discovery process and high-performance computing (HPC) can be used to speed up the most expensive calculations. Supporting a local HPC infrastructure is both costly and time-consuming, and, therefore, many research groups are moving from in-house solutions to remote-distributed computing platforms. Areas covered: The authors focus on the use of distributed technologies, solutions, and infrastructures to gain access to HPC capabilities, software tools, and datasets to run the complex simulations required in computational drug discovery (CDD). Expert opinion: The use of computational tools can decrease the time to market of new drugs. HPC has a crucial role in handling the complex algorithms and large volumes of data required to achieve specificity and avoid undesirable side-effects. Distributed computing environments have clear advantages over in-house solutions in terms of cost and sustainability. The use of infrastructures relying on virtualization reduces set-up costs. Distributed computing resources can be difficult to access, although web-based solutions are becoming increasingly available. There is a trade-off between cost-effectiveness and accessibility in using on-demand computing resources rather than free/academic resources. Graphics processing unit computing, with its outstanding parallel computing power, is becoming increasingly important.
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Affiliation(s)
- Antonio Jesús Banegas-Luna
- a Bioinformatics and High Performance Computing Research Group (BIO-HPC) , Universidad Católica de Murcia (UCAM) , Murcia , Spain
| | - Baldomero Imbernón
- a Bioinformatics and High Performance Computing Research Group (BIO-HPC) , Universidad Católica de Murcia (UCAM) , Murcia , Spain
| | - Antonio Llanes Castro
- a Bioinformatics and High Performance Computing Research Group (BIO-HPC) , Universidad Católica de Murcia (UCAM) , Murcia , Spain
| | - Alfonso Pérez-Garrido
- a Bioinformatics and High Performance Computing Research Group (BIO-HPC) , Universidad Católica de Murcia (UCAM) , Murcia , Spain
| | - José Pedro Cerón-Carrasco
- a Bioinformatics and High Performance Computing Research Group (BIO-HPC) , Universidad Católica de Murcia (UCAM) , Murcia , Spain
| | - Sandra Gesing
- b Center for Research Computing , University of Notre Dame , Notre Dame , IN , USA
| | - Ivan Merelli
- c Institute for Biomedical Technologies , National Research Council of Italy , Segrate (Milan) , Italy
| | - Daniele D'Agostino
- d Institute for Applied Mathematics and Information Technologies "E. Magenes" , National Research Council of Italy , Genoa , Italy
| | - Horacio Pérez-Sánchez
- a Bioinformatics and High Performance Computing Research Group (BIO-HPC) , Universidad Católica de Murcia (UCAM) , Murcia , Spain
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25
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A Task-Oriented Knowledge Base for Geospatial Problem-Solving. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2018. [DOI: 10.3390/ijgi7110423] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In recent years, the rapid development of cloud computing and web technologies has led to a significant advancement to chain geospatial information services (GI services) in order to solve complex geospatial problems. However, the construction of a problem-solving workflow requires considerable expertise for end-users. Currently, few studies design a knowledge base to capture and share geospatial problem-solving knowledge. This paper abstracts a geospatial problem as a task that can be further decomposed into multiple subtasks. The task distinguishes three distinct granularities: Geooperator, Atomic Task, and Composite Task. A task model is presented to define the outline of problem solution at a conceptual level that closely reflects the processes for problem-solving. A task-oriented knowledge base that leverages an ontology-based approach is built to capture and share task knowledge. This knowledge base provides the potential for reusing task knowledge when faced with a similar problem. Conclusively, the details of implementation are described through using a meteorological early-warning analysis as an example.
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Welivita A, Perera I, Meedeniya D, Wickramarachchi A, Mallawaarachchi V. Managing Complex Workflows in Bioinformatics: An Interactive Toolkit With GPU Acceleration. IEEE Trans Nanobioscience 2018; 17:199-208. [PMID: 29994533 DOI: 10.1109/tnb.2018.2837122] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Bioinformatics research continues to advance at an increasing scale with the help of techniques such as next-generation sequencing and the availability of tool support to automate bioinformatics processes. With this growth, a large amount of biological data gets accumulated at an unprecedented rate, demanding high-performance and high-throughput computing technologies for processing such datasets. Use of hardware accelerators, such as graphics processing units (GPUs) and distributed computing, accelerates the processing of big data in high-performance computing environments. They enable higher degrees of parallelism to be achieved, thereby increasing the throughput. In this paper, we introduce BioWorkflow, an interactive workflow management system to automate the bioinformatics analyses with the capability of scheduling parallel tasks with the use of GPU-accelerated and distributed computing. This paper describes a case study carried out to evaluate the performance of a complex workflow with branching executed by BioWorkflow. The results indicate the gains of $\times 2.89$ magnitude by utilizing GPUs and gains in speed by average $\times 2.832$ magnitude (over $n = 5$ scenarios) by parallel execution of graph nodes during multiple sequence alignment calculations. Combined speed-ups are achieved $\times 1.71$ times for complex workflows. This confirms the expected higher speed-ups when having parallelism through GPU-acceleration and concurrent execution of workflow nodes than the mainstream sequential workflow execution. The tool also provides a comprehensive user interface with better interactivity for managing complex workflows; a system usability scale score of 82.9 is confirmed high usability for the system.
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Abstract
Ever return from a meeting feeling elated by all those exciting talks, yet unsure how all those presented glamorous and/or exciting tools can be useful in your research? Or do you have a great piece of software you want to share, yet only a handful of people visited your poster? We have all been there, and that is why we organized the Matchmaking for Computational and Experimental Biologists Session at the latest ISCB/GLBIO’2017 meeting in Chicago (May 15-17, 2017). The session exemplifies a novel approach, mimicking “matchmaking”, to encouraging communication, making connections and fostering collaborations between computational and non-computational biologists. More specifically, the session facilitates face-to-face communication between researchers with similar or differing research interests, which we feel are critical for promoting productive discussions and collaborations. To accomplish this, three short scheduled talks were delivered, focusing on RNA-seq, integration of clinical and genomic data, and chromatin accessibility analyses. Next, small-table developer-led discussions, modeled after speed-dating, enabled each developer (including the speakers) to introduce a specific tool and to engage potential users or other developers around the table. Notably, we asked the audience whether any other tool developers would want to showcase their tool and we thus added four developers as moderators of these small-table discussions. Given the positive feedback from the tool developers, we feel that this type of session is an effective approach for promoting valuable scientific discussion, and is particularly helpful in the context of conferences where the number of participants and activities could hamper such interactions.
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Affiliation(s)
- Ewy Mathé
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA
| | - Ben Busby
- National Center for Biotechnology Information (NCBI), National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Helen Piontkivska
- Department of Biological Sciences and School of Biomedical Sciences, Kent State University, Kent, OH, 44242, USA
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28
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Kausar S, Falcao AO. An automated framework for QSAR model building. J Cheminform 2018; 10:1. [PMID: 29340790 PMCID: PMC5770354 DOI: 10.1186/s13321-017-0256-5] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Accepted: 12/27/2017] [Indexed: 01/13/2023] Open
Abstract
Background In-silico quantitative structure–activity relationship (QSAR) models based tools are widely used to screen huge databases of compounds in order to determine the biological properties of chemical molecules based on their chemical structure. With the passage of time, the exponentially growing amount of synthesized and known chemicals data demands computationally efficient automated QSAR modeling tools, available to researchers that may lack extensive knowledge of machine learning modeling. Thus, a fully automated and advanced modeling platform can be an important addition to the QSAR community. Results In the presented workflow the process from data preparation to model building and validation has been completely automated. The most critical modeling tasks (data curation, data set characteristics evaluation, variable selection and validation) that largely influence the performance of QSAR models were focused. It is also included the ability to quickly evaluate the feasibility of a given data set to be modeled. The developed framework is tested on data sets of thirty different problems. The best-optimized feature selection methodology in the developed workflow is able to remove 62–99% of all redundant data. On average, about 19% of the prediction error was reduced by using feature selection producing an increase of 49% in the percentage of variance explained (PVE) compared to models without feature selection. Selecting only the models with a modelability score above 0.6, average PVE scores were 0.71. A strong correlation was verified between the modelability scores and the PVE of the models produced with variable selection. Conclusions We developed an extendable and highly customizable fully automated QSAR modeling framework. This designed workflow does not require any advanced parameterization nor depends on users decisions or expertise in machine learning/programming. With just a given target or problem, the workflow follows an unbiased standard protocol to develop reliable QSAR models by directly accessing online manually curated databases or by using private data sets. The other distinctive features of the workflow include prior estimation of data modelability to avoid time-consuming modeling trials for non modelable data sets, an efficient variable selection procedure and the facility of output availability at each modeling task for the diverse application and reproduction of historical predictions. The results reached on a selection of thirty QSAR problems suggest that the approach is capable of building reliable models even for challenging problems. Electronic supplementary material The online version of this article (10.1186/s13321-017-0256-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Samina Kausar
- LaSIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, 1749-016, Lisbon, Portugal.,BioISI: Biosystems and Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, 1749-016, Lisbon, Portugal
| | - Andre O Falcao
- LaSIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, 1749-016, Lisbon, Portugal. .,BioISI: Biosystems and Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, 1749-016, Lisbon, Portugal.
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29
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Guitton Y, Tremblay-Franco M, Le Corguillé G, Martin JF, Pétéra M, Roger-Mele P, Delabrière A, Goulitquer S, Monsoor M, Duperier C, Canlet C, Servien R, Tardivel P, Caron C, Giacomoni F, Thévenot EA. Create, run, share, publish, and reference your LC–MS, FIA–MS, GC–MS, and NMR data analysis workflows with the Workflow4Metabolomics 3.0 Galaxy online infrastructure for metabolomics. Int J Biochem Cell Biol 2017; 93:89-101. [DOI: 10.1016/j.biocel.2017.07.002] [Citation(s) in RCA: 65] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2017] [Revised: 06/14/2017] [Accepted: 07/10/2017] [Indexed: 12/11/2022]
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30
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Güntsch A, Hyam R, Hagedorn G, Chagnoux S, Röpert D, Casino A, Droege G, Glöckler F, Gödderz K, Groom Q, Hoffmann J, Holleman A, Kempa M, Koivula H, Marhold K, Nicolson N, Smith VS, Triebel D. Actionable, long-term stable and semantic web compatible identifiers for access to biological collection objects. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2017; 2017:3053443. [PMID: 28365724 PMCID: PMC5467547 DOI: 10.1093/database/bax003] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2016] [Accepted: 01/11/2017] [Indexed: 11/12/2022]
Abstract
With biodiversity research activities being increasingly shifted to the web, the need for a system of persistent and stable identifiers for physical collection objects becomes increasingly pressing. The Consortium of European Taxonomic Facilities agreed on a common system of HTTP-URI-based stable identifiers which is now rolled out to its member organizations. The system follows Linked Open Data principles and implements redirection mechanisms to human-readable and machine-readable representations of specimens facilitating seamless integration into the growing semantic web. The implementation of stable identifiers across collection organizations is supported with open source provider software scripts, best practices documentations and recommendations for RDF metadata elements facilitating harmonized access to collection information in web portals. Database URL : http://cetaf.org/cetaf-stable-identifiers.
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Affiliation(s)
- Anton Güntsch
- Botanic Garden and Botanical Museum Berlin, Freie Universität Berlin, Berlin, Germany
| | - Roger Hyam
- Royal Botanic Garden Edinburgh, Edinburgh, UK
| | - Gregor Hagedorn
- Museum für Naturkunde Berlin, Leibniz-Institute for Evolution and Biodiversity, Berlin, Germany
| | - Simon Chagnoux
- Museum National d'Histoire Naturelle Paris, Paris, France
| | - Dominik Röpert
- Botanic Garden and Botanical Museum Berlin, Freie Universität Berlin, Berlin, Germany
| | - Ana Casino
- CETAF-Consortium of European Taxonomic Facilities, Brussels, Belgium
| | - Gabi Droege
- Botanic Garden and Botanical Museum Berlin, Freie Universität Berlin, Berlin, Germany
| | - Falko Glöckler
- Museum für Naturkunde Berlin, Leibniz-Institute for Evolution and Biodiversity, Berlin, Germany
| | - Karsten Gödderz
- CETAF-Consortium of European Taxonomic Facilities, Brussels, Belgium
| | | | - Jana Hoffmann
- Museum für Naturkunde Berlin, Leibniz-Institute for Evolution and Biodiversity, Berlin, Germany
| | - Ayco Holleman
- Naturalis Biodiversity Center, Leiden, The Netherlands
| | - Matúš Kempa
- Institute of Botany, Plant Science and Biodiversity Center, Slovak Academy of Sciences, Bratislava, Slovakia
| | - Hanna Koivula
- Finnish Museum of Natural History, University of Helsinki, Helsinki, Finland
| | - Karol Marhold
- Institute of Botany, Plant Science and Biodiversity Center, Slovak Academy of Sciences, Bratislava, Slovakia.,Department of Botany, Faculty of Science, Charles University, Praha, Czech Republic
| | - Nicky Nicolson
- Biodiversity Informatics & Spatial Analysis, Royal Botanic Gardens, Kew, London, UK
| | | | - Dagmar Triebel
- SNSB IT Center, Staatliche Naturwissenschaftliche Sammlungen Bayerns, München, Germany
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31
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Curcin V. Embedding data provenance into the Learning Health System to facilitate reproducible research. Learn Health Syst 2017; 1:e10019. [PMID: 31245557 PMCID: PMC6516719 DOI: 10.1002/lrh2.10019] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Revised: 08/04/2016] [Accepted: 10/09/2016] [Indexed: 12/11/2022] Open
Abstract
INTRODUCTION The learning health system (LHS) community has taken up the challenge of bringing the complex relationship between clinical research and practice into this brave new world. At the heart of the LHS vision is the notion of routine capture, transformation, and dissemination of data and knowledge, with various use cases, such as clinical studies, quality improvement initiatives, and decision support, constructed on top of specific routes that the data is taking through the system. In order to stop this increased data volume and analytical complexity from obfuscating the research process, it is essential to establish trust in the system through implementing reproducibility and auditability throughout the workflow. METHODS Data provenance technologies can automatically capture the trace of the research task and resulting data, thereby facilitating reproducible research. While some computational domains, such as bioinformatics, have embraced the technology through provenance-enabled execution middlewares, disciplines based on distributed, heterogeneous software, such as medical research, are only starting on the road to adoption, motivated by the institutional pressures to improve transparency and reproducibility. RESULTS Guided by the experiences of the TRANSFoRm project, we present the opportunities that data provenance offers to the LHS community. We illustrate how provenance can facilitate documenting 21 CFR Part 11 compliance for Food and Drug Administration submissions and provide auditability for decisions made by the decision support tools and discuss the transformational effect of routine provenance capture on data privacy, study reporting, and publishing medical research. CONCLUSIONS If the scaling up of the LHS is to succeed, we have to embed mechanisms to verify trust in the system inside our research instruments. In the research world increasingly reliant on electronic tools, provenance gives us a lingua franca to achieve traceability, which we have shown to be essential to building these mechanisms. To realize the vision of making computable provenance a feasible approach to implementing reproducibility in the LHS, we have to provide viable mechanisms for adoption. These include defining meaningful provenance models for problem domains and also introducing provenance support to existing tools in a minimally invasive manner.
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Affiliation(s)
- Vasa Curcin
- Division of Health and Social Care ResearchKing's College LondonLondonUK
- Department of InformaticsKing's College LondonLondonUK
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32
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Moutsatsos IK, Hossain I, Agarinis C, Harbinski F, Abraham Y, Dobler L, Zhang X, Wilson CJ, Jenkins JL, Holway N, Tallarico J, Parker CN. Jenkins-CI, an Open-Source Continuous Integration System, as a Scientific Data and Image-Processing Platform. SLAS DISCOVERY 2016; 22:238-249. [PMID: 27899692 PMCID: PMC5322829 DOI: 10.1177/1087057116679993] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
High-throughput screening generates large volumes of heterogeneous data that require a diverse set of computational tools for management, processing, and analysis. Building integrated, scalable, and robust computational workflows for such applications is challenging but highly valuable. Scientific data integration and pipelining facilitate standardized data processing, collaboration, and reuse of best practices. We describe how Jenkins-CI, an “off-the-shelf,” open-source, continuous integration system, is used to build pipelines for processing images and associated data from high-content screening (HCS). Jenkins-CI provides numerous plugins for standard compute tasks, and its design allows the quick integration of external scientific applications. Using Jenkins-CI, we integrated CellProfiler, an open-source image-processing platform, with various HCS utilities and a high-performance Linux cluster. The platform is web-accessible, facilitates access and sharing of high-performance compute resources, and automates previously cumbersome data and image-processing tasks. Imaging pipelines developed using the desktop CellProfiler client can be managed and shared through a centralized Jenkins-CI repository. Pipelines and managed data are annotated to facilitate collaboration and reuse. Limitations with Jenkins-CI (primarily around the user interface) were addressed through the selection of helper plugins from the Jenkins-CI community.
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Affiliation(s)
| | - Imtiaz Hossain
- 2 Centre for Proteomic Chemistry, NIBR, Postfach, Basel, Switzerland
| | - Claudia Agarinis
- 3 Developmental and Molecular Pathways, NIBR, Postfach, Basel, Switzerland
| | | | - Yann Abraham
- 5 The Janssen Pharmaceutical Companies of Johnson & Johnson, Beerse, Vlaanderen, Belgium
| | - Luc Dobler
- 6 République et Canton du Jura, Switzerland
| | - Xian Zhang
- 2 Centre for Proteomic Chemistry, NIBR, Postfach, Basel, Switzerland
| | | | - Jeremy L Jenkins
- 1 Developmental and Molecular Pathways, NIBR, Cambridge, MA, USA
| | - Nicholas Holway
- 7 Scientific Computing, NIBR Informatics, Novartis, Postfach, Basel, Switzerland
| | - John Tallarico
- 1 Developmental and Molecular Pathways, NIBR, Cambridge, MA, USA
| | - Christian N Parker
- 3 Developmental and Molecular Pathways, NIBR, Postfach, Basel, Switzerland
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33
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Curcin V, Fairweather E, Danger R, Corrigan D. Templates as a method for implementing data provenance in decision support systems. J Biomed Inform 2016; 65:1-21. [PMID: 27856379 DOI: 10.1016/j.jbi.2016.10.022] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Revised: 10/19/2016] [Accepted: 10/31/2016] [Indexed: 11/26/2022]
Abstract
Decision support systems are used as a method of promoting consistent guideline-based diagnosis supporting clinical reasoning at point of care. However, despite the availability of numerous commercial products, the wider acceptance of these systems has been hampered by concerns about diagnostic performance and a perceived lack of transparency in the process of generating clinical recommendations. This resonates with the Learning Health System paradigm that promotes data-driven medicine relying on routine data capture and transformation, which also stresses the need for trust in an evidence-based system. Data provenance is a way of automatically capturing the trace of a research task and its resulting data, thereby facilitating trust and the principles of reproducible research. While computational domains have started to embrace this technology through provenance-enabled execution middlewares, traditionally non-computational disciplines, such as medical research, that do not rely on a single software platform, are still struggling with its adoption. In order to address these issues, we introduce provenance templates - abstract provenance fragments representing meaningful domain actions. Templates can be used to generate a model-driven service interface for domain software tools to routinely capture the provenance of their data and tasks. This paper specifies the requirements for a Decision Support tool based on the Learning Health System, introduces the theoretical model for provenance templates and demonstrates the resulting architecture. Our methods were tested and validated on the provenance infrastructure for a Diagnostic Decision Support System that was developed as part of the EU FP7 TRANSFoRm project.
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Affiliation(s)
- Vasa Curcin
- Division of Health and Social Care Research, King's College London, London, United Kingdom.
| | - Elliot Fairweather
- Division of Health and Social Care Research, King's College London, London, United Kingdom.
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Miksa T, Rauber A, Mina E. Identifying impact of software dependencies on replicability of biomedical workflows. J Biomed Inform 2016; 64:232-254. [PMID: 27789415 DOI: 10.1016/j.jbi.2016.10.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2016] [Revised: 09/01/2016] [Accepted: 10/23/2016] [Indexed: 01/02/2023]
Abstract
Complex data driven experiments form the basis of biomedical research. Recent findings warn that the context in which the software is run, that is the infrastructure and the third party dependencies, can have a crucial impact on the final results delivered by a computational experiment. This implies that in order to replicate the same result, not only the same data must be used, but also it must be run on an equivalent software stack. In this paper we present the VFramework that enables assessing replicability of workflows. It identifies whether any differences in software dependencies among two executions of the same workflow exist and whether they have impact on the produced results. We also conduct a case study in which we investigate the impact of software dependencies on replicability of Taverna workflows used in biomedical research of Huntington's disease. We re-execute analysed workflows in environments differing in operating system distribution and configuration. The results show that the VFramework can be used to identify the impact of software dependencies on the replicability of biomedical workflows. Furthermore, we observe that despite the fact that the workflows are executed in a controlled environment, they still depend on specific tools installed in the environment. The context model used by the VFramework improves the deficiencies of provenance traces and documents also such tools. Based on our findings we define guidelines for workflow owners that enable them to improve replicability of their workflows.
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Affiliation(s)
- Tomasz Miksa
- SBA Research & Vienna University of Technology, Wien, Austria.
| | - Andreas Rauber
- SBA Research & Vienna University of Technology, Wien, Austria
| | - Eleni Mina
- Leiden University Medical Center, Leiden, The Netherlands
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Veit J, Sachsenberg T, Chernev A, Aicheler F, Urlaub H, Kohlbacher O. LFQProfiler and RNP(xl): Open-Source Tools for Label-Free Quantification and Protein-RNA Cross-Linking Integrated into Proteome Discoverer. J Proteome Res 2016; 15:3441-8. [PMID: 27476824 DOI: 10.1021/acs.jproteome.6b00407] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Modern mass spectrometry setups used in today's proteomics studies generate vast amounts of raw data, calling for highly efficient data processing and analysis tools. Software for analyzing these data is either monolithic (easy to use, but sometimes too rigid) or workflow-driven (easy to customize, but sometimes complex). Thermo Proteome Discoverer (PD) is a powerful software for workflow-driven data analysis in proteomics which, in our eyes, achieves a good trade-off between flexibility and usability. Here, we present two open-source plugins for PD providing additional functionality: LFQProfiler for label-free quantification of peptides and proteins, and RNP(xl) for UV-induced peptide-RNA cross-linking data analysis. LFQProfiler interacts with existing PD nodes for peptide identification and validation and takes care of the entire quantitative part of the workflow. We show that it performs at least on par with other state-of-the-art software solutions for label-free quantification in a recently published benchmark ( Ramus, C.; J. Proteomics 2016 , 132 , 51 - 62 ). The second workflow, RNP(xl), represents the first software solution to date for identification of peptide-RNA cross-links including automatic localization of the cross-links at amino acid resolution and localization scoring. It comes with a customized integrated cross-link fragment spectrum viewer for convenient manual inspection and validation of the results.
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Affiliation(s)
- Johannes Veit
- Center for Bioinformatics, University of Tübingen , 72074 Tübingen, Germany
| | - Timo Sachsenberg
- Center for Bioinformatics, University of Tübingen , 72074 Tübingen, Germany
| | - Aleksandar Chernev
- Center for Bioinformatics, University of Tübingen , 72074 Tübingen, Germany.,Bioanalytics Group, University Medical Center Göttingen , 37077 Göttingen, Germany
| | - Fabian Aicheler
- Center for Bioinformatics, University of Tübingen , 72074 Tübingen, Germany
| | - Henning Urlaub
- Bioanalytical Mass Spectrometry, Max Planck Institute for Biophysical Chemistry , 37077 Göttingen, Germany.,Bioanalytics Group, University Medical Center Göttingen , 37077 Göttingen, Germany
| | - Oliver Kohlbacher
- Center for Bioinformatics, University of Tübingen , 72074 Tübingen, Germany.,Quantitative Biology Center, University of Tübingen , 72076 Tübingen, Germany.,Biomolecular Interactions, Max Planck Institute for Developmental Biology , 72076 Tübingen, Germany
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Mina E, van Roon-Mom W, Hettne K, van Zwet E, Goeman J, Neri C, A.C. ’t Hoen P, Mons B, Roos M. Common disease signatures from gene expression analysis in Huntington's disease human blood and brain. Orphanet J Rare Dis 2016; 11:97. [PMID: 27476530 PMCID: PMC4968014 DOI: 10.1186/s13023-016-0475-2] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Accepted: 06/22/2016] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Huntington's disease (HD) is a devastating brain disorder with no effective treatment or cure available. The scarcity of brain tissue makes it hard to study changes in the brain and impossible to perform longitudinal studies. However, peripheral pathology in HD suggests that it is possible to study the disease using peripheral tissue as a monitoring tool for disease progression and/or efficacy of novel therapies. In this study, we investigated if blood can be used to monitor disease severity and progression in brain. Since previous attempts using only gene expression proved unsuccessful, we compared blood and brain Huntington's disease signatures in a functional context. METHODS Microarray HD gene expression profiles from three brain regions were compared to the transcriptome of HD blood generated by next generation sequencing. The comparison was performed with a combination of weighted gene co-expression network analysis and literature based functional analysis (Concept Profile Analysis). Uniquely, our comparison of blood and brain datasets was not based on (the very limited) gene overlap but on the similarity between the gene annotations in four different semantic categories: "biological process", "cellular component", "molecular function" and "disease or syndrome". RESULTS We identified signatures in HD blood reflecting a broad pathophysiological spectrum, including alterations in the immune response, sphingolipid biosynthetic processes, lipid transport, cell signaling, protein modification, spliceosome, RNA splicing, vesicle transport, cell signaling and synaptic transmission. Part of this spectrum was reminiscent of the brain pathology. The HD signatures in caudate nucleus and BA4 exhibited the highest similarity with blood, irrespective of the category of semantic annotations used. BA9 exhibited an intermediate similarity, while cerebellum had the least similarity. We present two signatures that were shared between blood and brain: immune response and spinocerebellar ataxias. CONCLUSIONS Our results demonstrate that HD blood exhibits dysregulation that is similar to brain at a functional level, but not necessarily at the level of individual genes. We report two common signatures that can be used to monitor the pathology in brain of HD patients in a non-invasive manner. Our results are an exemplar of how signals in blood data can be used to represent brain disorders. Our methodology can be used to study disease specific signatures in diseases where heterogeneous tissues are involved in the pathology.
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Affiliation(s)
- Eleni Mina
- Department of Human Genetics, Leiden University Medical Center, PO Box 9600, Leiden, 2300 RC The Netherlands
| | - Willeke van Roon-Mom
- Department of Human Genetics, Leiden University Medical Center, PO Box 9600, Leiden, 2300 RC The Netherlands
| | - Kristina Hettne
- Department of Human Genetics, Leiden University Medical Center, PO Box 9600, Leiden, 2300 RC The Netherlands
| | - Erik van Zwet
- Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, PO Box 9600, Leiden, 2300 RC The Netherlands
| | - Jelle Goeman
- Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, PO Box 9600, Leiden, 2300 RC The Netherlands
| | - Christian Neri
- CNRS, UMR 8256, Laboratory of Neuronal Cell Biology and Pathology, Institute of Biology Paris-Seine, 9 quai Saint Bernard, Paris, 75005 France
- Sorbonnes Universités, University Pierre and Marie Curie (UPMC) Univ Paris 06, 4 place Jussieu, Paris, 75005 France
| | - Peter A.C. ’t Hoen
- Department of Human Genetics, Leiden University Medical Center, PO Box 9600, Leiden, 2300 RC The Netherlands
| | - Barend Mons
- Department of Human Genetics, Leiden University Medical Center, PO Box 9600, Leiden, 2300 RC The Netherlands
| | - Marco Roos
- Department of Human Genetics, Leiden University Medical Center, PO Box 9600, Leiden, 2300 RC The Netherlands
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A scientific workflow framework for 13C metabolic flux analysis. J Biotechnol 2016; 232:12-24. [DOI: 10.1016/j.jbiotec.2015.12.032] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2015] [Revised: 12/14/2015] [Accepted: 12/17/2015] [Indexed: 12/15/2022]
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38
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Sahoo SS, Wei A, Valdez J, Wang L, Zonjy B, Tatsuoka C, Loparo KA, Lhatoo SD. NeuroPigPen: A Scalable Toolkit for Processing Electrophysiological Signal Data in Neuroscience Applications Using Apache Pig. Front Neuroinform 2016; 10:18. [PMID: 27375472 PMCID: PMC4895075 DOI: 10.3389/fninf.2016.00018] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2016] [Accepted: 05/17/2016] [Indexed: 11/13/2022] Open
Abstract
The recent advances in neurological imaging and sensing technologies have led to rapid increase in the volume, rate of data generation, and variety of neuroscience data. This “neuroscience Big data” represents a significant opportunity for the biomedical research community to design experiments using data with greater timescale, large number of attributes, and statistically significant data size. The results from these new data-driven research techniques can advance our understanding of complex neurological disorders, help model long-term effects of brain injuries, and provide new insights into dynamics of brain networks. However, many existing neuroinformatics data processing and analysis tools were not built to manage large volume of data, which makes it difficult for researchers to effectively leverage this available data to advance their research. We introduce a new toolkit called NeuroPigPen that was developed using Apache Hadoop and Pig data flow language to address the challenges posed by large-scale electrophysiological signal data. NeuroPigPen is a modular toolkit that can process large volumes of electrophysiological signal data, such as Electroencephalogram (EEG), Electrocardiogram (ECG), and blood oxygen levels (SpO2), using a new distributed storage model called Cloudwave Signal Format (CSF) that supports easy partitioning and storage of signal data on commodity hardware. NeuroPigPen was developed with three design principles: (a) Scalability—the ability to efficiently process increasing volumes of data; (b) Adaptability—the toolkit can be deployed across different computing configurations; and (c) Ease of programming—the toolkit can be easily used to compose multi-step data processing pipelines using high-level programming constructs. The NeuroPigPen toolkit was evaluated using 750 GB of electrophysiological signal data over a variety of Hadoop cluster configurations ranging from 3 to 30 Data nodes. The evaluation results demonstrate that the toolkit is highly scalable and adaptable, which makes it suitable for use in neuroscience applications as a scalable data processing toolkit. As part of the ongoing extension of NeuroPigPen, we are developing new modules to support statistical functions to analyze signal data for brain connectivity research. In addition, the toolkit is being extended to allow integration with scientific workflow systems. NeuroPigPen is released under BSD license at: https://sites.google.com/a/case.edu/neuropigpen/.
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Affiliation(s)
- Satya S Sahoo
- Division of Medical Informatics, School of Medicine, Case Western Reserve UniversityCleveland, OH, USA; Electrical Engineering and Computer Science Department, School of Engineering, Case Western Reserve UniversityCleveland, OH, USA
| | - Annan Wei
- Electrical Engineering and Computer Science Department, School of Engineering, Case Western Reserve University Cleveland, OH, USA
| | - Joshua Valdez
- Division of Medical Informatics, School of Medicine, Case Western Reserve University Cleveland, OH, USA
| | - Li Wang
- Division of Medical Informatics, School of Medicine, Case Western Reserve University Cleveland, OH, USA
| | - Bilal Zonjy
- Department of Neurology, School of Medicine, Case Western Reserve University Cleveland, OH, USA
| | - Curtis Tatsuoka
- Department of Neurology, School of Medicine, Case Western Reserve University Cleveland, OH, USA
| | - Kenneth A Loparo
- Electrical Engineering and Computer Science Department, School of Engineering, Case Western Reserve University Cleveland, OH, USA
| | - Samden D Lhatoo
- Department of Neurology, School of Medicine, Case Western Reserve University Cleveland, OH, USA
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Hosny A, Vera-Licona P, Laubenbacher R, Favre T. AlgoRun: a Docker-based packaging system for platform-agnostic implemented algorithms. ACTA ACUST UNITED AC 2016; 32:2396-8. [PMID: 27153722 DOI: 10.1093/bioinformatics/btw120] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2015] [Accepted: 02/26/2016] [Indexed: 11/13/2022]
Abstract
MOTIVATION There is a growing need in bioinformatics for easy-to-use software implementations of algorithms that are usable across platforms. At the same time, reproducibility of computational results is critical and often a challenge due to source code changes over time and dependencies. RESULTS The approach introduced in this paper addresses both of these needs with AlgoRun, a dedicated packaging system for implemented algorithms, using Docker technology. Implemented algorithms, packaged with AlgoRun, can be executed through a user-friendly interface directly from a web browser or via a standardized RESTful web API to allow easy integration into more complex workflows. The packaged algorithm includes the entire software execution environment, thereby eliminating the common problem of software dependencies and the irreproducibility of computations over time. AlgoRun-packaged algorithms can be published on http://algorun.org, a centralized searchable directory to find existing AlgoRun-packaged algorithms. AVAILABILITY AND IMPLEMENTATION AlgoRun is available at http://algorun.org and the source code under GPL license is available at https://github.com/algorun CONTACT laubenbacher@uchc.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Paola Vera-Licona
- Center for Quantitative Medicine Department of Cell Biology Institute for Systems Genomics, UConn Health, CT, USA
| | - Reinhard Laubenbacher
- Center for Quantitative Medicine Department of Cell Biology Institute for Systems Genomics, UConn Health, CT, USA Jackson Laboratory for Genomic Medicine, CT, USA
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40
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Tosta FE, Braganholo V, Murta L, Mattoso M. Improving workflow design by mining reusable tasks. JOURNAL OF THE BRAZILIAN COMPUTER SOCIETY 2015. [DOI: 10.1186/s13173-015-0035-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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41
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SeqMule: automated pipeline for analysis of human exome/genome sequencing data. Sci Rep 2015; 5:14283. [PMID: 26381817 PMCID: PMC4585643 DOI: 10.1038/srep14283] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2015] [Accepted: 08/21/2015] [Indexed: 11/16/2022] Open
Abstract
Next-generation sequencing (NGS) technology has greatly helped us identify disease-contributory variants for Mendelian diseases. However, users are often faced with issues such as software compatibility, complicated configuration, and no access to high-performance computing facility. Discrepancies exist among aligners and variant callers. We developed a computational pipeline, SeqMule, to perform automated variant calling from NGS data on human genomes and exomes. SeqMule integrates computational-cluster-free parallelization capability built on top of the variant callers, and facilitates normalization/intersection of variant calls to generate consensus set with high confidence. SeqMule integrates 5 alignment tools, 5 variant calling algorithms and accepts various combinations all by one-line command, therefore allowing highly flexible yet fully automated variant calling. In a modern machine (2 Intel Xeon X5650 CPUs, 48 GB memory), when fast turn-around is needed, SeqMule generates annotated VCF files in a day from a 30X whole-genome sequencing data set; when more accurate calling is needed, SeqMule generates consensus call set that improves over single callers, as measured by both Mendelian error rate and consistency. SeqMule supports Sun Grid Engine for parallel processing, offers turn-key solution for deployment on Amazon Web Services, allows quality check, Mendelian error check, consistency evaluation, HTML-based reports. SeqMule is available at http://seqmule.openbioinformatics.org.
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Nicola G, Berthold MR, Hedrick MP, Gilson MK. Connecting proteins with drug-like compounds: Open source drug discovery workflows with BindingDB and KNIME. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2015; 2015:bav087. [PMID: 26384374 PMCID: PMC4572361 DOI: 10.1093/database/bav087] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2015] [Accepted: 08/17/2015] [Indexed: 12/24/2022]
Abstract
Today's large, public databases of protein-small molecule interaction data are creating important new opportunities for data mining and integration. At the same time, new graphical user interface-based workflow tools offer facile alternatives to custom scripting for informatics and data analysis. Here, we illustrate how the large protein-ligand database BindingDB may be incorporated into KNIME workflows as a step toward the integration of pharmacological data with broader biomolecular analyses. Thus, we describe a collection of KNIME workflows that access BindingDB data via RESTful webservices and, for more intensive queries, via a local distillation of the full BindingDB dataset. We focus in particular on the KNIME implementation of knowledge-based tools to generate informed hypotheses regarding protein targets of bioactive compounds, based on notions of chemical similarity. A number of variants of this basic approach are tested for seven existing drugs with relatively ill-defined therapeutic targets, leading to replication of some previously confirmed results and discovery of new, high-quality hits. Implications for future development are discussed. Database URL: www.bindingdb.org.
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Affiliation(s)
- George Nicola
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA,
| | - Michael R Berthold
- Department of Computer and Information Science, Konstanz University, 78457 Konstanz, Germany, and
| | | | - Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA,
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A Digital Repository and Execution Platform for Interactive Scholarly Publications in Neuroscience. Neuroinformatics 2015; 14:23-40. [PMID: 26306864 DOI: 10.1007/s12021-015-9276-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
The CARMEN Virtual Laboratory (VL) is a cloud-based platform which allows neuroscientists to store, share, develop, execute, reproduce and publicise their work. This paper describes new functionality in the CARMEN VL: an interactive publications repository. This new facility allows users to link data and software to publications. This enables other users to examine data and software associated with the publication and execute the associated software within the VL using the same data as the authors used in the publication. The cloud-based architecture and SaaS (Software as a Service) framework allows vast data sets to be uploaded and analysed using software services. Thus, this new interactive publications facility allows others to build on research results through reuse. This aligns with recent developments by funding agencies, institutions, and publishers with a move to open access research. Open access provides reproducibility and verification of research resources and results. Publications and their associated data and software will be assured of long-term preservation and curation in the repository. Further, analysing research data and the evaluations described in publications frequently requires a number of execution stages many of which are iterative. The VL provides a scientific workflow environment to combine software services into a processing tree. These workflows can also be associated with publications and executed by users. The VL also provides a secure environment where users can decide the access rights for each resource to ensure copyright and privacy restrictions are met.
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Velloso H, Vialle RA, Ortega JM. BOWS (bioinformatics open web services) to centralize bioinformatics tools in web services. BMC Res Notes 2015; 8:206. [PMID: 26032494 PMCID: PMC4467627 DOI: 10.1186/s13104-015-1190-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2014] [Accepted: 05/20/2015] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Bioinformaticians face a range of difficulties to get locally-installed tools running and producing results; they would greatly benefit from a system that could centralize most of the tools, using an easy interface for input and output. Web services, due to their universal nature and widely known interface, constitute a very good option to achieve this goal. RESULTS Bioinformatics open web services (BOWS) is a system based on generic web services produced to allow programmatic access to applications running on high-performance computing (HPC) clusters. BOWS intermediates the access to registered tools by providing front-end and back-end web services. Programmers can install applications in HPC clusters in any programming language and use the back-end service to check for new jobs and their parameters, and then to send the results to BOWS. Programs running in simple computers consume the BOWS front-end service to submit new processes and read results. BOWS compiles Java clients, which encapsulate the front-end web service requisitions, and automatically creates a web page that disposes the registered applications and clients. CONCLUSIONS Bioinformatics open web services registered applications can be accessed from virtually any programming language through web services, or using standard java clients. The back-end can run in HPC clusters, allowing bioinformaticians to remotely run high-processing demand applications directly from their machines.
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Affiliation(s)
- Henrique Velloso
- Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG, Brazil.
| | - Ricardo A Vialle
- Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG, Brazil.
| | - J Miguel Ortega
- Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG, Brazil.
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Trends in IT Innovation to Build a Next Generation Bioinformatics Solution to Manage and Analyse Biological Big Data Produced by NGS Technologies. BIOMED RESEARCH INTERNATIONAL 2015; 2015:904541. [PMID: 26125026 PMCID: PMC4466500 DOI: 10.1155/2015/904541] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2014] [Revised: 04/01/2015] [Accepted: 04/01/2015] [Indexed: 02/07/2023]
Abstract
Sequencing the human genome began in 1994, and 10 years of work were necessary in order to provide a nearly complete sequence. Nowadays, NGS technologies allow sequencing of a whole human genome in a few days. This deluge of data challenges scientists in many ways, as they are faced with data management issues and analysis and visualization drawbacks due to the limitations of current bioinformatics tools. In this paper, we describe how the NGS Big Data revolution changes the way of managing and analysing data. We present how biologists are confronted with abundance of methods, tools, and data formats. To overcome these problems, focus on Big Data Information Technology innovations from web and business intelligence. We underline the interest of NoSQL databases, which are much more efficient than relational databases. Since Big Data leads to the loss of interactivity with data during analysis due to high processing time, we describe solutions from the Business Intelligence that allow one to regain interactivity whatever the volume of data is. We illustrate this point with a focus on the Amadea platform. Finally, we discuss visualization challenges posed by Big Data and present the latest innovations with JavaScript graphic libraries.
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46
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Bauer C, Stec K, Glintschert A, Gruden K, Schichor C, Or-Guil M, Selbig J, Schuchhardt J. BioMiner: Paving the Way for Personalized Medicine. Cancer Inform 2015; 14:55-63. [PMID: 26005322 PMCID: PMC4406277 DOI: 10.4137/cin.s20910] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2014] [Revised: 12/17/2014] [Accepted: 12/25/2014] [Indexed: 01/03/2023] Open
Abstract
Personalized medicine is promising a revolution for medicine and human biology in the 21st century. The scientific foundation for this revolution is accomplished by analyzing biological high-throughput data sets from genomics, transcriptomics, proteomics, and metabolomics. Currently, access to these data has been limited to either rather simple Web-based tools, which do not grant much insight or analysis by trained specialists, without firsthand involvement of the physician. Here, we present the novel Web-based tool “BioMiner,” which was developed within the scope of an international and interdisciplinary project (SYSTHER†) and gives access to a variety of high-throughput data sets. It provides the user with convenient tools to analyze complex cross-omics data sets and grants enhanced visualization abilities. BioMiner incorporates transcriptomic and cross-omics high-throughput data sets, with a focus on cancer. A public instance of BioMiner along with the database is available at http://systherDB.microdiscovery.de/, login and password: “systher”; a tutorial detailing the usage of BioMiner can be found in the Supplementary File.
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Affiliation(s)
- Chris Bauer
- Research and Development, MicroDiscovery GmbH, Berlin, Germany
| | - Karol Stec
- Research and Development, MicroDiscovery GmbH, Berlin, Germany
| | | | - Kristina Gruden
- Department for Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, Slovenia
| | - Christian Schichor
- Department of Neurosurgery, Klinikum der Ludwig-Maximilian-Universität München, Munich, Germany
| | - Michal Or-Guil
- Systems Immunology Lab, Department of Biology, Humboldt University, Berlin, Germany. ; Research Center ImmunoSciences, Charité University of Medicine Berlin, Berlin, Germany
| | - Joachim Selbig
- Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
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47
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Smedley D, Haider S, Durinck S, Pandini L, Provero P, Allen J, Arnaiz O, Awedh MH, Baldock R, Barbiera G, Bardou P, Beck T, Blake A, Bonierbale M, Brookes AJ, Bucci G, Buetti I, Burge S, Cabau C, Carlson JW, Chelala C, Chrysostomou C, Cittaro D, Collin O, Cordova R, Cutts RJ, Dassi E, Di Genova A, Djari A, Esposito A, Estrella H, Eyras E, Fernandez-Banet J, Forbes S, Free RC, Fujisawa T, Gadaleta E, Garcia-Manteiga JM, Goodstein D, Gray K, Guerra-Assunção JA, Haggarty B, Han DJ, Han BW, Harris T, Harshbarger J, Hastings RK, Hayes RD, Hoede C, Hu S, Hu ZL, Hutchins L, Kan Z, Kawaji H, Keliet A, Kerhornou A, Kim S, Kinsella R, Klopp C, Kong L, Lawson D, Lazarevic D, Lee JH, Letellier T, Li CY, Lio P, Liu CJ, Luo J, Maass A, Mariette J, Maurel T, Merella S, Mohamed AM, Moreews F, Nabihoudine I, Ndegwa N, Noirot C, Perez-Llamas C, Primig M, Quattrone A, Quesneville H, Rambaldi D, Reecy J, Riba M, Rosanoff S, Saddiq AA, Salas E, Sallou O, Shepherd R, Simon R, Sperling L, Spooner W, Staines DM, Steinbach D, Stone K, Stupka E, Teague JW, Dayem Ullah AZ, Wang J, Ware D, Wong-Erasmus M, Youens-Clark K, Zadissa A, Zhang SJ, Kasprzyk A. The BioMart community portal: an innovative alternative to large, centralized data repositories. Nucleic Acids Res 2015; 43:W589-98. [PMID: 25897122 PMCID: PMC4489294 DOI: 10.1093/nar/gkv350] [Citation(s) in RCA: 501] [Impact Index Per Article: 55.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2015] [Accepted: 04/02/2015] [Indexed: 01/17/2023] Open
Abstract
The BioMart Community Portal (www.biomart.org) is a community-driven effort to provide a unified interface to biomedical databases that are distributed worldwide. The portal provides access to numerous database projects supported by 30 scientific organizations. It includes over 800 different biological datasets spanning genomics, proteomics, model organisms, cancer data, ontology information and more. All resources available through the portal are independently administered and funded by their host organizations. The BioMart data federation technology provides a unified interface to all the available data. The latest version of the portal comes with many new databases that have been created by our ever-growing community. It also comes with better support and extensibility for data analysis and visualization tools. A new addition to our toolbox, the enrichment analysis tool is now accessible through graphical and web service interface. The BioMart community portal averages over one million requests per day. Building on this level of service and the wealth of information that has become available, the BioMart Community Portal has introduced a new, more scalable and cheaper alternative to the large data stores maintained by specialized organizations.
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Affiliation(s)
- Damian Smedley
- Wellcome Trust Sanger Institute, Welcome Trust Genome Campus, Hinxton, CB10 1SD, UK
| | - Syed Haider
- The Weatherall Institute Of Molecular Medicine, University of Oxford, Oxford, OX3 9DS, UK
| | - Steffen Durinck
- Genentech, Inc. 1 DNA Way South San Francisco, CA 94080, USA
| | - Luca Pandini
- Center for Translational Genomics and Bioinformatics San Raffaele Scientific Institute, Via Olgettina 58, 20132 Milan, Italy
| | - Paolo Provero
- Center for Translational Genomics and Bioinformatics San Raffaele Scientific Institute, Via Olgettina 58, 20132 Milan, Italy Dept of Molecular Biotechnology and Health Sciences University of Turin, Italy
| | - James Allen
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Olivier Arnaiz
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris Sud, 1 avenue de la terrasse, 91198 Gif sur Yvette, France
| | - Mohammad Hamza Awedh
- Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Richard Baldock
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, EH4 2XU, UK
| | - Giulia Barbiera
- Center for Translational Genomics and Bioinformatics San Raffaele Scientific Institute, Via Olgettina 58, 20132 Milan, Italy
| | | | - Tim Beck
- Department of Genetics, University of Leicester, University Road, Leicester, LE1 7RH, UK
| | - Andrew Blake
- MRC Harwell, Harwell Science and Innovation Campus, Oxfordshire, OX11 0RD, UK
| | | | - Anthony J Brookes
- Department of Genetics, University of Leicester, University Road, Leicester, LE1 7RH, UK
| | - Gabriele Bucci
- Center for Translational Genomics and Bioinformatics San Raffaele Scientific Institute, Via Olgettina 58, 20132 Milan, Italy
| | - Iwan Buetti
- Center for Translational Genomics and Bioinformatics San Raffaele Scientific Institute, Via Olgettina 58, 20132 Milan, Italy
| | - Sarah Burge
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | | | | | - Claude Chelala
- Centre for Molecular Oncology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
| | | | - Davide Cittaro
- Center for Translational Genomics and Bioinformatics San Raffaele Scientific Institute, Via Olgettina 58, 20132 Milan, Italy
| | | | - Raul Cordova
- International Potato Center (CIP), Lima, 1558, Peru
| | - Rosalind J Cutts
- Centre for Molecular Oncology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
| | - Erik Dassi
- Laboratory of Translational Genomics, Centre for Integrative Biology, University of Trento, Trento, Italy
| | - Alex Di Genova
- Center for Mathematical Modeling and Center for Genome Regulation, University of Chile, Beauchef 851, 7th floor, Chile
| | - Anis Djari
- Plate-forme bio-informatique Genotoul, Mathématiques et Informatique Appliquées de Toulouse, INRA, Castanet-Tolosan, France
| | | | | | - Eduardo Eyras
- Catalan Institute for Research and Advanced Studies (ICREA), Passeig Lluis Companys 23, E-08010 Barcelona, Spain Universitat Pompeu Fabra, Dr Aiguader 88 E-08003 Barcelona, Spain
| | | | - Simon Forbes
- Wellcome Trust Sanger Institute, Welcome Trust Genome Campus, Hinxton, CB10 1SD, UK
| | - Robert C Free
- Department of Genetics, University of Leicester, University Road, Leicester, LE1 7RH, UK
| | | | - Emanuela Gadaleta
- Centre for Molecular Oncology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
| | - Jose M Garcia-Manteiga
- Center for Translational Genomics and Bioinformatics San Raffaele Scientific Institute, Via Olgettina 58, 20132 Milan, Italy
| | - David Goodstein
- Department of Energy, Joint Genome Institute, Walnut Creek, USA
| | - Kristian Gray
- HUGO Gene Nomenclature Committee (HGNC), European Bioinformatics Institute (EMBL-EBI) Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK
| | - José Afonso Guerra-Assunção
- Centre for Molecular Oncology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
| | - Bernard Haggarty
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, EH4 2XU, UK
| | - Dong-Jin Han
- Medicinal Bioconvergence Research Center, College of Pharmacy, Seoul National University, Seoul 151-742, Republic of Korea Department of Molecular Medicine and Biopharmaceutical Sciences, Seoul National University, Seoul 151-742, Republic of Korea
| | - Byung Woo Han
- Research Institute of Pharmaceutical Sciences, College of Pharmacy, Seoul National University, Seoul 151-742, Republic of Korea Information Center for Bio-pharmacological Network, Seoul National University, Suwon 443-270, Republic of Korea
| | - Todd Harris
- Ontario Institute for Cancer Research, Toronto, M5G 0A3, Canada
| | - Jayson Harshbarger
- RIKEN Center for Life Science Technologies (CLST), Division of Genomic Technologies (DGT), Kanagawa, 230-0045, Japan
| | - Robert K Hastings
- Department of Genetics, University of Leicester, University Road, Leicester, LE1 7RH, UK
| | - Richard D Hayes
- Department of Energy, Joint Genome Institute, Walnut Creek, USA
| | - Claire Hoede
- Plate-forme bio-informatique Genotoul, Mathématiques et Informatique Appliquées de Toulouse, INRA, Castanet-Tolosan, France
| | - Shen Hu
- School of Dentistry and Dental Research Institute, University of California Los Angeles (UCLA), Los Angeles, CA 90095-1668, USA
| | | | - Lucie Hutchins
- Mouse Genomic Informatics Group, The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | - Zhengyan Kan
- Oncology Computational Biology, Pfizer, La Jolla, USA
| | - Hideya Kawaji
- RIKEN Center for Life Science Technologies (CLST), Division of Genomic Technologies (DGT), Kanagawa, 230-0045, Japan RIKEN Preventive Medicine and Diagnosis Innovation Program, Saitama 351-0198, Japan
| | - Aminah Keliet
- INRA URGI Centre de Versailles, bâtiment 18 Route de Saint Cyr 78026 Versailles, France
| | - Arnaud Kerhornou
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Sunghoon Kim
- Medicinal Bioconvergence Research Center, College of Pharmacy, Seoul National University, Seoul 151-742, Republic of Korea Department of Molecular Medicine and Biopharmaceutical Sciences, Seoul National University, Seoul 151-742, Republic of Korea
| | - Rhoda Kinsella
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Christophe Klopp
- Plate-forme bio-informatique Genotoul, Mathématiques et Informatique Appliquées de Toulouse, INRA, Castanet-Tolosan, France
| | - Lei Kong
- Center for Bioinformatics, State Key Laboratory of Protein and Plant Gene Research, College of Life Sciences, Peking University, Beijing, 100871, P.R. China
| | - Daniel Lawson
- VectorBase, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK
| | - Dejan Lazarevic
- Center for Translational Genomics and Bioinformatics San Raffaele Scientific Institute, Via Olgettina 58, 20132 Milan, Italy
| | - Ji-Hyun Lee
- Medicinal Bioconvergence Research Center, College of Pharmacy, Seoul National University, Seoul 151-742, Republic of Korea Research Institute of Pharmaceutical Sciences, College of Pharmacy, Seoul National University, Seoul 151-742, Republic of Korea Information Center for Bio-pharmacological Network, Seoul National University, Suwon 443-270, Republic of Korea
| | - Thomas Letellier
- INRA URGI Centre de Versailles, bâtiment 18 Route de Saint Cyr 78026 Versailles, France
| | - Chuan-Yun Li
- Institute of Molecular Medicine, Peking University, Beijing, China
| | - Pietro Lio
- Computer Laboratory, University of Cambridge, Cambridge, CB3 0FD, UK
| | - Chu-Jun Liu
- Institute of Molecular Medicine, Peking University, Beijing, China
| | - Jie Luo
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Alejandro Maass
- Center for Mathematical Modeling and Center for Genome Regulation, University of Chile, Beauchef 851, 7th floor, Chile Department of Mathematical Engineering, University of Chile, Av. Beauchef 851, 5th floor, Santiago, Chile
| | - Jerome Mariette
- Plate-forme bio-informatique Genotoul, Mathématiques et Informatique Appliquées de Toulouse, INRA, Castanet-Tolosan, France
| | - Thomas Maurel
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Stefania Merella
- Center for Translational Genomics and Bioinformatics San Raffaele Scientific Institute, Via Olgettina 58, 20132 Milan, Italy
| | - Azza Mostafa Mohamed
- Departament of Biochemistry, Faculty of Science for Girls, King Abdulaziz University, Jeddah, Saudi Arabia
| | | | - Ibounyamine Nabihoudine
- Plate-forme bio-informatique Genotoul, Mathématiques et Informatique Appliquées de Toulouse, INRA, Castanet-Tolosan, France
| | - Nelson Ndegwa
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, PO Box 281, 17177 Stockholm, Sweden
| | - Céline Noirot
- Plate-forme bio-informatique Genotoul, Mathématiques et Informatique Appliquées de Toulouse, INRA, Castanet-Tolosan, France
| | | | - Michael Primig
- Inserm U1085 IRSET, University of Rennes 1, 35042 Rennes, France
| | - Alessandro Quattrone
- Laboratory of Translational Genomics, Centre for Integrative Biology, University of Trento, Trento, Italy
| | - Hadi Quesneville
- INRA URGI Centre de Versailles, bâtiment 18 Route de Saint Cyr 78026 Versailles, France
| | - Davide Rambaldi
- Center for Translational Genomics and Bioinformatics San Raffaele Scientific Institute, Via Olgettina 58, 20132 Milan, Italy
| | | | - Michela Riba
- Center for Translational Genomics and Bioinformatics San Raffaele Scientific Institute, Via Olgettina 58, 20132 Milan, Italy
| | - Steven Rosanoff
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Amna Ali Saddiq
- Department of Biological Sciences, Faculty of Science for Girls, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Elisa Salas
- International Potato Center (CIP), Lima, 1558, Peru
| | | | - Rebecca Shepherd
- Wellcome Trust Sanger Institute, Welcome Trust Genome Campus, Hinxton, CB10 1SD, UK
| | | | - Linda Sperling
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris Sud, 1 avenue de la terrasse, 91198 Gif sur Yvette, France
| | - William Spooner
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA Eagle Genomics Ltd., Babraham Research Campus, Cambridge, CB22 3AT, UK
| | - Daniel M Staines
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Delphine Steinbach
- INRA URGI Centre de Versailles, bâtiment 18 Route de Saint Cyr 78026 Versailles, France
| | - Kevin Stone
- Mouse Genomic Informatics Group, The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | - Elia Stupka
- Center for Translational Genomics and Bioinformatics San Raffaele Scientific Institute, Via Olgettina 58, 20132 Milan, Italy
| | - Jon W Teague
- Wellcome Trust Sanger Institute, Welcome Trust Genome Campus, Hinxton, CB10 1SD, UK
| | - Abu Z Dayem Ullah
- Centre for Molecular Oncology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
| | - Jun Wang
- Center for Bioinformatics, State Key Laboratory of Protein and Plant Gene Research, College of Life Sciences, Peking University, Beijing, 100871, P.R. China
| | - Doreen Ware
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Marie Wong-Erasmus
- Human Longevity, Inc. 10835 Road to the Cure 140 San Diego, CA 92121, USA
| | - Ken Youens-Clark
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Amonida Zadissa
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Shi-Jian Zhang
- Institute of Molecular Medicine, Peking University, Beijing, China
| | - Arek Kasprzyk
- Center for Translational Genomics and Bioinformatics San Raffaele Scientific Institute, Via Olgettina 58, 20132 Milan, Italy Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
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48
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Göde B, Holzmüller-Laue S, Thurow K. Neue Wege der standardkonformen Gesamtprozesssteuerung in der Life Science Automation. CHEM-ING-TECH 2015. [DOI: 10.1002/cite.201400032] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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49
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Börnigen D, Moon YS, Rahnavard G, Waldron L, McIver L, Shafquat A, Franzosa EA, Miropolsky L, Sweeney C, Morgan XC, Garrett WS, Huttenhower C. A reproducible approach to high-throughput biological data acquisition and integration. PeerJ 2015; 3:e791. [PMID: 26157642 PMCID: PMC4493686 DOI: 10.7717/peerj.791] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2014] [Accepted: 02/04/2015] [Indexed: 12/25/2022] Open
Abstract
Modern biological research requires rapid, complex, and reproducible integration of multiple experimental results generated both internally and externally (e.g., from public repositories). Although large systematic meta-analyses are among the most effective approaches both for clinical biomarker discovery and for computational inference of biomolecular mechanisms, identifying, acquiring, and integrating relevant experimental results from multiple sources for a given study can be time-consuming and error-prone. To enable efficient and reproducible integration of diverse experimental results, we developed a novel approach for standardized acquisition and analysis of high-throughput and heterogeneous biological data. This allowed, first, novel biomolecular network reconstruction in human prostate cancer, which correctly recovered and extended the NFκB signaling pathway. Next, we investigated host-microbiome interactions. In less than an hour of analysis time, the system retrieved data and integrated six germ-free murine intestinal gene expression datasets to identify the genes most influenced by the gut microbiota, which comprised a set of immune-response and carbohydrate metabolism processes. Finally, we constructed integrated functional interaction networks to compare connectivity of peptide secretion pathways in the model organisms Escherichia coli, Bacillus subtilis, and Pseudomonas aeruginosa.
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Affiliation(s)
- Daniela Börnigen
- Biostatistics Department, Harvard School of Public Health, Boston, MA, USA.,The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yo Sup Moon
- Biostatistics Department, Harvard School of Public Health, Boston, MA, USA
| | - Gholamali Rahnavard
- Biostatistics Department, Harvard School of Public Health, Boston, MA, USA.,The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Levi Waldron
- Biostatistics Department, Harvard School of Public Health, Boston, MA, USA.,City University of New York School of Public Health, Hunter College, New York, NY, USA
| | - Lauren McIver
- Biostatistics Department, Harvard School of Public Health, Boston, MA, USA
| | - Afrah Shafquat
- Biostatistics Department, Harvard School of Public Health, Boston, MA, USA
| | - Eric A Franzosa
- Biostatistics Department, Harvard School of Public Health, Boston, MA, USA.,The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Larissa Miropolsky
- Biostatistics Department, Harvard School of Public Health, Boston, MA, USA
| | | | - Xochitl C Morgan
- Biostatistics Department, Harvard School of Public Health, Boston, MA, USA.,The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Wendy S Garrett
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, MA, USA.,Department of Medicine, Harvard Medical School, Boston, MA, USA.,Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Curtis Huttenhower
- Biostatistics Department, Harvard School of Public Health, Boston, MA, USA.,The Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Enabling the deployment of mobile services for end-users: the SMS approach. SERVICE ORIENTED COMPUTING AND APPLICATIONS 2015. [DOI: 10.1007/s11761-013-0141-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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