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  • 1.
    Alvarsson, Jonathan
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Lampa, Samuel
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Schaal, Wesley
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Andersson, Claes
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Wikberg, Jarl E. S.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Large-scale ligand-based predictive modelling using support vector machines2016In: Journal of Cheminformatics, ISSN 1758-2946, E-ISSN 1758-2946, Vol. 8, article id 39Article in journal (Refereed)
    Abstract [en]

    The increasing size of datasets in drug discovery makes it challenging to build robust and accurate predictive models within a reasonable amount of time. In order to investigate the effect of dataset sizes on predictive performance and modelling time, ligand-based regression models were trained on open datasets of varying sizes of up to 1.2 million chemical structures. For modelling, two implementations of support vector machines (SVM) were used. Chemical structures were described by the signatures molecular descriptor. Results showed that for the larger datasets, the LIBLINEAR SVM implementation performed on par with the well-established libsvm with a radial basis function kernel, but with dramatically less time for model building even on modest computer resources. Using a non-linear kernel proved to be infeasible for large data sizes, even with substantial computational resources on a computer cluster. To deploy the resulting models, we extended the Bioclipse decision support framework to support models from LIBLINEAR and made our models of logD and solubility available from within Bioclipse.

  • 2.
    Ameur, Adam
    et al.
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology. Natl Genom Infrastruct, Sci Life Lab, Stockholm, Sweden..
    Dahlberg, Johan
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Molecular Medicine. Natl Genom Infrastruct, Sci Life Lab, Stockholm, Sweden.
    Olason, Pall
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology. Natl Bioinformat Infrastruct, Sci Life Lab, Stockholm, Sweden..
    Vezzi, Francesco
    Natl Genom Infrastruct, Sci Life Lab, Stockholm, Sweden.;Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Stockholm, Sweden..
    Karlsson, Robert
    Karolinska Inst, Dept Med Epidemiol & Biostat, Stockholm, Sweden..
    Martin, Marcel
    Natl Bioinformat Infrastruct, Sci Life Lab, Stockholm, Sweden.;Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Stockholm, Sweden..
    Viklund, Johan
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics. Natl Bioinformat Infrastruct, Sci Life Lab, Stockholm, Sweden..
    Kähäri, Andreas
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics. Natl Bioinformat Infrastruct, Sci Life Lab, Stockholm, Sweden..
    Lundin, Par
    Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Stockholm, Sweden..
    Che, Huiwen
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Thutkawkorapin, Jessada
    Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden..
    Eisfeldt, Jesper
    Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden..
    Lampa, Samuel
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Natl Bioinformat Infrastruct, Sci Life Lab, Stockholm, Sweden.
    Dahlberg, Mats
    Natl Bioinformat Infrastruct, Sci Life Lab, Stockholm, Sweden.;Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Stockholm, Sweden..
    Hagberg, Jonas
    Natl Bioinformat Infrastruct, Sci Life Lab, Stockholm, Sweden.;Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Stockholm, Sweden..
    Jareborg, Niclas
    Natl Bioinformat Infrastruct, Sci Life Lab, Stockholm, Sweden.;Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Stockholm, Sweden..
    Liljedahl, Ulrika
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Molecular Medicine. Natl Genom Infrastruct, Sci Life Lab, Stockholm, Sweden.
    Jonasson, Inger
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology. Natl Genom Infrastruct, Sci Life Lab, Stockholm, Sweden..
    Johansson, Åsa
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medicinsk genetik och genomik.
    Feuk, Lars
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medicinsk genetik och genomik.
    Lundeberg, Joakim
    Natl Genom Infrastruct, Sci Life Lab, Stockholm, Sweden.;Royal Inst Technol, Div Gene Technol, Sch Biotechnol, Sci Life Lab, Stockholm, Sweden..
    Syvänen, Ann-Christine
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Molecular Medicine. Natl Genom Infrastruct, Sci Life Lab, Stockholm, Sweden.
    Lundin, Sverker
    Royal Inst Technol, Div Gene Technol, Sch Biotechnol, Sci Life Lab, Stockholm, Sweden..
    Nilsson, Daniel
    Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden..
    Nystedt, Björn
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Molecular Evolution. Natl Bioinformat Infrastruct, Sci Life Lab, Stockholm, Sweden..
    Magnusson, Patrik K. E.
    Natl Genom Infrastruct, Sci Life Lab, Stockholm, Sweden.;Karolinska Inst, Dept Med Epidemiol & Biostat, Stockholm, Sweden..
    Gyllensten, Ulf B.
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medicinsk genetik och genomik.
    SweGen: a whole-genome data resource of genetic variability in a cross-section of the Swedish population2017In: European Journal of Human Genetics, ISSN 1018-4813, E-ISSN 1476-5438, Vol. 25, no 11, p. 1253-1260Article in journal (Refereed)
    Abstract [en]

    Here we describe the SweGen data set, a comprehensive map of genetic variation in the Swedish population. These data represent a basic resource for clinical genetics laboratories as well as for sequencing-based association studies by providing information on genetic variant frequencies in a cohort that is well matched to national patient cohorts. To select samples for this study, we first examined the genetic structure of the Swedish population using high-density SNP-array data from a nation-wide cohort of over 10 000 Swedish-born individuals included in the Swedish Twin Registry. A total of 1000 individuals, reflecting a cross-section of the population and capturing the main genetic structure, were selected for whole-genome sequencing. Analysis pipelines were developed for automated alignment, variant calling and quality control of the sequencing data. This resulted in a genome-wide collection of aggregated variant frequencies in the Swedish population that we have made available to the scientific community through the website https://swefreq.nbis.se. A total of 29.2 million single-nucleotide variants and 3.8 million indels were detected in the 1000 samples, with 9.9 million of these variants not present in current databases. Each sample contributed with an average of 7199 individual-specific variants. In addition, an average of 8645 larger structural variants (SVs) were detected per individual, and we demonstrate that the population frequencies of these SVs can be used for efficient filtering analyses. Finally, our results show that the genetic diversity within Sweden is substantial compared with the diversity among continental European populations, underscoring the relevance of establishing a local reference data set.

  • 3.
    Grüning, Björn A.
    et al.
    Univ Freiburg, Dept Comp Sci, Bioinformat Grp, Georges Koehler Allee 106, D-79110 Freiburg, Germany;Univ Freiburg, Ctr Biol Syst Anal ZBSA, Habsburgerstr 49, D-79104 Freiburg, Germany.
    Lampa, Samuel
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Stockholm Univ, Natl Bioinformat Infrastruct Sweden, Sci Life Lab, Dept Biochem & Biophys, Svante Arrhenius Vag 16C, S-10691 Solna, Sweden.
    Vaudel, Marc
    Univ Bergen, Dept Clin Sci, KG Jebsen Ctr Diabet Res, Postboks 7804, N-5020 Bergen, Norway;Haukeland Hosp, Ctr Med Genet & Mol Med, Postboks 7804, N-5020 Bergen, Norway.
    Blankenberg, Daniel
    Cleveland Clin, Lerner Res Inst, Genom Med Inst, 9500 Euclid Ave NE50, Cleveland, OH 44106 USA.
    Software engineering for scientific big data analysis2019In: GigaScience, ISSN 2047-217X, E-ISSN 2047-217X, Vol. 8, no 5, article id giz054Article, review/survey (Refereed)
    Abstract [en]

    The increasing complexity of data and analysis methods has created an environment where scientists, who may not have formal training, are finding themselves playing the impromptu role of software engineer. While several resources are available for introducing scientists to the basics of programming, researchers have been left with little guidance on approaches needed to advance to the next level for the development of robust, large-scale data analysis tools that are amenable to integration into workflow management systems, tools, and frameworks. The integration into such workflow systems necessitates additional requirements on computational tools, such as adherence to standard conventions for robustness, data input, output, logging, and flow control. Here we provide a set of 10 guidelines to steer the creation of command-line computational tools that are usable, reliable, extensible, and in line with standards of modern coding practices.

  • 4.
    Lampa, Samuel
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Department of Biochemistry and Biophysics, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Stockholm University, Stockholm, Sweden.
    Reproducible Data Analysis in Drug Discovery with Scientific Workflows and the Semantic Web2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The pharmaceutical industry is facing a research and development productivity crisis. At the same time we have access to more biological data than ever from recent advancements in high-throughput experimental methods. One suggested explanation for this apparent paradox has been that a crisis in reproducibility has affected also the reliability of datasets providing the basis for drug development. Advanced computing infrastructures can to some extent aid in this situation but also come with their own challenges, including increased technical debt and opaqueness from the many layers of technology required to perform computations and manage data. In this thesis, a number of approaches and methods for dealing with data and computations in early drug discovery in a reproducible way are developed. This has been done while striving for a high level of simplicity in their implementations, to improve understandability of the research done using them. Based on identified problems with existing tools, two workflow tools have been developed with the aim to make writing complex workflows particularly in predictive modelling more agile and flexible. One of the tools is based on the Luigi workflow framework, while the other is written from scratch in the Go language. We have applied these tools on predictive modelling problems in early drug discovery to create reproducible workflows for building predictive models, including for prediction of off-target binding in drug discovery. We have also developed a set of practical tools for working with linked data in a collaborative way, and publishing large-scale datasets in a semantic, machine-readable format on the web. These tools were applied on demonstrator use cases, and used for publishing large-scale chemical data. It is our hope that the developed tools and approaches will contribute towards practical, reproducible and understandable handling of data and computations in early drug discovery.

    List of papers
    1. Large-scale ligand-based predictive modelling using support vector machines
    Open this publication in new window or tab >>Large-scale ligand-based predictive modelling using support vector machines
    Show others...
    2016 (English)In: Journal of Cheminformatics, ISSN 1758-2946, E-ISSN 1758-2946, Vol. 8, article id 39Article in journal (Refereed) Published
    Abstract [en]

    The increasing size of datasets in drug discovery makes it challenging to build robust and accurate predictive models within a reasonable amount of time. In order to investigate the effect of dataset sizes on predictive performance and modelling time, ligand-based regression models were trained on open datasets of varying sizes of up to 1.2 million chemical structures. For modelling, two implementations of support vector machines (SVM) were used. Chemical structures were described by the signatures molecular descriptor. Results showed that for the larger datasets, the LIBLINEAR SVM implementation performed on par with the well-established libsvm with a radial basis function kernel, but with dramatically less time for model building even on modest computer resources. Using a non-linear kernel proved to be infeasible for large data sizes, even with substantial computational resources on a computer cluster. To deploy the resulting models, we extended the Bioclipse decision support framework to support models from LIBLINEAR and made our models of logD and solubility available from within Bioclipse.

    Keywords
    Predictive modelling; Support vector machine; Bioclipse; Molecular signatures; QSAR
    National Category
    Pharmaceutical Sciences Bioinformatics (Computational Biology)
    Research subject
    Bioinformatics
    Identifiers
    urn:nbn:se:uu:diva-248959 (URN)10.1186/s13321-016-0151-5 (DOI)000381186100001 ()27516811 (PubMedID)
    Funder
    Swedish National Infrastructure for Computing (SNIC), b2013262 b2015001Science for Life Laboratory - a national resource center for high-throughput molecular bioscienceeSSENCE - An eScience Collaboration
    Available from: 2015-04-09 Created: 2015-04-09 Last updated: 2018-08-28Bibliographically approved
    2. Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles
    Open this publication in new window or tab >>Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles
    2016 (English)In: Journal of Cheminformatics, ISSN 1758-2946, E-ISSN 1758-2946, Vol. 8, article id 67Article in journal (Refereed) Published
    Abstract [en]

    Predictive modelling in drug discovery is challenging to automate as it often contains multiple analysis steps and might involve cross-validation and parameter tuning that create complex dependencies between tasks. With large-scale data or when using computationally demanding modelling methods, e-infrastructures such as high-performance or cloud computing are required, adding to the existing challenges of fault-tolerant automation. Workflow management systems can aid in many of these challenges, but the currently available systems are lacking in the functionality needed to enable agile and flexible predictive modelling. We here present an approach inspired by elements of the flow-based programming paradigm, implemented as an extension of the Luigi system which we name SciLuigi. We also discuss the experiences from using the approach when modelling a large set of biochemical interactions using a shared computer cluster.

    Keywords
    Predictive modelling, Machine learning, Workflows, Drug discovery, Flow-based programming
    National Category
    Computer Systems
    Identifiers
    urn:nbn:se:uu:diva-315089 (URN)10.1186/s13321-016-0179-6 (DOI)000391703900001 ()
    Funder
    eSSENCE - An eScience CollaborationSwedish e‐Science Research CenterSwedish National Infrastructure for Computing (SNIC), b2013262
    Available from: 2017-02-09 Created: 2017-02-09 Last updated: 2018-08-28Bibliographically approved
    3. RDFIO: extending Semantic MediaWiki for interoperable biomedical data management
    Open this publication in new window or tab >>RDFIO: extending Semantic MediaWiki for interoperable biomedical data management
    Show others...
    2017 (English)In: Journal of Biomedical Semantics, ISSN 2041-1480, E-ISSN 2041-1480, Vol. 8, article id 35Article in journal (Refereed) Published
    Abstract [en]

    BACKGROUND: Biological sciences are characterised not only by an increasing amount but also the extreme complexity of its data. This stresses the need for efficient ways of integrating these data in a coherent description of biological systems. In many cases, biological data needs organization before integration. This is not seldom a collaborative effort, and it is thus important that tools for data integration support a collaborative way of working. Wiki systems with support for structured semantic data authoring, such as Semantic MediaWiki, provide a powerful solution for collaborative editing of data combined with machine-readability, so that data can be handled in an automated fashion in any downstream analyses. Semantic MediaWiki lacks a built-in data import function though, which hinders efficient round-tripping of data between interoperable Semantic Web formats such as RDF and the internal wiki format.

    RESULTS: To solve this deficiency, the RDFIO suite of tools is presented, which supports importing of RDF data into Semantic MediaWiki, with metadata needed to export it again in the same RDF format, or ontology. Additionally, the new functionality enables mash-ups of automated data imports combined with manually created data presentations. The application of the suite of tools is demonstrated by importing drug discovery related data about rare diseases from Orphanet and acid dissociation constants from Wikidata. The RDFIO suite of tools is freely available for download via pharmb.io/project/rdfio .

    CONCLUSIONS: Through a set of biomedical demonstrators, it is demonstrated how the new functionality enables a number of usage scenarios where the interoperability of SMW and the wider Semantic Web is leveraged for biomedical data sets, to create an easy to use and flexible platform for exploring and working with biomedical data.

    Keywords
    MediaWiki, RDF, SPARQL, Semantic MediaWiki, Semantic Web, Wiki, Wikidata
    National Category
    Bioinformatics (Computational Biology)
    Research subject
    Bioinformatics
    Identifiers
    urn:nbn:se:uu:diva-329195 (URN)10.1186/s13326-017-0136-y (DOI)000409081000001 ()28870259 (PubMedID)
    Funder
    eSSENCE - An eScience CollaborationSwedish e‐Science Research CenterEU, FP7, Seventh Framework Programme
    Available from: 2017-09-10 Created: 2017-09-10 Last updated: 2018-08-28Bibliographically approved
    4. A confidence predictor for logD using conformal regression and a support-vector machine
    Open this publication in new window or tab >>A confidence predictor for logD using conformal regression and a support-vector machine
    Show others...
    2018 (English)In: Journal of Cheminformatics, ISSN 1758-2946, E-ISSN 1758-2946, Vol. 10, no 1, article id 17Article in journal (Refereed) Published
    Abstract [en]

    Lipophilicity is a major determinant of ADMET properties and overall suitability of drug candidates. We have developed large-scale models to predict water-octanol distribution coefficient (logD) for chemical compounds, aiding drug discovery projects. Using ACD/logD data for 1.6 million compounds from the ChEMBL database, models are created and evaluated by a support-vector machine with a linear kernel using conformal prediction methodology, outputting prediction intervals at a specified confidence level. The resulting model shows a predictive ability of [Formula: see text] and with the best performing nonconformity measure having median prediction interval of [Formula: see text] log units at 80% confidence and [Formula: see text] log units at 90% confidence. The model is available as an online service via an OpenAPI interface, a web page with a molecular editor, and we also publish predictive values at 90% confidence level for 91 M PubChem structures in RDF format for download and as an URI resolver service.

    Keywords
    Conformal prediction, LogD, Machine learning, QSAR, RDF, Support-vector machine
    National Category
    Bioinformatics (Computational Biology)
    Research subject
    Bioinformatics
    Identifiers
    urn:nbn:se:uu:diva-347779 (URN)10.1186/s13321-018-0271-1 (DOI)000429065900001 ()29616425 (PubMedID)
    Funder
    EU, Horizon 2020, 731075
    Available from: 2018-04-06 Created: 2018-04-06 Last updated: 2018-08-28Bibliographically approved
    5. Predicting off-target binding profiles with confidence using Conformal Prediction
    Open this publication in new window or tab >>Predicting off-target binding profiles with confidence using Conformal Prediction
    Show others...
    2018 (English)In: Frontiers in Pharmacology, ISSN 1663-9812, E-ISSN 1663-9812, Vol. 9, article id 1256Article in journal (Refereed) Published
    Abstract [en]

    Ligand-based models can be used in drug discovery to obtain an early indication of potential off-target interactions that could be linked to adverse effects. Another application is to combine such models into a panel, allowing to compare and search for compounds with similar profiles. Most contemporary methods and implementations however lack valid measures of confidence in their predictions, and only providing point predictions. We here describe the use of conformal prediction for predicting off-target interactions with models trained on data from 31 targets in the ExCAPE dataset, selected for their utility in broad early hazard assessment. Chemicals were represented by the signature molecular descriptor and support vector machines were used as the underlying machine learning method. By using conformal prediction, the results from predictions come in the form of confidence p-values for each class. The full pre-processing and model training process is openly available as scientific workflows on GitHub, rendering it fully reproducible. We illustrate the usefulness of the methodology on a set of compounds extracted from DrugBank. The resulting models are published online and are available via a graphical web interface and an OpenAPI interface for programmatic access.

    Keywords
    target profiles, predictive modelling, conformal prediction, machine learning, off-target, adverse effects
    National Category
    Pharmacology and Toxicology
    Research subject
    Pharmacology
    Identifiers
    urn:nbn:se:uu:diva-357894 (URN)10.3389/fphar.2018.01256 (DOI)000449322200002 ()30459617 (PubMedID)
    Funder
    EU, Horizon 2020, 731075
    Available from: 2018-08-21 Created: 2018-08-21 Last updated: 2019-01-15Bibliographically approved
    6. SciPipe: A workflow library for agile development of complex and dynamic bioinformatics pipelines
    Open this publication in new window or tab >>SciPipe: A workflow library for agile development of complex and dynamic bioinformatics pipelines
    2019 (English)In: GigaScience, ISSN 2047-217X, E-ISSN 2047-217X, Vol. 8, no 5, article id giz044Article in journal (Refereed) Published
    Abstract [en]

    Background: The complex nature of biological data has driven the development of specialized software tools. Scientific workflow management systems simplify the assembly of such tools into pipelines, assist with job automation, and aid reproducibility of analyses. Many contemporary workflow tools are specialized or not designed for highly complex workflows, such as with nested loops, dynamic scheduling, and parametrization, which is common in, e.g., machine learning. Findings: SciPipe is a workflow programming library implemented in the programming language Go, for managing complex and dynamic pipelines in bioinformatics, cheminformatics, and other fields. SciPipe helps in particular with workflow constructs common in machine learning, such as extensive branching, parameter sweeps, and dynamic scheduling and parametrization of downstream tasks. SciPipe builds on flow-based programming principles to support agile development of workflows based on a library of self-contained, reusable components. It supports running subsets of workflows for improved iterative development and provides a data-centric audit logging feature that saves a full audit trace for every output file of a workflow, which can be converted to other formats such as HTML, TeX, and PDF on demand. The utility of SciPipe is demonstrated with a machine learning pipeline, a genomics, and a transcriptomics pipeline. Conclusions: SciPipe provides a solution for agile development of complex and dynamic pipelines, especially in machine learning, through a flexible application programming interface suitable for scientists used to programming or scripting.

    Keywords
    Scientific Workflow Management Systems, Workflow tools, Workflows, Pipelines, Reproducibility, Machine Learning, Flow-based Programming, Go, Golang
    National Category
    Bioinformatics (Computational Biology)
    Research subject
    Bioinformatics
    Identifiers
    urn:nbn:se:uu:diva-358347 (URN)10.1093/gigascience/giz044 (DOI)000474856100002 ()31029061 (PubMedID)
    Funder
    eSSENCE - An eScience CollaborationSwedish e‐Science Research CenterEU, Horizon 2020, 654241
    Available from: 2018-08-27 Created: 2018-08-27 Last updated: 2019-08-13Bibliographically approved
  • 5.
    Lampa, Samuel
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Alvarsson, Jonathan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Arvidsson Mc Shane, Staffan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Berg, Arvid
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Ahlberg, Ernst
    Predictive Compound ADME & Safety, Drug Safety & Metabolism, AstraZeneca IMED Biotech Unit, Mölndal, Sweden.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Predicting off-target binding profiles with confidence using Conformal Prediction2018In: Frontiers in Pharmacology, ISSN 1663-9812, E-ISSN 1663-9812, Vol. 9, article id 1256Article in journal (Refereed)
    Abstract [en]

    Ligand-based models can be used in drug discovery to obtain an early indication of potential off-target interactions that could be linked to adverse effects. Another application is to combine such models into a panel, allowing to compare and search for compounds with similar profiles. Most contemporary methods and implementations however lack valid measures of confidence in their predictions, and only providing point predictions. We here describe the use of conformal prediction for predicting off-target interactions with models trained on data from 31 targets in the ExCAPE dataset, selected for their utility in broad early hazard assessment. Chemicals were represented by the signature molecular descriptor and support vector machines were used as the underlying machine learning method. By using conformal prediction, the results from predictions come in the form of confidence p-values for each class. The full pre-processing and model training process is openly available as scientific workflows on GitHub, rendering it fully reproducible. We illustrate the usefulness of the methodology on a set of compounds extracted from DrugBank. The resulting models are published online and are available via a graphical web interface and an OpenAPI interface for programmatic access.

  • 6.
    Lampa, Samuel
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Alvarsson, Jonathan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles2016In: Journal of Cheminformatics, ISSN 1758-2946, E-ISSN 1758-2946, Vol. 8, article id 67Article in journal (Refereed)
    Abstract [en]

    Predictive modelling in drug discovery is challenging to automate as it often contains multiple analysis steps and might involve cross-validation and parameter tuning that create complex dependencies between tasks. With large-scale data or when using computationally demanding modelling methods, e-infrastructures such as high-performance or cloud computing are required, adding to the existing challenges of fault-tolerant automation. Workflow management systems can aid in many of these challenges, but the currently available systems are lacking in the functionality needed to enable agile and flexible predictive modelling. We here present an approach inspired by elements of the flow-based programming paradigm, implemented as an extension of the Luigi system which we name SciLuigi. We also discuss the experiences from using the approach when modelling a large set of biochemical interactions using a shared computer cluster.

  • 7.
    Lampa, Samuel
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab. Department of Biochemistry and Biophysics, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Stockholm University, Stockholm, Sweden.
    Dahlö, Martin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Alvarsson, Jonathan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    SciPipe: A workflow library for agile development of complex and dynamic bioinformatics pipelines2019In: GigaScience, ISSN 2047-217X, E-ISSN 2047-217X, Vol. 8, no 5, article id giz044Article in journal (Refereed)
    Abstract [en]

    Background: The complex nature of biological data has driven the development of specialized software tools. Scientific workflow management systems simplify the assembly of such tools into pipelines, assist with job automation, and aid reproducibility of analyses. Many contemporary workflow tools are specialized or not designed for highly complex workflows, such as with nested loops, dynamic scheduling, and parametrization, which is common in, e.g., machine learning. Findings: SciPipe is a workflow programming library implemented in the programming language Go, for managing complex and dynamic pipelines in bioinformatics, cheminformatics, and other fields. SciPipe helps in particular with workflow constructs common in machine learning, such as extensive branching, parameter sweeps, and dynamic scheduling and parametrization of downstream tasks. SciPipe builds on flow-based programming principles to support agile development of workflows based on a library of self-contained, reusable components. It supports running subsets of workflows for improved iterative development and provides a data-centric audit logging feature that saves a full audit trace for every output file of a workflow, which can be converted to other formats such as HTML, TeX, and PDF on demand. The utility of SciPipe is demonstrated with a machine learning pipeline, a genomics, and a transcriptomics pipeline. Conclusions: SciPipe provides a solution for agile development of complex and dynamic pipelines, especially in machine learning, through a flexible application programming interface suitable for scientists used to programming or scripting.

  • 8.
    Lampa, Samuel
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Dahlö, Martin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Alvarsson, Jonathan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    SciPipe-Turning Scientific Workflows into Computer Programs2019In: Computing in science & engineering (Print), ISSN 1521-9615, E-ISSN 1558-366X, Vol. 21, no 3, p. 109-113Article in journal (Refereed)
  • 9.
    Lampa, Samuel
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Dahlö, Martin
    Uppsala University, Science for Life Laboratory, SciLifeLab.
    Olason, Pall I
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology.
    Hagberg, Jonas
    Uppsala University, Science for Life Laboratory, SciLifeLab.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Lessons learned from implementing a national infrastructure in Sweden for storage and analysis of next-generation sequencing data2013In: GigaScience, ISSN 2047-217X, E-ISSN 2047-217X, Vol. 2, no 1, p. 1-10Article in journal (Refereed)
    Abstract [en]

    Analyzing and storing data and results from next-generation sequencing (NGS) experiments is a challenging task, hampered by ever-increasing data volumes and frequent updates of analysis methods and tools. Storage and computation have grown beyond the capacity of personal computers and there is a need for suitable e-infrastructures for processing. Here we describe UPPNEX, an implementation of such an infrastructure, tailored to the needs of data storage and analysis of NGS data in Sweden serving various labs and multiple instruments from the major sequencing technology platforms. UPPNEX comprises resources for high-performance computing, large-scale and high-availability storage, an extensive bioinformatics software suite, up-to-date reference genomes and annotations, a support function with system and application experts as well as a web portal and support ticket system. UPPNEX applications are numerous and diverse, and include whole genome-, de novo- and exome sequencing, targeted resequencing, SNP discovery, RNASeq, and methylation analysis. There are over 300 projects that utilize UPPNEX and include large undertakings such as the sequencing of the flycatcher and Norwegian spruce. We describe the strategic decisions made when investing in hardware, setting up maintenance and support, allocating resources, and illustrate major challenges such as managing data growth. We conclude with summarizing our experiences and observations with UPPNEX to date, providing insights into the successful and less successful decisions made.

  • 10.
    Lampa, Samuel
    et al.
    Uppsala University, Science for Life Laboratory, SciLifeLab.
    Hagberg, Jonas
    Uppsala University, Science for Life Laboratory, SciLifeLab.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    UPPNEX: A solution for next generation sequencing data management and analysis2012In: EMBnet.journal, ISSN 2226-6089, Vol. 17, no Suppl. B, p. 44-44Article in journal (Other academic)
  • 11.
    Lampa, Samuel
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Willighagen, Egon
    Maastricht Univ, Dept Bioinformat BiGCaT, NUTRIM, POB 616,UNS50 Box 19, NL-6200 MD Maastricht, Netherlands.
    Kohonen, Pekka
    Karolinska Inst, Inst Environm Med, SE-17177 Stockholm, Sweden.; Misvik Biol Oy, Div Toxicol, Turku, Finland. .
    King, Ali
    FanDuel Inc, Edinburgh, Midlothian, Scotland.
    Vrandečić, Denny
    Google Inc, 345 Spear St, San Francisco, CA USA.
    Grafström, Roland
    Karolinska Inst, Inst Environm Med, SE-17177 Stockholm, Sweden.; Misvik Biol Oy, Div Toxicol, Turku, Finland..
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    RDFIO: extending Semantic MediaWiki for interoperable biomedical data management2017In: Journal of Biomedical Semantics, ISSN 2041-1480, E-ISSN 2041-1480, Vol. 8, article id 35Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Biological sciences are characterised not only by an increasing amount but also the extreme complexity of its data. This stresses the need for efficient ways of integrating these data in a coherent description of biological systems. In many cases, biological data needs organization before integration. This is not seldom a collaborative effort, and it is thus important that tools for data integration support a collaborative way of working. Wiki systems with support for structured semantic data authoring, such as Semantic MediaWiki, provide a powerful solution for collaborative editing of data combined with machine-readability, so that data can be handled in an automated fashion in any downstream analyses. Semantic MediaWiki lacks a built-in data import function though, which hinders efficient round-tripping of data between interoperable Semantic Web formats such as RDF and the internal wiki format.

    RESULTS: To solve this deficiency, the RDFIO suite of tools is presented, which supports importing of RDF data into Semantic MediaWiki, with metadata needed to export it again in the same RDF format, or ontology. Additionally, the new functionality enables mash-ups of automated data imports combined with manually created data presentations. The application of the suite of tools is demonstrated by importing drug discovery related data about rare diseases from Orphanet and acid dissociation constants from Wikidata. The RDFIO suite of tools is freely available for download via pharmb.io/project/rdfio .

    CONCLUSIONS: Through a set of biomedical demonstrators, it is demonstrated how the new functionality enables a number of usage scenarios where the interoperability of SMW and the wider Semantic Web is leveraged for biomedical data sets, to create an easy to use and flexible platform for exploring and working with biomedical data.

  • 12.
    Lapins, Maris
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Arvidsson, Staffan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Lampa, Samuel
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Berg, Arvid
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Schaal, Wesley
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Alvarsson, Jonathan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    A confidence predictor for logD using conformal regression and a support-vector machine2018In: Journal of Cheminformatics, ISSN 1758-2946, E-ISSN 1758-2946, Vol. 10, no 1, article id 17Article in journal (Refereed)
    Abstract [en]

    Lipophilicity is a major determinant of ADMET properties and overall suitability of drug candidates. We have developed large-scale models to predict water-octanol distribution coefficient (logD) for chemical compounds, aiding drug discovery projects. Using ACD/logD data for 1.6 million compounds from the ChEMBL database, models are created and evaluated by a support-vector machine with a linear kernel using conformal prediction methodology, outputting prediction intervals at a specified confidence level. The resulting model shows a predictive ability of [Formula: see text] and with the best performing nonconformity measure having median prediction interval of [Formula: see text] log units at 80% confidence and [Formula: see text] log units at 90% confidence. The model is available as an online service via an OpenAPI interface, a web page with a molecular editor, and we also publish predictive values at 90% confidence level for 91 M PubChem structures in RDF format for download and as an URI resolver service.

  • 13. Peters, Kristian
    et al.
    Bradbury, James
    Bergmann, Sven
    Capuccini, Marco
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Cascante, Marta
    de Atauri, Pedro
    Ebbels, Timothy M. D.
    Foguet, Carles
    Glen, Robert
    Gonzalez-Beltran, Alejandra
    Günther, Ulrich L.
    Handakas, Evangelos
    Hankemeier, Thomas
    Haug, Kenneth
    Herman, Stephanie
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Chemistry.
    Holub, Petr
    Izzo, Massimiliano
    Jacob, Daniel
    Johnson, David
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Informatics and Media.
    Jourdan, Fabien
    Kale, Namrata
    Karaman, Ibrahim
    Khalili, Bita
    Emami Khoonsari, Payam
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Chemistry.
    Kultima, Kim
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Chemistry.
    Lampa, Samuel
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Larsson, Anders
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Ludwig, Christian
    Moreno, Pablo
    Neumann, Steffen
    Novella, Jon Ander
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    O'Donovan, Claire
    Pearce, Jake T. M.
    Peluso, Alina
    Piras, Marco Enrico
    Pireddu, Luca
    Reed, Michelle A. C.
    Rocca-Serra, Philippe
    Roger, Pierrick
    Rosato, Antonio
    Rueedi, Rico
    Ruttkies, Christoph
    Sadawi, Noureddin
    Salek, Reza M.
    Sansone, Susanna-Assunta
    Selivanov, Vitaly
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Schober, Daniel
    Thévenot, Etienne A.
    Tomasoni, Mattia
    van Rijswijk, Merlijn
    van Vliet, Michael
    Viant, Mark R.
    Weber, Ralf J. M.
    Zanetti, Gianluigi
    Steinbeck, Christoph
    PhenoMeNal: Processing and analysis of metabolomics data in the cloud2019In: GigaScience, ISSN 2047-217X, E-ISSN 2047-217X, Vol. 8, no 2Article in journal (Refereed)
  • 14.
    Spjuth, Ola
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Bongcam-Rudloff, Erik
    Carrasco Hernández, Guillermo
    Forer, Lukas
    Giovacchini, Mario
    Guimera, Roman Valls
    Kallio, Aleksi
    Korpelainen, Eija
    Kańduła, Maciej M.
    Krachunov, Milko
    Kreil, David P.
    Kulev, Ognyan
    Łabaj, Paweł P.
    Lampa, Samuel
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Pireddu, Luca
    Schönherr, Sebastian
    Siretskiy, Alexey
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
    Vassilev, Dimitar
    Experiences with workflows for automating data-intensive bioinformatics2015In: Biology Direct, ISSN 1745-6150, E-ISSN 1745-6150, Vol. 10, article id 43Article, review/survey (Refereed)
  • 15.
    Willighagen, Egon
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Alvarsson, Jonathan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Andersson, Annsofie
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Eklund, Martin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Lampa, Samuel
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Lapins, Maris
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Wikberg, Jarl
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Linking the Resource Description Framework to cheminformatics and proteochemometrics2011In: Journal of Biomedical Semantics, ISSN 2041-1480, E-ISSN 2041-1480, Vol. 2, no Suppl 1, p. 6-Article in journal (Refereed)
    Abstract [en]

    BACKGROUND :

    Semantic web technologies are finding their way into the life sciences. Ontologies and semantic markup have already been used for more than a decade in molecular sciences, but have not found widespread use yet. The semantic web technology Resource Description Framework (RDF) and related methods show to be sufficiently versatile to change that situation.

    RESULTS :

    The work presented here focuses on linking RDF approaches to existing molecular chemometrics fields, including cheminformatics, QSAR modeling and proteochemometrics. Applications are presented that link RDF technologies to methods from statistics and cheminformatics, including data aggregation, visualization, chemical identification, and property prediction. They demonstrate how this can be done using various existing RDF standards and cheminformatics libraries. For example, we show how IC50 and Ki values are modeled for a number of biological targets using data from the ChEMBL database.

    CONCLUSIONS :

    We have shown that existing RDF standards can suitably be integrated into existing molecular chemometrics methods. Platforms that unite these technologies, like Bioclipse, makes this even simpler and more transparent. Being able to create and share workflows that integrate data aggregation and analysis (visual and statistical) is beneficial to interoperability and reproducibility. The current work shows that RDF approaches are sufficiently powerful to support molecular chemometrics workflows.

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