Data management and data enrichment for systems biology projects

Wittig, Ulrike; Rey, Maja; Weidemann, Andreas; Müller, Wolfgang G.

doi:10.1016/j.jbiotec.2017.06.007

“…The Tellurium Notebook system by Sauro et al (in review) 31 offers support for SED-ML to help reproduce quantitative simulation of biological networks. Future work should consider bringing this feature for qualitative models as well, in order to better meet FAIR (Findability, Accessibility, Interoperability, and Reusability) recommendations (Wittig et al, 2017 ).…”

Section: Discussionmentioning

confidence: 99%

The CoLoMoTo Interactive Notebook: Accessible and Reproducible Computational Analyses for Qualitative Biological Networks

Naldi

¹

,

Hernandez

²

,

Levy

³

et al. 2018

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Analysing models of biological networks typically relies on workflows in which different software tools with sensitive parameters are chained together, many times with additional manual steps. The accessibility and reproducibility of such workflows is challenging, as publications often overlook analysis details, and because some of these tools may be difficult to install, and/or have a steep learning curve. The CoLoMoTo Interactive Notebook provides a unified environment to edit, execute, share, and reproduce analyses of qualitative models of biological networks. This framework combines the power of different technologies to ensure repeatability and to reduce users' learning curve of these technologies. The framework is distributed as a Docker image with the tools ready to be run without any installation step besides Docker, and is available on Linux, macOS, and Microsoft Windows. The embedded computational workflows are edited with a Jupyter web interface, enabling the inclusion of textual annotations, along with the explicit code to execute, as well as the visualization of the results. The resulting notebook files can then be shared and re-executed in the same environment. To date, the CoLoMoTo Interactive Notebook provides access to the software tools GINsim, BioLQM, Pint, MaBoSS, and Cell Collective, for the modeling and analysis of Boolean and multi-valued networks. More tools will be included in the future. We developed a Python interface for each of these tools to offer a seamless integration in the Jupyter web interface and ease the chaining of complementary analyses.

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“…The data management part of de.NBI-SysBio has the mission to serve customers with standardized high-quality scientific data and data services. The data management system “SEEK for Science” has been developed by the transnational FAIRDOM project 5 as a sharing space for projects fitted to the needs of systems and synthetic biology as well as systems medicine. The database SABIO-RK provides structured, standardized and annotated kinetic data with a focus on supporting the computational modelling community to create models of biochemical reaction networks as well as on allowing experimentalists to gain further knowledge about enzymatic activities and reaction properties.…”

Section: Bioinformatics Training In Germany By Denbimentioning

confidence: 99%

“…Therefore, de.NBI trainers work together and within different existing German and European (training) communities, e.g. the Galaxy Training Network 2 , The Carpentries 4 , FAIRDOM 5 etc., to connect de.NBI to researchers and to the most important topics in the life sciences community ( Figure 1 ). These topics were introduced in the de.NBI training activities, which range from basic skills to advanced data analysis and expert hackathons including 1–14 day training courses, webinars, mentoring, online training and one-week summer schools 3 .…”

Section: Introductionmentioning

confidence: 99%

The de.NBI / ELIXIR-DE training platform - Bioinformatics training in Germany and across Europe within ELIXIR

Wibberg

¹

,

Batut

²

,

Belmann

³

et al. 2020

Self Cite

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The German Network for Bioinformatics Infrastructure (de.NBI) is a national and academic infrastructure funded by the German Federal Ministry of Education and Research (BMBF). The de.NBI provides (i) service, (ii) training, and (iii) cloud computing to users in life sciences research and biomedicine in Germany and Europe and (iv) fosters the cooperation of the German bioinformatics community with international network structures. The de.NBI members also run the German node (ELIXIR-DE) within the European ELIXIR infrastructure. The de.NBI / ELIXIR-DE training platform, also known as special interest group 3 (SIG 3) ‘Training & Education’, coordinates the bioinformatics training of de.NBI and the German ELIXIR node. The network provides a high-quality, coherent, timely, and impactful training program across its eight service centers. Life scientists learn how to handle and analyze biological big data more effectively by applying tools, standards and compute services provided by de.NBI. Since 2015, more than 300 training courses were carried out with about 6,000 participants and these courses received recommendation rates of almost 90% (status as of July 2020). In addition to face-to-face training courses, online training was introduced on the de.NBI website in 2016 and guidelines for the preparation of e-learning material were established in 2018. In 2016, ELIXIR-DE joined the ELIXIR training platform. Here, the de.NBI / ELIXIR-DE training platform collaborates with ELIXIR in training activities, advertising training courses via TeSS and discussions on the exchange of data for training events essential for quality assessment on both the technical and administrative levels. The de.NBI training program trained thousands of scientists from Germany and beyond in many different areas of bioinformatics.

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“…Tellurium Notebook system by [42] offers support for SED-ML to help reproduce quantitative simulation of biological networks. Future work should consider bringing this feature for qualitative models as well, in order to better meet FAIR (Findability, Accessibility, Interoperability, and Reusability) recommendations [53].…”

Section: Extending the Colomoto Interactive Notebookmentioning

confidence: 99%

The CoLoMoTo Interactive Notebook: Accessible and Reproducible Computational Analyses for Qualitative Biological Networks

Naldi

¹

,

Hernandez

²

,

Levy

³

et al. 2018

Preprint

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Analysing models of biological networks typically relies on workflows in which different software tools with sensitive parameters are chained together, many times with additional manual steps.The accessibility and reproducibility of such workflows is challenging, as publications often overlook analysis details, and because some of these tools may be difficult to install, and/or have a steep learning curve. The CoLoMoTo Interactive Notebook provides a unified environment to edit, execute, share, and reproduce analyses of qualitative models of biological networks. This framework combines the power of different technologies to ensure repeatability and to reduce users' learning curve of these technologies. The framework is distributed as a Docker image with the tools ready to be run without any installation step besides Docker, and is available on Linux, macOS, and Microsoft Windows. The embedded computational workflows are edited with a Jupyter web interface, enabling the inclusion of textual annotations, along with the explicit code to execute, as well as the visualisation of the results.The resulting notebook files can then be shared and re-executed in the same environment. To date, the CoLoMoTo Interactive Notebook provides access to software tools including GINsim, BioLQM, Pint, MaBoSS, and Cell Collective for the modelling and analysis of Boolean and multi-valued networks.More tools will be included in the future. We developed a Python interface for each of these tools to offer a seamless integration in the Jupyter web interface and ease the chaining of complementary analyses. Python programming languageRecently, the scientific community has been increasingly concerned about difficulties in reproducing already published results. In the context of preclinical studies, observed difficulties to reproduce important findings have raised controversy (see e.g. [7,15,40,43], and [8] for a review on this topic). Although not invalidating the findings, these observations have shaken the community. In 2016, a Nature survey pointed to the multi-factorial origin of this "reproducibility crisis" [4]. Factors related to computational analyses were highlighted, in particular the unavailability of code and methods, along with the technical expertise required to reproduce the computations.The scientific community is progressively addressing this problem. Prestigious conferences (such as two major conferences from the database community, namely, VLDB 1 and SIGMOD 2 ) and journals such as PNAS 3 , Biostatistics [38], Nature [41] and Science [54], to name only a few, now encourage or even require published results to be accompanied by all the information necessary to reproduce them. While the reproducibility challenges have first been observed in domains where deluge of data were quickly becoming available (e.g., Next Generation Sequencing data analyses), the problem is now present in many (if not all) communities where computational analyses and simulations are performed. In particular, the System Biology community is facing a pro...

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Data management and data enrichment for systems biology projects

Cited by 21 publications

References 36 publications

The CoLoMoTo Interactive Notebook: Accessible and Reproducible Computational Analyses for Qualitative Biological Networks

The CoLoMoTo Interactive Notebook: Accessible and Reproducible Computational Analyses for Qualitative Biological Networks

The de.NBI / ELIXIR-DE training platform - Bioinformatics training in Germany and across Europe within ELIXIR

The CoLoMoTo Interactive Notebook: Accessible and Reproducible Computational Analyses for Qualitative Biological Networks

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