MOTIVATION: Molecular dynamics simulation is very useful but computationally demanding method of studying dynamics of biomolecular systems. Many enhanced sampling methods were developed in order to obtain the desired results in available computational time. Metadynamics and its variants are common enhanced sampling methods used for this purpose. Metadynamics simulations allow the user to gather large amounts of data, which have to be analyzed to elucidate the properties of the studied system. RESULTS: Here, we present metadynminer.py, a Python package that allows easy and user-friendly analysis and visualization of the results obtained from metadynamics simulations. The built-in functions automate frequent tasks and make the package easy to use for new users, while its many customization options and object-oriented nature allow for integration into specialized data analysis workflows by more advanced users. AVAILABILITY AND IMPLEMENTATION: The "metadynminer.py" Python package is available under the GPL-3.0 license via PyPi and Conda. The development version is available on GitHub along with issue support (https://github.com/Jan8be/metadynminer.py). Documentation, tutorial and Jupyter Notebook (provided through the public mybinder.org service) are available at https://metadynreporter.cz.

Systems medicine (SM) has emerged as a powerful tool for studying the human body at the systems level with the aim of improving our understanding, prevention and treatment of complex diseases. Being able to automatically extract relevant features needed for a given task from high-dimensional, heterogeneous data, deep learning (DL) holds great promise in this endeavour. This review paper addresses the main developments of DL algorithms and a set of general topics where DL is decisive, namely, within the SM landscape. It discusses how DL can be applied to SM with an emphasis on the applications to predictive, preventive and precision medicine. Several key challenges have been highlighted including delivering clinical impact and improving interpretability. We used some prototypical examples to highlight the relevance and significance of the adoption of DL in SM, one of them is involving the creation of a model for personalized Parkinson's disease. The review offers valuable insights and informs the research in DL and SM.

ELIXIR is a pan-European intergovernmental organisation for life science that aims to coordinate bioinformatics resources in a single infrastructure across Europe; bioinformatics training is central to its strategy, which aims to develop a training community that spans all ELIXIR member states. In an evidence-based approach for strengthening bioinformatics training programmes across Europe, the ELIXIR Training Platform, led by the ELIXIR EXCELERATE Quality and Impact Assessment Subtask in collaboration with the ELIXIR Training Coordinators Group, has implemented an assessment strategy to measure quality and impact of its entire training portfolio. Here, we present ELIXIR's framework for assessing training quality and impact, which includes the following: specifying assessment aims, determining what data to collect in order to address these aims, and our strategy for centralised data collection to allow for ELIXIR-wide analyses. In addition, we present an overview of the ELIXIR training data collected over the past 4 years. We highlight the importance of a coordinated and consistent data collection approach and the relevance of defining specific metrics and answer scales for consortium-wide analyses as well as for comparison of data across iterations of the same course.