The MPI Bioinformatics Toolkit (https://toolkit.tuebingen.mpg.de) provides interactive access to a wide range of the best‐performing bioinformatics tools and databases, including the state‐of‐the‐art protein sequence comparison methods HHblits and HHpred. The Toolkit currently includes 35 external and in‐house tools, covering functionalities such as sequence similarity searching, prediction of sequence features, and sequence classification. Due to this breadth of functionality, the tight interconnection of its constituent tools, and its ease of use, the Toolkit has become an important resource for biomedical research and for teaching protein sequence analysis to students in the life sciences. In this article, we provide detailed information on utilizing the three most widely accessed tools within the Toolkit: HHpred for the detection of homologs, HHpred in conjunction with MODELLER for structure prediction and homology modeling, and CLANS for the visualization of relationships in large sequence datasets. © 2020 The Authors. Basic Protocol 1: Sequence similarity searching using HHpred Alternate Protocol: Pairwise sequence comparison using HHpred Support Protocol: Building a custom multiple sequence alignment using PSI‐BLAST and forwarding it as input to HHpred Basic Protocol 2: Calculation of homology models using HHpred and MODELLER Basic Protocol 3: Cluster analysis using CLANS

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Cellular appendages such as cilia and flagella represent universal tools enabling cells and microbes, among other essential functionalities, to propel themselves in diverse environments. In its planktonic, i.e. freely swimming, state the unicellular bi-flagellated microbe Chlamydomonas reinhardtii employs a periodic breaststroke-like flagellar beating to displace the surrounding fluid. Another flagella-mediated motility mode is observed for surface-associated Chlamydomonas cells, which glide along the surface by means of force transduction through an intraflagellar transport machinery. Experiments and statistical motility analysis demonstrate that this gliding motility enhances clustering and supports self-organization of Chlamydomonas populations. We employ Minkowski functionals to characterize the spatiotemporal organization of the surface-associated cell monolayer. We find that simulations based on a purely mechanistic approach cannot capture the observed non-random cell configurations. Quantitative agreement with experimental data however is achieved when considering a minimal cognitive model of the flagellar mechanosensing.

Diabetes mellitus is a common condition which has several serious complications associated with it. In this paper a mixture model, based on one previously used to predict the onset of AIDS, is used to predict the onset of one of these complications, diabetic retinopathy, the major cause of adult blindness in the U.K. This model differs from the previous AIDS model by introducing covariates into the model and using a wider choice of mixture distributions. The fit and distributional assumptions of the model are then discussed for this example. The model is fitted to the data by maximum likelihood. It is important that the training set contains balanced numbers of individuals with and without retinopathy.