UNLABELLED: Supervised classification based on support vector machines (SVMs) has successfully been used for the prediction of cis-regulatory modules (CRMs). However, no integrated tool using such heterogeneous data as position-specific scoring matrices, ChIP-seq data or conservation scores is currently available. Here, we present LedPred, a flexible SVM workflow that predicts new regulatory sequences based on the annotation of known CRMs, which are associated to a large variety of feature types. LedPred is provided as an R/Bioconductor package connected to an online server to avoid installation of non-R software. Due to the heterogeneous CRM feature integration, LedPred excels at the prediction of regulatory sequences in Drosophila and mouse datasets compared with similar SVM-based software. AVAILABILITY AND IMPLEMENTATION: LedPred is available on GitHub: https://github.com/aitgon/LedPred and Bioconductor: http://bioconductor.org/packages/release/bioc/html/LedPred.html under the MIT license. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Given the complexity of developmental networks, it is often difficult to predict the effect of genetic perturbations, even within coding genes. Regulatory factors generally have pleiotropic effects, exhibit partially redundant roles, and regulate highly interconnected pathways with ample cross-talk. Here, we delineate a logical model encompassing 48 components and 82 regulatory interactions involved in mesoderm specification during Drosophila development, thereby providing a formal integration of all available genetic information from the literature. The four main tissues derived from mesoderm correspond to alternative stable states. We demonstrate that the model can predict known mutant phenotypes and use it to systematically predict the effects of over 300 new, often non-intuitive, loss- and gain-of-function mutations, and combinations thereof. We further validated several novel predictions experimentally, thereby demonstrating the robustness of model. Logical modelling can thus contribute to formally explain and predict regulatory outcomes underlying cell fate decisions.

This paper reports a study that estimated visitor positions, visiting patterns, and inter-human relationships at a science museum using information from RFID readers. In the science museum, we exhibited humanoid robots. Visitors were invited to wear RFID tags to interact with the robots. Visitor behavior was simultaneously observed using 20 RFID readers, distributed throughout the entire floor, that roughly measured the distances of nearby tags. We integrated the outputs from all RFID readers to estimate visitor trajectories that were used to analyze three perspectives: space, visiting patterns, and relationships. Regarding space, we identified crowded and uncrowded areas. We found several typical visiting patterns, such as visited at every exhibit and directly going to robot area. We also identified atypical visiting behavior. Regarding relationships, for example, we estimated 68% coverage of group-member relationships with 91% reliability.