Implicit certificates are gaining ever more prominence in constrained embedded devices, in both the internet of things (IoT) and automotive domains. They present a resource-efficient security solution against common threat concerns. The computational requirements are not the main issue anymore, with the focus now shifting to determining a good balance between the provided security level and the derived threat model. A security aspect that often gets overlooked is the establishment of secure communication sessions, as most design solutions are based only on the use of static key derivation, and therefore lack the perfect forward secrecy. This leaves the transmitted data open for potential future exposures as keys are tied to the certificates rather than the communication sessions. We aim to close this gap and present a design that utilizes the Station to Station (STS) protocol with implicit certificates. In addition, we propose potential protocol optimization implementation steps and run a comprehensive study on the performance and security level between the proposed design and the state-of-the-art key derivation protocols. In our comparative study, we show that we are able to mitigate many session-related security vulnerabilities that would otherwise remain open with only a slight computational increase of 20% compared to a static elliptic curve digital signature algorithm (ECDSA) key derivation.

The growing awareness of environmental sustain-ability has led to new investments in the field of electric vehicles. One of the most expensive and important components of electric vehicles are their batteries, with battery management systems (BMS) being responsible for their control. New regulations, such as those of the European Union, aim to introduce battery passports as a way to track battery lifecycle from manufacturing, over second-life use, to recycling. Given the vast amount of data generated during the lifecycle of a battery, the current research is focused on combining BMS with cloud connectivity. However, not much research has yet been done in the area of BMS cloud security and secure data logging. To address this gap, we propose a novel solution for secure BMS data acquisition for on-premise and cloud environments. In this paper, we make two main contributions: a secure data structure for BMS logging and a secure architecture for transferring BMS data from its source to cloud and end systems. We demonstrate the feasibility of the design by developing a prototype with real components and evaluate it in terms of security and performance.

Motivation: Estimating the abundance of transposable elements (TEs) in populations (or tissues) promises to answer many open research questions. However, progress is hampered by the lack of concordance between different approaches for TE identification and thus potentially unreliable results. Results: To address this problem, we developed SimulaTE a tool that generates TE landscapes for populations using a newly developed domain specific language (DSL). The simple syntax of our DSL allows for easily building even complex TE landscapes that have, for example, nested, truncated and highly diverged TE insertions. Reads may be simulated for the populations using different sequencing technologies (PacBio, Illumina paired-ends) and strategies (sequencing individuals and pooled populations). The comparison between the expected (i.e. simulated) and the observed results will guide researchers in finding the most suitable approach for a particular research question. Availability and implementation: SimulaTE is implemented in Python and available at https://sourceforge.net/projects/simulates/. Manual https://sourceforge.net/p/simulates/wiki/Home/#manual; Test data and tutorials https://sourceforge.net/p/simulates/wiki/Home/#walkthrough; Validation https://sourceforge.net/p/simulates/wiki/Home/#validation. Contact: [email protected].

Bioinformatics (2017) DOI: https://doi.org/10.1093/bioinformatics/btx772 The author of the above paper wishes to inform readers that the initials ‘RK’ were erroneously added to the author’s name. The paper has now been corrected online.

Evolve and Resequencing (E&R) studies allow us to monitor adaptation at the genomic level. By sequencing evolving populations at regular time intervals, E&R studies promise to shed light on some of the major open questions in evolutionary biology such as the repeatability of evolution and the molecular basis of adaptation. However, data interpretation, statistical analysis and the experimental design of E&R studies increasingly require simulations of evolving populations, a task that is difficult to accomplish with existing tools, which may i) be too slow, ii) require substantial reformatting of data, iii) not support an adaptive scenario of interest or iv) not sufficiently capture the biology of the used model organism. Therefore we developed MimicrEE2, a multi-threaded Java program for genome-wide forward simulations of evolving populations. MimicrEE2 enables the convenient usage of available genomic resources, supports biological particulars of model organism frequently used in E&R studies and offers a wide range of different adaptive models (selective sweeps, polygenic adaptation, epistasis). Due to its user-friendly and efficient design MimicrEE2 will facilitate simulations of E&R studies even for small labs with limited bioinformatics expertise or computational resources. Additionally, the scripts provided for executing MimicrEE2 on a computer cluster permit the coverage even of a large parameter space. MimicrEE2 runs on any computer with Java installed. It is distributed under the GPLv3 license at https://sourceforge.net/projects/mimicree2/.

MOTIVATION: Recent advances in high-throughput sequencing (HTS) have made it possible to monitor genomes in great detail. New experiments not only use HTS to measure genomic features at one time point but also monitor them changing over time with the aim of identifying significant changes in their abundance. In population genetics, for example, allele frequencies are monitored over time to detect significant frequency changes that indicate selection pressures. Previous attempts at analyzing data from HTS experiments have been limited as they could not simultaneously include data at intermediate time points, replicate experiments and sources of uncertainty specific to HTS such as sequencing depth. RESULTS: We present the beta-binomial Gaussian process model for ranking features with significant non-random variation in abundance over time. The features are assumed to represent proportions, such as proportion of an alternative allele in a population. We use the beta-binomial model to capture the uncertainty arising from finite sequencing depth and combine it with a Gaussian process model over the time series. In simulations that mimic the features of experimental evolution data, the proposed method clearly outperforms classical testing in average precision of finding selected alleles. We also present simulations exploring different experimental design choices and results on real data from Drosophila experimental evolution experiment in temperature adaptation. AVAILABILITY AND IMPLEMENTATION: R software implementing the test is available at https://github.com/handetopa/BBGP.

SUMMARY: An analysis of gene set [e.g. Gene Ontology (GO)] enrichment assumes that all genes are sampled independently from each other with the same probability. These assumptions are violated in genome-wide association (GWA) studies since (i) longer genes typically have more single-nucleotide polymorphisms resulting in a higher probability of being sampled and (ii) overlapping genes are sampled in clusters. Herein, we introduce Gowinda, a software specifically designed to test for enrichment of gene sets in GWA studies. We show that GO tests on GWA data could result in a substantial number of false-positive GO terms. Permutation tests implemented in Gowinda eliminate these biases, but maintain sufficient power to detect enrichment of GO terms. Since sufficient resolution for large datasets requires millions of permutations, we use multi-threading to keep computation times reasonable. AVAILABILITY AND IMPLEMENTATION: Gowinda is implemented in Java (v1.6) and freely available on http://code.google.com/p/gowinda/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Manual: http://code.google.com/p/gowinda/wiki/Manual. Test data and tutorial: http://code.google.com/p/gowinda/wiki/Tutorial. VALIDATION: http://code.google.com/p/gowinda/wiki/VALIDATION.

SUMMARY: Sequencing pooled DNA samples (Pool-Seq) is the most cost-effective approach for the genome-wide comparison of population samples. Here, we introduce PoPoolation2, the first software tool specifically designed for the comparison of populations with Pool-Seq data. PoPoolation2 implements a range of commonly used measures of differentiation (F(ST), Fisher's exact test and Cochran-Mantel-Haenszel test) that can be applied on different scales (windows, genes, exons, SNPs). The result may be visualized with the widely used Integrated Genomics Viewer. AVAILABILITY AND IMPLEMENTATION: PoPoolation2 is implemented in Perl and R. It is freely available on http://code.google.com/p/popoolation2/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Manual: http://code.google.com/p/popoolation2/wiki/Manual Test data and tutorial: http://code.google.com/p/popoolation2/wiki/Tutorial Validation: http://code.google.com/p/popoolation2/wiki/Validation.

BACKGROUND: Next generation sequencing technologies hold great potential for many biological questions. While mainly used for genomic sequencing, they are also very promising for gene expression profiling. Sequencing of cDNA does not only provide an estimate of the absolute expression level, it can also be used for the identification of allele specific gene expression. RESULTS: We developed PanGEA, a tool which enables a fast and user-friendly analysis of allele specific gene expression using the 454 technology. PanGEA allows mapping of 454-ESTs to genes or whole genomes, displaying gene expression profiles, identification of SNPs and the quantification of allele specific gene expression. The intuitive GUI of PanGEA facilitates a flexible and interactive analysis of the data. PanGEA additionally implements a modification of the Smith-Waterman algorithm which deals with incorrect estimates of homopolymer length as occuring in the 454 technology CONCLUSION: To our knowledge, PanGEA is the first tool which facilitates the identification of allele specific gene expression. PanGEA is distributed under the Mozilla Public License and available at: http://www.kofler.or.at/bioinformatics/PanGEA

UNLABELLED: SciRoKo is a user-friendly software tool for the identification of microsatellites in genomic sequences. The combination of an extremely fast search algorithm with a built-in summary statistic tool makes SciRoKo an excellent tool for full genome analysis. Compared to other already existing tools, SciRoKo also allows the analysis of compound microsatellites. AVAILABILITY: free for use: www.kofler.or.at/Bioinformatics. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.