MOTIVATION: Currently, more than 40 sequence tandem repeat detectors are published, providing heterogeneous, partly complementary, partly conflicting results. RESULTS: We present TRAL, a tandem repeat annotation library that allows running and parsing of various detection outputs, clustering of redundant or overlapping annotations, several statistical frameworks for filtering false positive annotations, and importantly a tandem repeat annotation and refinement module based on circular profile hidden Markov models (cpHMMs). Using TRAL, we evaluated the performance of a multi-step tandem repeat annotation workflow on 547 085 sequences in UniProtKB/Swiss-Prot. The researcher can use these results to predict run-times for specific datasets, and to choose annotation complexity accordingly. AVAILABILITY AND IMPLEMENTATION: TRAL is an open-source Python 3 library and is available, together with documentation and tutorials via http://www.vital-it.ch/software/tral. CONTACT: [email protected].

UNLABELLED: Many time-consuming analyses of next -: generation sequencing data can be addressed with modern cloud computing. The Apache Hadoop-based solutions have become popular in genomics BECAUSE OF: their scalability in a cloud infrastructure. So far, most of these tools have been used for batch data processing rather than interactive data querying. The SparkSeq software has been created to take advantage of a new MapReduce framework, Apache Spark, for next-generation sequencing data. SparkSeq is a general-purpose, flexible and easily extendable library for genomic cloud computing. It can be used to build genomic analysis pipelines in Scala and run them in an interactive way. SparkSeq opens up the possibility of customized ad hoc secondary analyses and iterative machine learning algorithms. This article demonstrates its scalability and overall fast performance by running the analyses of sequencing datasets. Tests of SparkSeq also prove that the use of cache and HDFS block size can be tuned for the optimal performance on multiple worker nodes. AVAILABILITY AND IMPLEMENTATION: Available under open source Apache 2.0 license: https://bitbucket.org/mwiewiorka/sparkseq/.

Software safety certification involves checking that the software design meets the (software) safety requirements. In practice, inspections are one of the primary vehicles for ensuring that safety requirements are satisfied by the design. Unless the safety-related aspects of the design are clearly delineated, the inspections conducted by safety assessors would have to consider the entire design, although only small fragments of the design may be related to safety. In a model-driven development context, this means that the assessors have to browse through large models, understand them, and identify the safety-related fragments. This is time-consuming and error-prone, specially noting that the assessors are often third-party regulatory bodies who were not involved in the design. To address this problem, we describe in this paper a prototype tool called, SafeSlice, that enables one to automatically extract the safety-related slices (fragments) of design models. The main enabler for our slicing technique is the traceability between the safety requirements and the design, established by following a structured design methodology that we propose. Our work is grounded on SysML, which is being increasingly used for expressing the design of safety-critical systems. We have validated our work through two case studies and a control experiment which we briefly outline in the paper.