Rapid molecular typing of bacterial pathogens is critical for public health epidemiology, surveillance and infection control, yet routine use of whole genome sequencing (WGS) for these purposes poses significant challenges. Here we present SRST2, a read mapping-based tool for fast and accurate detection of genes, alleles and multi-locus sequence types (MLST) from WGS data. Using >900 genomes from common pathogens, we show SRST2 is highly accurate and outperforms assembly-based methods in terms of both gene detection and allele assignment. We include validation of SRST2 within a public health laboratory, and demonstrate its use for microbial genome surveillance in the hospital setting. In the face of rising threats of antimicrobial resistance and emerging virulence among bacterial pathogens, SRST2 represents a powerful tool for rapidly extracting clinically useful information from raw WGS data.Source code is available from http://katholt.github.io/srst2/.

The myelination of axons is a crucial step during vertebrate central nervous system (CNS) development, allowing for rapid and energy efficient saltatory conduction of nerve impulses. Accordingly, the differentiation of oligodendrocytes, the myelinating cells of the CNS, and their expression of myelin genes are under tight transcriptional control. We previously identified a putative transcription factor, Myelin Regulatory Factor (Myrf), as being vital for CNS myelination. Myrf is required for the generation of CNS myelination during development and also for its maintenance in the adult. It has been controversial, however, whether Myrf directly regulates transcription, with reports of a transmembrane domain and lack of nuclear localization. Here we show that Myrf is a membrane-associated transcription factor that undergoes an activating proteolytic cleavage to separate its transmembrane domain-containing C-terminal region from a nuclear-targeted N-terminal region. Unexpectedly, this cleavage event occurs via a protein domain related to the autoproteolytic intramolecular chaperone domain of the bacteriophage tail spike proteins, the first time this domain has been found to play a role in eukaryotic proteins. Using ChIP-Seq we show that the N-terminal cleavage product directly binds the enhancer regions of oligodendrocyte-specific and myelin genes. This binding occurs via a defined DNA-binding consensus sequence and strongly promotes the expression of target genes. These findings identify Myrf as a novel example of a membrane-associated transcription factor and provide a direct molecular mechanism for its regulation of oligodendrocyte differentiation and CNS myelination. Oligodendrocytes are a highly specialized cell type that surround axons of the vertebrate central nervous system with myelin, electrically insulating them and allowing rapid and energy-efficient propagation of nerve signals. We previously identified a protein, MYRF, that is required for the final stages of oligodendrocyte differentiation and myelination. Although we proposed that MYRF might act as a transcription factor, it remains uncertain whether this is true, given that MYRF and related proteins contain a transmembrane domain that might preclude localization to the nucleus. Here, we show that the MYRF protein undergoes an activating cleavage event to release the functional transcription factor from the transmembrane domain that otherwise anchors it to the endoplasmic reticulum. Unexpectedly, this cleavage event is mediated by a portion of MYRF that is related to a self-cleaving domain found in bacteriophage proteins. This distinguishes it from other membrane-associated transcription factors that are cleaved via regulated proteolysis within the membrane bilayer. We find that the N-terminal product of MYRF cleavage directly binds to a wide range of genes involved in myelination, stimulating their expression. Many of these MYRF binding sites identify previously uncharacterized enhancers for these myelin genes.

Summary: Bpipe is a simple, dedicated programming language for defining and executing bioinformatics pipelines. It specializes in enabling users to turn existing pipelines based on shell scripts or command line tools into highly flexible, adaptable and maintainable workflows with a minimum of effort. Bpipe ensures that pipelines execute in a controlled and repeatable fashion and keeps audit trails and logs to ensure that experimental results are reproducible. Requiring only Java as a dependency, Bpipe is fully self-contained and cross-platform, making it very easy to adopt and deploy into existing environments. Availability and implementation: Bpipe is freely available from http://bpipe.org under a BSD License. Contact:simon.sadedin@mcri.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.

Scientific research relies on computer software, yet software is not always developed following practices that ensure its quality and sustainability. This manuscript does not aim to propose new software development best practices, but rather to provide simple recommendations that encourage the adoption of existing best practices. Software development best practices promote better quality software, and better quality software improves the reproducibility and reusability of research. These recommendations are designed around Open Source values, and provide practical suggestions that contribute to making research software and its source code more discoverable, reusable and transparent. This manuscript is aimed at developers, but also at organisations, projects, journals and funders that can increase the quality and sustainability of research software by encouraging the adoption of these recommendations.

Genetic variant effect prediction algorithms are used extensively in clinical genomics and research to determine the likely consequences of amino acid substitutions on protein function. It is vital that we better understand their accuracies and limitations because published performance metrics are confounded by serious problems of circularity and error propagation. Here, we derive three independent, functionally determined human mutation datasets, UniFun, BRCA1-DMS and TP53-TA, and employ them, alongside previously described datasets, to assess the pre-eminent variant effect prediction tools. Apparent accuracies of variant effect prediction tools were influenced significantly by the benchmarking dataset. Benchmarking with the assay-determined datasets UniFun and BRCA1-DMS yielded areas under the receiver operating characteristic curves in the modest ranges of 0.52 to 0.63 and 0.54 to 0.75, respectively, considerably lower than observed for other, potentially more conflicted datasets. These results raise concerns about how such algorithms should be employed, particularly in a clinical setting. Contemporary variant effect prediction tools are unlikely to be as accurate at the general prediction of functional impacts on proteins as reported prior. Use of functional assay-based datasets that avoid prior dependencies promises to be valuable for the ongoing development and accurate benchmarking of such tools.