Assessing and assuring interoperability of a genomics file format.

MOTIVATION: Bioinformatics software tools operate largely through the use of specialized genomics file formats. Often these formats lack formal specification, making it difficult or impossible for the creators of these tools to robustly test them for correct handling of input and output. This causes problems in interoperability between different tools that, at best, wastes time and frustrates users. At worst, interoperability issues could lead to undetected errors in scientific results. RESULTS: We developed a new verification system, Acidbio, which tests for correct behavior in bioinformatics software packages. We crafted tests to unify correct behavior when tools encounter various edge cases-potentially unexpected inputs that exemplify the limits of the format. To analyze the performance of existing software, we tested the input validation of 80 Bioconda packages that parsed the Browser Extensible Data (BED) format. We also used a fuzzing approach to automatically perform additional testing. Of 80 software packages examined, 75 achieved less than 70% correctness on our test suite. We categorized multiple root causes for the poor performance of different types of software. Fuzzing detected other errors that the manually designed test suite could not. We also created a badge system that developers can use to indicate more precisely which BED variants their software accepts and to advertise the software's performance on the test suite. AVAILABILITY AND IMPLEMENTATION: Acidbio is available at https://github.com/hoffmangroup/acidbio. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Segway 2.0: Gaussian mixture models and minibatch training.

Summary: Segway performs semi-automated genome annotation, discovering joint patterns across multiple genomic signal datasets. We discuss a major new version of Segway and highlight its ability to model data with substantially greater accuracy. Major enhancements in Segway 2.0 include the ability to model data with a mixture of Gaussians, enabling capture of arbitrarily complex signal distributions, and minibatch training, leading to better learned parameters. Availability and implementation: Segway and its source code are freely available for download at http://segway.hoffmanlab.org. We have made available scripts (https://doi.org/10.5281/zenodo.802939) and datasets (https://doi.org/10.5281/zenodo.802906) for this paper's analysis. Contact: michael.hoffman@utoronto.ca. Supplementary information: Supplementary data are available at Bioinformatics online.

Technical issues in performing PET studies in pediatric patients.

The purpose of this review is to familiarize the reader with aspects of PET that are important to its performance in pediatric patients. Recognition of differences in applying PET technology to children than to adults should result in higher quality scans in pediatric patients. The reader should be able to recognize key differences in performing PET scans in pediatric patients and to recall basic indications for PET scanning in children. High-quality PET imaging of pediatric patients is challenging and requires consideration of issues common to pediatric nuclear medicine but uncommon to imaging of adult patients. These include intravenous access, sedation, fasting, consent, and clearance of activity from the urinary tract. This article focuses on technical differences involved in pediatric PET compared with adult PET and serves as a guide to enhance the quality of scans and to ensure the safety and comfort of pediatric patients. Upon reading this article, the reader will be familiar with the aspects of PET that pertain to pediatric patients, know how to apply PET imaging techniques to pediatric patients, and know the indications for PET scanning in children.