The increasing accessibility of next-generation sequencing has empowered researchers to investigate somatic mutations in cancer. The complexity of variant analysis pipelines, terminology, and tool selection remains a major barrier, especially for those new to the field or working in translational settings. To address this challenge, we present a practical framework that guides researchers through the critical steps of variant interrogation in tumor samples. This guide is broken into four phases: Planning-laying the foundation for thoughtful experimental design and a clear understanding of sequencing outputs; Gathering Resources-assembling the tools, reference data, and variant annotation sets required for analysis; Filtering and Validation-executing a systematic approach to prioritize meaningful variants; and Dissemination and Storage-ensuring findings are reproducible and accessible through transparent reporting and data sharing. Developed with an emphasis on accessibility, reproducibility, and clinical relevance, this framework equips researchers with the guidance to navigate variant analysis with confidence and rigor.

Because tests are important to the development process, developers need to know when a test suite is missing tests. Missing tests - tests that should be included in a test suite but are not - reduce the utility that developers can derive from a test suite. Currently, developers find missing tests by using coverage information such as line coverage or mutation coverage. However, coverage metrics are limited in their ability to reveal missing tests and show only what code needs to be tested, not how to test it. We present a method for finding missing tests that addresses the shortcomings of coverage metrics based on the fact that similar code entities are often tested in the same way. We are able to find what code is missing tests by identifying code entities which are not tested in the same way as other similar entities. We then show how a code entity with a missing test should be tested by leveraging the tests written for those similar entities. Our results show that our approach offers several benefits over a coverage-based approach and is able to find missing tests in a range of software projects while generating few erroneous identifications of missing tests.

MOTIVATION: To understand the evolution of molecular function within protein families, it is important to identify those amino acid residues responsible for functional divergence; i.e. those sites in a protein family that affect cofactor, protein or substrate binding preferences; affinity; catalysis; flexibility; or folding. Type I functional divergence (FD) results from changes in conservation (evolutionary rate) at a site between protein subfamilies, whereas type II FD occurs when there has been a shift in preferences for different amino acid chemical properties. A variety of methods have been developed for identifying both site types in protein subfamilies, both from phylogenetic and information-theoretic angles. However, evaluation of the performance of these methods has typically relied upon a handful of reasonably well-characterized biological datasets or analyses of a single biological example. While experimental validation of many truly functionally divergent sites (true positives) can be relatively straightforward, determining that particular sites do not contribute to functional divergence (i.e. false positives and true negatives) is much more difficult, resulting in noisy 'gold standard' examples. RESULTS: We describe a novel, phylogeny-based functional divergence classifier, FunDi. Unlike previous approaches, FunDi uses a unified mixture model-based approach to detect type I and type II FD. To assess FunDi's overall classification performance relative to other methods, we introduce two methods for simulating functionally divergent datasets. We find that the FunDi method performs better than several other predictors over a wide variety of simulation conditions. AVAILABILITY: http://rogerlab.biochem.dal.ca/Software CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.