MOTIVATION: Several algorithms exist for detecting copy number variants (CNVs) from human exome sequencing read depth, but previous tools have not been well suited for large population studies on the order of tens or hundreds of thousands of exomes. Their limitations include being difficult to integrate into automated variant-calling pipelines and being ill-suited for detecting common variants. To address these issues, we developed a new algorithm--Copy number estimation using Lattice-Aligned Mixture Models (CLAMMS)--which is highly scalable and suitable for detecting CNVs across the whole allele frequency spectrum. RESULTS: In this note, we summarize the methods and intended use-case of CLAMMS, compare it to previous algorithms and briefly describe results of validation experiments. We evaluate the adherence of CNV calls from CLAMMS and four other algorithms to Mendelian inheritance patterns on a pedigree; we compare calls from CLAMMS and other algorithms to calls from SNP genotyping arrays for a set of 3164 samples; and we use TaqMan quantitative polymerase chain reaction to validate CNVs predicted by CLAMMS at 39 loci (95% of rare variants validate; across 19 common variant loci, the mean precision and recall are 99% and 94%, respectively). In the Supplementary Materials (available at the CLAMMS Github repository), we present our methods and validation results in greater detail. AVAILABILITY AND IMPLEMENTATION: https://github.com/rgcgithub/clamms (implemented in C). CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Memory leaks are caused by software programs that prevent the reclamation of memory that is no longer in use. They can cause significant slowdowns, exhaustion of available storage space and, eventually, application crashes. Detecting memory leaks is challenging because real-world applications are built on multiple layers of software frameworks, making it difficult for a developer to know whether observed references to objects are legitimate or the cause of a leak. We present a graph mining solution to this problem wherein we analyze heap dumps to automatically identify subgraphs which could represent potential memory leak sources. Although heap dumps are commonly analyzed in existing heap profiling tools, our work is the first to apply a graph grammar mining solution to this problem. Unlike classical graph mining work, we show that it suffices to mine the dominator tree of the heap dump, which is significantly smaller than the underlying graph. Our approach identifies not just leaking candidates and their structure, but also provides aggregate information about the access path to the leaks. We demonstrate several synthetic as well as real-world examples of heap dumps for which our approach provides more insight into the problem than state-of-the-art tools such as Eclipse's MAT.