MOTIVATION The processing of k-mers (subsequences of length k) is at the foundation of many sequence processing algorithms in bioinformatics, including k-mer counting for genome size estimation, genome assembly, and taxonomic classification for metagenomics. Minimizers-ordered m-mers where m < k - are often used to group k-mers into bins as a first step in such processing. However, minimizers are known to generate bins of very different sizes, which can pose challenges for distributed and parallel processing, as well as generally increase memory requirements. Furthermore, although various minimizer orderings have been proposed, their practical value for improving tool efficiency has not yet been fully explored. RESULTS We present Discount, a distributed k-mer counting tool based on Apache Spark, which we use to investigate the behaviour of various minimizer orderings in practice when applied to metagenomics data. Using this tool, we then introduce the universal frequency ordering, a new combination of frequencysampled minimizers and universal k-mer hitting sets, which yields both evenly distributed binning and small bin sizes. We show that this ordering allows Discount to perform distributed k-mer counting on a large dataset in as little as 1/8 of the memory of comparable approaches, making it the most efficient out-of-core distributed k-mer counting method available. AVAILABILITY Discount is GPL licensed and available at https://github.com/jtnystrom/discount. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

MOTIVATION: The processing of k-mers (subsequences of length k) is at the foundation of many sequence processing algorithms in bioinformatics, including k-mer counting for genome size estimation, genome assembly, and taxonomic classification for metagenomics. Minimizers-ordered m-mers where m < k-are often used to group k-mers into bins as a first step in such processing. However, minimizers are known to generate bins of very different sizes, which can pose challenges for distributed and parallel processing, as well as generally increase memory requirements. Furthermore, although various minimizer orderings have been proposed, their practical value for improving tool efficiency has not yet been fully explored. RESULTS: We present Discount, a distributed k-mer counting tool based on Apache Spark, which we use to investigate the behaviour of various minimizer orderings in practice when applied to metagenomics data. Using this tool, we then introduce the universal frequency ordering, a new combination of frequency-sampled minimizers and universal k-mer hitting sets, which yields both evenly distributed binning and small bin sizes. We show that this ordering allows Discount to perform distributed k-mer counting on a large dataset in as little as 1/8 of the memory of comparable approaches, making it the most efficient out-of-core distributed k-mer counting method available. AVAILABILITY AND IMPLEMENTATION: Discount is GPL licensed and available at https://github.com/jtnystrom/discount. The data underlying this article are available in the article and in its online supplementary material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: In early stage drug development, it is desirable to assess the toxicity of compounds as quickly as possible. Biomarker genes can help predict whether a candidate drug will adversely affect a given individual, but they are often difficult to discover. In addition, the mechanism of toxicity of many drugs and common compounds is not yet well understood. The Japanese Toxicogenomics Project provides a large database of systematically collected microarray samples from rats (liver, kidney and primary hepatocytes) and human cells (primary hepatocytes) after exposure to 170 different compounds in different dosages and at different time intervals. However, until now, no intuitive user interface has been publically available, making it time consuming and difficult for individual researchers to explore the data. RESULTS: We present Toxygates, a user-friendly integrated analysis platform for this database. Toxygates combines a large microarray dataset with the ability to fetch semantic linked data, such as pathways, compound-protein interactions and orthologs, on demand. It can also perform pattern-based compound ranking with respect to the expression values of a set of relevant candidate genes. By using Toxygates, users can freely interrogate the transcriptome's response to particular compounds and conditions, which enables deep exploration of toxicity mechanisms.

We present a virtualisation and method specialisation framework for Java that facilitates efficient, dynamic modification of the behaviour of object accesses at run time. The technique works by virtualising all method calls and field accesses associated with selected classes so that all corresponding object accesses are via the invocation of a virtual method. Different access behaviours are then supported by allowing arbitrary specialisations of those methods to be defined. The virtualisation overheads are partially recovered by allowing the JVM's optimisation subsystem to perform guarded inlining of specialised methods. We describe an implementation based on the Jikes RVM and show how the framework can be used to implement an 'implicit' readbarrier that supports incremental garbage collection. The performance overhead of full virtualisation, and the performance of the implicit read barrier compared with an existing conventional, explicit barrier, are evaluated using SPEC JVM98 and DaCapo benchmarks. The net virtualisation costs are shown to be remarkably low and the implicit barrier is shown to outperform the explicit barrier substantially in most cases. Other potential applications, including object proxying, caching, and relocation, and instrumentation are also discussed.

We present generic extensions to the GCspy visualisation frame-work that make it suitable for tracking the way continuous dynamic memory allocators such as dlmalloc or incremental and concurrent garbage collectors make use of heap memory. These extensions include sample-driven client-server communication, in-cremental stream updates and client-controlled stream update frequency. Additional extensions to the current GCspy client are also described. These include hierarchical driver grouping and hierarchical visualisation, zooming, and the ability to define and view relationships between tiles in different spaces. We also introduce a heuristics engine that is responsible for flipping GCspy from its decoupled 'observation' mode to a synchronous 'single-step' mode, and describe a backtrace facility that can trace the server-side call sequence that led to the triggering of a specified event, such as the allocation or freeing of a block of memory. This enables aspects of the allocator (fragmentation, block ordering, splitting and coalescing policies, etc.) to be understood in the context of a particular application and potential optimisations to be identified. The effectiveness of the enhanced framework is demonstrated with a complete integration with dlmalloc. The framework is evaluated in terms of both performance and its ability to explore contrived modifications to dlmalloc's coalescing policy.