MOTIVATION: Shotgun sequence read data derived from xenograft material contains a mixture of reads arising from the host and reads arising from the graft. Classifying the read mixture to separate the two allows for more precise analysis to be performed. RESULTS: We present a technique, with an associated tool Xenome, which performs fast, accurate and specific classification of xenograft-derived sequence read data. We have evaluated it on RNA-Seq data from human, mouse and human-in-mouse xenograft datasets. AVAILABILITY: Xenome is available for non-commercial use from http://www.nicta.com.au/bioinformatics.

MOTIVATION: The de novo assembly of short read high-throughput sequencing data poses significant computational challenges. The volume of data is huge; the reads are tiny compared to the underlying sequence, and there are significant numbers of sequencing errors. There are numerous software packages that allow users to assemble short reads, but most are either limited to relatively small genomes (e.g. bacteria) or require large computing infrastructure or employ greedy algorithms and thus often do not yield high-quality results. RESULTS: We have developed Gossamer, an implementation of the de Bruijn approach to assembly that requires close to the theoretical minimum of memory, but still allows efficient processing. Our results show that it is space efficient and produces high-quality assemblies. AVAILABILITY: Gossamer is available for non-commercial use from http://www.genomics.csse.unimelb.edu.au/product-gossamer.php.

Abstract C-Rules is a business rules management system developed by Constraint Technologies International ( www.constrainttechnologies.com ) that is designed for use in transport, travel and logistics problems. Individual businesses within these industries often need to solve the same kinds of problems related to scheduling of operations, resource allocation, staff rostering and so on, but each organisation has its own rules and procedures. Furthermore, these problems tend to be combinatorially challenging: before a final solution is chosen, many potential choices need to be generated, evaluated and compared. In C-Rules, users define rules that describe various aspects of a problem. These rules can be invoked from an application, which is typically either an optimising solver or an interactive planning tool. Rules can be used to encode evaluation criteria, such as the legality or cost of a proposed solution, or values like configuration parameters that may be used by the host application to tune or direct its progress. At its core, C-Rules provides a functional expression language that affords users power and flexibility when formulating rules. In this paper we will describe our experiences of using functional programming both at the end-user level and at the implementation level. We highlight some of the benefits of basing our rule system on features such as higher-order functions, referential transparency and static, polymorphic typing. We also outline some of our experiences in using Haskell to build an efficient compiler for the core language.

C-Rules is a business rules management system developed by Constraint Technologies International1 (CTI), designed for use in transportation problems. Users define rules describing various aspects of a problem, such as solution costs and legality, which are then queried from a host application, typically an optimising solver. At its core, C-Rules provides a functional expression language which affords users both power and flexibility when formulating rules. In this paper we will describe our experiences of using functional programming both at the end-user level, as well as at the implementation level. We highlight some of the benefits we, and the product's users, have enjoyed from the decision to base our rule system on features such as: higher-order functions, referential transparency, and static, polymorphic typing. We also outline some of our experiences in using Haskell to build an efficient compiler for the core language.

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Proceedings of the 2003 ACM SIGPLAN Haskell Workshop

An unsatisfiable set of constraints is minimal if all its (strict) subsets aresatisfiable.A number of forms of error diagnosis, including circuit error diagnosis and type error diagnosis, require finding all minimal unsatisfiable subsets of a given set of constraints (representing an error), in order to generate the best explanation of the error. In this paper we give algorithms for efficiently determining all minimal unsatisfiable subsets for any kind of constraints. We show how taking into account notions of independence of constraints and using incremental constraint solvers can significantly improve the calculation of these subsets.