BACKGROUND: Computer languages can be domain-related, and in the case of multidisciplinary projects, knowledge of several languages will be needed in order to quickly implements ideas. Moreover, each computer language has relative strong points, making some languages better suited than others for a given task to be implemented. The Bioconductor project, based on the R language, has become a reference for the numerical processing and statistical analysis of data coming from high-throughput biological assays, providing a rich selection of methods and algorithms to the research community. At the same time, Python has matured as a rich and reliable language for the agile development of prototypes or final implementations, as well as for handling large data sets. RESULTS: The data structures and functions from Bioconductor can be exposed to Python as a regular library. This allows a fully transparent and native use of Bioconductor from Python, without one having to know the R language and with only a small community of translators required to know both. To demonstrate this, we have implemented such Python representations for key infrastructure packages in Bioconductor, letting a Python programmer handle annotation data, microarray data, and next-generation sequencing data. CONCLUSIONS: Bioconductor is now not solely reserved to R users. Building a Python application using Bioconductor functionality can be done just like if Bioconductor was a Python package. Moreover, similar principles can be applied to other languages and libraries. Our Python package is available at: http://pypi.python.org/pypi/rpy2-bioconductor-extensions/.

UNLABELLED: Gene expression index calculations from Affymetrix GeneChips have been dominated by the Affymetrix MAS, dChip, and RMA methods. A new method to estimate the gene expression value utilizing the probe sequence information named position-dependent nearest-neighbor (PDNN) has been suggested by Zhang et al. (2003). Here we describe an open source implementation of the PDNN method for the statistical language R. AVAILABILITY: The package can be downloaded from http://www.bioconductor.org/repository/devel/package/html/affypdnn.html CONTACT: [email protected].

MOTIVATION: The processing of the Affymetrix GeneChip data has been a recent focus for data analysts. Alternatives to the original procedure have been proposed and some of these new methods are widely used. RESULTS: The affy package is an R package of functions and classes for the analysis of oligonucleotide arrays manufactured by Affymetrix. The package is currently in its second release, affy provides the user with extreme flexibility when carrying out an analysis and make it possible to access and manipulate probe intensity data. In this paper, we present the main classes and functions in the package and demonstrate how they can be used to process probe-level data. We also demonstrate the importance of probe-level analysis when using the Affymetrix GeneChip platform.

BACKGROUND: Short oligonucleotide arrays have several probes measuring the expression level of each target transcript. Therefore the selection of probes is a key component for the quality of measurements. However, once probes have been selected and synthesized on an array, it is still possible to re-evaluate the results using an updated mapping of probes to genes, taking into account the latest biological knowledge available. METHODS: We investigated how probes found on recent commercial microarrays for human genes (Affymetrix HG-U133A) were matching a recent curated collection of human transcripts: the NCBI RefSeq database. We also built mappings and used them in place of the original probe to genes associations provided by the manufacturer of the arrays. RESULTS: In a large number of cases, 36%, the probes matching a reference sequence were consistent with the grouping of probes by the manufacturer of the chips. For the remaining cases there were discrepancies and we show how that can affect the analysis of data. CONCLUSIONS: While the probes on Affymetrix arrays remain the same for several years, the biological knowledge concerning the genomic sequences evolves rapidly. Using up-to-date knowledge can apparently change the outcome of an analysis.

This paper reports on the design and the evaluation of transmission control mechanisms specifically designed for multiplayer, distributed (serverless), interactive Internet applications. Distributed synchronization and dead reckoning are the main elements of this transmission control infrastructure. These mechanisms have been implemented in a fully distributed, multiplayer game application, i.e., one in which each entity in a game session computes its own local view of the session. The role of each entity is consequently to periodically send its own state to all other session participants (using RTP/UDP/IP multicast) and to periodically compute its own local view of the global game state using information received from the other participants. A detailed experimental analysis is provided using MBone and LAN experiments. We investigate how the "quality" of the game is influenced by the frequency at which players exchange state information, as well as by network impairments such as packet loss and transmission delay.

This paper describes the design and the prototyping of a compiling tool for the automated implementation of distributed applications: ALFred. This compiler starts from the formal specification of an application written in ESTEREL and then integrates end-to-end communication functions tailored to the application characteristics (described in the specification); it finally produces a high performance implementation. The paper describes the communication architecture associated with the approach. The compiler consists of a control compiler, also called ALF compiler, and a data manipulation compiler (the ILP compiler) that combines data manipulation functions in an efficient way (the ILP loop). The ALFred compiler has been designed to allow the development and the analysis of non-layered high performance communication architectures based on ALF and ILP.