Deep-sea life requires adaptation to high pressure, an extreme yet common condition given that oceans cover 70% of Earth's surface and have an average depth of 3800 meters. Survival at such depths requires specific adaptation but, compared with other extreme conditions, high pressure has received little attention. Recently, Photobacterium profundum strain SS9 has been adopted as a model for piezophily. Here we report its genome sequence (6.4 megabase pairs) and transcriptome analysis. The results provide a first glimpse into the molecular basis for life in the largest portion of the biosphere, revealing high metabolic versatility.

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The adoption of agent technologies and multi-agent systems constitutes an emerging area in bioinformatics. In this article, we report on the activity of the Working Group on Agents in Bioinformatics (BIOAGENTS) founded during the first AgentLink III Technical Forum meeting on the 2nd of July, 2004, in Rome. The meeting provided an opportunity for seeding collaborations between the agent and bioinformatics communities to develop a different (agent-based) approach of computational frameworks both for data analysis and management in bioinformatics and for systems modelling and simulation in computational and systems biology. The collaborations gave rise to applications and integrated tools that we summarize and discuss in context of the state of the art in this area. We investigate on future challenges and argue that the field should still be explored from many perspectives ranging from bio-conceptual languages for agent-based simulation, to the definition of bio-ontology-based declarative languages to be used by information agents, and to the adoption of agents for computational grids.

We present the Human Muscle Gene Map (HMGM), the first comprehensive and updated high-resolution expression map of human skeletal muscle. The 1078 entries of the map were obtained by merging data retrieved from UniGene with the RH mapping information on 46 novel muscle transcripts, which showed no similarity to any known sequence. In the map, distances are expressed in megabase pairs. About one-quarter of the map entries represents putative novel genes. Genes known to be specifically expressed in muscle account for <4% of the total. The genomic distribution of the map entries confirmed the previous finding that muscle genes are selectively concentrated in chromosomes 17, 19, and X. Five chromosomal regions are suspected to have a significant excess of muscle genes. Present data support the hypothesis that the biochemical and functional properties of differentiated muscle cells may result from the transcription of a very limited number of muscle-specific genes along with the activity of a large number of genes, shared with other tissues, but showing different levels of expression in muscle.[The sequence data described in this paper have been submitted to the EMBL data library under accession nos. F23198–F23242.]

Large-scale parallel measurements of the expression of many thousands genes are now available with high-density array made with collections of cDNA fragments, or oligonucleotide corresponding to different transcripts. These technologies have been applied to cancer investigations since the availability of such a large number of markers makes DNA array a powerful diagnostic tool for tumour and patient classification. Over the last two years, a series of computational tools have been developed for the analysis of different aspects of gene profiling. Our work tries to compare a series of supervised statistical techniques on the basis of their ability to correctly classify different types of tumours. A simulation approach was initially used to control the huge source of variation among and between patients, and to evaluate the ability of algorithms to classify tumours in relation to different types of experimental variables. Different techniques for reduction of data dimension were then added to the discriminant analysis and compared according to their ability to capture the main genetic information. The simulation results have been tested by applying the selected classification algorithms to two experimental microarray datasets of human cancers, and by measuring the correspondent rates of misclassification. Our analyses identify in these datasets a series of genes principally involved in tumour characterization. The functional role of these discriminant transcripts is discussed.