Briefings in Bioinformatics Advance Access originally published online on December 6, 2006
Briefings in Bioinformatics 2007 8(1):1; doi:10.1093/bib/bbl046
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Editorial
We wish readers a prosperous year and remind you that the journal, in addition to the Advance Access electronic edition, will be published every two months so there will be six issues in 2007 instead of the four in 2006. In this first issue for 2007, we have articles about gene nomenclature, three aspects of gene expression studies and computing agents.Nothing in biology can be understood except in the light of evolution. Thus, it is not only a practical science, which gathers and interprets experimental data, but also a historical discipline in which evolutionary models, data and hypotheses have to be evaluated, hopefully by statistical inference. This leaves a lot of room for individual methodology and choice of models which may not even be explicitly formulated. By taking evidence from a variety of sources and combining them, the HCOP database is designed to increase our confidence that the history of genes has been correctly inferred. The orthology assertion is a statement that asserts that a certain premise about evolutionary relationships is true, and is useful for numerous downstream deductions. Some criteria are considered stronger evidence than others. For example, functional complementation is stronger evidence than nucleotide sequence similarity. Multiple criteria for specific orthologies generally imply stronger evidence. Conserved map location may be a circular argument for orthology.
Transcriptomics, that is gene expression studies, is probably still the most active area in molecular biology, although we can expect proteomics and metabolomics to catch up. Nucleotide sequence data now amount to over 100 Gigabases, of which a large part consists of transcripts. Nagaraj, Gasser and Ranganathan provide a contemporary look at the value of EST analysis and the methods that are available. Hsu et al. emphasize the importance of careful experimental design in order to robustly estimate transcript levels and provide tests of significance. Boulesteix and Strimmer suggest that the method of partial least squares (PLS) provides a very powerful tool for further analyses of these data.
Agent-based computing may seem futuristic and hard to grasp amongst a plethora of complex software solutions. Luck and McBurney [1] provide a succinct definition as follows: "Agents can be defined as autonomous, problem-solving computational entities capable of effective operation in dynamic and open environments. Agents are often deployed in environments in which they interact, and sometimes cooperate, with other agents (including both people and software) that have possibly conflicting aims. Such environments are known as multi-agent systems." They point out that research in object-orientated technologies began some 30 years before Java became main stream. Since agent technology is still young, the field lacks proven methodologies, tools, and complementary products and services, the availability of which would act to reduce the costs and risks associated with it. The application domains for which agent technologies are best suited typically require coordination and collaboration between multiple organizations, a factor that complicates adoption decisions by the companies or organizations involved. This is as true in the bioinformatics area as in any other field of endeavor.
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Reference
- Luck M, McBurney P. Agent-based computing for next generation applications. 2005 ITNOW, September, British Computer Society.
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