When studying complex environments where the composing microorganisms are unknown, exploratory tools able to tackle with the biological diversity have to be proposed. DNA microarrays can be a good answer if we are in a position to propose all the probes that could target a specific enzyme. In addition, in the context of new metabolic pathways discovery, it appears that a full backtranslation of oligopeptides is also a promising approach. In both contexts it is preferable to have all the complete nucleic sequences corresponding to an enzyme of interest. In this paper, we revisit existing bioinformatics applications, which bring partial reverse translation solutions, and we propose an algorithm based on input oligopeptide degeneracy able to efficiently compute a complete backtranslation of oligopeptides. This algorithm is precious for the discovery of new organisms and we show its performance on simulated and real biological datasets.

MOTIVATION: Microbial diversity is still largely unknown in most environments, such as soils. In order to get access to this microbial 'black-box', the development of powerful tools such as microarrays are necessary. However, the reliability of this approach relies on probe efficiency, in particular sensitivity, specificity and explorative power, in order to obtain an image of the microbial communities that is close to reality. RESULTS: We propose a new probe design algorithm that is able to select microarray probes targeting SSU rRNA at any phylogenetic level. This original approach, implemented in a program called 'PhylArray', designs a combination of degenerate and non-degenerate probes for each target taxon. Comparative experimental evaluations indicate that probes designed with PhylArray yield a higher sensitivity and specificity than those designed by conventional approaches. Applying the combined PhyArray/GoArrays strategy helps to optimize the hybridization performance of short probes. Finally, hybridizations with environmental targets have shown that the use of the PhylArray strategy can draw attention to even previously unknown bacteria.

MOTIVATION: The use of oligonucleotide microarray technology requires a very detailed attention to the design of specific probes spotted on the solid phase. These problems are far from being commonplace since they refer to complex physicochemical constraints. Whereas there are more and more publicly available programs for microarray oligonucleotide design, most of them use the same algorithm or criteria to design oligos, with only little variation. RESULTS: We show that classical approaches used in oligo design software may be inefficient under certain experimental conditions, especially when dealing with complex target mixtures. Indeed, our biological model is a human obligate parasite, the microsporidia Encephalitozoon cuniculi. Targets that are extracted from biological samples are composed of a mixture of pathogen transcripts and host cell transcripts. We propose a new approach to design oligonucleotides which combines good specificity with a potentially high sensitivity. This approach is original in the biological point of view as well as in the algorithmic point of view. We also present an experimental validation of this new strategy by comparing results obtained with standard oligos and with our composite oligos. A specific E.cuniculi microarray will overcome the difficulty to discriminate the parasite mRNAs from the host cell mRNAs demonstrating the power of the microarray approach to elucidate the lifestyle of an intracellular pathogen using mix mRNAs.