MOTIVATION: The use of DNA microarrays allows the monitoring of the extreme microbial diversity encountered in complex samples like environmental ones as well as that of their functional capacities. However, no probe design software currently available is adapted to easily design efficient and explorative probes for functional gene arrays. RESULTS: We present a new efficient functional microarray probe design algorithm called HiSpOD (High Specific Oligo Design). This uses individual nucleic sequences or consensus sequences produced by multiple alignments to design highly specific probes. Indeed, to bypass crucial problem of cross-hybridizations, probe specificity is assessed by similarity search against a large formatted database dedicated to microbial communities containing about 10 million coding sequences (CDS). For experimental validation, a microarray targeting genes encoding enzymes involved in chlorinated solvent biodegradation was built. The results obtained from a contaminated environmental sample proved the specificity and the sensitivity of probes designed with the HiSpOD program. AVAILABILITY: http://fc.isima.fr/~g2im/hispod/.

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.