Summary Complete backtranslation is the step of generating all possible nucleic acid sequences from a protein sequence. This is a time‐consuming task that can provide unreasonable quantities of data. Complete backtranslation was recently used to initiate probe design for functional DNA microarrays from conserved peptidic regions, in order to assess the full microbial gene diversity present in complex environments. In this article, we present an efficient parallelization method to compute a complete backtranslation of short peptides to select probes for functional microarrays. We implemented a software that uses meta‐programming and a model‐driven engineering approach to automatically generate source codes to perform complete backtranslation on different architectures: PCs, Symmetric Multiprocessors servers, computing clusters, or a computing grid. Our software is filtering the generated oligonucleotides with usual selection criteria for the design of microarray probes. It uses load balancing and can be easily integrated in probe design software for functional microarrays. We present its performance on both simulated and real biological datasets. The obtained results show a significant computing speedup on different platforms and an important gain of about 40% of disk space when filtering oligonucleotides. Copyright © 2014 John Wiley & Sons, Ltd.

The selection of oligonucleotide probes for micro arrays is still very difficult task. With the rapid growth of environmental databases (metagenomics programs coupled to next generation sequencing), the computational capacity requirements of probe design algorithms have hugely increased. The use of parallel and distributed architectures can considerably reduce the complexity and the computational time of these algorithms. In this paper we present a new efficient algorithm of oligonucleotide probe selection for an individual specific nucleic acid sequence or a group of sequences. We used a model driven engineering approach to simultaneous design of thousands of sensitive, specific, isothermal and explorative probes, on both PC, multiprocessor, cluster and grid computing with on the one hand a significant computing speedup and on the other hand an improved quality of the resulting probes when compared to equivalent software.

SUMMARY: KASpOD is a web service dedicated to the design of signature sequences using a k-mer-based algorithm. Such highly specific and explorative oligonucleotides are then suitable for various goals, including Phylogenetic Oligonucleotide Arrays. AVAILABILITY: http://g2im.u-clermont1.fr/kaspod. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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/.

BACKGROUND: Microorganisms display vast diversity, and each one has its own set of genes, cell components and metabolic reactions. To assess their huge unexploited metabolic potential in different ecosystems, we need high throughput tools, such as functional microarrays, that allow the simultaneous analysis of thousands of genes. However, most classical functional microarrays use specific probes that monitor only known sequences, and so fail to cover the full microbial gene diversity present in complex environments. We have thus developed an algorithm, implemented in the user-friendly program Metabolic Design, to design efficient explorative probes. RESULTS: First we have validated our approach by studying eight enzymes involved in the degradation of polycyclic aromatic hydrocarbons from the model strain Sphingomonas paucimobilis sp. EPA505 using a designed microarray of 8,048 probes. As expected, microarray assays identified the targeted set of genes induced during biodegradation kinetics experiments with various pollutants. We have then confirmed the identity of these new genes by sequencing, and corroborated the quantitative discrimination of our microarray by quantitative real-time PCR. Finally, we have assessed metabolic capacities of microbial communities in soil contaminated with aromatic hydrocarbons. Results show that our probe design (sensitivity and explorative quality) can be used to study a complex environment efficiently. CONCLUSIONS: We successfully use our microarray to detect gene expression encoding enzymes involved in polycyclic aromatic hydrocarbon degradation for the model strain. In addition, DNA microarray experiments performed on soil polluted by organic pollutants without prior sequence assumptions demonstrate high specificity and sensitivity for gene detection. Metabolic Design is thus a powerful, efficient tool that can be used to design explorative probes and monitor metabolic pathways in complex environments, and it may also be used to study any group of genes. The Metabolic Design software is freely available from the authors and can be downloaded and modified under general public license.

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.