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To take advantage of the computing power offered by grid and opportunistic resources, the CERN Large Hadron Collider (LHC) experiments have adopted the Pilot-Job paradigm. In this work, we study the DIRAC Site Director, one of the existing Pilot-Job provisioning solutions, mainly developed and used by the beauty experiment (LHCb). The purpose is to improve the Pilot-Job submission rates and the throughput of the jobs on grid resources. To analyze the DIRAC Site Director mechanisms and assess our contributions, we collected data over 12 months from the LHCbDIRAC instance. We extracted data from the DIRAC databases and the logs. Data include (i) evolution of the number of Pilot-Jobs/jobs over time; (ii) slots available in grid Sites; (iii) number of jobs processed per Pilot-Job.

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In recent years, high-throughput molecular tools have led to an exponential growth of available 16S rRNA gene sequences. Incorporating such data, molecular tools based on target-probe hybridization were developed to monitor microbial communities within complex environments. Unfortunately, only a few 16S rRNA gene-targeted probe collections were described. Here, we present PhylOPDb, an online resource for a comprehensive phylogenetic oligonucleotide probe database. PhylOPDb provides a convivial and easy-to-use web interface to browse both regular and explorative 16S rRNA-targeted probes. Such probes set or subset could be used to globally monitor known and unknown prokaryotic communities through various techniques including DNA microarrays, polymerase chain reaction (PCR), fluorescent in situ hybridization (FISH), targeted gene capture or in silico rapid sequence identification. PhylOPDb contains 74 003 25-mer probes targeting 2178 genera including Bacteria and Archaea. Database URL: http://g2im.u-clermont1.fr/phylopdb/

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Phylogenetic Oligonucleotide Arrays (POAs) were recently adapted for studying the huge microbial communities in a flexible and easy-to-use way. POA coupled with the use of explorative probes to detect the unknown part is now one of the most powerful approaches for a better understanding of microbial community functioning. However, the selection of probes remains a very difficult task. The rapid growth of environmental databases has led to an exponential increase of data to be managed for an efficient design. Consequently, the use of high performance computing facilities is mandatory. In this paper, we present an efficient parallelization method to select known and explorative oligonucleotide probes at large scale using computing grids. We implemented a software that generates and monitors thousands of jobs over the European Computing Grid Infrastructure (EGI). We also developed a new algorithm for the construction of a high-quality curated phylogenetic database to avoid erroneous design due to bad sequence affiliation. We present here the performance and statistics of our method on real biological datasets based on a phylogenetic prokaryotic database at the genus level and a complete design of about 20,000 probes for 2,069 genera of prokaryotes.

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In the context of new metabolic pathways discovery, a full backtranslation of oligopeptides can be a promising approach. When studying complex environments where the composing microorganisms are unknown it is also preferable to have all the complete nucleic sequences corresponding to an enzyme of interest. In this paper, we revisit the existing bioinformatics applications, which bring partial reverse translation solutions, and we compare two algorithms based on oligopeptide degeneracy able to efficiently compute a complete backtranslation of oligopeptides. Such algorithms are precious for the discovery of new organisms and we show their performances on simulated and real biological data sets.

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