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The SWISS-PROT protein knowledgebase (http://www.expasy.org/sprot/ and http://www.ebi.ac.uk/swissprot/) connects amino acid sequences with the current knowledge in the Life Sciences. Each protein entry provides an interdisciplinary overview of relevant information by bringing together experimental results, computed features and sometimes even contradictory conclusions. Detailed expertise that goes beyond the scope of SWISS-PROT is made available via direct links to specialised databases. SWISS-PROT provides annotated entries for all species, but concentrates on the annotation of entries from human (the HPI project) and other model organisms to ensure the presence of high quality annotation for representative members of all protein families. Part of the annotation can be transferred to other family members, as is already done for microbes by the High-quality Automated and Manual Annotation of microbial Proteomes (HAMAP) project. Protein families and groups of proteins are regularly reviewed to keep up with current scientific findings. Complementarily, TrEMBL strives to comprise all protein sequences that are not yet represented in SWISS-PROT, by incorporating a perpetually increasing level of mostly automated annotation. Researchers are welcome to contribute their knowledge to the scientific community by submitting relevant findings to SWISS-PROT at swiss-prot@expasy.org

Genomic projects heavily depend on genome annotations and are limited by the current deficiencies in the published predictions of gene structure and function. It follows that, improved annotation will allow better data mining of genomes, and more secure planning and design of experiments. The purpose of the GeneFarm project is to obtain homogeneous, reliable, documented and traceable annotations for Arabidopsis nuclear genes and gene products, and to enter them into an added-value database. This re-annotation project is being performed exhaustively on every member of each gene family. Performing a family-wide annotation makes the task easier and more efficient than a gene-by-gene approach since many features obtained for one gene can be extrapolated to some or all the other genes of a family. A complete annotation procedure based on the most efficient prediction tools available is being used by 16 partner laboratories, each contributing annotated families from its field of expertise. A database, named GeneFarm, and an associated user-friendly interface to query the annotations have been developed. More than 3000 genes distributed over 300 families have been annotated and are available at http://genoplante-info.infobiogen.fr/Genefarm/. Furthermore, collaboration with the Swiss Institute of Bioinformatics is underway to integrate the GeneFarm data into the protein knowledgebase Swiss-Prot

[Conférence. 2007-01-10], Collection : Grands thèmes et grandes figures littéraires, Collection : Grands thèmes et grandes figures littéraires, Collection : Conférences de la Bibliothèque nationale de France, Collection : Conférences de la Bibliothèque nationale de France, Enregistrement : (France) Paris, BnF, 10-01-2007, Durée : 01:53:49, Thème : Littérature

We present in this paper a generic object-oriented benchmark (OCB: the Object Clustering Benchmark) that has been designed to evaluate the performances of Object-Oriented Data-bases (OODBs), and more specifically the performances of clustering policies within OODBs. OCB is generic because its sample database may be customized to fit any of the databases in-troduced by the main existing benchmarks, e.g., OO1 (Object Operation 1) or OO7. The first version of OCB was purposely clustering-oriented due to a clustering-oriented workload, but OCB has been thoroughly extended to be able to suit other purposes. Eventually, OCB's code is compact and easily portable. OCB has been validated through two implementations: one within the O2 OODB and another one within the Texas persistent object store. The perfor-mances of a specific clustering policy called DSTC (Dynamic, Statistical, Tunable Clustering) have also been evaluated with OCB.

Background: Tef (Eragrostis tef), an indigenous cereal critical to food security in the Horn of Africa, is rich in minerals and protein, resistant to many biotic and abiotic stresses and safe for diabetics as well as sufferers of immune reactions to wheat gluten. We present the genome of tef, the first species in the grass subfamily Chloridoideae and the first allotetraploid assembled de novo. We sequenced the tef genome for marker-assisted breeding, to shed light on the molecular mechanisms conferring tef’s desirable nutritional and agronomic properties, and to make its genome publicly available as a community resource.Results: The draft genome contains 672 Mbp representing 87% of the genome size estimated from flow cytometry. We also sequenced two transcriptomes, one from a normalized RNA library and another from unnormalized RNASeq data. The normalized RNA library revealed around 38000 transcripts that were then annotated by the SwissProt group. The CoGe comparative genomics platform was used to compare the tef genome to other genomes, notably sorghum. Scaffolds comprising approximately half of the genome size were ordered by syntenic alignment to sorghum producing tef pseudo- chromosomes, which were sorted into A and B genomes as well as compared to the genetic map of tef. The draft genome was used to identify novel SSR markers, investigate target genes for abiotic stress resistance studies, and understand the evolution of the prolamin family of proteins that are responsible for the immune response to gluten.Conclusions: It is highly plausible that breeding targets previously identified in other cereal crops will also be valuable breeding targets in tef. The draft genome and transcriptome will be of great use for identifying these targets for genetic improvement of this orphan crop that is vital for feeding 50 million people in the Horn of Africa.

We present in this paper a generic object-oriented benchmark (the Object Clustering Benchmark) that has been designed to evaluate the performances of clustering policies in object-oriented databases. OCB is generic because its sample database may be customized to fit the databases introduced by the main existing benchmarks (e.g., OO1). OCB's current form is clustering-oriented because of its clustering-oriented workload, but it can be easily adapted to other purposes. Lastly, OCB's code is compact and easily portable. OCB has been implemented in a real system (Texas, running on a Sun workstation), in order to test a specific clustering policy called DSTC. A few results concerning this test are presented.

Performance of object-oriented database systems (OODBs) is still an issue to both designers and users nowadays. The aim of this paper is to propose a generic discrete-event random simulation model, called VOODB, in order to evaluate the performances of OODBs in general, and the performances of optimization methods like clustering in particular. Such optimization methods undoubtedly improve the performances of OODBs. Yet, they also always induce some kind of overhead for the system. Therefore, it is important to evaluate their exact impact on the overall performances. VOODB has been designed as a generic discrete-event random simulation model by putting to use a modelling approach, and has been validated by simulating the behavior of the O2 OODB and the Texas persistent object store. Since our final objective is to compare object clustering algorithms, some experiments have also been conducted on the DSTC clustering technique, which is implemented in Texas. To validate VOODB, performance results obtained by simulation for a given experiment have been compared to the results obtained by benchmarking the real systems in the same conditions. Benchmarking and simulation performance evaluations have been observed to be consistent, so it appears that simulation can be a reliable approach to evaluate the performances of OODBs.

We present in this paper three dynamic clustering techniques for Object-Oriented Databases (OODBs). The first two, Dynamic, Statistical & Tunable Clustering (DSTC) and StatClust, exploit both comprehensive usage statistics and the inter-object reference graph. They are quite elaborate. However, they are also complex to implement and induce a high overhead. The third clustering technique, called Detection & Reclustering of Objects (DRO), is based on the same principles, but is much simpler to implement. These three clustering algorithm have been implemented in the Texas persistent object store and compared in terms of clustering efficiency (i.e., overall performance increase) and overhead using the Object Clustering Benchmark (OCB). The results obtained showed that DRO induced a lighter overhead while still achieving better overall performance.

Efficient query processing is a critical requirement for data warehousing systems as decision support applications often require minimum response times to answer complex, ad-hoc queries having aggregations, multiways joins over vast repositories of data. This can be achieved by fragmenting warehouse data. The data fragmentation concept in the context of distributed databases aims to reduce query execution time and facilitates the parallel execution of queries. In this paper, we propose a methodology for applying the fragmentation technique in a data warehouse star schema to reduce the total query execution cost. We present an algorithm for fragmenting the tables of a star schema. During the fragmentation process, we observe that the choice of the dimension tables used in fragmenting the fact table plays an important role on overall performance. Therefore, we develop a greedy algorithm in selecting `best' dimension tables. We propose an analytical cost model for executing a set of OLAP queries on a fragmented star schema. Finally, we conduct some experiments to evaluate the utility of the fragmentation for efficiently executing OLAP queries.

not available.

Efficient query processing is a critical requirement for data warehousing systems as decision support applications often require minimum response times to answer complex, ad-hoc queries having aggregations, multiways joins over vast repositories of data. This can be achieved by fragmenting warehouse data. The data fragmentation concept in the context of distributed databases aims to reduce query execution time and facilitates the parallel execution of queries. In this paper, we propose a methodology for applying the fragmentation technique in a data warehouse star schema to reduce the total query execution cost. We present an algorithm for fragmenting the tables of a star schema. During the fragmentation process, we observe that the choice of the dimension tables used in fragmenting the fact table plays an important role on overall performance. Therefore, we develop a greedy algorithm in selecting `best' dimension tables. We propose an analytical cost model for executing a set of OLAP queries on a fragmented star schema. Finally, we conduct some experiments to evaluate the utility of the fragmentation for efficiently executing OLAP queries.

not available.

Efficient query processing is a critical requirement for data warehousing systems as decision support applications often require minimum response times to answer complex, ad-hoc queries having aggregations, multiways joins over vast repositories of data. This can be achieved by fragmenting warehouse data. The data fragmentation concept in the context of distributed databases aims to reduce query execution time and facilitates the parallel execution of queries. In this paper, we propose a methodology for applying the fragmentation technique in a data warehouse star schema to reduce the total query execution cost. We present an algorithm for fragmenting the tables of a star schema. During the fragmentation process, we observe that the choice of the dimension tables used in fragmenting the fact table plays an important role on overall performance. Therefore, we develop a greedy algorithm in selecting `best' dimension tables. We propose an analytical cost model for executing a set of OLAP queries on a fragmented star schema. Finally, we conduct some experiments to evaluate the utility of the fragmentation for efficiently executing OLAP queries.

not available.

Contient une table des matières, Avec mode texte

Background: Tef (Eragrostis tef), an indigenous cereal critical to food security in the Horn of Africa, is rich in minerals and protein, resistant to many biotic and abiotic stresses and safe for diabetics as well as sufferers of immune reactions to wheat gluten. We present the genome of tef, the first species in the grass subfamily Chloridoideae and the first allotetraploid assembled de novo. We sequenced the tef genome for marker-assisted breeding, to shed light on the molecular mechanisms conferring tef’s desirable nutritional and agronomic properties, and to make its genome publicly available as a community resource.Results: The draft genome contains 672 Mbp representing 87% of the genome size estimated from flow cytometry. We also sequenced two transcriptomes, one from a normalized RNA library and another from unnormalized RNASeq data. The normalized RNA library revealed around 38000 transcripts that were then annotated by the SwissProt group. The CoGe comparative genomics platform was used to compare the tef genome to other genomes, notably sorghum. Scaffolds comprising approximately half of the genome size were ordered by syntenic alignment to sorghum producing tef pseudo- chromosomes, which were sorted into A and B genomes as well as compared to the genetic map of tef. The draft genome was used to identify novel SSR markers, investigate target genes for abiotic stress resistance studies, and understand the evolution of the prolamin family of proteins that are responsible for the immune response to gluten.Conclusions: It is highly plausible that breeding targets previously identified in other cereal crops will also be valuable breeding targets in tef. The draft genome and transcriptome will be of great use for identifying these targets for genetic improvement of this orphan crop that is vital for feeding 50 million people in the Horn of Africa.

The SWISS-PROT protein knowledgebase (http://www.expasy.org/sprot/ and http://www.ebi.ac.uk/swissprot/) connects amino acid sequences with the current knowledge in the Life Sciences. Each protein entry provides an interdisciplinary overview of relevant information by bringing together experimental results, computed features and sometimes even contradictory conclusions. Detailed expertise that goes beyond the scope of SWISS-PROT is made available via direct links to specialised databases. SWISS-PROT provides annotated entries for all species, but concentrates on the annotation of entries from human (the HPI project) and other model organisms to ensure the presence of high quality annotation for representative members of all protein families. Part of the annotation can be transferred to other family members, as is already done for microbes by the High-quality Automated and Manual Annotation of microbial Proteomes (HAMAP) project. Protein families and groups of proteins are regularly reviewed to keep up with current scientific findings. Complementarily, TrEMBL strives to comprise all protein sequences that are not yet represented in SWISS-PROT, by incorporating a perpetually increasing level of mostly automated annotation. Researchers are welcome to contribute their knowledge to the scientific community by submitting relevant findings to SWISS-PROT at swiss-prot@expasy.org

Genomic projects heavily depend on genome annotations and are limited by the current deficiencies in the published predictions of gene structure and function. It follows that, improved annotation will allow better data mining of genomes, and more secure planning and design of experiments. The purpose of the GeneFarm project is to obtain homogeneous, reliable, documented and traceable annotations for Arabidopsis nuclear genes and gene products, and to enter them into an added-value database. This re-annotation project is being performed exhaustively on every member of each gene family. Performing a family-wide annotation makes the task easier and more efficient than a gene-by-gene approach since many features obtained for one gene can be extrapolated to some or all the other genes of a family. A complete annotation procedure based on the most efficient prediction tools available is being used by 16 partner laboratories, each contributing annotated families from its field of expertise. A database, named GeneFarm, and an associated user-friendly interface to query the annotations have been developed. More than 3000 genes distributed over 300 families have been annotated and are available at http://genoplante-info.infobiogen.fr/Genefarm/. Furthermore, collaboration with the Swiss Institute of Bioinformatics is underway to integrate the GeneFarm data into the protein knowledgebase Swiss-Prot