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The number of introns varies considerably among different organisms. This can be explained by the differences in the rates of intron gain and loss. Two factors that are likely to influence these rates are selection for or against introns and the mutation rate that generates the novel intron or the intronless copy. Although it has been speculated that stronger selection for a compact genome might result in a higher rate of intron loss and a lower rate of intron gain, clear evidence is lacking, and the role of selection in determining these rates has not been established. Here, we studied the gain and loss of introns in the two closely related species Arabidopsis thaliana and A. lyrata as it was recently shown that A. thaliana has been undergoing a faster genome reduction driven by selection. We found that A. thaliana has lost six times more introns than A. lyrata since the divergence of the two species but gained very few introns. We suggest that stronger selection for genome reduction probably resulted in the much higher intron loss rate in A. thaliana, although further analysis is required as we could not find evidence that the loss rate increased in A. thaliana as opposed to having decreased in A. lyrata compared with the rate in the common ancestor. We also examined the pattern of the intron gains and losses to better understand the mechanisms by which they occur. Microsimilarity was detected between the splice sites of several gained and lost introns, suggesting that nonhomologous end joining repair of double-strand breaks might be a common pathway not only for intron gain but also for intron loss.

Background: Large-scale identification of the interrelationships between different components of the cell, such as the interactions between proteins, has recently gained great interest. However, unraveling large-scale protein-protein interaction maps is laborious and expensive. Moreover, assessing the reliability of the interactions can be cumbersome., Results: In this study, we have developed a computational method that exploits the existing knowledge on protein-protein interactions in diverse species through orthologous relations on the one hand, and functional association data on the other hand to predict and filter protein-protein interactions in Arabidopsis thaliana. A highly reliable set of protein-protein interactions is predicted through this integrative approach making use of existing protein-protein interaction data from yeast, human, C. elegans and D. melanogaster. Localization, biological process, and co-expression data are used as powerful indicators for protein-protein interactions. The functional repertoire of the identified interactome reveals interactions between proteins functioning in well-conserved as well as plant-specific biological processes. We observe that although common mechanisms (e.g. actin polymerization) and components (e.g. ARPs, actin-related proteins) exist between different lineages, they are active in specific processes such as growth, cancer metastasis and trichome development in yeast, human and Arabidopsis, respectively., Conclusion: We conclude that the integration of orthology with functional association data is adequate to predict protein-protein interactions. Through this approach, a high number of novel protein-protein interactions with diverse biological roles is discovered. Overall, we have predicted a reliable set of protein-protein interactions suitable for further computational as well as experimental analyses.

Motivation: In this age of complete genome sequencing, finding the location and structure of genes is crucial for further molecular research. The accurate prediction of intron boundaries largely facilitates the correct prediction of gene structure in nuclear genomes. Many tools for localizing these boundaries on DNA sequences have been developed and are available to researchers through the internet. Nevertheless, these tools still make many false positive predictions., Results: This manuscript presents a novel publicly available splice site prediction tool named SpliceMachine that (i) shows state-of-the-art prediction performance on Arabidopsis thaliana and human sequences, (ii) performs a computationally fast annotation and (iii) can be trained by the user on its own data., Availability: Results, figures and software are available at http://www.bioinformatics.psb.ugent.be/supplementary_data/, Contact: sven.degroeve@psb.ugent.be; yves.vandepeer@psb.ugent.be.

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.

Microarray transcript profiling and RNA interference are two new technologies crucial for large-scale gene function studies in multicellular eukaryotes. Both rely on sequence-specific hybridization between complementary nucleic acid strands, inciting us to create a collection of gene-specific sequence tags (GSTs) representing at least 21,500 Arabidopsis genes and which are compatible with both approaches. The GSTs were carefully selected to ensure that each of them shared no significant similarity with any other region in the Arabidopsis genome. They were synthesized by PCR amplification from genomic DNA. Spotted microarrays fabricated from the GSTs show good dynamic range, specificity, and sensitivity in transcript profiling experiments. The GSTs have also been transferred to bacterial plasmid vectors via recombinational cloning protocols. These cloned GSTs constitute the ideal starting point for a variety of functional approaches, including reverse genetics. We have subcloned GSTs on a large scale into vectors designed for gene silencing in plant cells. We show that in planta expression of GST hairpin RNA results in the expected phenotypes in silenced Arabidopsis lines. These versatile GST resources provide novel and powerful tools for functional genomics.

MicroRNAs (miRNAs) are an extensive class of tiny RNA molecules that regulate the expression of target genes by means of complementary base pair interactions. Although the first miRNAs were discovered in Caenorhabditis elegans, >300 miRNAs were recently documented in animals and plants, both by cloning methods and computational predictions. We present a genome-wide computational approach to detect miRNA genes in the Arabidopsis thaliana genome. Our method is based on the conservation of short sequences between the genomes of Arabidopsis and rice (Oryza sativa) and on properties of the secondary structure of the miRNA precursor. The method was fine-tuned to take into account plant-specific properties, such as the variable length of the miRNA precursor sequences. In total, 91 potential miRNA genes were identified, of which 58 had at least one nearly perfect match with an Arabidopsis mRNA, constituting the potential targets of those miRNAs. In addition to already known transcription factors involved in plant development, the targets also comprised genes involved in several other cellular processes, such as sulfur assimilation and ubiquitin-dependent protein degradation. These findings considerably broaden the scope of miRNA functions in plants.

Motivation: Feature subset selection is an important preprocessing step for classification. In biology, where structures or processes are described by a large number of features, the elimination of irrelevant and redundant information in a reasonable amount of time has a number of advantages. It enables the classification system to achieve good or even better solutions with a restricted subset of features, allows for a faster classification, and it helps the human expert focus on a relevant subset of features, hence providing useful biological knowledge., Results: We present a heuristic method based on Estimation of Distribution Algorithms to select relevant subsets of features for splice site prediction in Arabidopsis thaliana. We show that this method performs a fast detection of relevant feature subsets using the technique of constrained feature subsets. Compared to the traditional greedy methods the gain in speed can be up to one order of magnitude, with results being comparable or even better than the greedy methods. This makes it a very practical solution for classification tasks that can be solved using a relatively small amount of discriminative features (or feature dependencies), but where the initial set of potential discriminative features is rather large.

The Complete Arabidopsis Transcriptome Micro Array (CATMA) database contains gene sequence tag (GST) and gene model sequences for over 70% of the predicted genes in the Arabidopsis thaliana genome as well as primer sequences for GST amplification and a wide range of supplementary information. All CATMA GST sequences are specific to the gene for which they were designed, and all gene models were predicted from a complete reannotation of the genome using uniform parameters. The database is searchable by sequence name, sequence homology or direct SQL query, and is available through the CATMA website at http://www.catma.org/.

Cyclin-dependent kinases and cyclins regulate with the help of different interacting proteins the progression through the eukaryotic cell cycle. A high-quality, homology-based annotation protocol was applied to determine the core cell cycle genes in the recently completed Arabidopsis genome sequence. In total, 61 genes were identified belonging to seven selected families of cell cycle regulators, for which 30 are new or corrections of the existing annotation. A new class of putative cell cycle regulators was found that probably are competitors of E2F/DP transcription factors, which mediate the G1-to-S progression. In addition, the existing nomenclature for cell cycle genes of Arabidopsis was updated, and the physical positions of all genes were compared with segmentally duplicated blocks in the genome, showing that 22 core cell cycle genes emerged through block duplications. This genome-wide analysis illustrates the complexity of the plant cell cycle machinery and provides a tool for elucidating the function of new family members in the future.

Motivation: The large amount of available annotated Arabidopsis thaliana sequences allows the induction of splice site prediction models with supervised learning algorithms (see Haussler (1998) for a review and references). These algorithms need information sources or features from which the models can be computed. For splice site prediction, the features we consider in this study are the presence or absence of certain nucleotides in close proximity to the splice site. Since it is not known how many and which nucleotides are relevant for splice site prediction, the set of features is chosen large enough such that the probability that all relevant information sources are in the set is very high. Using only those features that are relevant for constructing a splice site prediction system might improve the system and might also provide us with useful biological knowledge. Using fewer features will of course also improve the prediction speed of the system., Results: A wrapper-based feature subset selection algorithm using a support vector machine or a naive Bayes prediction method was evaluated against the traditional method for selecting features relevant for splice site prediction. Our results show that this wrapper approach selects features that improve the performance against the use of all features and against the use of the features selected by the traditional method., Availability: The data and additional interactive graphs on the selected feature subsets are available at http://www.psb.rug.ac.be/gps

The type I MADS-box genes constitute a largely unexplored subfamily of the extensively studied MADS-box gene family, well known for its role in flower development. Genes of the type I MADS-box subfamily possess the characteristic MADS box but are distinguished from type II MADS-box genes by the absence of the keratin-like box. In this in silico study, we have structurally annotated all 47 members of the type I MADS-box gene family in Arabidopsis thaliana and exerted a thorough analysis of the C-terminal regions of the translated proteins. On the basis of conserved motifs in the C-terminal region, we could classify the gene family into three main groups, two of which could be further subdivided. Phylogenetic trees were inferred to study the evolutionary relationships within this large MADS-box gene subfamily. These suggest for plant type I genes a dynamic of evolution that is significantly different from the mode of both animal type I (SRF) and plant type II (MIKC-type) gene phylogeny. The presence of conserved motifs in the majority of these genes, the identification of Oryza sativa MADS-box type I homologues, and the detection of expressed sequence tags for Arabidopsis thaliana and other plant type I genes suggest that these genes are indeed of functional importance to plants. It is therefore even more intriguing that, from an experimental point of view, almost nothing is known about the function of these MADS-box type I genes. [ABSTRACT FROM AUTHOR]

Annotation of the first few complete plant genomes has revealed that plants have many genes. For Arabidopsis, over 26500 gene loci have been predicted, whereas for rice, the number adds up to 41000. Recent analysis of the poplar genome suggests more than 45000 genes, and partial sequence data from Medicago and Lotus also suggest that these plants contain more than 40000 genes. Nevertheless, estimations suggest that ancestral angiosperms had no more than 12000–14000 genes. One explanation for the large increase in gene number during angiosperm evolution is gene duplication. It has been shown previously that the retention of duplicates following small- and large-scale duplication events in plants is substantial. Taking into account the function of genes that have been duplicated, we are now beginning to understand why many plant genes might have been retained, and how their retention might be linked to the typical lifestyle of plants. [Copyright &y& Elsevier]