Biomedical subjects

Pierre Rouzé

Publications and source records attributed to Pierre Rouzé.

At least 19 recordsLinked to original sources

PubMed2007

In search of the small ones: improved prediction of short exons in vertebrates, plants, fungi and protists.

MOTIVATION: Prediction of the coding potential for stretches of DNA is crucial in gene calling and genome annotation, where it is used to identify potential exons and to position their boundaries in conjunction with functional sites, such as splice sites and translation initiation sites. The ability to discriminate between coding and non-coding sequences relates to the structure of coding sequences, which are organized in codons, and by their biased usage. For statistical reasons, the longer the sequences, the easier it is to detect this codon bias. However, in many eukaryotic genomes, where genes harbour many introns, both introns and exons might be small and hard to distinguish based on coding potential. RESULTS: Here, we present novel approaches that specifically aim at a better detection of coding potential in short sequences. The methods use complementary sequence features, combined with identification of which features are relevant in discriminating between coding and non-coding sequences. These newly developed methods are evaluated on different species, representative of four major eukaryotic kingdoms, and extensively compared to state-of-the-art Markov models, which are often used for predicting coding potential. The main conclusions drawn from our analyses are that (1) combining complementary sequence features clearly outperforms current Markov models for coding potential prediction in short sequence fragments, (2) coding potential prediction benefits from length-specific models, and these models are not necessarily the same for different sequence lengths and (3) comparing the results across several species indicates that, although our combined method consistently performs extremely well, there are important differences across genomes. SUPPLEMENTARY DATA: http://bioinformatics.psb.ugent.be/.

Algorithms↗

PubMed2006

Genome analysis of the smallest free-living eukaryote Ostreococcus tauri unveils many unique features.

The green lineage is reportedly 1,500 million years old, evolving shortly after the endosymbiosis event that gave rise to early photosynthetic eukaryotes. In this study, we unveil the complete genome sequence of an ancient member of this lineage, the unicellular green alga Ostreococcus tauri (Prasinophyceae). This cosmopolitan marine primary producer is the world's smallest free-living eukaryote known to date. Features likely reflecting optimization of environmentally relevant pathways, including resource acquisition, unusual photosynthesis apparatus, and genes potentially involved in C(4) photosynthesis, were observed, as was downsizing of many gene families. Overall, the 12.56-Mb nuclear genome has an extremely high gene density, in part because of extensive reduction of intergenic regions and other forms of compaction such as gene fusion. However, the genome is structurally complex. It exhibits previously unobserved levels of heterogeneity for a eukaryote. Two chromosomes differ structurally from the other eighteen. Both have a significantly biased G+C content, and, remarkably, they contain the majority of transposable elements. Many chromosome 2 genes also have unique codon usage and splicing, but phylogenetic analysis and composition do not support alien gene origin. In contrast, most chromosome 19 genes show no similarity to green lineage genes and a large number of them are specialized in cell surface processes. Taken together, the complete genome sequence, unusual features, and downsized gene families, make O. tauri an ideal model system for research on eukaryotic genome evolution, including chromosome specialization and green lineage ancestry.

Animals↗

PubMed2006

The small RNA world of plants.

RNA has many functions in addition to being a simple messenger between the genome and the proteome. Over two decades, several classes of small noncoding RNAs c. 21 nucleotides (nt) long have been uncovered in eukaryotic genomes, which appear to play a central role in diverse and fundamental processes. In plants, small RNA-based mechanisms are involved in genome stability, gene expression and defense. Many of the discoveries in this new "small RNA world" were made by plant biologists. Here, we discuss the three major classes of small RNAs that are found in the plant kingdom, namely small interfering RNAs, microRNAs, and the recently discovered trans-acting small interfering RNAs. Recent results shed light on the identification, integration and specialization of the different components (Dicer-like, Argonaute, and others) involved in the biogenesis of the different classes of small RNAs in plants. Owing to the development of better experimental and computational methods, an ever increasing number of small noncoding RNAs are uncovered in different plant genomes. In particular the well-studied microRNAs seem to act as key regulators in several different developmental pathways, with a marked preference for transcription factors as targets. In addition, an increasing amount of data suggest that they also play an important role in other mechanisms, such as response to stress or environmental changes.

Base Sequence↗

PubMed2005

Large-scale structural analysis of the core promoter in mammalian and plant genomes.

DNA encodes at least two independent levels of functional information. The first level is for encoding proteins and sequence targets for DNA-binding factors, while the second one is contained in the physical and structural properties of the DNA molecule itself. Although the physical and structural properties are ultimately determined by the nucleotide sequence itself, the cell exploits these properties in a way in which the sequence itself plays no role other than to support or facilitate certain spatial structures. In this work, we focus on these structural properties, comparing them between different organisms and assessing their ability to describe the core promoter. We prove the existence of distinct types of core promoters, based on a clustering of their structural profiles. These results indicate that the structural profiles are much conserved within plants (Arabidopsis and rice) and animals (human and mouse), but differ considerably between plants and animals. Furthermore, we demonstrate that these structural profiles can be an alternative way of describing the core promoter, in addition to more classical motif or IUPAC-based approaches. Using the structural profiles as discriminatory elements to separate promoter regions from non-promoter regions, reliable models can be built to identify core-promoter regions using a strictly computational approach.

Animals↗

PubMed2005

GeneFarm, structural and functional annotation of Arabidopsis gene and protein families by a network of experts.

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.

Arabidopsis↗

PubMed2005

EST data suggest that poplar is an ancient polyploid.

We analysed the publicly available expressed sequence tag (EST) collections for the genus Populus to examine whether evidence can be found for large-scale gene-duplication events in the evolutionary past of this genus. The ESTs were clustered into unigenes for each poplar species examined. Gene families were constructed for all proteins deduced from these unigenes, and K(S) dating was performed on all paralogs within a gene family. The fraction of paralogs was then plotted against the K(S) values, which resulted in a distribution reflecting the age of duplicated genes in poplar. Sufficient EST data were available for seven different poplar species spanning four of the six sections of the genus Populus. For all these species, there was evidence that a large-scale gene-duplication event had occurred. From our analysis it is clear that all poplar species have shared the same large-scale gene-duplication event, suggesting that this event must have occurred in the ancestor of poplar, or at least very early in the evolution of the Populus genus.

Biological Evolution↗

PubMed2004

SpliceMachine: predicting splice sites from high-dimensional local context representations.

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.

Algorithms↗

PubMed2004

Detection of 91 potential conserved plant microRNAs in Arabidopsis thaliana and Oryza sativa identifies important target genes.

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.

Arabidopsis↗

PubMed2004

Evidence that microRNA precursors, unlike other non-coding RNAs, have lower folding free energies than random sequences.

MOTIVATION: Most non-coding RNAs are characterized by a specific secondary and tertiary structure that determines their function. Here, we investigate the folding energy of the secondary structure of non-coding RNA sequences, such as microRNA precursors, transfer RNAs and ribosomal RNAs in several eukaryotic taxa. Statistical biases are assessed by a randomization test, in which the predicted minimum free energy of folding is compared with values obtained for structures inferred from randomly shuffling the original sequences. RESULTS: In contrast with transfer RNAs and ribosomal RNAs, the majority of the microRNA sequences clearly exhibit a folding free energy that is considerably lower than that for shuffled sequences, indicating a high tendency in the sequence towards a stable secondary structure. A possible usage of this statistical test in the framework of the detection of genuine miRNA sequences is discussed.

Animals↗

PubMed2004

Feature selection for splice site prediction: a new method using EDA-based feature ranking.

BACKGROUND: The identification of relevant biological features in large and complex datasets is an important step towards gaining insight in the processes underlying the data. Other advantages of feature selection include the ability of the classification system to attain good or even better solutions using a restricted subset of features, and a faster classification. Thus, robust methods for fast feature selection are of key importance in extracting knowledge from complex biological data. RESULTS: In this paper we present a novel method for feature subset selection applied to splice site prediction, based on estimation of distribution algorithms, a more general framework of genetic algorithms. From the estimated distribution of the algorithm, a feature ranking is derived. Afterwards this ranking is used to iteratively discard features. We apply this technique to the problem of splice site prediction, and show how it can be used to gain insight into the underlying biological process of splicing. CONCLUSION: We show that this technique proves to be more robust than the traditional use of estimation of distribution algorithms for feature selection: instead of returning a single best subset of features (as they normally do) this method provides a dynamical view of the feature selection process, like the traditional sequential wrapper methods. However, the method is faster than the traditional techniques, and scales better to datasets described by a large number of features.

Adenosine↗

PubMed2004

Versatile gene-specific sequence tags for Arabidopsis functional genomics: transcript profiling and reverse genetics applications.

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.

Arabidopsis↗

PubMed2003

Automatic design of gene-specific sequence tags for genome-wide functional studies.

MOTIVATION: The availability of complete genome sequences allows the identification of short DNA segments that are specific to each annotated gene. Such unique gene sequence tags (GSTs) replace advantageously cDNAs in microarray transcript profiling experiments. In particular, probes corresponding to individual members of multigene families can be chosen carefully to avoid cross-hybridization events. RESULTS: The Specific Primer and Amplicon Design Software (SPADS) was constructed to delineate the more divergent regions in each gene by comparing them with a completely annotated genome sequence and to select optimal primer pairs for the polymerase chain reaction amplification of one divergent region per gene. SPADS is a unique integrated tool to design specific GSTs from any public or private genome sequences and allows the user to fine-tune GST size and specificity. SPADS has been used to obtain probes for whole genome and family-wide transcript profiling, as well as inserts for gene-specific knock-out experiments. AVAILABILITY: The GENOPLANTE SPADS source code and web interface are available upon request. The online version is accessible via http://genoplante-info.infobiogen.fr/spads and via http://oberon.fvms.ugent.be:8080/SPADS/

Algorithms↗

PubMed2003

AFLPinSilico, simulating AFLP fingerprints.

SUMMARY: A drawback of the Amplified Fragment Length Polymorphism (AFLP) fingerprinting method is the difficulty to correlate the different fragments with their DNA sequence. The AFLPinSilico application presented here simulates AFLP experiments run on either cDNA or genomic sequences, producing virtual fingerprints that allow high throughput identification of AFLP fragments. The program also enables biologists to manage experiments through simulations done beforehand, thereby reducing the number of experiments that have to be run. AFLPinSilico is available through the www or as a stand-alone version, through a command line executable (available upon request, for any platform running PERL).

Algorithms↗

PubMed2003

CATMA: a complete Arabidopsis GST database.

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/.

Arabidopsis↗

PubMed2003

Genomewide structural annotation and evolutionary analysis of the type I MADS-box genes in plants.

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.

Amino Acid Motifs↗

PubMed2003

Fast feature selection using a simple estimation of distribution algorithm: a case study on splice site prediction.

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.

Algorithms↗

PubMed2003

Computational approaches to identify promoters and cis-regulatory elements in plant genomes.

The identification of promoters and their regulatory elements is one of the major challenges in bioinformatics and integrates comparative, structural, and functional genomics. Many different approaches have been developed to detect conserved motifs in a set of genes that are either coregulated or orthologous. However, although recent approaches seem promising, in general, unambiguous identification of regulatory elements is not straightforward. The delineation of promoters is even harder, due to its complex nature, and in silico promoter prediction is still in its infancy. Here, we review the different approaches that have been developed for identifying promoters and their regulatory elements. We discuss the detection of cis-acting regulatory elements using word-counting or probabilistic methods (so-called "search by signal" methods) and the delineation of promoters by considering both sequence content and structural features ("search by content" methods). As an example of search by content, we explored in greater detail the association of promoters with CpG islands. However, due to differences in sequence content, the parameters used to detect CpG islands in humans and other vertebrates cannot be used for plants. Therefore, a preliminary attempt was made to define parameters that could possibly define CpG and CpNpG islands in Arabidopsis, by exploring the compositional landscape around the transcriptional start site. To this end, a data set of more than 5,000 gene sequences was built, including the promoter region, the 5'-untranslated region, and the first introns and coding exons. Preliminary analysis shows that promoter location based on the detection of potential CpG/CpNpG islands in the Arabidopsis genome is not straightforward. Nevertheless, because the landscape of CpG/CpNpG islands differs considerably between promoters and introns on the one side and exons (whether coding or not) on the other, more sophisticated approaches can probably be developed for the successful detection of "putative" CpG and CpNpG islands in plants.

Computational Biology↗

PubMed2002

Current methods of gene prediction, their strengths and weaknesses.

While the genomes of many organisms have been sequenced over the last few years, transforming such raw sequence data into knowledge remains a hard task. A great number of prediction programs have been developed that try to address one part of this problem, which consists of locating the genes along a genome. This paper reviews the existing approaches to predicting genes in eukaryotic genomes and underlines their intrinsic advantages and limitations. The main mathematical models and computational algorithms adopted are also briefly described and the resulting software classified according to both the method and the type of evidence used. Finally, the several difficulties and pitfalls encountered by the programs are detailed, showing that improvements are needed and that new directions must be considered.

Algorithms↗