Optimal clone identifier for genomic shotgun libraries: OC Identifier tool

In DNA microarray experiments, the gene fragments that are spotted on the slides are usually obtained by the synthesis of specific oligonucleotides that are able to amplify genes through PCR. Shotgun library sequences are an alternative to synthesis of primers for the study of each gene in the genome. The possibility of putting thousands of gene sequences into a single slide allows the use of shotgun clones in order to proceed with microarray analysis without a completely sequenced genome. We developed an OC Identifier tool (optimal clone identifier for genomic shotgun libraries) for the identification of unique genes in shotgun libraries based on a partially sequenced genome; this allows simultaneous use of clones in projects such as transcriptome and phylogeny studies, using comparative genomic hybridization and genome assembly. The OC Identifier tool allows comparative genome analysis, biological databases, query language in relational databases, and provides bioinformatics tools to identify clones that contain unique genes as alternatives to primer synthesis. The OC Identifier allows analysis of clones during the sequencing phase, making it possible to select genes of interest for construction of a DNA microarray. ©FUNPEC-RP.

Identification of "pathologs" (disease-related genes) from the RIKEN mouse cDNA dataset using human curation plus FACTS, a new biological information extraction system

Background: A major goal in the post-genomic era is to identify and characterise disease susceptibility genes and to apply this knowledge to disease prevention and treatment. Rodents and humans have remarkably similar genomes and share closely related biochemical, physiological and pathological pathways. In this work we utilised the latest information on the mouse transcriptome as revealed by the RIKEN FANTOM2 project to identify novel human disease-related candidate genes. We define a new term patholog to mean a homolog of a human disease-related gene encoding a product ( transcript, anti-sense or protein) potentially relevant to disease. Rather than just focus on Mendelian inheritance, we applied the analysis to all potential pathologs regardless of their inheritance pattern. Results: Bioinformatic analysis and human curation of 60,770 RIKEN full-length mouse cDNA clones produced 2,578 sequences that showed similarity ( 70 - 85% identity) to known human-disease genes. Using a newly developed biological information extraction and annotation tool ( FACTS) in parallel with human expert analysis of 17,051 MEDLINE scientific abstracts we identified 182 novel potential pathologs. Of these, 36 were identified by computational tools only, 49 by human expert analysis only and 97 by both methods. These pathologs were related to neoplastic ( 53%), hereditary ( 24%), immunological ( 5%), cardio-vascular (4%), or other (14%), disorders. Conclusions: Large scale genome projects continue to produce a vast amount of data with potential application to the study of human disease. For this potential to be realised we need intelligent strategies for data categorisation and the ability to link sequence data with relevant literature. This paper demonstrates the power of combining human expert annotation with FACTS, a newly developed bioinformatics tool, to identify novel pathologs from within large-scale mouse transcript datasets.

Approches bio-informatiques appliquées aux technologies émergentes en génomique

Les études génétiques, telles que les études de liaison ou d’association, ont permis d’acquérir une plus grande connaissance sur l’étiologie de plusieurs maladies affectant les populations humaines. Même si une dizaine de milliers d’études génétiques ont été réalisées sur des centaines de maladies ou autres traits, une grande partie de leur héritabilité reste inexpliquée. Depuis une dizaine d’années, plusieurs percées dans le domaine de la génomique ont été réalisées. Par exemple, l’utilisation des micropuces d’hybridation génomique comparative à haute densité a permis de démontrer l’existence à grande échelle des variations et des polymorphismes en nombre de copies. Ces derniers sont maintenant détectables à l’aide de micropuce d’ADN ou du séquençage à haut débit. De plus, des études récentes utilisant le séquençage à haut débit ont permis de démontrer que la majorité des variations présentes dans l’exome d’un individu étaient rares ou même propres à cet individu. Ceci a permis la conception d’une nouvelle micropuce d’ADN permettant de déterminer rapidement et à faible coût le génotype de plusieurs milliers de variations rares pour un grand ensemble d’individus à la fois. Dans ce contexte, l’objectif général de cette thèse vise le développement de nouvelles méthodologies et de nouveaux outils bio-informatiques de haute performance permettant la détection, à de hauts critères de qualité, des variations en nombre de copies et des variations nucléotidiques rares dans le cadre d’études génétiques. Ces avancées permettront, à long terme, d’expliquer une plus grande partie de l’héritabilité manquante des traits complexes, poussant ainsi l’avancement des connaissances sur l’étiologie de ces derniers. Un algorithme permettant le partitionnement des polymorphismes en nombre de copies a donc été conçu, rendant possible l’utilisation de ces variations structurales dans le cadre d’étude de liaison génétique sur données familiales. Ensuite, une étude exploratoire a permis de caractériser les différents problèmes associés aux études génétiques utilisant des variations en nombre de copies rares sur des individus non reliés. Cette étude a été réalisée avec la collaboration du Wellcome Trust Centre for Human Genetics de l’University of Oxford. Par la suite, une comparaison de la performance des algorithmes de génotypage lors de leur utilisation avec une nouvelle micropuce d’ADN contenant une majorité de marqueurs rares a été réalisée. Finalement, un outil bio-informatique permettant de filtrer de façon efficace et rapide des données génétiques a été implémenté. Cet outil permet de générer des données de meilleure qualité, avec une meilleure reproductibilité des résultats, tout en diminuant les chances d’obtenir une fausse association.

Quantitative proteomics methods for the analysis of histone post-translational modifications

Les histones sont des protéines nucléaires hautement conservées chez les cellules des eucaryotes. Elles permettent d’organiser et de compacter l’ADN sous la forme de nucléosomes, ceux-ci representant les sous unités de base de la chromatine. Les histones peuvent être modifiées par de nombreuses modifications post-traductionnelles (PTMs) telles que l’acétylation, la méthylation et la phosphorylation. Ces modifications jouent un rôle essentiel dans la réplication de l’ADN, la transcription et l’assemblage de la chromatine. L’abondance de ces modifications peut varier de facon significative lors du developpement des maladies incluant plusieurs types de cancer. Par exemple, la perte totale de la triméthylation sur H4K20 ainsi que l’acétylation sur H4K16 sont des marqueurs tumoraux spécifiques a certains types de cancer chez l’humain. Par conséquent, l’étude de ces modifications et des événements determinant la dynamique des leurs changements d’abondance sont des atouts importants pour mieux comprendre les fonctions cellulaires et moléculaires lors du développement de la maladie. De manière générale, les modifications des histones sont étudiées par des approches biochimiques telles que les immuno-buvardage de type Western ou les méthodes d’immunoprécipitation de la chromatine (ChIP). Cependant, ces approches présentent plusieurs inconvénients telles que le manque de spécificité ou la disponibilité des anticorps, leur coût ou encore la difficulté de les produire et de les valider. Au cours des dernières décennies, la spectrométrie de masse (MS) s’est avérée être une méthode performante pour la caractérisation et la quantification des modifications d’histones. La MS offre de nombreux avantages par rapport aux techniques traditionnelles. Entre autre, elle permet d’effectuer des analyses reproductibles, spécifiques et facilite l’etude d’un large spectre de PTMs en une seule analyse. Dans cette thèse, nous présenterons le développement et l’application de nouveaux outils analytiques pour l’identification et à la quantification des PTMs modifiant les histones. Dans un premier temps, une méthode a été développée pour mesurer les changements d’acétylation spécifiques à certains sites des histones. Cette méthode combine l’analyse des histones intactes et les méthodes de séquençage peptidique afin de déterminer les changements d’acétylation suite à la réaction in vitro par l’histone acétyltransférase (HAT) de levure Rtt109 en présence de ses chaperonnes (Asf1 ou Vps75). Dans un second temps, nous avons développé une méthode d’analyse des peptides isomériques des histones. Cette méthode combine la LC-MS/MS à haute résolution et un nouvel outil informatique appelé Iso-PeptidAce qui permet de déconvoluer les spectres mixtes de peptides isomériques. Nous avons évalué Iso-PeptidAce avec un mélange de peptides synthétiques isomériques. Nous avons également validé les performances de cette approche avec des histones isolées de cellules humaines érythroleucémiques (K562) traitées avec des inhibiteurs d’histones désacétylases (HDACi) utilisés en clinique, et des histones de Saccharomyces cerevisiae liées au facteur d’assemblage de la chromatine (CAF-1) purifiées par chromatographie d’affinité. Enfin, en utilisant la méthode présentée précédemment, nous avons fait une analyse approfondie de la spécificité de plusieurs HATs et HDACs chez Schizosaccharomyces pombe. Nous avons donc déterminé les niveaux d’acétylation d’histones purifiées à partir de cellules contrôles ou de souches mutantes auxquelles il manque une HAT ou HDAC. Notre analyse nous a permis de valider plusieurs cibles connues des HATs et HDACs et d’en identifier de nouvelles. Nos données ont également permis de définir le rôle des différentes HATs et HDACs dans le maintien de l’équilibre d’acétylation des histones. Dans l’ensemble, nous anticipons que les méthodes décrites dans cette thèse permettront de résoudre certains défis rencontrés dans l’étude de la chromatine. De plus, ces données apportent de nouvelles connaissances pour l’élaboration d’études génétiques et biochimiques utilisant S. pombe.

Satellyptus: analysis and database of microsatellites from ESTs of Eucalyptus

The main goal of our research was to search for SSRs in the Eucalyptus EST FORESTs database (using a software for mining SSR-motifs). With this objective, we created a database for cataloging Eucalyptus EST-derived SSRs, and developed a bioinformatics tool, named Satellyptus, for finding and analyzing microsatellites in the Eucalyptus EST database. The search for microsatellites in the FORESTs database containing 71,115 Eucalyptus EST sequences (52.09 Mb) revealed 20,530 SSRs in 15,621 ESTs. The SSR abundance detected on the Eucalyptus ESTs database (29% or one microsatellite every four sequences) is considered very high for plants. Amongst the categories of SSR motifs, the dimeric (37%) and trimeric ones (33%) predominated. The AG/CT motif was the most frequent (35.15%) followed by the trimeric CCG/CGG (12.81%). From a random sample of 1,217 sequences, 343 microsatellites in 265 SSR-containing sequences were identified. Approximately 48% of these ESTs containing microsatellites were homologous to proteins with known biological function. Most of the microsatellites detected in Eucalyptus ESTs were positioned at either the 5 or 3 end. Our next priority involves the design of flanking primers for codominant SSR loci, which could lead to the development of a set of microsatellite-based markers suitable for marker-assisted Eucalyptus breeding programs.

Elementos de regulação e microRNAs envolvidos na modulação dos níveis de hemoglobina fetal em indivíduos portadores de beta-hemoglobinopatias

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Searching oligo sets of human chromosome 12 using evolutionary strategies

DNA Microarray is a powerful tool to measure the level of a mixed population of nucleic acids at one time, which has great impact in many aspects of life sciences research. In order to distinguish nucleic acids with very similar composition by hybridization, it is necessary to design microarray probes with high specificities and sensitivities. Highly specific probes correspond to probes having unique DNA sequences; whereas highly sensitive probes correspond to those with melting temperature within a desired range and having no secondary structure. The selection of these probes from a set of functional DNA sequences (exons) constitutes a computationally expensive discrete non-linear search problem. We delegate the search task to a simple yet effective Evolution Strategy algorithm. The computational efficiency is also greatly improved by making use of an available bioinformatics tool.

OPPL-Galaxy, a Galaxy tool for enhancing ontology exploitation as part of bioinformatics workflows

Biomedical ontologies are key elements for building up the Life Sciences Semantic Web. Reusing and building biomedical ontologies requires flexible and versatile tools to manipulate them efficiently, in particular for enriching their axiomatic content. The Ontology Pre Processor Language (OPPL) is an OWL-based language for automating the changes to be performed in an ontology. OPPL augments the ontologists’ toolbox by providing a more efficient, and less error-prone, mechanism for enriching a biomedical ontology than that obtained by a manual treatment. Results We present OPPL-Galaxy, a wrapper for using OPPL within Galaxy. The functionality delivered by OPPL (i.e. automated ontology manipulation) can be combined with the tools and workflows devised within the Galaxy framework, resulting in an enhancement of OPPL. Use cases are provided in order to demonstrate OPPL-Galaxy’s capability for enriching, modifying and querying biomedical ontologies. Conclusions Coupling OPPL-Galaxy with other bioinformatics tools of the Galaxy framework results in a system that is more than the sum of its parts. OPPL-Galaxy opens a new dimension of analyses and exploitation of biomedical ontologies, including automated reasoning, paving the way towards advanced biological data analyses.

ProbFAST: Probabilistic Functional Analysis System Tool

Background: The post-genomic era has brought new challenges regarding the understanding of the organization and function of the human genome. Many of these challenges are centered on the meaning of differential gene regulation under distinct biological conditions and can be performed by analyzing the Multiple Differential Expression (MDE) of genes associated with normal and abnormal biological processes. Currently MDE analyses are limited to usual methods of differential expression initially designed for paired analysis. Results: We proposed a web platform named ProbFAST for MDE analysis which uses Bayesian inference to identify key genes that are intuitively prioritized by means of probabilities. A simulated study revealed that our method gives a better performance when compared to other approaches and when applied to public expression data, we demonstrated its flexibility to obtain relevant genes biologically associated with normal and abnormal biological processes. Conclusions: ProbFAST is a free accessible web-based application that enables MDE analysis on a global scale. It offers an efficient methodological approach for MDE analysis of a set of genes that are turned on and off related to functional information during the evolution of a tumor or tissue differentiation. ProbFAST server can be accessed at http://gdm.fmrp.usp.br/probfast.

Arrow Plot: a new graphical tool for selecting up and down regulated genes and genes differentially expressed on samples subgroups

Background: A common task in analyzing microarray data is to determine which genes are differentially expressed across two (or more) kind of tissue samples or samples submitted under experimental conditions. Several statistical methods have been proposed to accomplish this goal, generally based on measures of distance between classes. It is well known that biological samples are heterogeneous because of factors such as molecular subtypes or genetic background that are often unknown to the experimenter. For instance, in experiments which involve molecular classification of tumors it is important to identify significant subtypes of cancer. Bimodal or multimodal distributions often reflect the presence of subsamples mixtures. Consequently, there can be genes differentially expressed on sample subgroups which are missed if usual statistical approaches are used. In this paper we propose a new graphical tool which not only identifies genes with up and down regulations, but also genes with differential expression in different subclasses, that are usually missed if current statistical methods are used. This tool is based on two measures of distance between samples, namely the overlapping coefficient (OVL) between two densities and the area under the receiver operating characteristic (ROC) curve. The methodology proposed here was implemented in the open-source R software. Results: This method was applied to a publicly available dataset, as well as to a simulated dataset. We compared our results with the ones obtained using some of the standard methods for detecting differentially expressed genes, namely Welch t-statistic, fold change (FC), rank products (RP), average difference (AD), weighted average difference (WAD), moderated t-statistic (modT), intensity-based moderated t-statistic (ibmT), significance analysis of microarrays (samT) and area under the ROC curve (AUC). On both datasets all differentially expressed genes with bimodal or multimodal distributions were not selected by all standard selection procedures. We also compared our results with (i) area between ROC curve and rising area (ABCR) and (ii) the test for not proper ROC curves (TNRC). We found our methodology more comprehensive, because it detects both bimodal and multimodal distributions and different variances can be considered on both samples. Another advantage of our method is that we can analyze graphically the behavior of different kinds of differentially expressed genes. Conclusion: Our results indicate that the arrow plot represents a new flexible and useful tool for the analysis of gene expression profiles from microarrays.

MAMOT: hidden Markov modeling tool.

Hidden Markov models (HMMs) are probabilistic models that are well adapted to many tasks in bioinformatics, for example, for predicting the occurrence of specific motifs in biological sequences. MAMOT is a command-line program for Unix-like operating systems, including MacOS X, that we developed to allow scientists to apply HMMs more easily in their research. One can define the architecture and initial parameters of the model in a text file and then use MAMOT for parameter optimization on example data, decoding (like predicting motif occurrence in sequences) and the production of stochastic sequences generated according to the probabilistic model. Two examples for which models are provided are coiled-coil domains in protein sequences and protein binding sites in DNA. A wealth of useful features include the use of pseudocounts, state tying and fixing of selected parameters in learning, and the inclusion of prior probabilities in decoding. AVAILABILITY: MAMOT is implemented in C++, and is distributed under the GNU General Public Licence (GPL). The software, documentation, and example model files can be found at http://bcf.isb-sib.ch/mamot

AssociationViewer: a scalable and integrated software tool for visualization of large-scale variation data in genomic context.

SUMMARY: We present a tool designed for visualization of large-scale genetic and genomic data exemplified by results from genome-wide association studies. This software provides an integrated framework to facilitate the interpretation of SNP association studies in genomic context. Gene annotations can be retrieved from Ensembl, linkage disequilibrium data downloaded from HapMap and custom data imported in BED or WIG format. AssociationViewer integrates functionalities that enable the aggregation or intersection of data tracks. It implements an efficient cache system and allows the display of several, very large-scale genomic datasets. AVAILABILITY: The Java code for AssociationViewer is distributed under the GNU General Public Licence and has been tested on Microsoft Windows XP, MacOSX and GNU/Linux operating systems. It is available from the SourceForge repository. This also includes Java webstart, documentation and example datafiles.

The GATO gene annotation tool for research laboratories

Large-scale genome projects have generated a rapidly increasing number of DNA sequences. Therefore, development of computational methods to rapidly analyze these sequences is essential for progress in genomic research. Here we present an automatic annotation system for preliminary analysis of DNA sequences. The gene annotation tool (GATO) is a Bioinformatics pipeline designed to facilitate routine functional annotation and easy access to annotated genes. It was designed in view of the frequent need of genomic researchers to access data pertaining to a common set of genes. In the GATO system, annotation is generated by querying some of the Web-accessible resources and the information is stored in a local database, which keeps a record of all previous annotation results. GATO may be accessed from everywhere through the internet or may be run locally if a large number of sequences are going to be annotated. It is implemented in PHP and Perl and may be run on any suitable Web server. Usually, installation and application of annotation systems require experience and are time consuming, but GATO is simple and practical, allowing anyone with basic skills in informatics to access it without any special training. GATO can be downloaded at [http://mariwork.iq.usp.br/gato/]. Minimum computer free space required is 2 MB.

AutoFACT: An Automatic Functional Annotation and Classification Tool

Affiliation: Centre Robert-Cedergren de l'Université de Montréal en bio-informatique et génomique & Département de biochimie, Université de Montréal