Design and implementation of bioinformatics tools for large scale genome annotation

The continuous increase of genome sequencing projects produced a huge amount of data in the last 10 years: currently more than 600 prokaryotic and 80 eukaryotic genomes are fully sequenced and publically available. However the sole sequencing process of a genome is able to determine just raw nucleotide sequences. This is only the first step of the genome annotation process that will deal with the issue of assigning biological information to each sequence. The annotation process is done at each different level of the biological information processing mechanism, from DNA to protein, and cannot be accomplished only by in vitro analysis procedures resulting extremely expensive and time consuming when applied at a this large scale level. Thus, in silico methods need to be used to accomplish the task. The aim of this work was the implementation of predictive computational methods to allow a fast, reliable, and automated annotation of genomes and proteins starting from aminoacidic sequences. The first part of the work was focused on the implementation of a new machine learning based method for the prediction of the subcellular localization of soluble eukaryotic proteins. The method is called BaCelLo, and was developed in 2006. The main peculiarity of the method is to be independent from biases present in the training dataset, which causes the over‐prediction of the most represented examples in all the other available predictors developed so far. This important result was achieved by a modification, made by myself, to the standard Support Vector Machine (SVM) algorithm with the creation of the so called Balanced SVM. BaCelLo is able to predict the most important subcellular localizations in eukaryotic cells and three, kingdom‐specific, predictors were implemented. In two extensive comparisons, carried out in 2006 and 2008, BaCelLo reported to outperform all the currently available state‐of‐the‐art methods for this prediction task. BaCelLo was subsequently used to completely annotate 5 eukaryotic genomes, by integrating it in a pipeline of predictors developed at the Bologna Biocomputing group by Dr. Pier Luigi Martelli and Dr. Piero Fariselli. An online database, called eSLDB, was developed by integrating, for each aminoacidic sequence extracted from the genome, the predicted subcellular localization merged with experimental and similarity‐based annotations. In the second part of the work a new, machine learning based, method was implemented for the prediction of GPI‐anchored proteins. Basically the method is able to efficiently predict from the raw aminoacidic sequence both the presence of the GPI‐anchor (by means of an SVM), and the position in the sequence of the post‐translational modification event, the so called ω‐site (by means of an Hidden Markov Model (HMM)). The method is called GPIPE and reported to greatly enhance the prediction performances of GPI‐anchored proteins over all the previously developed methods. GPIPE was able to predict up to 88% of the experimentally annotated GPI‐anchored proteins by maintaining a rate of false positive prediction as low as 0.1%. GPIPE was used to completely annotate 81 eukaryotic genomes, and more than 15000 putative GPI‐anchored proteins were predicted, 561 of which are found in H. sapiens. In average 1% of a proteome is predicted as GPI‐anchored. A statistical analysis was performed onto the composition of the regions surrounding the ω‐site that allowed the definition of specific aminoacidic abundances in the different considered regions. Furthermore the hypothesis that compositional biases are present among the four major eukaryotic kingdoms, proposed in literature, was tested and rejected. All the developed predictors and databases are freely available at: BaCelLo http://gpcr.biocomp.unibo.it/bacello eSLDB http://gpcr.biocomp.unibo.it/esldb GPIPE http://gpcr.biocomp.unibo.it/gpipe

A clustering method for robust and reliable large scale functional and structural protein sequence annotation

Bioinformatics, in the last few decades, has played a fundamental role to give sense to the huge amount of data produced. Obtained the complete sequence of a genome, the major problem of knowing as much as possible of its coding regions, is crucial. Protein sequence annotation is challenging and, due to the size of the problem, only computational approaches can provide a feasible solution. As it has been recently pointed out by the Critical Assessment of Function Annotations (CAFA), most accurate methods are those based on the transfer-by-homology approach and the most incisive contribution is given by cross-genome comparisons. In the present thesis it is described a non-hierarchical sequence clustering method for protein automatic large-scale annotation, called “The Bologna Annotation Resource Plus” (BAR+). The method is based on an all-against-all alignment of more than 13 millions protein sequences characterized by a very stringent metric. BAR+ can safely transfer functional features (Gene Ontology and Pfam terms) inside clusters by means of a statistical validation, even in the case of multi-domain proteins. Within BAR+ clusters it is also possible to transfer the three dimensional structure (when a template is available). This is possible by the way of cluster-specific HMM profiles that can be used to calculate reliable template-to-target alignments even in the case of distantly related proteins (sequence identity < 30%). Other BAR+ based applications have been developed during my doctorate including the prediction of Magnesium binding sites in human proteins, the ABC transporters superfamily classification and the functional prediction (GO terms) of the CAFA targets. Remarkably, in the CAFA assessment, BAR+ placed among the ten most accurate methods. At present, as a web server for the functional and structural protein sequence annotation, BAR+ is freely available at http://bar.biocomp.unibo.it/bar2.0.

Computational methods for genome screening

Motivation An actual issue of great interest, both under a theoretical and an applicative perspective, is the analysis of biological sequences for disclosing the information that they encode. The development of new technologies for genome sequencing in the last years, opened new fundamental problems since huge amounts of biological data still deserve an interpretation. Indeed, the sequencing is only the first step of the genome annotation process that consists in the assignment of biological information to each sequence. Hence given the large amount of available data, in silico methods became useful and necessary in order to extract relevant information from sequences. The availability of data from Genome Projects gave rise to new strategies for tackling the basic problems of computational biology such as the determination of the tridimensional structures of proteins, their biological function and their reciprocal interactions. Results The aim of this work has been the implementation of predictive methods that allow the extraction of information on the properties of genomes and proteins starting from the nucleotide and aminoacidic sequences, by taking advantage of the information provided by the comparison of the genome sequences from different species. In the first part of the work a comprehensive large scale genome comparison of 599 organisms is described. 2,6 million of sequences coming from 551 prokaryotic and 48 eukaryotic genomes were aligned and clustered on the basis of their sequence identity. This procedure led to the identification of classes of proteins that are peculiar to the different groups of organisms. Moreover the adopted similarity threshold produced clusters that are homogeneous on the structural point of view and that can be used for structural annotation of uncharacterized sequences. The second part of the work focuses on the characterization of thermostable proteins and on the development of tools able to predict the thermostability of a protein starting from its sequence. By means of Principal Component Analysis the codon composition of a non redundant database comprising 116 prokaryotic genomes has been analyzed and it has been showed that a cross genomic approach can allow the extraction of common determinants of thermostability at the genome level, leading to an overall accuracy in discriminating thermophilic coding sequences equal to 95%. This result outperform those obtained in previous studies. Moreover, we investigated the effect of multiple mutations on protein thermostability. This issue is of great importance in the field of protein engineering, since thermostable proteins are generally more suitable than their mesostable counterparts in technological applications. A Support Vector Machine based method has been trained to predict if a set of mutations can enhance the thermostability of a given protein sequence. The developed predictor achieves 88% accuracy.

Computational methods for the analysis of protein structure and function

The vast majority of known proteins have not yet been experimentally characterized and little is known about their function. The design and implementation of computational tools can provide insight into the function of proteins based on their sequence, their structure, their evolutionary history and their association with other proteins. Knowledge of the three-dimensional (3D) structure of a protein can lead to a deep understanding of its mode of action and interaction, but currently the structures of <1% of sequences have been experimentally solved. For this reason, it became urgent to develop new methods that are able to computationally extract relevant information from protein sequence and structure. The starting point of my work has been the study of the properties of contacts between protein residues, since they constrain protein folding and characterize different protein structures. Prediction of residue contacts in proteins is an interesting problem whose solution may be useful in protein folding recognition and de novo design. The prediction of these contacts requires the study of the protein inter-residue distances related to the specific type of amino acid pair that are encoded in the so-called contact map. An interesting new way of analyzing those structures came out when network studies were introduced, with pivotal papers demonstrating that protein contact networks also exhibit small-world behavior. In order to highlight constraints for the prediction of protein contact maps and for applications in the field of protein structure prediction and/or reconstruction from experimentally determined contact maps, I studied to which extent the characteristic path length and clustering coefficient of the protein contacts network are values that reveal characteristic features of protein contact maps. Provided that residue contacts are known for a protein sequence, the major features of its 3D structure could be deduced by combining this knowledge with correctly predicted motifs of secondary structure. In the second part of my work I focused on a particular protein structural motif, the coiled-coil, known to mediate a variety of fundamental biological interactions. Coiled-coils are found in a variety of structural forms and in a wide range of proteins including, for example, small units such as leucine zippers that drive the dimerization of many transcription factors or more complex structures such as the family of viral proteins responsible for virus-host membrane fusion. The coiled-coil structural motif is estimated to account for 5-10% of the protein sequences in the various genomes. Given their biological importance, in my work I introduced a Hidden Markov Model (HMM) that exploits the evolutionary information derived from multiple sequence alignments, to predict coiled-coil regions and to discriminate coiled-coil sequences. The results indicate that the new HMM outperforms all the existing programs and can be adopted for the coiled-coil prediction and for large-scale genome annotation. Genome annotation is a key issue in modern computational biology, being the starting point towards the understanding of the complex processes involved in biological networks. The rapid growth in the number of protein sequences and structures available poses new fundamental problems that still deserve an interpretation. Nevertheless, these data are at the basis of the design of new strategies for tackling problems such as the prediction of protein structure and function. Experimental determination of the functions of all these proteins would be a hugely time-consuming and costly task and, in most instances, has not been carried out. As an example, currently, approximately only 20% of annotated proteins in the Homo sapiens genome have been experimentally characterized. A commonly adopted procedure for annotating protein sequences relies on the "inheritance through homology" based on the notion that similar sequences share similar functions and structures. This procedure consists in the assignment of sequences to a specific group of functionally related sequences which had been grouped through clustering techniques. The clustering procedure is based on suitable similarity rules, since predicting protein structure and function from sequence largely depends on the value of sequence identity. However, additional levels of complexity are due to multi-domain proteins, to proteins that share common domains but that do not necessarily share the same function, to the finding that different combinations of shared domains can lead to different biological roles. In the last part of this study I developed and validate a system that contributes to sequence annotation by taking advantage of a validated transfer through inheritance procedure of the molecular functions and of the structural templates. After a cross-genome comparison with the BLAST program, clusters were built on the basis of two stringent constraints on sequence identity and coverage of the alignment. The adopted measure explicity answers to the problem of multi-domain proteins annotation and allows a fine grain division of the whole set of proteomes used, that ensures cluster homogeneity in terms of sequence length. A high level of coverage of structure templates on the length of protein sequences within clusters ensures that multi-domain proteins when present can be templates for sequences of similar length. This annotation procedure includes the possibility of reliably transferring statistically validated functions and structures to sequences considering information available in the present data bases of molecular functions and structures.

Studio molecolare dei meccanismi dell'autoincompatibilità gametofitica in pero europeo (Pyrus communis)

Self-incompatibility (SI) systems have evolved in many flowering plants to prevent self-fertilization and thus promote outbreeding. Pear and apple, as many of the species belonging to the Rosaceae, exhibit RNase-mediated gametophytic self-incompatibility, a widespread system carried also by the Solanaceae and Plantaginaceae. Pear orchards must for this reason contain at least two different cultivars that pollenize each other; to guarantee an efficient cross-pollination, they should have overlapping flowering periods and must be genetically compatible. This compatibility is determined by the S-locus, containing at least two genes encoding for a female (pistil) and a male (pollen) determinant. The female determinant in the Rosaceae, Solanaceae and Plantaginaceae system is a stylar glycoprotein with ribonuclease activity (S-RNase), that acts as a specific cytotoxin in incompatible pollen tubes degrading cellular RNAs. Since its identification, the S-RNase gene has been intensively studied and the sequences of a large number of alleles are available in online databases. On the contrary, the male determinant has been only recently identified as a pollen-expressed protein containing a F-box motif, called S-Locus F-box (abbreviated SLF or SFB). Since F-box proteins are best known for their participation to the SCF (Skp1 - Cullin - F-box) E3 ubiquitine ligase enzymatic complex, that is involved in protein degradation through the 26S proteasome pathway, the male determinant is supposed to act mediating the ubiquitination of the S-RNases, targeting them for the degradation in compatible pollen tubes. Attempts to clone SLF/SFB genes in the Pyrinae produced no results until very recently; in apple, the use of genomic libraries allowed the detection of two F-box genes linked to each S haplotype, called SFBB (S-locus F-Box Brothers). In Japanese pear, three SFBB genes linked to each haplotype were cloned from pollen cDNA. The SFBB genes exhibit S haplotype-specific sequence divergence and pollen-specific expression; their multiplicity is a feature whose interpretation is unclear: it has been hypothesized that all of them participate in the S-specific interaction with the RNase, but it is also possible that only one of them is involved in this function. Moreover, even if the S locus male and female determinants are the only responsible for the specificity of the pollen-pistil recognition, many other factors are supposed to play a role in GSI; these are not linked to the S locus and act in a S-haplotype independent manner. They can have a function in regulating the expression of S determinants (group 1 factors), modulating their activity (group 2) or acting downstream, in the accomplishment of the reaction of acceptance or rejection of the pollen tube (group 3). This study was aimed to the elucidation of the molecular mechanism of GSI in European pear (Pyrus communis) as well as in the other Pyrinae; it was divided in two parts, the first focusing on the characterization of male determinants, and the second on factors external to the S locus. The research of S locus F-box genes was primarily aimed to the identification of such genes in European pear, for which sequence data are still not available; moreover, it allowed also to investigate about the S locus structure in the Pyrinae. The analysis was carried out on a pool of varieties of the three species Pyrus communis (European pear), Pyrus pyrifolia (Japanese pear), and Malus × domestica (apple); varieties carrying S haplotypes whose RNases are highly similar were chosen, in order to check whether or not the same level of similarity is maintained also between the male determinants. A total of 82 sequences was obtained, 47 of which represent the first S-locus F-box genes sequenced from European pear. The sequence data strongly support the hypothesis that the S locus structure is conserved among the three species, and presumably among all the Pyrinae; at least five genes have homologs in the analysed S haplotypes, but the number of F-box genes surrounding the S-RNase could be even greater. The high level of sequence divergence and the similarity between alleles linked to highly conserved RNases, suggest a shared ancestral polymorphism also for the F-box genes. The F-box genes identified in European pear were mapped on a segregating population of 91 individuals from the cross 'Abbé Fétel' × 'Max Red Bartlett'. All the genes were placed on the linkage group 17, where the S locus has been placed both in pear and apple maps, and resulted strongly associated to the S-RNase gene. The linkage with the RNase was perfect for some of the F-box genes, while for others very rare single recombination events were identified. The second part of this study was focused on the research of other genes involved in the SI response in pear; it was aimed on one side to the identification of genes differentially expressed in compatible and incompatible crosses, and on the other to the cloning and characterization of the transglutaminase (TGase) gene, whose role may be crucial in pollen rejection. For the identification of differentially expressed genes, controlled pollinations were carried out in four combinations (self pollination, incompatible, half-compatible and fully compatible cross-pollination); expression profiles were compared through cDNA-AFLP. 28 fragments displaying an expression pattern related to compatibility or incompatibility were identified, cloned and sequenced; the sequence analysis allowed to assign a putative annotation to a part of them. The identified genes are involved in very different cellular processes or in defense mechanisms, suggesting a very complex change in gene expression following the pollen/pistil recognition. The pool of genes identified with this technique offers a good basis for further study toward a better understanding of how the SI response is carried out. Among the factors involved in SI response, moreover, an important role may be played by transglutaminase (TGase), an enzyme involved both in post-translational protein modification and in protein cross-linking. The TGase activity detected in pear styles was significantly higher when pollinated in incompatible combinations than in compatible ones, suggesting a role of this enzyme in the abnormal cytoskeletal reorganization observed during pollen rejection reaction. The aim of this part of the work was thus to identify and clone the pear TGase gene; the PCR amplification of fragments of this gene was achieved using primers realized on the alignment between the Arabidopsis TGase gene sequence and several apple EST fragments; the full-length coding sequence of the pear TGase gene was then cloned from cDNA, and provided a precious tool for further study of the in vitro and in vivo action of this enzyme.

Creazione e sviluppo di corpora multimediali. Nuove metodologie di ricerca nella traduzione audiovisiva

The construction and use of multimedia corpora has been advocated for a while in the literature as one of the expected future application fields of Corpus Linguistics. This research project represents a pioneering experience aimed at applying a data-driven methodology to the study of the field of AVT, similarly to what has been done in the last few decades in the macro-field of Translation Studies. This research was based on the experience of Forlixt 1, the Forlì Corpus of Screen Translation, developed at the University of Bologna’s Department of Interdisciplinary Studies in Translation, Languages and Culture. As a matter of fact, in order to quantify strategies of linguistic transfer of an AV product, we need to take into consideration not only the linguistic aspect of such a product but all the meaning-making resources deployed in the filmic text. Provided that one major benefit of Forlixt 1 is the combination of audiovisual and textual data, this corpus allows the user to access primary data for scientific investigation, and thus no longer rely on pre-processed material such as traditional annotated transcriptions. Based on this rationale, the first chapter of the thesis sets out to illustrate the state of the art of research in the disciplinary fields involved. The primary objective was to underline the main repercussions on multimedia texts resulting from the interaction of a double support, audio and video, and, accordingly, on procedures, means, and methods adopted in their translation. By drawing on previous research in semiotics and film studies, the relevant codes at work in visual and acoustic channels were outlined. Subsequently, we concentrated on the analysis of the verbal component and on the peculiar characteristics of filmic orality as opposed to spontaneous dialogic production. In the second part, an overview of the main AVT modalities was presented (dubbing, voice-over, interlinguistic and intra-linguistic subtitling, audio-description, etc.) in order to define the different technologies, processes and professional qualifications that this umbrella term presently includes. The second chapter focuses diachronically on various theories’ contribution to the application of Corpus Linguistics’ methods and tools to the field of Translation Studies (i.e. Descriptive Translation Studies, Polysystem Theory). In particular, we discussed how the use of corpora can favourably help reduce the gap existing between qualitative and quantitative approaches. Subsequently, we reviewed the tools traditionally employed by Corpus Linguistics in regard to the construction of traditional “written language” corpora, to assess whether and how they can be adapted to meet the needs of multimedia corpora. In particular, we reviewed existing speech and spoken corpora, as well as multimedia corpora specifically designed to investigate Translation. The third chapter reviews Forlixt 1's main developing steps, from a technical (IT design principles, data query functions) and methodological point of view, by laying down extensive scientific foundations for the annotation methods adopted, which presently encompass categories of pragmatic, sociolinguistic, linguacultural and semiotic nature. Finally, we described the main query tools (free search, guided search, advanced search and combined search) and the main intended uses of the database in a pedagogical perspective. The fourth chapter lists specific compilation criteria retained, as well as statistics of the two sub-corpora, by presenting data broken down by language pair (French-Italian and German-Italian) and genre (cinema’s comedies, television’s soapoperas and crime series). Next, we concentrated on the discussion of the results obtained from the analysis of summary tables reporting the frequency of categories applied to the French-Italian sub-corpus. The detailed observation of the distribution of categories identified in the original and dubbed corpus allowed us to empirically confirm some of the theories put forward in the literature and notably concerning the nature of the filmic text, the dubbing process and Italian dubbed language’s features. This was possible by looking into some of the most problematic aspects, like the rendering of socio-linguistic variation. The corpus equally allowed us to consider so far neglected aspects, such as pragmatic, prosodic, kinetic, facial, and semiotic elements, and their combination. At the end of this first exploration, some specific observations concerning possible macrotranslation trends were made for each type of sub-genre considered (cinematic and TV genre). On the grounds of this first quantitative investigation, the fifth chapter intended to further examine data, by applying ad hoc models of analysis. Given the virtually infinite number of combinations of categories adopted, and of the latter with searchable textual units, three possible qualitative and quantitative methods were designed, each of which was to concentrate on a particular translation dimension of the filmic text. The first one was the cultural dimension, which specifically focused on the rendering of selected cultural references and on the investigation of recurrent translation choices and strategies justified on the basis of the occurrence of specific clusters of categories. The second analysis was conducted on the linguistic dimension by exploring the occurrence of phrasal verbs in the Italian dubbed corpus and by ascertaining the influence on the adoption of related translation strategies of possible semiotic traits, such as gestures and facial expressions. Finally, the main aim of the third study was to verify whether, under which circumstances, and through which modality, graphic and iconic elements were translated into Italian from an original corpus of both German and French films. After having reviewed the main translation techniques at work, an exhaustive account of possible causes for their non-translation was equally provided. By way of conclusion, the discussion of results obtained from the distribution of annotation categories on the French-Italian corpus, as well as the application of specific models of analysis allowed us to underline possible advantages and drawbacks related to the adoption of a corpus-based approach to AVT studies. Even though possible updating and improvement were proposed in order to help solve some of the problems identified, it is argued that the added value of Forlixt 1 lies ultimately in having created a valuable instrument, allowing to carry out empirically-sound contrastive studies that may be usefully replicated on different language pairs and several types of multimedia texts. Furthermore, multimedia corpora can also play a crucial role in L2 and translation teaching, two disciplines in which their use still lacks systematic investigation.

Investigating the role of single point mutations in the human proteome: a computational study

In the post genomic era with the massive production of biological data the understanding of factors affecting protein stability is one of the most important and challenging tasks for highlighting the role of mutations in relation to human maladies. The problem is at the basis of what is referred to as molecular medicine with the underlying idea that pathologies can be detailed at a molecular level. To this purpose scientific efforts focus on characterising mutations that hamper protein functions and by these affect biological processes at the basis of cell physiology. New techniques have been developed with the aim of detailing single nucleotide polymorphisms (SNPs) at large in all the human chromosomes and by this information in specific databases are exponentially increasing. Eventually mutations that can be found at the DNA level, when occurring in transcribed regions may then lead to mutated proteins and this can be a serious medical problem, largely affecting the phenotype. Bioinformatics tools are urgently needed to cope with the flood of genomic data stored in database and in order to analyse the role of SNPs at the protein level. In principle several experimental and theoretical observations are suggesting that protein stability in the solvent-protein space is responsible of the correct protein functioning. Then mutations that are found disease related during DNA analysis are often assumed to perturb protein stability as well. However so far no extensive analysis at the proteome level has investigated whether this is the case. Also computationally methods have been developed to infer whether a mutation is disease related and independently whether it affects protein stability. Therefore whether the perturbation of protein stability is related to what it is routinely referred to as a disease is still a big question mark. In this work we have tried for the first time to explore the relation among mutations at the protein level and their relevance to diseases with a large-scale computational study of the data from different databases. To this aim in the first part of the thesis for each mutation type we have derived two probabilistic indices (for 141 out of 150 possible SNPs): the perturbing index (Pp), which indicates the probability that a given mutation effects protein stability considering all the “in vitro” thermodynamic data available and the disease index (Pd), which indicates the probability of a mutation to be disease related, given all the mutations that have been clinically associated so far. We find with a robust statistics that the two indexes correlate with the exception of all the mutations that are somatic cancer related. By this each mutation of the 150 can be coded by two values that allow a direct comparison with data base information. Furthermore we also implement computational methods that starting from the protein structure is suited to predict the effect of a mutation on protein stability and find that overpasses a set of other predictors performing the same task. The predictor is based on support vector machines and takes as input protein tertiary structures. We show that the predicted data well correlate with the data from the databases. All our efforts therefore add to the SNP annotation process and more importantly found the relationship among protein stability perturbation and the human variome leading to the diseasome.

Bioinformatic methods in applied genomic research

Here I will focus on three main topics that best address and include the projects I have been working in during my three year PhD period that I have spent in different research laboratories addressing both computationally and practically important problems all related to modern molecular genomics. The first topic is the use of livestock species (pigs) as a model of obesity, a complex human dysfunction. My efforts here concern the detection and annotation of Single Nucleotide Polymorphisms. I developed a pipeline for mining human and porcine sequences. Starting from a set of human genes related with obesity the platform returns a list of annotated porcine SNPs extracted from a new set of potential obesity-genes. 565 of these SNPs were analyzed on an Illumina chip to test the involvement in obesity on a population composed by more than 500 pigs. Results will be discussed. All the computational analysis and experiments were done in collaboration with the Biocomputing group and Dr.Luca Fontanesi, respectively, under the direction of prof. Rita Casadio at the Bologna University, Italy. The second topic concerns developing a methodology, based on Factor Analysis, to simultaneously mine information from different levels of biological organization. With specific test cases we develop models of the complexity of the mRNA-miRNA molecular interaction in brain tumors measured indirectly by microarray and quantitative PCR. This work was done under the supervision of Prof. Christine Nardini, at the “CAS-MPG Partner Institute for Computational Biology” of Shangai, China (co-founded by the Max Planck Society and the Chinese Academy of Sciences jointly) The third topic concerns the development of a new method to overcome the variety of PCR technologies routinely adopted to characterize unknown flanking DNA regions of a viral integration locus of the human genome after clinical gene therapy. This new method is entirely based on next generation sequencing and it reduces the time required to detect insertion sites, decreasing the complexity of the procedure. This work was done in collaboration with the group of Dr. Manfred Schmidt at the Nationales Centrum für Tumorerkrankungen (Heidelberg, Germany) supervised by Dr. Annette Deichmann and Dr. Ali Nowrouzi. Furthermore I add as an Appendix the description of a R package for gene network reconstruction that I helped to develop for scientific usage (http://www.bioconductor.org/help/bioc-views/release/bioc/html/BUS.html).

Functional and molecular characterization of ethylene responsive factor genes involved in grape ripening

Grape berry is considered a non climacteric fruit, but there are some evidences that ethylene plays a role in the control of berry ripening. This PhD thesis aimed to give insights in the role of ethylene and ethylene-related genes in the regulation of grape berry ripening. During this study a small increase in ethylene concentration one week before véraison has been measured in Vitis vinifera L. ‘Pinot Noir’ grapes confirming previous findings in ‘Cabernet Sauvignon’. In addition, ethylene-related genes have been identified in the grapevine genome sequence. Similarly to other species, biosynthesis and ethylene receptor genes are present in grapevine as multi-gene families and their expression appeared tissue or developmental specific. All the other elements of the ethylene signal transduction cascade were also identified in the grape genome. Among them, there were ethylene response factors (ERF) which modulate the transcription of many effector genes in response to ethylene. In this study seven grapevine ERFs have been characterized and they showed tissue and berry development specific expression profiles. Two sequences, VvERF045 and VvERF063, seemed likely involved in berry ripening control due to their expression profiles and their sequence annotation. VvERF045 was induced before véraison and was specific of the ripe berry, by sequence similarity it was likely a transcription activator. VvERF063 displayed high sequence similarity to repressors of transcription and its expression, very high in green berries, was lowest at véraison and during ripening. To functionally characterize VvERF045 and VvERF063, a stable transformation strategy was chosen. Both sequences were cloned in vectors for over-expression and silencing and transferred in grape by Agrobacterium-mediated or biolistic-mediated gene transfer. In vitro, transgenic VvERF045 over-expressing plants displayed an epinastic phenotype whose extent was correlated to the transgene expression level. Four pathogen stress response genes were significantly induced in the transgenic plants, suggesting a putative function of VvERF045 in biotic stress defense during berry ripening. Further molecular analysis on the transgenic plants will help in identifying the actual VvERF045 target genes and together with the phenotypic characterization of the adult transgenic plants, will allow to extensively define the role of VvERF045 in berry ripening.

Metodi computazionali per l'annotazione di genomi e proteomi

Il progresso tecnologico nel campo della biologia molecolare, pone la comunità scientifica di fronte all’esigenza di dare un’interpretazione all’enormità di sequenze biologiche che a mano a mano vanno a costituire le banche dati, siano esse proteine o acidi nucleici. In questo contesto la bioinformatica gioca un ruolo di primaria importanza. Un nuovo livello di possibilità conoscitive è stato introdotto con le tecnologie di Next Generation Sequencing (NGS), per mezzo delle quali è possibile ottenere interi genomi o trascrittomi in poco tempo e con bassi costi. Tra le applicazioni del NGS più rilevanti ci sono senza dubbio quelle oncologiche che prevedono la caratterizzazione genomica di tessuti tumorali e lo sviluppo di nuovi approcci diagnostici e terapeutici per il trattamento del cancro. Con l’analisi NGS è possibile individuare il set completo di variazioni che esistono nel genoma tumorale come varianti a singolo nucleotide, riarrangiamenti cromosomici, inserzioni e delezioni. Va però sottolineato che le variazioni trovate nei geni vanno in ultima battuta osservate dal punto di vista degli effetti a livello delle proteine in quanto esse sono le responsabili più dirette dei fenotipi alterati riscontrabili nella cellula tumorale. L’expertise bioinformatica va quindi collocata sia a livello dell’analisi del dato prodotto per mezzo di NGS ma anche nelle fasi successive ove è necessario effettuare l’annotazione dei geni contenuti nel genoma sequenziato e delle relative strutture proteiche che da esso sono espresse, o, come nel caso dello studio mutazionale, la valutazione dell’effetto della variazione genomica. È in questo contesto che si colloca il lavoro presentato: da un lato lo sviluppo di metodologie computazionali per l’annotazione di sequenze proteiche e dall’altro la messa a punto di una pipeline di analisi di dati prodotti con tecnologie NGS in applicazioni oncologiche avente come scopo finale quello della individuazione e caratterizzazione delle mutazioni genetiche tumorali a livello proteico.

Identification and genetic diversity in phytoplasmas associated with diseases of cassava and other agronomic relevant crops in south-east Asia and Latin America

Identification and genetic diversity of phytoplasmas infecting tropical plant species, selected among those most agronomically relevant in South-east Asia and Latin America were studied. Correlation between evolutionary divergence of relevant phytoplasma strains and their geographic distribution by comparison on homologous genes of phytoplasma strains detected in the same or related plant species in other geographical areas worldwide was achieved. Molecular diversity was studied on genes coding ribosomal proteins, groEL, tuf and amp besides phytoplasma 16S rRNA. Selected samples infected by phytoplasmas belonging to diverse ribosomal groups were also studied by in silico RFLP followed by phylogenetic analyses. Moreover a partial genome annotation of a ‘Ca. P. brasiliense’ strain was done towards future application for epidemiological studies. Phytoplasma presence in cassava showing frog skin (CFSD) and witches’ broom (CWB) diseases in Costa Rica - Paraguay and in Vietnam – Thailand, respectively, was evaluated. In both cases, the diseases were associated with phytoplasmas related to aster yellows, apple proliferation and “stolbur” groups, while only phytoplasma related to X-disease group in CFSD, and to hibiscus witches’ broom, elm yellows and clover proliferation groups in CWB. Variability was found among strains belonging to the same ribosomal group but having different geographic origin and associated with different disease. Additionally, a dodder transmission assay to elucidate the role of phytoplasmas in CWB disease was carried out, and resulted in typical phytoplasma symptoms in periwinkle plants associated with the presence of aster yellows-related strains. Lethal wilt disease, a severe disease of oil palm in Colombia that is spreading throughout South America was also studied. Phytoplasmas were detected in symptomatic oil palm and identified as ‘Ca. P. asteris’, ribosomal subgroup 16SrI-B, and were distinguished from other aster yellows phytoplasmas used as reference strains; in particular, from an aster yellows strain infecting corn in the same country.

Graph algorithms for bioinformatics

Biological data are inherently interconnected: protein sequences are connected to their annotations, the annotations are structured into ontologies, and so on. While protein-protein interactions are already represented by graphs, in this work I am presenting how a graph structure can be used to enrich the annotation of protein sequences thanks to algorithms that analyze the graph topology. We also describe a novel solution to restrict the data generation needed for building such a graph, thanks to constraints on the data and dynamic programming. The proposed algorithm ideally improves the generation time by a factor of 5. The graph representation is then exploited to build a comprehensive database, thanks to the rising technology of graph databases. While graph databases are widely used for other kind of data, from Twitter tweets to recommendation systems, their application to bioinformatics is new. A graph database is proposed, with a structure that can be easily expanded and queried.