Federica Viti

A multilevel data integration resource for breast cancer study

BackgroundBreast cancer is one of the most common cancer types. Due to the complexity of this disease, it is important to face its study with an integrated and multilevel approach, from genes, transcripts and proteins to molecular networks, cell populations and tissues. According to the systems biology perspective, the biological functions arise from complex networks: in this context, concepts like molecular pathways, protein-protein interactions (PPIs), mathematical models and ontologies play an important role for dissecting such complexity.ResultsIn this work we present the Genes-to-Systems Breast Cancer (G2SBC) Database, a resource which integrates data about genes, transcripts and proteins reported in literature as altered in breast cancer cells. Beside the data integration, we provide an ontology based query system and analysis tools related to intracellular pathways, PPIs, protein structure and systems modelling, in order to facilitate the study of breast cancer using a multilevel perspective. The resource is available at the URL http://www.itb.cnr.it/breastcancer.ConclusionsThe G2SBC Database represents a systems biology oriented data integration approach devoted to breast cancer. By means of the analysis capabilities provided by the web interface, it is possible to overcome the limits of reductionist resources, enabling predictions that can lead to new experiments.

myMIR: a genome-wide microRNA targets identification and annotation tool

miRNA target genes prediction represents a crucial step in miRNAs functional characterization. In this context, the challenging issue remains predictions accuracy and recognition of false positive results. In this article myMIR, a web based system for increasing reliability of miRNAs predicted targets lists, is presented. myMIR implements an integrated pipeline for computing ranked miRNA::target lists and provides annotations for narrowing them down. The system relies on knowledge base data, suitably integrated in order to extend the functional characterization of targeted genes to miRNAs, by highlighting the search on over-represented annotation terms. Validation results show a dramatic reduction in the quantity of predictions and an increase in the sensitivity, when compared to other methods. This improves the predictions accuracy and allows the formulation of novel hypotheses on miRNAs functional involvement.

SNPranker 2.0: a gene-centric data mining tool for diseases associated SNP prioritization in GWAS.

BackgroundThe capability of correlating specific genotypes with human diseases is a complex issue in spite of all advantages arisen from high-throughput technologies, such as Genome Wide Association Studies (GWAS). New tools for genetic variants interpretation and for Single Nucleotide Polymorphisms (SNPs) prioritization are actually needed. Given a list of the most relevant SNPs statistically associated to a specific pathology as result of a genotype study, a critical issue is the identification of genes that are effectively related to the disease by re-scoring the importance of the identified genetic variations. Vice versa, given a list of genes, it can be of great importance to predict which SNPs can be involved in the onset of a particular disease, in order to focus the research on their effects.ResultsWe propose a new bioinformatics approach to support biological data mining in the analysis and interpretation of SNPs associated to pathologies. This system can be employed to design custom genotyping chips for disease-oriented studies and to re-score GWAS results. The proposed method relies (1) on the data integration of public resources using a gene-centric database design, (2) on the evaluation of a set of static biomolecular annotations, defined as features, and (3) on the SNP scoring function, which computes SNP scores using parameters and weights set by users. We employed a machine learning classifier to set default feature weights and an ontological annotation layer to enable the enrichment of the input gene set. We implemented our method as a web tool called SNPranker 2.0 (http://www.itb.cnr.it/snpranker), improving our first published release of this system. A user-friendly interface allows the input of a list of genes, SNPs or a biological process, and to customize the features set with relative weights. As result, SNPranker 2.0 returns a list of SNPs, localized within input and ontologically enriched genes, combined with their prioritization scores.ConclusionsDifferent databases and resources are already available for SNPs annotation, but they do not prioritize or re-score SNPs relying on a-priori biomolecular knowledge. SNPranker 2.0 attempts to fill this gap through a user-friendly integrated web resource. End users, such as researchers in medical genetics and epidemiology, may find in SNPranker 2.0 a new tool for data mining and interpretation able to support SNPs analysis. Possible scenarios are GWAS data re-scoring, SNPs selection for custom genotyping arrays and SNPs/diseases association studies.

A Grid-based solution for management and analysis of microarrays in distributed experiments

Several systems have been presented in the last years in order to manage the complexity of large microarray experiments. Although good results have been achieved, most systems tend to lack in one or more fields. A Grid based approach may provide a shared, standardized and reliable solution for storage and analysis of biological data, in order to maximize the results of experimental efforts. A Grid framework has been therefore adopted due to the necessity of remotely accessing large amounts of distributed data as well as to scale computational performances for terabyte datasets. Two different biological studies have been planned in order to highlight the benefits that can emerge from our Grid based platform. The described environment relies on storage services and computational services provided by the gLite Grid middleware. The Grid environment is also able to exploit the added value of metadata in order to let users better classify and search experiments. A state-of-art Grid portal has been implemented in order to hide the complexity of framework from end users and to make them able to easily access available services and data. The functional architecture of the portal is described. As a first test of the system performances, a gene expression analysis has been performed on a dataset of Affymetrix GeneChip® Rat Expression Array RAE230A, from the ArrayExpress database. The sequence of analysis includes three steps: (i) group opening and image set uploading, (ii) normalization, and (iii) model based gene expression (based on PM/MM difference model). Two different Linux versions (sequential and parallel) of the dChip software have been developed to implement the analysis and have been tested on a cluster. From results, it emerges that the parallelization of the analysis process and the execution of parallel jobs on distributed computational resources actually improve the performances. Moreover, the Grid environment have been tested both against the possibility of uploading and accessing distributed datasets through the Grid middleware and against its ability in managing the execution of jobs on distributed computational resources. Results from the Grid test will be discussed in a further paper.

Osteogenic Differentiation of MSC through Calcium Signaling Activation: Transcriptomics and Functional Analysis.

The culture of progenitor mesenchymal stem cells (MSC) onto osteoconductive materials to induce a proper osteogenic differentiation and mineralized matrix regeneration represents a promising and widely diffused experimental approach for tissue-engineering (TE) applications in orthopaedics. Among modern biomaterials, calcium phosphates represent the best bone substitutes, due to their chemical features emulating the mineral phase of bone tissue. Although many studies on stem cells differentiation mechanisms have been performed involving calcium-based scaffolds, results often focus on highlighting production of in vitro bone matrix markers and in vivo tissue ingrowth, while information related to the biomolecular mechanisms involved in the early cellular calcium-mediated differentiation is not well elucidated yet. Genetic programs for osteogenesis have been just partially deciphered, and the description of the different molecules and pathways operative in these differentiations is far from complete, as well as the activity of calcium in this process. The present work aims to shed light on the involvement of extracellular calcium in MSC differentiation: a better understanding of the early stage osteogenic differentiation program of MSC seeded on calcium-based biomaterials is required in order to develop optimal strategies to promote osteogenesis through the use of new generation osteoconductive scaffolds. A wide spectrum of analysis has been performed on time-dependent series: gene expression profiles are obtained from samples (MSC seeded on calcium-based scaffolds), together with related microRNAs expression and in vivo functional validation. On this basis, and relying on literature knowledge, hypotheses are made on the biomolecular players activated by the biomaterial calcium-phosphate component. Interestingly, a key role of miR-138 was highlighted, whose inhibition markedly increases osteogenic differentiation in vitro and enhance ectopic bone formation in vivo. Moreover, there is evidence that Ca-P substrate triggers osteogenic differentiation through genes (SMAD and RAS family) that are typically regulated during dexamethasone (DEX) induced differentiation.

Ontology-based, Tissue MicroArray oriented, image centered tissue bank

BackgroundTissue MicroArray technique is becoming increasingly important in pathology for the validation of experimental data from transcriptomic analysis. This approach produces many images which need to be properly managed, if possible with an infrastructure able to support tissue sharing between institutes. Moreover, the available frameworks oriented to Tissue MicroArray provide good storage for clinical patient, sample treatment and block construction information, but their utility is limited by the lack of data integration with biomolecular information.ResultsIn this work we propose a Tissue MicroArray web oriented system to support researchers in managing bio-samples and, through the use of ontologies, enables tissue sharing aimed at the design of Tissue MicroArray experiments and results evaluation. Indeed, our system provides ontological description both for pre-analysis tissue images and for post-process analysis image results, which is crucial for information exchange. Moreover, working on well-defined terms it is then possible to query web resources for literature articles to integrate both pathology and bioinformatics data.ConclusionsUsing this system, users associate an ontology-based description to each image uploaded into the database and also integrate results with the ontological description of biosequences identified in every tissue. Moreover, it is possible to integrate the ontological description provided by the user with a full compliant gene ontology definition, enabling statistical studies about correlation between the analyzed pathology and the most commonly related biological processes.

GEMMA - A Grid environment for microarray management and analysis in bone marrow stem cells experiments

Microarray techniques are successfully used to investigate thousands gene expression profiling in a variety of genomic analyses such as gene identification, drug discovery and clinical diagnosis, providing a large amount of genomic data for the overall research community. A Grid based Environment for distributed Microarray data Management and Analysis (GEMMA) is being built. This platform is planned to provide shared, standardized and reliable tools for managing and analyzing biological data related to bone marrow stem cell cultures, in order to maximize the results of distributed experiments. Different microarray analysis algorithms may be offered to the end-user, through a web interface. A set of modular and independent applications may be published on the portal, and either single algorithms or a combination of them might be invoked by the user, through a workflow strategy. Services may be implemented within an existing Grid computing infrastructure to solve problems concerning both large datasets storage (data intensive problem) and large computational times (computing intensive problem). Moreover, experimental data annotation may be collected according to the same rules and stored through the Grid portal, by using a metadata schema, which allows a comprehensive and replicable sharing of microarray experiments among different researchers. The environment has been tested, so far, as regards performance results concerning Grid parallelization of a microarray based gene expression analysis. First results show a very promising speedup ratio.

SNPRanker: a tool for identification and scoring of SNPs associated to target genes.

The identification of genes and SNPs involved in human diseases remains a challenge. Many public resources, databases and applications, collect biological data and perform annotations, increasing the global biological knowledge. The need of SNPs prioritization is emerging with the development of new high-throughput genotyping technologies, which allow to develop customized disease-oriented chips. Therefore, given a list of genes related to a specific biological process or disease as input, a crucial issue is finding the most relevant SNPs to analyse. The selection of these SNPs may rely on the relevant a-priori knowledge of biomolecular features characterising all the annotated SNPs and genes of the provided list. The bioinformatics approach described here allows to retrieve a ranked list of significant SNPs from a set of input genes, such as candidate genes associated with a specific disease. The system enriches the genes set by including other genes, associated to the original ones by ontological similarity evaluation. The proposed method relies on the integration of data from public resources in a vertical perspective (from genomics to systems biology data), the evaluation of features from biomolecular knowledge, the computation of partial scores for SNPs and finally their ranking, relying on their global score. Our approach has been implemented into a web based tool called SNPRanker, which is accessible through at the URL http://www.itb.cnr.it/snpranker . An interesting application of the presented system is the prioritisation of SNPs related to genes involved in specific pathologies, in order to produce custom arrays.

The biophysics of piezo1 and piezo2 mechanosensitive channels.

The ability to sense mechanical stimuli and elaborate a response to them is a fundamental process in all organisms, driving crucial mechanisms ranging from cell volume regulation up to organ development or regeneration. Nevertheless, only in few cases the underlying molecular players are known. In particular, mammals possess a large variety of mechanoreceptors, providing highly specialized functions in sensory cells, but also several housekeeping molecular systems are involved in the complex mechanism of mechanotransduction. Recently, a new class of almost ubiquitous membrane channels has been identified in mammalians, namely piezo1 and piezo2, that is thought to play a crucial role in the mechanobiology of mammals. This review focuses on recent findings on these novel channels, and highlights open biophysical questions that largely remain to be addressed.

Ontology-based resources for bioinformatics analysis

A number of specific web accessible databases are developed in order to shed light into biomolecular data, providing novel perspectives about particular scientific problems or presenting innovative data integration approaches. Ontologies constitute an important enhancement, since they allow a better representation of biological data, by providing a hierarchical structure to organise information, enabling more effective queries, statistical analysis and semantic web searching. Here we present our experience in exploiting ontologies to enrich biomolecular databases in diverse biomolecular contexts. The semantic layer improves data organisation, accessibility and analysis and represents an invaluable support to identify relations among biological components.