Abstract: Improvements of bio-nano-technologies and biomolecular techniques have led to increasing production of high-throughput experimental data. Spotted cDNA microarray is one of the most diffuse technologies, used in single research laboratories and in biotechnology service facilities. Although they are routinely performed, spotted microarray experiments are complex procedures entailing several experimental steps and actors with different technical skills and roles. During an experiment, involved actors, who can also be located in a distance, need to access and share specific experiment information according to their roles. Furthermore, complete information describing all experimental steps must be orderly collected to allow subsequent correct interpretation of experimental results. To satisfy such requirements, we developed MicroGen, a Web based system for managing information and workflow in the production pipeline of spotted microarray experiments. Our aim was to realize a multi-database system able to store all data completely characterizing different spotted microarray experiments according to the Minimum Information About Microarray Experiments (MIAME) standard, and to support the collaborative work required among multidisciplinary actors and roles involved in microarray experiment production.

Abstract: High-throughput technologies create the necessity to integrate the resulting gene expression data with information mined from large amounts of gene annotations within several different biomolecular databanks. Most of these databanks can be queried only via web, for a single gene at a time, and query results are generally available in HTML format. Although some databanks provide batch retrieval of data via FTP, this requires expertise and resources for locally re-implementing the databank. Web wrappers can automate extraction of the information of numerous genes from different web-based databanks. As the content of a dynamic web page can change from one query to another (e.g. tables with extra rows or missing fields), such wrappers should be able to locate and extract data of interest inside different HTML pages. Unfortunately, HTML tags describe the visual formatting of data, not their semantics. Thus, human-readability and machinereadability are often not equivalent. Wrapper generation tools help creating a wrapper for a specific source, i.e. a web-based biomolecular databank with its own HTML layout. First, the user is invited via a Graphic User Interface to select data of interest inside one or more sample HTML pages. Then, the system saves this information as an extraction template for that specific source. The long term goal is to generate wrappers that scale well with the number of processed web pages.

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Abstract: Analysis of inherited diseases and their associated phenotypes is of great importance to gain insight into the underlying genetic interactions and could ultimately give clinically useful insights into disease processes, including complex diseases influenced by multiple genetic loci. Nevertheless, to date few computational contributions have been proposed for this purpose mainly due to lack of controlled clinical information easily accessible and structured for computational genome-wise analyses. To enable performing comprehensive evaluations of gene annotations sparsely available in numerous different databanks accessible via Internet, we previously developed GFINDer, a Web server that dynamically aggregates functional annotations of user uploaded gene lists and allows performing their statistical analysis and mining (http://www.bioinformatics.polimi.it/GFINDer/). Exploiting and structuring information present in textual form in the Online Mendelian Inheritance in Man (OMIM) databank, we developed and made available within GFINDer new original modules specifically devoted to the analysis of inherited disorder related genes. They allow annotating large numbers of user classified biomolecular sequence identifiers with morbidity and clinical information, classifying them according to genetic disease and phenotypic location categories, and statistically analyzing the obtained classifications.

Abstract: Statistical and clustering analyses of gene expression results from high-throughput microarray experiments produce lists of hundreds of genes candidate regulated, or with particular expression profile patterns, in the conditions under study. Independently of the microarray platforms and analysis methods used to identify and cluster differentially expressed genes, the common task any researcher faces is to translate the identified lists of genes into a better understanding of the patho-physiological phenomena involved. To this aim, many biological annotations are available within numerous heterogeneous and widely distributed databases. Although several tools have been developed for annotating lists of genes, most of them do not provide methods to evaluate the relevance of the retrieved annotations for the considered set of genes, or to estimate the functional bias introduced by the gene set present on the specific array used to identify the considered gene list. Lately, few tools have been proposed that use gene annotations provided through the Gene Ontology (GO) [1] controlled vocabularies to enrich lists of genes with biological information. Some of them (e.g. Affymetrix Data Mining Tool, DAVID, FatiGO, GoMiner, MAPPFinder) also present the GO categories more relevant for a given set of genes according to the number of genes of the considered set belonging to a given category, or in relation to their statistical evaluation performed using some basic tests. To extend these functionalities we created GFINDer (i.e. Genome Function INtegrated Discoverer, http://www.medinfopoli.polimi.it/GFINDer/), a web server able to automatically provide large-scale lists of user-classified genes with the statistically significant functional profiles that biologically characterize the different gene classes in a considered gene list.