Computational tools for the interactive exploration of proteomics data and automatic bio-networks reconstruction

Revolutionary improvements in high-throughput technologies, also called 'omics' technologies, enable qualitative and quantitative monitoring of various biomolecule classes, providing broader insights into fundamental biological processes of living systems. In order to investigate, at the same time, a whole genome, transcriptome or proteome of a cell, a tissue or an organism, modern high-throughput instruments lead to the generation of a vast amounts of raw experimental data. For example, in genomics applications, next generation sequencing instruments can produce nearly 1 terabyte of data from each sample run. Data creation in today’s research is exponentially more rapid than anything we anticipated even a decade ago, and biomedical data generation is exceeding researchers’ ability to capitalize on the data. Omics studies generating large amounts of data continue to proliferate, producing billions of data points and providing opportunities for the original researchers and other investigators to use these results in their own work to advance our knowledge of biology and biomedicine. Moreover, much of this information was collected within biomedical publications and in heterogeneous databases. The discovery and extraction of useful information from unstructured sources, as biomedical literature and public available database, are a trivial tasks, but necessary to enable a deep knowledge and understanding of the state of the art in a specific field of interest. Based on these premises, the increasing availability of 'omics' data represents an unprecedented opportunity for bioinformatics researchers, but also a major challenge behind the need for novel systems biology approaches. The development of new approaches, software, and tools are requested to improve access to these data and store data, for its annotation and integration and stimulate the ability to make new discoveries using them. Moreover, the value of 'omics' data is greatly enhanced when bioinformatics and systems biology strategies allow the integration of several data sources. In particular, a systems biology approach facilitates a multi-targeted approach, allows the integration of experimental and literature data, leading to a deeper understanding of physiologically complex processes and cellular functions. The first objective of this dissertation is discuss the problems related to the management of high volume of experimental data, and how extract meaningful informations form biomedical literature and other open source of biomedical data. Later this dissertation describe the bioinformatics tool and software that can store interactively and neatly proteomics data, perform analysis and meta-analyses for obtaining new insights and understanding from the large amount of data generated in high-throughput screening. Finally, this dissertation aims to provide evidence of the effectiveness of systems biology approaches to integrate experimental 'omics' data and informations form biomedical literature.