Involving different fields as computer science, mathematics, statistics and biology, bioinformatics aims to deep understanding of the biological processes at the molecular level with a multidisciplinary approach. As biological information is being made as computerized databases and their sizes are steadily growing, molecular biologists need effective and efficient tools to manage the massive volumes of biological data generated. Bioinformatics was born to deal with this kind of issues: design and deploy efficient software tools to understand and organise the information associated with biomolecules, on a large scale. Bioinformatics nowadays has an essential role both, in deciphering genomic, transcriptomic and proteomic data generated by high throughput experimental technologies, and in organizing information gathered from traditional biology and medicine. The evolution of bioinformatics, which started with sequence analysis and has led to high-throughput whole genome or transcriptome annotation today, is now going to be directed towards recently emerging areas of integrative and translational genomics, and ultimately personalized medicine. Major research areas include sequence alignment, gene finding, genome assembly, protein structure alignment, protein structure prediction, prediction of gene expression and protein-protein interactions and the modelling of evolution.
Therefore considerable efforts are required to provide the necessary infrastructure for high-performance computing, sophisticated algorithms, advanced data management capabilities, and well trained and educated personnel to design, maintain and use these environments.
Starting from these considerations, the group is actively interested to these topics:
a) Analysis of microarrays (gene expression and genotyping).
b) Analysis of Next generation sequencing data.
c) Analysis of GWAS (Genome-Wide Association Studies).
Analysis of microarrays (gene expression and genotyping)
Microarray technology is a versatile technique that can be used in a rich diversity of approaches to help understand cancer development, to improve patient treatment and management, and to identify those predisposed to develop cancer.
Microarrays are devices displaying hundreds, or even thousands of specific oligonucleotide probes, precisely located on a small-format solid support. These array-based technologies offer both research and potentially clinical (patient-specific) application due to the ability of the multiple probe sets to simultaneously interrogate multiple genetic markers (SNPs) or the activity (i.e. the expression) of thousands of genes at once, from an individual.
The analysis of this kind of data is quite complex as involves different steps including image analysis, data preprocessing, quality assessment, statistical analysis.
Analysis of sequencing data
Next generation sequencing technology (NextGen) enables accelerated scientific discovery using a broad array of applications, including both RNA and DNA applications, and promises to transform genomic, transcriptomic, and epigenomic research.
The rapid rate of NextGen technology development has forced researchers to rethink data management strategies from the ground up. Single instrument runs can produce terabytes of data and data analysis for a small group of samples may involve analyzing entire genomes or screening hundreds of millions of sequence reads.
Traditional approaches to data management, storage, distribution, analysis and visualization are insufficient to manage the quantity and complexity of NextGen data.
Raw sequence data should be run through automated, application-specific analysis pipelines to produce well characterized, biologically meaningful data sets such as gene lists or variant reports. Researchers need immediate access to analyzed data and visualization tools.
Thus data analysis and management are becoming of particular importance as represent the rate limiting step for NextGen experiments.
Analysis of GWAS (Genome-Wide Association Studies)
A genome-wide association study is an approach that involves rapidly scanning markers across the complete sets of DNA, or genomes, of many people to find genetic variations associated with a particular disease. Once new genetic associations are identified, researchers can use the information to develop better strategies to detect, treat and prevent the disease. Such studies are particularly useful in finding genetic variations that contribute to common, complex diseases, such as asthma, cancer, diabetes, heart disease and mental illnesses. This approach requires large datasets that are being deposited in public databases with increasing frequency. The analysis is performed through statistical algorithms that are usually computational intensive and difficult to use.
The lab research in bioinformatics field has two important objectives:
1) the first one is more focused on technological issues, related to the study and implementation of new methods and tools to manage and analyze bioinformatics data. It means dealing with complex issues such as heterogeneity, data distribution, computational and storage needs, collaborative environments, quality control and reproducibility. In fact, the richness and enormity of information everyday produced by high-throughput technologies require more and more the combined use of different platforms and tools, in a virtual collaborative environment where users at different level of expertise and background can cooperate achieving more performance results;
2) the second objective mainly concerns medical and scientific challenges: to support doctors in predicting clinical outcome of cancer patients through the analysis of genomic profiles in the primary tumour and identifying which patients will respond to which drugs. This is the context of a new area called "Personalized Medicine" addressed to combine all different types of data (clinical, environmental, and genetic) to predict what diseases a person is at risk for and to identify medical treatments that will work for that specific person.
Development of a low cost integrated system for the multiple diagnosis of infectious diseases, degenerative disease and tumors
Laboratory of Interdisciplinary Technologies in Bioinformatics (LITBIO)
Design and development of a Grid-based framework for Genome-Wide Association Studies (GWAS)
Survival Online: a web-based service for the analysis of correlations between gene expression and clinical and follow-up data
This is the download link for the R package concerning the Multidimensional Integrative Quality Assessment Method ( MIQAM )