In this thesis, I propose new algorithms and models to address biological problems. Computer science in fact plays a key role in proteomics and genetics research due to the advent of big datasets. In the context of protein study, I developed new methods for protein function prediction based on information retrieval principles. By using heterogeneous source of knowledge, like graph search and sequence similarity, I designed a tool called INGA that can be used to annotate entire genomes. It has been benchmarked during the Critical Assessment of Function Annotation challenge, and it proved to be one of the most effective approach for function inference.
To better characterize proteins from the structural point of view, I proposed a protein conformers detection strategy based on residue interaction network (RIN) data. RIN graphs were extended to deal with the time-dependent protein coordinate fluctuations, and were generated by clustering algorithms. An implementation called RING MD highlighted effectively the key amino acids known to be functionally relevant in Ubiquitin. These amino acids in fact are very important to explain the protein three-dimensional dynamics. With the same rationale, RIN graphs were used also to predict the impact of mutations within a protein structure. By combining information about a mutant node in the network and its features, an artificial neural network was trained to estimate the free Gibbs energy change of a protein. Extreme changes in the internal energy might lead to the protein unfolding, and possibly to disease. The reduction of a protein flexibility may hamper its function as well. As an example, the extreme fluctuations observed in intrinsically disordered proteins (IDPs) are fundamental for their activities. To better understand IDPs, I contributed in the collection of the largest dataset of disordered regions. In the following analysis, it was shown what are the typical functions of these sequences and the biological processes where they are involved. Due to the importance of their detection, a comprehensive assessment of disorder predictors was performed to show what are the state-of-the-art methods and their limitations.
In the context of genetics, I focused on phenotype prediction. During the Critical Assessment of Genome Interpretation (CAGI), I proposed new approaches for the analysis of exome data to prioritize the risk of Crohn's disease and abnormal cholesterol levels. These are often defined as complex disease, since the mechanism behind their insurgence is still unknown. In my study, human samples with an enrichment of mutations in critical genes were predicted to have an high genetic risk. In addition to disease associated genes, protein interaction networks were considered to better account for variants accumulation in biological pathways. Such strategy was shown to be among the best approaches by CAGI organizers. In the simpler case of Mendelian traits, with BOOGIE I designed a method for human blood groups prediction based on exome data. It uses a specialized version of nearest neighbor algorithm in order to match the gene variants in an unannotated exome with the ones available in a reference knowledge base. The most similar hit is used to transfer the blood group. With an accuracy above 90%, BOOGIE is a proof-of-concept that shows the potential applications of genetic prediction, and can be easily extended to any Mendelian trait.
To summarize, this thesis is a partial answer to the exponential growth of sequences available that need further experiments. By integrating heterogeneous information and designing new predictive models based on machine learning, I developed novel tools for biological data analysis and classification. All implementations are freely available for the community and might be helpful during future investigations like in drug design and disease studies
