RNA binding proteins (RBPs) and non-coding RNAs (ncRNAs) are key actors in post-transcriptional gene regulation. By being able to bind messenger RNA (mRNA) they modulate many regulatory processes. In the last years, the increasing interest in this level of regulation favored the development of many NGS-based experimental techniques to detect RNA-protein interactions, and the consequent release of a considerable amount of interaction data on a growing number of eukaryotic RBPs. Despite the continuous advances in the experimental procedures, these techniques are still far from fully uncovering, on their own, the global RNA-protein interaction system. For instance, the available interaction data still covers a small fraction (less than 10%) of the known human RBPs. Moreover, experimentally determined interactions are often noisy and cell-line dependent. Importantly, obtaining genome-wide experimental evidence of combinatorial interactions of RBPs is still an experimental challenge. Machine learning approaches are able to learn from the data and generalize the information contained in them. This might give useful insights to help the investigation of the post-transcriptional regulation. In this work, three machine learning contributions are proposed. They aim at addressing the three above-mentioned shortcomings of the experimental techniques, to help researchers unveiling some yet uncharacterized aspects of post-transcriptional gene regulation. The first contribution is RNAcommender, a tool capable of suggesting RNA targets to unexplored RBPs at a genome-wide level. RNAcommender is a recommender system that propagates the available interaction data, considering biologically relevant aspects of the RNA-protein interactions, such as protein domains and RNA predicted secondary structure. The second contribution is ProtScan, a tool that models RNA-protein interactions at a single-nucleotide resolution. Learning models from experimentally determined interactions allows to denoise the data and to make predictions of the RBP binding preferences in conditions that are different from those of the experiment. The third and last contribution is PTRcombiner, a tool that unveils the combinatorial aspects of post-transcriptional gene regulation. It extracts clusters of mRNA co-regulators from the interaction annotations, and it automatically provides a biological analysis that might supply a functional characterization of the set of mRNAs targeted by a cluster of co-regulators, as well as of the binding dynamics of different RBPs belonging to the same cluster.

Machine Learning for Investigating Post-Transcriptional Regulation of Gene Expression / Corrado, Gianluca. - (2017), pp. 1-150.

Machine Learning for Investigating Post-Transcriptional Regulation of Gene Expression

Corrado, Gianluca
2017-01-01

Abstract

RNA binding proteins (RBPs) and non-coding RNAs (ncRNAs) are key actors in post-transcriptional gene regulation. By being able to bind messenger RNA (mRNA) they modulate many regulatory processes. In the last years, the increasing interest in this level of regulation favored the development of many NGS-based experimental techniques to detect RNA-protein interactions, and the consequent release of a considerable amount of interaction data on a growing number of eukaryotic RBPs. Despite the continuous advances in the experimental procedures, these techniques are still far from fully uncovering, on their own, the global RNA-protein interaction system. For instance, the available interaction data still covers a small fraction (less than 10%) of the known human RBPs. Moreover, experimentally determined interactions are often noisy and cell-line dependent. Importantly, obtaining genome-wide experimental evidence of combinatorial interactions of RBPs is still an experimental challenge. Machine learning approaches are able to learn from the data and generalize the information contained in them. This might give useful insights to help the investigation of the post-transcriptional regulation. In this work, three machine learning contributions are proposed. They aim at addressing the three above-mentioned shortcomings of the experimental techniques, to help researchers unveiling some yet uncharacterized aspects of post-transcriptional gene regulation. The first contribution is RNAcommender, a tool capable of suggesting RNA targets to unexplored RBPs at a genome-wide level. RNAcommender is a recommender system that propagates the available interaction data, considering biologically relevant aspects of the RNA-protein interactions, such as protein domains and RNA predicted secondary structure. The second contribution is ProtScan, a tool that models RNA-protein interactions at a single-nucleotide resolution. Learning models from experimentally determined interactions allows to denoise the data and to make predictions of the RBP binding preferences in conditions that are different from those of the experiment. The third and last contribution is PTRcombiner, a tool that unveils the combinatorial aspects of post-transcriptional gene regulation. It extracts clusters of mRNA co-regulators from the interaction annotations, and it automatically provides a biological analysis that might supply a functional characterization of the set of mRNAs targeted by a cluster of co-regulators, as well as of the binding dynamics of different RBPs belonging to the same cluster.
2017
XXIX
2017-2018
Ingegneria e scienza dell'Informaz (29/10/12-)
Information and Communication Technology
Passerini, Andrea
Viero, Gabriella
no
Inglese
Settore INF/01 - Informatica
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/368856
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