Biologists generally interrogate genomics data using web-based genome browsers that have limited analytical potential. New generation genome browsers such as the Integrated Genome Browser (IGB) have largely overcome this limitation and permit customized analysis to be implemented using plugins. We extend the functionality of IGB with two plugins which allow to enable molecular, network and structure biology, and to perform advanced pattern search in the genome browser tracks. 3D interactomes (networks of molecular interactions, which structures are either known or modeled) facilitate the identification of disease-relevant interactions that can then be specifically targeted by drugs. We developed a plugin for IGB, that uses advanced visualization techniques to integrate the analysis of genomics data with network and structural biology approaches. The plugin automatically maps genomic regions to protein sequence and interaction structures and identifies residues in contact with proteins, nucleic acids or small molecules. This allows the end user to generate hypotheses regarding drug- and ligand-dependent perturbations of PPI networks, and provides predictions as to how specific mutations might have an impact on drug resistance. We also integrated a pattern-search algorithm to provides biologists with the ability, once they identify an interesting genomic pattern, to look for similar occurrences in the data, thus facilitating genomic data access and use. For example, such patterns can describe gene expression regulatory DNA areas including heterogeneous (epi)genomic features (e.g. histone modification and/or different transcription factor binding regions). It is possible to define complex patterns based on perfect matches in genome tracks (regions that must match), partial matches (regions that are allowed to be absent), and negative matches (for instance to search for regions distant from transcription start sites). Plugins available at: http://cru.genomics.iit.it/igbmibundle/ and http://www-db.disi.unibo.it/research/GenData/SimSearch
Ceol, A., Montanari, P., Bartolini, I., Ciaccia, P., Patella, M., Ceri, S., et al. (2017). Advanced analysis in a genome browser: molecular interactions and pattern similarity search..
Advanced analysis in a genome browser: molecular interactions and pattern similarity search.
MONTANARI, PIERO;BARTOLINI, ILARIA;CIACCIA, PAOLO;PATELLA, MARCO;
2017
Abstract
Biologists generally interrogate genomics data using web-based genome browsers that have limited analytical potential. New generation genome browsers such as the Integrated Genome Browser (IGB) have largely overcome this limitation and permit customized analysis to be implemented using plugins. We extend the functionality of IGB with two plugins which allow to enable molecular, network and structure biology, and to perform advanced pattern search in the genome browser tracks. 3D interactomes (networks of molecular interactions, which structures are either known or modeled) facilitate the identification of disease-relevant interactions that can then be specifically targeted by drugs. We developed a plugin for IGB, that uses advanced visualization techniques to integrate the analysis of genomics data with network and structural biology approaches. The plugin automatically maps genomic regions to protein sequence and interaction structures and identifies residues in contact with proteins, nucleic acids or small molecules. This allows the end user to generate hypotheses regarding drug- and ligand-dependent perturbations of PPI networks, and provides predictions as to how specific mutations might have an impact on drug resistance. We also integrated a pattern-search algorithm to provides biologists with the ability, once they identify an interesting genomic pattern, to look for similar occurrences in the data, thus facilitating genomic data access and use. For example, such patterns can describe gene expression regulatory DNA areas including heterogeneous (epi)genomic features (e.g. histone modification and/or different transcription factor binding regions). It is possible to define complex patterns based on perfect matches in genome tracks (regions that must match), partial matches (regions that are allowed to be absent), and negative matches (for instance to search for regions distant from transcription start sites). Plugins available at: http://cru.genomics.iit.it/igbmibundle/ and http://www-db.disi.unibo.it/research/GenData/SimSearchI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
