Workshop on Biomedical and Bioinformatics Challenges for Computer Science (BBC) Session 2

Time and Date: 14:30 - 16:10 on 1st June 2015

Room: V206

Chair: Riccardo Dondi

319 GoD: An R-Package based on Ontologies for Prioritization of Genes with respect to Diseases. [abstract]
Abstract: Omics sciences are widely used to analyze diseases at a molecular level. Usually, results of omics experiments are a large list of candidate genes, proteins or other molecules. The interpretation of results and the filtering of candidate genes or proteins selected in an experiment is a challenge in some scenarios. This problem is particularly evident in clinical scenarios in which researchers are interested in the behaviour of few molecules related to some specific disease. The filtering requires the use of domain-specific knowledge that is often encoded into ontologies. To support this interpretation, we implemented GoD (Gene ranking based On Diseases), an algorithm that ranks a given set of genes based on ontology annotations. The algorithm orders genes by the semantic similarity computed between annotation of each gene and those describing the selected disease. We tested as proof-of-principle our software using Human Phenotype Ontology (HPO), Gene Ontology (GO) and Disease Ontology (DO) using the semantic similarity measures. The dedicated website is \url{}.
Mario Cannataro, Pietro Hiram Guzzi and Marianna Milano
693 Large Scale Comparative Visualisation of Regulatory Networks with TRNDiff [abstract]
Abstract: The advent of Next Generation Sequencing technologies has seen explosive growth in genomic datasets, and dense coverage of related organisms, supporting study of subtle, strain-specific variations as a determinant of function. Such data collections present fresh and complex challenges for bioinformatics, those of comparing models of complex relationships across hundreds and even thousands of sequences. Transcriptional Regulatory Network (TRN) structures document the influence of regulatory proteins called Transcription Factors (TFs) on associated Target Genes (TGs). TRNs are routinely inferred from model systems or iterative search, and analysis at these scales requires simultaneous displays of multiple networks well beyond those of existing network visualisation tools [1]. In this paper we describe TRNDiff, an open source tool supporting the comparative analysis and visualization of TRNs (and similarly structured data) from many genomes, allowing rapid identification of functional variations within species. The approach is demonstrated through a small scale multiple TRN analysis of the Fur iron-uptake system of Yersinia, suggesting a number of candidate virulence factors; and through a far larger study based on integration with the RegPrecise database ( - a collection of hundreds of manually curated and predicted transcription factor regulons drawn from across the entire spectrum of prokaryotic organisms. The tool is presently available in stand-alone and integrated form. Information may be found at the dedicated site, which includes example data, a short tutorial and links to a working version of the stand-alone system. The integrated regulon browser is currently available at the demonstration site Source code is freely available under a non-restrictive Apache 2.0 licence from the authors’ repository at
Xin-Yi Chua, Lawrence Buckingham, James Hogan
30 Epistatic Analysis of Clarkson Disease [abstract]
Abstract: Genome Wide Association Studies (GWAS) have predominantly focused on the association between single SNPs and disease. It is probable, however, that complex diseases are due to combined effects of multiple genetic variations, as opposed to single variations. Multi-SNP interactions, known as epistatic interactions, can potentially provide information about causes of complex diseases, and build on previous GWAS looking at associations between single SNPs and phenotypes. By applying epistatic analysis methods to GWAS datasets, it is possible to identify significant epistatic interactions, and map SNPs identified to genes allowing the construction of a gene network. A large number of studies have applied graph theory techniques to analyse gene networks from microarray data sets, using graph theory metrics to identify important hub genes in these networks. In this work, we present a graph theory study of SNP and gene interaction networks constructed for a Clarkson disease GWAS, as a result of applying epistatic interaction methods to identify significant epistatic interactions. This study identifies a number of genes and SNPs with potential roles for Clarkson disease that could not be found using traditional single SNP analysis, including a number located on chromosome 5q previously identified as being of interest for capillary malformation.
Alex Upton, Oswaldo Trelles, James Perkins
527 Multiple structural clustering of bromodomains of the bromo and extra terminal (BET) proteins highlights subtle differences in their structural dynamics and acetylated leucine binding pocket [abstract]
Abstract: BET proteins are epigenetic readers whose deregulation results in cancer and inflammation. We show that BET proteins (BRD2, BRD3, BRD4 and BRDT) are globally similar with subtle differences in the sequences and structures of their N-terminal bromodomain. Principal component analysis and non-negative matrix factorization reveal distinct structural clusters associated with specific BET family members, experimental methods, and source organisms. Subtle variations in structural dynamics are evident in the acetylated lysine (Kac) binding pocket of BET bromodomains. Using multiple structural clustering methods, we have also identified representative structures of BET proteins, which are potentially useful for developing potential therapeutic agents.
Suryani Lukman, Zeyar Aung, Kelvin Sim
633 Parallel Tools for Simulating the Depolarization Block on a Neural Model [abstract]
Abstract: The prototyping and the development of computational codes for biological models, in terms of reliability, efficient and portable building blocks allow to simulate real cerebral behaviours and to validate theories and experiments. A critical issue is the tuning of a model by means of several numerical simulations with the aim to reproduce real scenarios. This requires a huge amount of computational resources to assess the impact of parameters that influence the neuronal response. In this paper, we describe how parallel tools are adopted to simulate the so-called depolarization block of a CA1 pyramidal cell of hippocampus. Here, the high performance computing techniques are adopted in order to achieve a more efficient model simulation. Finally, we analyse the performance of this neural model, investigating the scalability and benefits on multi-core and on parallel and distributed architectures.
Salvatore Cuomo, Pasquale De Michele, Ardelio Galletti, Giovanni Ponti