A systematic approach to the interrogation and sharing of standardised biofilm signatures

Publicado em "6th International Conference on Practical Applications of Computational Biology & Bioinformatics", ISBN 978-3-642-28838-8The study of microorganism consortia, also known as biofilms, is associated to a number of applications in biotechnology, ecotechnology and clinical domains. A public repository on existing biofilm studies would aid in the design of new studies as well as promote collaborative and incremental work. However, bioinformatics approaches are hampered by the limited access to existing data. Scientific publications summarise the studies whilst results are kept in researchers’ private ad hoc files. Since the collection and ability to compare existing data is imperative to move forward in biofilm analysis, the present work has addressed the development of a systematic computer-amenable approach to biofilm data organisation and standardisation. A set of in-house studies involving pathogens and employing different state-of-the-art devices and methods of analysis was used to validate the approach. The approach is now supporting the activities of BiofOmics, a public repository on biofilm signatures (http://biofomics.org).The authors thank, among others, Rosario Oliveira, Maria Joao Vieira, Idalina Machado, Nuno Cerca, Mariana Henriques, Pilar Teixeira, Douglas Monteiro, Melissa Negri, Susana Lopes, Carina Almeida and Helder Lopes, for submitting their data. The financial support from IBB-CEB, Fundacao para a Ciencia e Tecnologia (FCT) and European Community fund FEDER (Program COMPETE), project PTDC/SAU-ESA/646091/2006/FCOMP-01-0124-FEDER-007480, are also gratefully acknowledged

Dense Subgraphs with Restrictions and Applications to Gene Annotation Graphs

Abstract. In this paper, we focus on finding complex annotation patterns representing novel and interesting hypotheses from gene annotation data. We define a generalization of the densest subgraph problem by adding an additional distance restriction (defined by a separate metric) to the nodes of the subgraph. We show that while this generalization makes the problem NP-hard for arbitrary metrics, when the metric comes from the distance metric of a tree, or an interval graph, the problem can be solved optimally in polynomial time. We also show that the densest subgraph problem with a specified subset of vertices that have to be included in the solution can be solved optimally in polynomial time. In addition, we consider other extensions when not just one solution needs to be found, but we wish to list all subgraphs of almost maximum density as well. We apply this method to a dataset of genes and their annotations obtained from The Arabidopsis Information Resource (TAIR). A user evaluation confirms that the patterns found in the distance restricted densest subgraph for a dataset of photomorphogenesis genes are indeed validated in the literature; a control dataset validates that these are not random patterns. Interestingly, the complex annotation patterns potentially lead to new and as yet unknown hypotheses. We perform experiments to determine the properties of the dense subgraphs, as we vary parameters, including the number of genes and the distance.