CATCHprofiles: Clustering and Alignment Tool for ChIP Profiles

Chromatin Immuno Precipitation (ChIP) profiling detects in vivo protein-DNA binding, and has revealed a large combinatorial complexity in the binding of chromatin associated proteins and their post-translational modifications. To fully explore the spatial and combinatorial patterns in ChIP-profiling data and detect potentially meaningful patterns, the areas of enrichment must be aligned and clustered, which is an algorithmically and computationally challenging task. We have developed CATCHprofiles, a novel tool for exhaustive pattern detection in ChIP profiling data. CATCHprofiles is built upon a computationally efficient implementation for the exhaustive alignment and hierarchical clustering of ChIP profiling data. The tool features a graphical interface for examination and browsing of the clustering results. CATCHprofiles requires no prior knowledge about functional sites, detects known binding patterns “ab initio”, and enables the detection of new patterns from ChIP data at a high resolution, exemplified by the detection of asymmetric histone and histone modification patterns around H2A.Z-enriched sites. CATCHprofiles' capability for exhaustive analysis combined with its ease-of-use makes it an invaluable tool for explorative research based on ChIP profiling data

Retroperitoneal liposarcomas: The experience of a tertiary Asian center

10.1186/1477-7819-9-12World Journal of Surgical Oncology9

Evidence of an Antimicrobial-Immunomodulatory Role of Atlantic Salmon Cathelicidins during Infection with Yersinia ruckeri

Cathelicidins are a family of antimicrobial peptides that act as effector molecules of the innate immune system with broad-spectrum antimicrobial properties. These evolutionary conserved cationic host-defence peptides are integral components of the immune response of fish, which are generally believed to rely heavily on innate immune defences to invading pathogens. In this study we showed that Atlantic salmon cathelicidin 1 and 2 (asCATH1 and asCATH2) stimulated peripheral blood leukocytes increasing the transcription of the chemokine interleukin-8. Further, functional differences were identified between the two cathelicidins. In the presence of serum, asCATH1 displayed greatly diminished host haemolytic activity, while the constitutively expressed asCATH2 had no haemolytic activity with or without serum. These findings support our hypothesis that fish cathelicidins exert their primary antimicrobial action at the site of pathogen invasion such as epithelial surfaces. Further, we hypothesise that like their mammalian counterparts in the presence of serum they act as mediators of the innate and adaptive immune response via the release of cytokines thus indirectly protecting against a variety of pathogens. We highlight the importance of this immunomodulatory role from the involvement of asCATHs during an infection with the fish pathogen Yersinia ruckeri. While we were able to demonstrate in vitro that asCATH1 and 2, possessed direct microbicidal activity against the fish pathogen, Vibrio anguillarum, and a common gram negative bacterium, Escherichia coli, little or no bactericidal activity was found against Y. ruckeri. The contribution of either asCATH in the immune response or as a potential virulence factor during yersiniosis is highlighted from the increased expression of asCATH1 and 2 mRNA during an in vivo challenge with Y. ruckeri . We propose that Atlantic salmon cathelicidins participate in the interplay between the innate and adaptive immune systems via the release of cytokines enabling a more effective response to invading pathogens

Using graph theory to analyze biological networks

Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system