Modular biological function is most effectively captured by combining molecular interaction data types.

Large-scale molecular interaction data sets have the potential to provide a comprehensive, system-wide understanding of biological function. Although individual molecules can be promiscuous in terms of their contribution to function, molecular functions emerge from the specific interactions of molecules giving rise to modular organisation. As functions often derive from a range of mechanisms, we demonstrate that they are best studied using networks derived from different sources.

MicroRNAs from the same precursor have different targeting properties.

Background: The processing of a microRNA results in an intermediate duplex of two potential mature products that derive from the two arms (5' and 3') of the precursor hairpin. It is often suggested that one of the sequences is degraded and the other is incorporated into the RNA-induced silencing complex. However, both precursor arms may give rise to functional levels of mature microRNA and the dominant product may change from species to species, from tissue to tissue, or between developmental stages.

An integrated transcriptomic and meta-analysis of hepatoma cells reveals factors that influence susceptibility to HCV infection.

Hepatitis C virus (HCV) is a global problem. To better understand HCV infection researchers employ in vitro HCV cell-culture (HCVcc) systems that use Huh-7 derived hepatoma cells that are particularly permissive to HCV infection. A variety of hyper-permissive cells have been subcloned for this purpose.

Patterns of HIV-1 protein interaction identify perturbed host-cellular subsystems.

Human immunodeficiency virus type 1 (HIV-1) exploits a diverse array of host cell functions in order to replicate. This is mediated through a network of virus-host interactions. A variety of recent studies have catalogued this information.

JNets: exploring networks by integrating annotation.

Background: A common method for presenting and studying biological interaction networks is visualization. Software tools can enhance our ability to explore network visualizations and improve our understanding of biological systems, particularly when these tools offer analysis capabilities. However, most published network visualizations are static representations that do not support user interaction.