MOTIVATION: MicroRNAs (miRNAs) are short sequences that negatively regulate gene expression. The current understanding of miRNA and their corresponding mRNA targets is primarily based on prediction programs. This study addresses the potential of a coordinated action of miRNAs to manipulate the human pathways. Specifically, we investigate the effectiveness of disrupting the topology of human pathway graphs through a regulation by miRNAs. RESULTS: From a set of miRNA candidates that is associated with a pathway, an exhaustive search for all possible doubles and triplets (coined miR-Duo, miR-Trios) is performed. The impact of each miR-combination on the connectivity of the pathway graph was quantified. About 170 human pathways were tested, and the miR-Duos and miR-Trios were scored for their ability to disrupt these pathway graphs. We show that 75% of all pathways are effectively disconnected by a small number of pathway-specific miR-Trios. Only 15% of the human pathways are resistant to fragmentation by miR-Duos or miR-Trios. Significantly, the combination of the most effective miR-Trios is unique. Thus, a specific regulation of a pathway within the cell is guaranteed. The impact of the selected miR-Duo/Trios on various diseases is discussed. CONCLUSIONS: The methodology presented shows that the synthesis of the topology of a network with a detailed understanding of the miRNAs' regulation is useful in exposing critical nodes of the network. We propose the miR-Trio approach as a basis for rationally designed perturbation experiments. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: The miRror application provides insights on microRNA (miRNA) regulation. It is based on the notion of a combinatorial regulation by an ensemble of miRNAs or genes. miRror integrates predictions from a dozen of miRNA resources that are based on complementary algorithms into a unified statistical framework. For miRNAs set as input, the online tool provides a ranked list of targets, based on set of resources selected by the user, according to their significance of being coordinately regulated. Symmetrically, a set of genes can be used as input to suggest a set of miRNAs. The user can restrict the analysis for the preferred tissue or cell line. miRror is suitable for analyzing results from miRNAs profiling, proteomics and gene expression arrays. AVAILABILITY: http://www.proto.cs.huji.ac.il/mirror

MOTIVATION: Animal toxins operate by binding to receptors and ion channels. These proteins are short and vary in sequence, structure and function. Sporadic discoveries have also revealed endogenous toxin-like proteins in non-venomous organisms. Viral proteins are the largest group of quickly evolving proteomes. We tested the hypothesis that toxin-like proteins exist in viruses and that they act to modulate functions of their hosts. RESULTS: We updated and improved a classifier for compact proteins resembling short animal toxins that is based on a machine-learning method. We applied it in a large-scale setting to identify toxin-like proteins among short viral proteins. Among the approximately 26 000 representatives of such short proteins, 510 sequences were positively identified. We focused on the 19 highest scoring proteins. Among them, we identified conotoxin-like proteins, growth factors receptor-like proteins and anti-bacterial peptides. Our predictor was shown to enhance annotation inference for many 'uncharacterized' proteins. We conclude that our protocol can expose toxin-like proteins in unexplored niches including metagenomics data and enhance the systematic discovery of novel cell modulators for drug development. AVAILABILITY: ClanTox is available at http://www.clantox.cs.huji.ac.il.