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The Bioinformatics Links Directory is an online community resource that contains a directory of freely available tools, databases, and resources for bioinformatics and molecular biology research. The listing of the servers published in this and previous issues of Nucleic Acids Research together with other useful tools and websites represents a rich repository of resources that are openly provided to the research community using internet technologies.

Large-scale transcriptome projects have shown that the number of RNA transcripts not coding for proteins (non-coding RNAs) is much larger than previously recognized. High-throughput technologies, coupled with bioinformatics approaches, have produced increasing amounts of data, highlighting the role of non-coding RNAs (ncRNAs) in biological processes. Data generated by these studies include diverse non-coding RNA classes from organisms of different kingdoms, which were obtained using different experimental and computational assays.

Recent studies have shown that dysfunctional microRNAs (miRNAs) are involved in the progression of various cancers. Dysfunctional miRNAs may jointly regulate their target genes and further alter the activities of canonical biological pathways. Identification of the pathways regulated by a group of dysfunctional miRNAs could help uncover the pathogenic mechanisms of cancer and facilitate development of new drug targets. Current miRNA-pathway analyses mainly use differentially-expressed miRNAs to predict the shared pathways on which they act.

MicroRNAs (miRNAs) are small non-coding RNAs that have been found in most of the eukaryotic organisms. They are involved in the regulation of gene expression at the post-transcriptional level in a sequence specific manner. MiRNAs are produced from their precursors by Dicer-dependent small RNA biogenesis pathway. Involvement of miRNAs in a wide range of biological processes makes them excellent candidates for studying gene function or for therapeutic applications. For this purpose, different RNA-based gene silencing techniques have been developed.

In the last years there has been an increasing effort to computationally model and predict the influence of regulators (transcription factors, miRNAs) on gene expression. Here we introduce biRte as a computationally attractive approach combining Bayesian inference of regulator activities with network reverse engineering. biRte integrates target gene predictions with different omics data entities (e.g. miRNA and mRNA data) into a joint probabilistic framework.

Cancer progression and development are initiated by aberrations in various molecular networks through coordinated changes across multiple genes and pathways. It is important to understand how these networks change under different stress conditions and/or patient-specific groups to infer differential patterns of activation and inhibition. Existing methods are limited to correlation networks that are independently estimated from separate group-specific data and without due consideration of relationships that are conserved across multiple groups.