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microRNAs are small RNA molecules that inhibit the translation of target genes. microRNA binding sites are located in the untranslated regions as well as in the coding domains. We describe TmiRUSite and TmiROSite scripts developed using python as tools for the extraction of nucleotide sequences for miRNA binding sites with their encoded amino acid residue sequences. The scripts allow for retrieving a set of additional sequences at left and at right from the binding site. The scripts presents all received data in table formats that are easy to analyse further.

miRNAfe is a comprehensive tool to extract features from RNA sequences. It is freely available as a web service, allowing a single access point to almost all state-of-the-art feature extraction methods used today in a variety of works from different authors. It has a very simple user interface, where the user only needs to load a file containing the input sequences and select the features to extract. As a result, the user obtains a text file with the features extracted, which can be used to analyze the sequences or as input to a miRNA prediction software.

RNAi is broadly used to map gene regulatory networks, but the identification of genes that are responsible for the observed phenotypes is challenging, assmall interfering RNAs (siRNAs) simultaneously downregulate the intended on targets and many partially complementary off targets. Additionally, the scarcity of publicly available control datasets hinders the development and comparative evaluation of computational methods for analyzing the data.

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.