Recognizing the modular organization of biological networks has greatly advanced our understanding of complex cellular systems. However, little is known about the modules that exist in miRNA-gene regulation systems, and even less is known about these modules’ role in specific biological processes and key regulation assemblies. Identifying functional miRNA-gene regulatory modules is a challenging task for several reasons. (i) One gene can be regulated by multiple miRNAs, and one miRNA can regulate a large number of genes. (ii) The miRNA–mRNA target relationships differ among tissues and conditions. (iii) Although miRNAs physically interact with mRNAs, ultimately miRNA regulation affects the quantities of proteins in cells rather than the quantities of mRNAs. (iv) The genomic data are generally noisy and incomplete.
Hosts a network-assisted hypothesis-generating server for C. elegans. WormNet includes a base gene network, which substantially improved predictions of RNAi phenotypes. The server generates various gene network-based hypotheses using three complementary network methods: (i) a phenotype-centric approach to ‘find new members for a pathway’; (ii) a gene-centric approach to ‘infer functions from network neighbors’ and (iii) a context-centric approach to ‘find context-associated hub genes’, which is a method to identify key genes that mediate physiology within a specific context.
Serves for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules. SNMNMF is a program that presents the following characteristics: (1) integrates prior knowledge such as a gene interaction network as a constraint on the solution space; (2) allows users to upload several different types of data; (3) and provides sparse solutions. Moreover, this tool can be used for many problems involving heterogeneous data sources.
Connects motif content of the regulatory sequences with the logarithm of expression fold changes of a set of genes. miReduce is able to correlate the motif content of the 3'UTRs with the genome wide mRNA log fold changes in experiments involving of one or more miRNAs.
Allows users for in-depth analysis of individual miRNAs in the context of synergistic surroundings. MFSN is a computational method to identify significantly functional synergistic miRNA pairs via functional modules which are jointly regulate by integrating predicted miRNA targets. As general biological networks, the MFSN is scale free, modular and has a small-world property.
Extends the regression framework of GroupMiR by using an additional type of data: protein interactions. PIMiM is a method for inferring condition-specific regulation of miRNAs and for identifying their targets. It combines sequence, expression and interaction data to discover miRNA-regulated modules of mRNAs. PIMiM can already be of use to researchers that collect mRNA and miRNA expression data.
A method to predict miRNA regulatory modules (MRMs) based on bicliques merging. BCM exploits the miRNA/mRNA expression profiles, target site predictions as well as the gene-gene interactions. Furthermore, the module number is automatically determined during the merging process. We apply our method to breast cancer and thyroid cancer datasets downloaded from TCGA. Comparing with alternative formalisms, we show that the modules identified by our method are more densely connected and functionally enriched.
Detects synergistic miRNA regulatory modules. Mirsynergy operates in two stages: it first forms microRNA regulatory modules (MiRMs) based on co-occurring microRNA (miRNA) targets and then expands each MiRM by greedily including (excluding) mRNAs into (from) the MiRM to maximize the synergy score, which is a function of miRNA-mRNA and gene-gene interactions.