Integrating sequence, expression and interaction data to determine condition-specific miRNA regulation

Abstract

Motivation: MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression post-transcriptionally. MiRNAs were shown to play an important role in development and disease, and accurately determining the networks regulated by these miRNAs in a specific condition is of great interest. Early work on miRNA target prediction has focused on using static sequence information. More recently, researchers have combined sequence and expression data to identify such targets in various conditions.

Results: We developed the Protein Interaction-based MicroRNA Modules (PIMiM), a regression-based probabilistic method that integrates sequence, expression and interaction data to identify modules of mRNAs controlled by small sets of miRNAs. We formulate an optimization problem and develop a learning framework to determine the module regulation and membership. Applying PIMiM to cancer data, we show that by adding protein interaction data and modeling cooperative regulation of mRNAs by a small number of miRNAs, PIMiM can accurately identify both miRNA and their targets improving on previous methods. We next used PIMiM to jointly analyze a number of different types of cancers and identified both common and cancer-type-specific miRNA regulators.

Supplementary information: Supplementary data are available at Bioinformatics online.

Fig. 1.

Data used as input for PIMiM. In addition the miRNA and mRNA expression data, PIMiM uses sequence-based predictions of miRNA–mRNA interactions and protein–protein interactions. These datasets are integrated as discussed in Section 2

Fig. 2.

Interactions between genes of the modules. We show an edge between two genes if they are members of a module and their interaction exists in the database. Each color corresponds to one module. Genes with no edges are omitted to improve visualization

Fig. 3.

MSigDB enrichment analysis: pathway enrichment analysis was done using 880 gene sets of canonical pathways (C2-CP) from MSigDB (Subramanian et al., 2005). P-values were computed using hypergeometric test (with 10 000 random permutations) on the intersection of the set of genes in each module with MSigDB gene sets. Benjamini–Hochberg procedure was used to control the false discovery rate. Top: Number of modules significantly enriched for at least one MSigDB category for different significance cut-offs. Bottom: Number of MSigDB categories identified as in enriched in at least one of the modules for different significance cut-off

Fig. 4.

The effect of protein interaction data to the result. We varied the value of β and tested the different metrics discussed in Section 4. As can be seen, both high and low values lead to reduced performance

Fig. 5.

Inferred miRNA modules of the three cancer types (BRCA, GBM and AML). The x-axis shows the modules learned for the three cancer types (each x-axis bar is subdivided into three with the color corresponding to the cancer type). The y-axis shows miRNAs ordered by hierarchical clustering of their module membership vector. In several cases, the same miRNAs are predicted for all or two of the three cancer types

Fig. 6.

miRNAs and mRNAs assigned to Module 11 in all three cancer types. Color indicates the specific cancer type for which the mRNA or miRNA was selected as part of the module