 Methodology article
 Open Access
 Published:
Systematic exploration of autonomous modules in noisy microRNAtarget networks for testing the generality of the ceRNA hypothesis
BMC Genomics volume 15, Article number: 1178 (2014)
Abstract
Background
In the competing endogenous RNA (ceRNA) hypothesis, different transcripts communicate through a competition for their common targeting microRNAs (miRNAs). Individual examples have clearly shown the functional importance of ceRNA in gene regulation and cancer biology. It remains unclear to what extent gene expression levels are regulated by ceRNA in general. One major hurdle to studying this problem is the intertwined connections in miRNAtarget networks, which makes it difficult to isolate the effects of individual miRNAs.
Results
Here we propose computational methods for decomposing a complex miRNAtarget network into largely autonomous modules called microRNAtarget biclusters (MTBs). Each MTB contains a relatively small number of densely connected miRNAs and mRNAs with few connections to other miRNAs and mRNAs. Each MTB can thus be individually analyzed with minimal crosstalk with other MTBs. Our approach differs from previous methods for finding modules in miRNAtarget networks by not making any preassumptions about expression patterns, thereby providing objective information for testing the ceRNA hypothesis. We show that the expression levels of miRNAs and mRNAs in an MTB are significantly more anticorrelated than random miRNAmRNA pairs and other validated and predicted miRNAtarget pairs, demonstrating the biological relevance of MTBs. We further show that there is widespread correlation of expression between mRNAs in same MTBs under a wide variety of parameter settings, and the correlation remains even when coregulatory effects are controlled for, which suggests potential widespread expression buffering between these mRNAs, which is consistent with the ceRNA hypothesis. Lastly, we also propose a potential use of MTBs in functional annotation of miRNAs.
Conclusions
MTBs can be used to help identify autonomous miRNAtarget modules for testing the generality of the ceRNA hypothesis experimentally. The identified modules can also be used to test other properties of miRNAtarget networks in general.
Background
MicroRNAs (miRNAs) are short endogenous RNAs that bind specific sites of messenger RNA (mRNA) targets called miRNA response elements (MREs) with partial or full sequence complementarity. The protein levels of the targets are regulated by the miRNAs through the promotion of RNA degradation or translational repression [1–4]. Based on the distribution of MREs on different mRNAs, one miRNA could target multiple mRNAs, and multiple miRNAs could target the same mRNA, leading to a complex network of miRNAmRNA interactions [5, 6].
While conventionally miRNAs are considered to regulate their mRNA targets, in theory mRNAs could also backregulate their targeting miRNAs by affecting their availability in binding other mRNAs [7, 8]. If the expression level of an mRNA is increased, more copies of its targeting miRNAs will bind to it and become less available for binding other targets. These other targets will be derepressed and their expression levels will increase. Similarly, if the expression level of an mRNA is decreased, more copies of its targeting miRNAs will become available. They will bind more to other targets and will decrease their expression levels. As a result, different targets of a miRNA can buffer each other [9, 10] and display a positive correlation of their expression levels [8]. In general, different transcripts (mRNAs and other noncoding RNAs) with MREs of the same miRNA may compete for the finite copies of the miRNA in a cell. This backregulation mechanism and its in vivo functional roles have been coined the competing endogenous RNA (ceRNA) hypothesis [8].
One interesting example that supports the ceRNA hypothesis was found between the tumor suppressor gene PTEN and its pseudogene PTENP1 [11]. The MREs of some miRNAs that target PTEN, including miR19b and miR20a, are preserved in the truncated 3’ end of the PTENP1 transcript, which allow it to act as a miRNA target decoy for PTEN. Indeed, the expression of both PTEN and PTENP1 was repressed by miR19b and miR20a in DU145 prostate cancer cells, and their expression levels exhibited a positive correlation across a large number of normal human tissues and prostate tumor samples. Functionally, PTENP1 was found to have tumor suppressive activity and was selectively lost in human cancer, which suggest a potential role of this pseudogene in the normal functioning of PTEN in tumor suppression. Additional evidence of the functional roles of ceRNA in human cancer was reported in the same study and a series of other studies [11–15]. Regulatory interactions between mRNAs that share common MREs had also been discovered in plants, a phenomenon known as “target mimicry” [7, 16].
At a more global scale, the idea that miRNA targets buffer each other has been used by a number of methods to study miRNAtarget interactions. Some methods identify the subset of computationally predicted miRNA targets with a positive correlation of expression as the more reliable targets [17–19]. Some methods identify “sponge” modulators of miRNAtarget interactions, which are RNAs whose expression is associated with changes in the mutual information between miRNAs and their targets [14]. All these methods assume a certain degree of generality of the ceRNA hypothesis, and require some highthroughput expression data as input.
In contrast, a transcriptomewide systematic test of the ceRNA hypothesis has been lacking. It has not been certain whether the buffering between miRNA targets is sufficiently strong to be reflected by their expression levels in general. Conceptually, this can be tested by a twostep procedure, namely (1) gathering a list of miRNAtarget pairs obtained by a method not considering their expression patterns, and (2) evaluating whether mRNAs targeted by same miRNAs are significantly more correlated in expression than other mRNAs (with proper control for effects due to coregulation, as discussed in detail below). While conceptually simple, there are a number of issues that make this kind of analysis practically difficult:

1.
Noisy miRNAtarget networks: Current computational methods for miRNA target prediction have limited accuracy and consistency [20], while the number of experimentally validated miRNAtarget pairs is small [21]. False positives and false negatives in the miRNA target predictions would make it difficult to identify mRNAs with common targeting miRNAs.

2.
Unshared targeting miRNAs: mRNAs targeted by a common miRNA may individually be targeted by other unshared miRNAs, which could affect their expression levels separately and lower their correlation.

3.
Unshared mRNA targets: miRNAs that target a common set of mRNAs may individually have additional unshared targets, which could dilute the buffering effect of their common set of mRNA targets.

4.
Partial effects at transcriptional level: The functional effects of miRNAs on their targets are only partially reflected by mRNA levels, while data about protein abundance are not as widely available.

5.
Other gene regulatory mechanisms: Gene expression is regulated by a complex system that involves many other components. Even if two mRNAs are competing for their common targeting miRNAs, their expression levels may not appear correlated if they are individually affected by some other regulatory mechanisms. In addition, a miRNA may affect the expression level of a gene indirectly through its targets that directly or indirectly regulate the gene, leading to expression patterns more difficult to analyze.
In this study, we propose computational methods for studying noisy miRNAtarget networks that can overcome the first three issues and tolerate the last two. The main idea is to identify small modules in the networks, which we call microRNAtarget biclusters (MTBs), without using any expression data as input. It is a novel concept inspired by the related work on biclustering in the literature of gene expression data analysis [22]. Each MTB consists of a set of miRNAs and a set of mRNAs, where (1) the miRNAs target most of the mRNAs in the MTB but few other mRNAs and (2) the mRNAs are targeted by most of the miRNAs in the MTB but few other miRNAs. Each MTB represents a network module that potentially maintains a largely autonomous regulation subsystem. By tuning the level of interactions linking members of an MTB to nonmembers, and the level of missing intraMTB interactions allowed, the impacts of false positives and false negatives in the interaction networks on the MTBs (issue 1) and the degree of independence of each MTB (issues 2 and 3) can be controlled.
To show the biological relevance of MTBs, we analyzed the expression patterns of the miRNAs and mRNAs in our MTBs using RNAseq data from matched cell lines produced by the ENCODE consortium [23, 24]. We show that the MTBs identified by our methods contain miRNAs more anticorrelated in expression with the mRNAs in the same MTBs than both their other targets and random mRNA sets. As proposed in a series of previous studies, this strong anticorrelation observed indicates that the mRNAs in our MTBs are likely true targets of the miRNAs in the same MTBs [25–32]. By using formal validation procedures and considering many different experimental settings, we show that our results are statistically significant and robust. These results suggest that despite the incomplete reflection of the effects of miRNAs at the transcription level (issue 4) and the presence of other transcriptional regulatory mechanisms (issue 5), it is still possible to systematically analyze the effects of miRNAs on their targets using RNAseq data.
Correspondingly, we observed stronger expression correlations among mRNAs in the same MTBs even if subtle effects due to coregulation are controlled for. We also found that mRNAs and miRNAs in the same MTBs have related biological functions. Overall, our results suggest widespread expression buffering between mRNAs commonly targeted by the same miRNAs, which is in line with the ceRNA hypothesis.
In the literature of computational analysis of miRNAs, the two main focuses have long been on identifying miRNAencoding regions from genomes [33–36] and on predicting the targets of individual miRNAs [37, 38]. In line with the latest trend of studying the interrelated miRNAtarget interactions from a network perspective [27, 39–44], our work introduces a new way to study these miRNAtarget networks by decomposing complex networks into simple modules that can be more easily analyzed.
Results and discussion
Defining MTBs and identifying them from miRNAtarget networks
We collected computationally predicted human miRNA targets from 5 prediction methods. We combined these predictions to form a highconfidence set and a highcoverage set of miRNAtarget predicted interactions, which consist of pairs predicted by at least one prediction method with high and moderate confidence, respectively. We also collected experimentally validated miRNA targets in human from a recent release of TarBase [21]. To study the effects of having validated interactions in these networks on our analyses, for both the highconfidence and highcoverage networks, we further considered either having only the computational predictions, or both the computational predictions and experimentally validated pairs combined, resulting in 4 integrated miRNAtarget networks in total (Table 1).Each of these networks can be represented either by a binary matrix or a bipartite graph (Figure 1). In the matrix representation, each row corresponds to an mRNA and each column corresponds to a miRNA. An element has value 1 if the miRNA represented by the column targets the mRNA represented by the row in the network, and 0 otherwise. In the graphical representation, each node in the first part represents an mRNA and each node in the second part represents a miRNA. There is an edge connecting a miRNA node and an mRNA node if the miRNA targets the mRNA.An idealized definition of an MTB is a set of miRNAs and mRNAs in which each of these miRNAs targets all these mRNAs but not any other mRNAs, and each of these mRNAs are targeted by all these miRNAs not any other miRNAs (Figure 1, type R). In the matrix representation, it corresponds to a submatrix (i.e., a subset of rows and columns not necessarily adjacent to each other) containing only 1’s, with all other elements on these rows and columns having value 0. In the graphical representation, it is a biclique (fullyconnected bipartite subgraph) with no extra edges incident on these nodes. If the miRNAtarget network was free of false positive and false negative errors, MTBs of this type would be perfect cases for testing the ceRNA hypothesis since they represent totally autonomous modules isolated from the other parts of the network.In practice, however, such ideal modules rarely exist in miRNAtarget networks. Even if they do exist, they may not be observed in our integrated networks due to possible false positives and false negatives in the networks. We thus defined a number of other MTB types with less stringent requirements, by allowing some missing 1’s in the submatrix and/or extra 1’s in other elements on the defining rows and columns. We first defined three other types that retain the restrictive (R) requirement that the submatrix should contain all 1’s (i.e., the miRNAs in an MTB should target all mRNAs in the MTB), but either only the defining columns are not allowed to have extra 1’s (i.e., only the miRNAs (mi) are restricted from having extra interactions), or only the defining rows are not allowed to have extra 1’s (i.e., only the mRNAs (m) are restricted from have extra interactions), or the general case (gen) that both are allowed. The corresponding MTB types are denoted as Rmi, Rm and Rgen, respectively. Analogously, we also defined four loose (L) types that allow 0’s in the submatrix (i.e., the miRNAs in an MTB are not required to target all mRNAs in the MTB), resulting in the L, Lmi, Lm and Lgen types (Figure 1). Having different types of MTB enabled us to control the impacts of false positives and false negatives in the input network, and the amount of crosstalk between MTBs.The different MTB types have drastically different numbers of possible occurrences in a network (Figure 1). For some types, there is at most a linear number of MTBs with respect to the number of mRNAs and miRNAs in the network (types R and L). For some other types, the maximum number of MTBs is exponential, but the number of maximal MTBs, i.e., MTBs not being submatrices of other MTBs, is linear (types Rmi, Rm, Lmi and Lm). Type Rgen could give an exponential number of maximal MTBs in theory, but in practice a tractable number is usually observed. Finally, type Lgen has an exponential number of MTBs, both in theory and in practice. Consequently, we developed a variety of algorithms to identify MTBs of the different types, from simple graph searching algorithms that can efficiently identify all MTBs of a certain type, to algorithms that only return a subset of MTBs with the highest scores based on the intraMTB density of interactions.
Expression of miRNAs and mRNAs in the same MTBs are significantly more anticorrelated than general miRNAtarget pairs
As a way to check whether the MTBs we identified represent modules with biological relevance, we examined the expression levels of the miRNAs and mRNAs in human cell lines obtained from RNAseq experiments performed by the ENCODE Project Consortium [23, 24]. The details of the analysis pipeline are given in Materials and Methods (see also Figure 2 and Additional file 1: Figure S1a). Briefly, for each miRNAmRNA pair in an MTB, we calculated the Pearson correlation of their expression across the cell lines. For each MTB, we then counted the fraction of pairs having correlation values more negative than a certain threshold t, multiple values of which (from 0.1 to 0.7) were tested. A large fraction of pairs having expression correlations more negative than the threshold would indicate that the regulatory effects of the miRNAs on the mRNAs were sufficiently strong to be observed in the expression data. To make sure that the negative correlations were not obtained by random chance, we compared these fractions with the corresponding fractions in random sets of expressed miRNAs and mRNAs of the same sizes as the identified MTBs. A pvalue was then computed to determine if the fractions from the MTBs were significantly higher than those from the random background.
In addition, we wanted to check if the negative correlations were simply a general phenomenon among miRNAs and their targets regardless of their MTB memberships. We therefore repeated the above procedure using a second background set of miRNAmRNA pairs that composed of miRNAs and their targets not participated in the same MTBs.From the results for the expressed union set with TarBase interactions (Figure 3), we see that for moderate values of the correlation threshold (0.1 to 0.4), for most MTB types, significantly more miRNAmRNA pairs in the MTBs were anticorrelated in expression than random miRNAmRNA pairs (panels a and b). For example, considering miRNAmRNA pairs with expression correlation > 0.1, all MTB types except type R had a significantly higher fraction of such pairs than random miRNAmRNA pairs at the p =0.01 level.Importantly, the miRNAmRNA pairs in the MTBs were also significantly more anticorrelated in expression than miRNAtarget pairs not in the same MTBs (Figure 3c,d), which suggests that the regulatory effects of miRNAs are either stronger or more clearly observed on their targets within the same MTBs than their other targets.
Significant pvalues were obtained for both the MTBs from the highconfidence set (panels a and c) and the highcoverage set (panels b and d). We have also repeated our procedure for the networks without the validated miRNAtarget interactions from TarBase (Additional file 1: Figure S2), and when related miRNAs with the same miRNA number but different modifiers (such as 5p and 3p) were grouped (Additional file 1: Figures S3 and S4). In all cases, the same general conclusion was drawn, that significantly more withinMTB miRNAmRNA pairs were strongly anticorrelated in expression than random pairs and miRNAtarget pairs not in the same MTBs. These consistent results show that MTB is a robust method for identifying miRNAmRNA modules with strong expression relationships despite the fact that gene expression data were not used in defining the MTBs.
Figure 4 shows the distributions of fractions of miRNAmRNA pairs satisfying the correlation threshold in an example setting. It can be seen that for some MTBs, almost all miRNAmRNA pairs (with a fraction close to 1) had expression correlations more negative than threshold t=−0.2. More generally, about twothird of the MTBs had more than 20% of their miRNAmRNA pairs satisfying this correlation threshold. In contrast, for both random groups of miRNAs and mRNAs, and other miRNAtarget pairs, almost none of them had expression correlation more negative than −0.2.
Comparing the different MTB types, the general types that allow both the miRNAs to have extraMTB targets and the mRNAs to be targeted by extraMTB miRNAs (Rgen and Lgen) produced more MTBs as expected (Figures 5, Additional file 1: Figure S5–S7). Interestingly, the MTBs of these two types also contained miRNAs and mRNAs with more significant anticorrelations, and over a broader range of correlation threshold values (Figure 3). In contrast, due to the rigid requirements of type R, no MTBs of this type could be discovered from the highcoverage set and few were identified from the highconfidence set. Even when MTBs of this type could be found, their miRNAmRNA anticorrelations of expression were not statistically significant. These results confirm the importance of explicitly considering nonfullyconnected miRNAmRNA modules and possible errors in the input miRNAmRNA interaction networks.
Nonexpression features can be used to identify MTBs with strong miRNAmRNA expression anticorrelation
While the MTBs in general contained a significantly higher fraction of miRNAs and mRNAs with strong expression anticorrelation, we were interested in knowing whether some simple features of the MTBs could help identify the subset of MTBs with particularly strong expression anticorrelation without referencing the expression data. This would be particularly useful in identifying the most interesting MTBs when expression data are not available. To explore this possibility, for each MTB we identified, we computed 7 nonexpression features, including the number of mRNAs and miRNAs in it, the density of 1’s in the MTB, in the same rows, columns or either but outside the MTB, and the MTB type. We then used these features to construct a Random Forest model [45] for predicting the fraction of miRNAmRNA pairs within the MTB with expression correlation more negative than t=−0.1. Based on the results of 10fold crossvalidation, the average area under the receiveroperator characteristics (AUC) of ten equalwidth fraction classes was 0.97, which is significantly higher than what would be expected for random predictions (AUC =0.5), indicating that the features were useful in identifying the MTBs with higher fractions of strong miRNAmRNA expression anticorrelation.
We then looked for the features most important for identifying MTBs with strong expression anticorrelations between their member miRNAs and mRNAs. An exhaustive search of feature combinations identified two features that were consistently the most important in a 10fold crossvalidation procedure, namely the number of mRNAs in an MTB and the fraction of miRNAs outside an MTB that target the mRNAs in the MTB. Basically, MTBs with very strong anticorrelations between their miRNAs and mRNAs have a relatively small number of mRNAs and these mRNAs are targeted by few other miRNAs outside the MTBs, which are consistent with the intuition that MTBs with these properties are more autonomous, although the exact relationships of these two features with the fractions passing the correlation threshold are not linear in general.
Comparison with a previous method
To further check if MTBs represent novel miRNAmRNA modules, we compared them with the miRNA regulatory modules (MRMs) identified by the Yoon and De Micheli method [46] from the same networks. It is one of the few methods in the literature that identify miRNAmRNA modules from a miRNAtarget network without requiring expression data as input. We applied the same procedure described above to check the fraction of miRNAmRNA pairs within each MRM identified by this method with expression correlation more negative than a threshold. We then collected all these fractions, and compared them with the corresponding fractions from the MTBs. Since type Lgen was found to be most biologically relevant in terms of miRNAmRNA expression anticorrelation, in this and subsequent analyses we focus on this type of MTBs.
We found that for all threshold values between t=−0.1 and t=−0.7, there was constantly a higher fraction of miRNAmRNA pairs within the MTBs passing the anticorrelation threshold than the MRMs identified by Yoon and De Micheli method as reflected by pvalues > 0.5 (Figure 6). In many settings, the difference in these fraction values was statistically significant. For example, for all threshold values between t=−0.1 and t=−0.5, there was always a significantly higher fraction of miRNAmRNA pairs in the MTBs passing the anticorrelation threshold than those in the MRMs at the p=0.01 level based on the highconfidence network with TarBase interactions. These results further confirm that the MTBs successfully identified groups of miRNAs and mRNAs with strong expression relationships from the miRNAtarget networks alone.
Potential widespread expression buffering between mRNAs in the same MTBs
After checking the biological relevance of MTBs, we then used them to study whether mRNAs commonly targeted by some miRNAs buffer each other in terms of their expression levels. We studied this question using three different methods.
First, we reasoned that if different mRNAs buffer each other, they should exhibit a positive correlation of expression levels across different cell types. To test if it was the case, we applied a procedure similar to the one we used for testing miRNAmRNA anticorrelations described above. Specifically, we asked whether a significantly higher fraction of mRNA pairs in the same MTBs had expression correlation more positive than a threshold t, as compared to random mRNA pairs and mRNA pairs targeted by the same miRNA but not in the same MTBs.
The results (Figure 7) show that indeed significantly more mRNA pairs within type Lgen MTBs were strongly correlated in expression than both types of background mRNA pairs at various values of t from 0.1 to 0.4, no matter we considered the highconfidence or highcoverage set of miRNA target predictions, and whether experimentally validated pairs from TarBase were included or not. The pvalues in the comparison with mRNA pairs targeted by same miRNAs but not in same MTBs as background were particularly significant (Figure 7b), indicating that MTBs helped discover mRNAs with strong expression correlations that could be hard to observe if all targets of a miRNA were considered together as a group.
We noticed that the positive correlations observed between mRNAs in the same MTBs are necessary but not sufficient for showing that they buffer each other. Since most mRNAs in the same MTB are expected to be targeted by the same miRNAs, a plausible alternative explanation is that the positive correlations were simply due to independent regulation by the same miRNAs without a feedback mechanism for the mRNAs to affect the expression level of each other. We argue that this coregulation mechanism cannot fully explain the significant positive correlations observed, because mRNAs targeted by same miRNAs but not in same MTBs were not as correlated in expression as those in the same MTBs. Also, one possible situation in which mRNAs cannot backregulate their targeting miRNAs, and thus they cannot buffer other mRNA targets, is when the miRNAs have saturated expression levels across different cell types. This was also unlikely the case since we observed significant anticorrelations between miRNAs and their mRNA targets in the same MTBs.
Nonetheless, the above arguments cannot rule out the possibility that the main function of MTBs was to identify the more reliable miRNAtarget pairs from the noisy interaction network, and thus coregulation effects between mRNAs in the same MTBs were still stronger than other mRNAs targeted by the same miRNAs according to the network.
To more directly distinguish between coregulation and buffering, we applied a second analysis method. The main idea is that if some mRNAs buffer each other, the expression level of one mRNA would provide some information for explaining the expression level of another mRNA, even when the expression level of the targeting miRNAs have already been considered. In other words, we wanted to test if one mRNA could help explain the expression level of another mRNA that could not be fully explained by the miRNA targets alone. This idea can be quantified by using partial correlation. Suppose R,T_{1} and T_{2} represent a miRNA regulator, target mRNA 1 and target mRNA 2, respectively. We define f(R,T_{1}) as the correlation between R and T_{1}, and f(R,T_{1}T_{2}) as the expected correlation between R and T_{1} given the level of T_{2}. The difference between them, d(R,T_{1},T_{2})=f(R,T_{1}T_{2})−f(R,T_{1}) would be negative if T_{2} provides additional information for explaining the expression anticorrelation between R and T_{1}, and it would be close to 0 if T_{2} provides no additional information, such as when T_{1} and T_{2} were independently regulated by R. A similar method based on conditional mutual information was previously used to identify sponge modulators in miRNAtarget networks [14].
Given R and T_{1} from an MTB, we compared the partial correlation values using other mRNAs from the same MTB as T_{2} with the values obtained by using other targets of R outside the MTB as T_{2}. The results (Figure 8) show that as expected, significantly more mRNAs from the same MTBs gave a strong negative value of d(R,T_{1},T_{2}) than other mRNA targets of the miRNAs, and the results were consistently obtained from all four miRNAtarget networks. These results suggest that the mRNAs in an MTB do help explain the expression levels of each other in addition to what the regulating miRNAs can explain.
Finally, we reasoned that if two mRNAs buffer each other, they should have an expression correlation stronger than other mRNA pairs coregulated by the same miRNA, even if we consider only those within the same MTBs. In other words, for any two mRNAs T_{1} and T_{2} from the same MTB, if d(R,T_{1},T_{2}) is strongly negative, f(T_{1},T_{2}) should be strongly positive. To test if this was the case, we picked the top x MTBs with most negative d(R,T_{1},T_{2}) values and bottom x MTBs with most positive d(R,T_{1},T_{2}) values. We then repeated the correlation analysis above (the first method) using either only the top MTBs or only the bottoms ones. For the 112 parameter settings we tested involving different input networks and different values of x and t, the top MTBs had equal or more significant pvalues in 103 of the cases (92% of the 112 settings). This result confirmed our intuition that the top MTBs with potentially stronger expression buffering among its member mRNAs had their expression levels more correlated.
Taken together, the results of the three methods show that the mRNAs in the same MTBs are significantly correlated in expression, and this cannot be explained purely by the fact that they are regulated by the same miRNAs. We propose that one likely alternative explanation is that these mRNAs buffer each other in terms of their expression levels.
mRNAs in same MTBs have related biological functions
In addition to expression correlations, another way to check the biological relevance of MTBs is to test whether the genes (mRNAs) in the same MTB are enriched in particular functional categories. We collected the Gene Ontology (GO) [47] annotation of the genes in each MTB, and computed the enrichment score of each GO term using both hypergeometric tests and EASE scores [48]. We then collected the most significant enrichment score of each MTB to form a distribution, and compared it with the corresponding distribution of a background set of mRNAs, where the background was either random sets of mRNAs with the same sizes as the MTBs, or mRNAs targeted by same miRNAs but not included in the same MTBs.
From the results (Figure 9 and Additional file 1: Figure S8), it is seen that the genes in the MTBs were indeed more functionally related than both types of background mRNA sets. The results were largely unaffected by the exact way to compute enrichment scores (hypergeometric test pvalues or EASE scores), although the results based on MTBs obtained from the highcoverage set of miRNAtarget interactions were more significant.
Functional enrichment of mRNAs in same MTBs is not only due to coexpression
Since the mRNAs in an MTB were correlated in expression in general, we further tested whether coexpression alone was sufficient to explain the functional enrichment. To test it, we sampled random sets of mRNAs with similar sizes and expression correlation profiles as the MTBs, and computed the hypergeometric test pvalues of the GO terms of the mRNAs in each set. We then compared the distribution of the most significant enrichment score from each of these sets with the scores from the MTBs.The functional enrichment scores of the MTBs were found to be significantly stronger than the scores from the random sets of genes with similar levels of coexpression (Figure 10), especially when MTBs were identified from the two highcoverage miRNAtarget networks. These results show that MTBs were able to identify groups of functionally related genes better than using coexpression information alone.
MTB as a way to annotate miRNA functions
Finally, we explored the potential application of MTBs in identifying functionally related miRNAs. Currently, functional annotation of miRNAs is far less complete than proteincoding genes. Since each MTB represents a largely autonomous module, we hypothesized that the miRNAs in an MTB were functionally related to one another and to the mRNAs in the same MTB. To check if this was the case, for each MTB, we identified GO terms that were significantly enriched (p >0.05) based on the GO term annotations of the mRNAs. We then checked if these enriched GO terms were also related to the functions of the miRNAs in the same MTB. Table 2 shows several interesting examples we identified.
In the first example (MTB 1), the mRNA encoding AAK1, MAPK1 and PDK3 were annotated with the GO term “protein serinethreonine kinase activity”. We found that several miRNAs in MTB 1 are able to target the activities of the MAPK family of serinethreonine protein kinases. miR320a has been shown to directly target MAPK1 activity to control the expression of proinflammatory cytokines in patients with myasthenia gravis [49]. Other miRNAs in the same MTB, miR17 [50, 51] and miR20b [50] can both target the MAPK signaling cascades to regulate cell cycle phase transition [50] and keratinocyte differentiation [51]. In addition, miR93 also directly modulates the activity of the protein serinethreonine kinase, LATS2 to control tumor angiogenesis and metastasis in human breast cancer cells [52].
In MTB 2, genes encoding CBX5, HMGA2 and RNF20 are annotated by the GO term “heterochromatin”. The expression of HMGA2, a nonhistone protein with important roles in chromosomal architecture and oncogenic transformation, is directly targeted by the let7 miRNA [53, 54]. Interestingly, HMGA2 also functions as a ceRNA of Tgfbr3 through the let7 miRNA family that commonly targets them, resulting in the promotion of lung cancer progression [55]. Furthermore, genomewide chromatinbinding analysis suggested that let7 and miR185 are heterochromatinbound miRNAs that can associate with AGO2 in the nucleus of senescent cells to mediate transcriptional gene silencing of proliferationpromoting genes [56]. Together, results from previously published studies supported our MTB classification of miR185 and let7 as miRNAs important in heterochromatinbinding and/or chromatinremodeling.
We next examined the functions of the miRNAs in MTB 3, the mRNAs of which are annotated by the GO term “Notch signaling pathway”. We found that miR34a inhibits cell proliferation in part by directly targeting the expression of CDK6 [57], an important cell cycle regulator whose expression is dependent on the Notch signaling pathway in T cell development [58]. Likewise, the level of miR497 has been shown to inversely correlate with CDK6 expression to regulate cell cycle progression [59]. Furthermore, both miR34a and miR449a have been shown to target the expression of Notch1 [60, 61], a member of the Notch family of receptors in human cancer cell lines. Two of the miRNAs in MTB 3 can also target ligands of the Notch receptors: miR34a is known to directly target Deltalike 1 [62] whereas miR15a targets the noncanonical notch ligand, Deltalike 1 homolog [63].
Finally, in MTB 4, the mRNAassociated GO term “proteasomemediated ubiquitindependent protein catabolic process” is also functionally related to the three miRNAs in the MTB. miR25 has been shown to directly target the E3 ubiquitin ligase, WWP2 [64] to control the reprogramming of somatic cells to induced pluripotent stem cells. miR363 directly inhibits a ubiquitinspecific protease, USP28 to promote proteasomemediated degradation of Myc in human hepatocellular carcinoma [65]. miR93, which lies in the miR106b25 cluster, has been shown to target the expression of βTRCP2, a component of the SCF ubiquitin ligase complex important in the ubiquitination and subsequent proteasomal degradation of target proteins [66].
Collectively, these examples demonstrate that enrichment analysis based on the annotation of GO terms to the mRNAs in an MTB could be used as a way to annotate the functions of miRNAs in the same MTB.
Discussion
In this study, we have shown that mRNAs in the same MTBs have significant expression correlations that cannot be explained purely by the fact that they are regulated by the same miRNAs. We have used multiple methods to show the high possibility that these mRNAs buffer each other in terms of expression, which suggests that ceRNAs could play an important role in the regulation of many mRNAs. In order to fully test the generality of the ceRNA hypothesis, it is necessary to perform perturbation experiments to see how the alteration of the expression level of one mRNA could affect other mRNAs regulated by the same miRNAs. Without such experimental data, in this study we do not aim at completely proving or disproving the generality of the ceRNA hypotheses. Instead, we think the MTBs represent small miRNAtarget modules that could be very useful in identifying candidate miRNAs and mRNAs of future experimental studies in testing hypotheses related to ceRNA.
The fact that more significant pvalues were observed for the MTB types with higher error tolerance (such as Rgen and Lgen) suggests that analysis results could indeed be misled by the errors present in the networks, and that trading off the purity of modules with some error tolerance is a reasonable strategy to handle the current noisy miRNAtarget networks. On the other hand, although the Rgen and Lgen types of MTBs had the most statistically significant results, there could also be interesting cases identified by the other types. For instance, type R MTBs theoretically represent fully autonomous modules with complete target sharing among its member miRNAs, which are ideal cases for studying the ceRNA hypothesis. When miRNAtarget networks become more complete and accurate in the future, more statistically significant results may be obtained from this and the other types of MTB with more stringent definitions.
One aspect of MTBs that we have not yet explored in this study is their celltype specificity. Since MTBs are defined purely based on the miRNAtarget connections, the different miRNAs and mRNAs in an MTB may not be all expressed in the same cell types. It would be interesting to study whether different miRNAs in an MTB usually coexpress in the same cell types and coregulate the common mRNA targets, or express in different cell types and act as alternative regulators.
In this work, we have focused on the use of expression data from ENCODE, which include matched mRNA and miRNA expression data from the same cell lines. We have also tested mRNAmRNA positive correlations within our identified MTBs using a larger data set originally obtained from more than 73,000 microarray experiments [67]. Statistically significant results were again observed, but within a narrower range of correlation threshold t. We will test the concept of MTBs using larger data sets in the future.
While we have defined eight different types of MTB, actually they can all be described by a general framework. A detailed discussion is given in the Additional file 1. Briefly, a general MTB can be defined as a submatrix with an associated score, which is a combination of (1) the missing 1’s in the MTB, (2) extra 1’s in other rows of the defining columns, and (3) extra 1’s in other columns of the defining rows. Each of the eight MTB types corresponds to a particular way to combine these three components. While this general model appears to be more of theoretical interests, it actually has a real application in helping define MTBs from weighted networks in which each miRNAmRNA pair is given a weight that indicates how likely they interact. Using such a weighted network would provide more information for analysis and avoid defining arbitrary thresholds to form a binary network.
Conclusion
In this study, we have introduced microRNAtarget biclusters (MTBs) as a method to systematically identify largely autonomous modules purely from the connections in a noisy miRNAtarget network. To cater for modules involving miRNAs that do not target all mRNAs in the module, and the presence of false positives and false negatives in the network, we have defined eight different types of MTB with different levels of autonomy and error tolerance. We have shown that for some MTB types, especially those with higher error tolerance, the identified modules are biologically relevant by having significant anticorrelations between their member miRNAs and mRNAs as compared to both random miRNAmRNA pairs and miRNAtarget pairs not in the same MTBs. We have checked the robustness of our method using different input networks (high confidence or high coverage, with or without experimentally validated interactions), different values of the correlation threshold t in computing pvalues, and whether to pregroup related miRNAs. The results were consistent across a wide spectrum of parameter settings.
The identified MTBs have enabled us to study how the expression patterns of their member mRNAs are related, with relatively small influence from other miRNAs and mRNAs outside the MTBs. Using three different analysis methods, namely direct expression correlation among the mRNAs, gain of miRNAmRNA anticorrelation information by conditioning on another mRNA, and separate correlation analyses of MTBs with the strongest and weakest information gain, we have shown that there is strong correlation between the expression levels of mRNAs in the same MTBs that can well be explained by expression buffering as stated in the ceRNA hypothesis. These results show that although the regulatory effects of miRNAs are only partially reflected by the expression levels of their target mRNAs, and mRNA expression is affected by other regulatory mechanisms, it is still possible to use transcript levels to study the effects of miRNAs by decomposing a complex and noisy network of miRNAtarget interactions into small modules that can be analyzed individually.
In the long term, the methods proposed in this study should be extended to model the hierarchical relationships between different MTBs and incorporate other regulatory mechanisms, to provide a more complete picture of the complex interactions between various types of biological objects in gene regulatory networks.
Materials and methods
Construction of miRNAtarget networks
We collected experimentally validated human miRNAtarget pairs from TarBase (v6.0) [21], which contained one of the most comprehensive sets of validated miRNAmRNA interactions. We considered only the experimental types that likely report direct miRNAmRNA interactions, namely PCR, ReportGeneAssay and Sequencing.
In addition, we gathered computationally predicted human miRNAmRNA interactions using 5 methods based on different prediction approaches, namely miRanda (Aug 2010) [6], miRGen (v2.0) [68], PicTar (Hg18) [69], PITA (v6) [70] and TargetScan (6.0) [71]. The dataset for miRanda used was the human predictions with “Good mirSVR score, Conserved miRNA”, downloaded from http://cbio.mskcc.org/microrna_data/human_predictions_S_C_aug2010.txt.gz. The miRGen data file was downloaded from http://diana.cslab.ece.ntua.gr/data/public/TF_GENEID_miRNA_sorted.txt. The dataset for PicTar was the PicTar2 predicted target genes with conservation at the mammals’ level based on RefSeq gene models and human hg18 reference assembly, downloaded from http://dorina1.mdcberlin.de/rbp_browser/hg18.html, choosing all genes in database in option 1 and all mammals’ miRNAs in database in option 2. For PITA, the Human Top predictions of miRNA targets were downloaded from http://genie.weizmann.ac.il/pubs/mir07/catalogs/PITA_targets_hg18_0_0_TOP.tab.gz. For TargetScan, the data used were the predicted conserved targets, downloaded form http://www.targetscan.org/vert_61/vert_61_data_download/Predicted_Targets_Info.txt.zip.
The gene names in all data files were converted to official gene symbols using the lookup table in HGNC [72]. Records with unrecognized gene names were ignored.
A highconfidence interaction network was constructed by taking the union of the 1,000 highestscoring predictions from each method (where the number for miRGen was slightly larger due to ties in prediction scores). A second network was constructed by adding to this network the experimentally validated interactions in TarBase. Similarly, two highcoverage interaction networks were constructed by taking the union of the 5,000 highestscoring predictions from each method, one with TarBase interactions and one without.
Expression data
To study the expression levels of miRNAs and mRNAs across different cell types, we collected RNAseq data in whole cells of human cell lines from ENCODE [23, 24], namely A549, AGO4450, BJ, GM12878, H1hESC, HeLaS3, K562, MCF7, NHEK and SKNSH, which contained the largest number of nonzero expression values for our mRNAs and miRNAs among all the human cell lines with RNAseq data available from ENCODE at the time of collection. We used long PolyA+ RNA data to compute expression levels of mRNAs, and short total RNA data for miRNAs. Expression levels were computed by the number of reads mapped to each gene per kilobase per million reads (RPKM). We combined values from multiple replicates of the same experiment by taking their average.
As our goal was to study expression relationships between miRNAs and mRNAs, we focused on the set of mRNAs and miRNAs with nonzero expression values in at least 8 of the 10 cell lines. Considering only these miRNAs and mRNAs, we obtained the four integrated networks used in our analyses, namely the highconfidence/highcoverage expressed union network with/without TarBase interactions (Table 1).
Definitions of MTBs and identification algorithms
As described in the Results section, we defined eight MTB types that differ in whether missing 1’s are allowed in the defining submatrix, and whether extra 1’s are allowed in the defining rows and columns outside the MTB (Figure 1). Here we provide detailed definitions of the eight types, and describe the corresponding algorithms for identifying the MTBs of each type from a miRNAtarget network. In our analyses, by default we considered only MTBs containing at least two mRNAs and at least two miRNAs. For the analysis of positive expression correlations between mRNA pairs, in order to avoid having only one correlation value per MTB, we further considered only MTBs with at least 3 mRNAs.
Type R
Type R is the most restrictive type that requires each participating miRNA to target all participating mRNAs but no other mRNAs, and each participating mRNA to be targeted by all participating miRNAs but no other miRNAs. In the matrix representation, an MTB of this type is a submatrix with all 1’s, and all other elements on the same rows and columns are 0’s. Since each row and each column can participate in at most one MTB, the total number of MTBs is at most m i n(R,C), where R and C are the sets of all rows (i.e., mRNAs) and all columns (i.e., miRNAs), respectively, and the notation X denotes the size of any set X.
We developed an algorithm to identify all MTBs of this type from a miRNAtarget network in linear time. The basic idea is to use the columns with 1’s in a row as its signature, and group all rows with the same signature together with the help of a hash table. Similarly, we defined the signature of a column as the rows at which it has 1’s, and grouped all columns with the same signature together. For each group of rows, if the columns in its signature do not have 1’s at other rows, it forms an MTB with these columns. Otherwise, by the definition of type R MTB, the whole group of rows cannot be members of any MTB. In this algorithm, whether there are other 1’s in these columns can be efficiently checked by the following method. Suppose r is the group of rows, c is the set of columns defining its signature, and j is one of these columns. All columns in c do not have other 1’s if and only if j belongs to a group with signature r for all j∈c.
The pseudocode of the whole algorithm is given in Additional file 1: Algorithm 1.
Type Rmi
Type Rmi is the same as type R except that the mRNAs of an MTB are allowed to be targeted by additional miRNAs. In the matrix representation, extra 1’s are allowed in other columns of the defining rows. There can be an exponential number of type Rmi MTBs, because if (r,c) is an MTB, then (r,c^{′}) is also an MTB for any set of columns c^{′}⊂c. On the other hand, if we define a maximal MTB as one that is not a submatrix of another MTB, then each column can participate in at most one maximal MTB. Therefore the total number of maximal MTBs is at most C.
We modified the algorithm for type R to identify all maximal MTBs of type Rmi. For each column, we defined its signature as the rows at which it has 1’s. We then grouped all columns with the same signature with the help of a hash table. Each resulting group of columns and the rows in their signatures form a maximal type Rmi MTB, with no additional checking required.
The pseudocode of the algorithm is given in Additional file 1: Algorithm 2.
Type Rm
Type Rm is the transpose of type Rmi. It allows each miRNA of an MTB to target other mRNAs outside the MTB, but does not allow the mRNAs to have other targeting miRNAs. Using the same argument for type Rmi, there can be an exponential number of type Rm MTBs, but at most R maximal MTBs.
The algorithm we used for identifying all maximal type Rm MTBs is analogous to the one for type Rmi, except that we grouped rows based on their signatures instead.
The pseudocode of the algorithm is given in Additional file 1: Algorithm 3.
Type Rgen
Type Rgen maintains the requirement that all miRNAs participating in an MTB must target all participating mRNAs, but the miRNAs are allowed to have other targets and the mRNAs are allowed to be targeted by other miRNAs. This type of MTBs is best described by the graphical representation, where each MTB is a biclique, i.e., a complete subgraph with all the miRNA nodes connecting to all the mRNA nodes. Again, there can be an exponential number of MTBs, as each subgraph of a type Rgen MTB is also a type Rgen MTB. There can also be an exponential number of maximal type Rgen MTB. For example, if there are 2^{C}−1 rows and the signature of each row is the same as its index, i.e., the first row has signature 000...001, the second row has signature 000...010, the third row has signature 000...011, and so on, then each of the 2^{C}−1 nonempty column combinations participates in a different maximal MTB. Because of the exponential number of possible maximal MTBs, and the fact that finding maximal bicliques is NP hard [73], in theory it is infeasible to identify all maximal type Rgen MTBs in a miRNAmRNA network.
In practice, however, both the number of maximal type Rgen MTBs and the size of each are small in the networks we studied. We therefore used an iterative algorithm to find all maximal type Rgen MTBs, based on the Apriori algorithm proposed for association rule mining [74, 75]. The basic idea is that if (r,c) is a type Rgen MTB, then for any c^{′}⊂c, (r,c^{′}) must also be a type Rgen MTB. One can then iteratively discover MTBs with two columns, three columns, and so on, by testing kcolumn sets in the kth iteration, constructed by merging two (k−1)column sets in the previous iteration.
The pseudocode of the algorithm is given in Additional file 1: Algorithm 4.
Type L
The definition of type L MTB involves three rules. First, each participating miRNA is allowed to target only some of the participating mRNAs, but it cannot target any other mRNAs. Second, each participating mRNA is allowed to be targeted by only some of the participating miRNAs, but it cannot be targeted by other miRNAs. Finally, in the graphical representation, the nodes that represent the participating rows and columns should all be connected, i.e., there should be a path between any two nodes. In other words, each type L MTB is a connected component. Since each row and each column can participate in at most one MTB, the total number of type L MTBs is at most m i n(R,C).
We used a standard breadthfirst search algorithm to find all connected components, i.e., all type L MTBs, in linear time.
The pseudocode of the algorithm is given in Additional file 1: Algorithm 5.
Type Lmi
Type Lmi MTB differs from type L by allowing the participating mRNAs of an MTB to be targeted by additional miRNAs outside the MTB. Since each type Rmi MTB is also a type Lmi MTB, there is at maximum an exponential number of type Lmi MTBs in a miRNAtarget network. Since each column can participate in at most one maximal MTB, there are no more than C maximal type Lmi MTBs.
It is easy to see that the set of maximal type Lmi MTBs is exactly the same as the set of maximal type L MTBs. The algorithm for finding all maximal type L MTBs can thus be used for finding all maximal type Lmi MTBs. However, we did not adopt this approach for two reasons. First, by doing so it would be meaningless to define type L and type Lmi MTBs as two separate types. Second, a maximal type Lmi MTB is likely to have many member miRNAs and mRNAs not having interactions, leading to a low density of interactions within the MTB.
We therefore developed an algorithm for finding highscoring type Lmi MTBs instead. The score of an MTB is defined as the density of 1’s in the defining submatrix, where the density of 1’s in an MTB is defined as the number of 1’s divided by the total number of elements in the submatrix. The algorithm starts with the set of maximal type Rmi MTBs, which all have an interaction density of one by definition. We then removed MTBs that are too similar to another one. After that, for each column not in any MTB, we tested if it was reasonable to add it and all rows with a 1 in that column to the MTB. If the resulting density of 1’s in the new MTB did not drop below a certain threshold (which we set to 0.3), we considered the addition of the column as reasonable. If there were multiple reasonable additions, we chose the one with the highest resulting density of 1’s and repeated the process. Otherwise, the current MTB was returned as one of the highscoring type Lmi MTBs.
The pseudocode of the algorithm is given in Additional file 1: Algorithm 6.
Type Lm
Type Lm MTB is the transpose of type Lmi MTB. All the discussions about type Lmi MTBs can be applied to type Lm MTBs by swapping the rows and columns.
The pseudocode of the algorithm for finding all highscoring type Lm MTBs is given in Additional file 1: Algorithm 7.
Type Lgen
Type Lgen has the most relaxed definition among the eight types, and is likely the most practical one. Each miRNA in a type Lgen MTB is allowed to target only some of the mRNAs in the MTB, and is allowed to target other mRNAs. Likewise, each mRNA is allowed to be targeted by only some of the miRNAs in the MTB, and is allowed to be targeted by other miRNAs. To avoid having completely unrelated miRNAs and mRNAs in the same MTB, we maintained the connectedness requirement from type L. Since type Rgen is a special case of type Lgen, there can also be an exponential number of type Lgen MTBs. On the other hand, the number of maximal MTBs is limited by the number of connected components in the network, which is at most O(m i n(R,C)).
Due to the exponential number of type Lgen MTBs, both theoretically and practically, it is infeasible to return all of them. On the other hand, it is also not meaningful to return all maximal MTBs, since they are usually very sparse and contain miRNAs and mRNAs that are only weakly connected. Therefore as in the cases of type Lmi and type Lm MTB, we adopted a different approach to return highscoring MTBs, which are MTBs with a high density of 1’s within the defining submatrices. We developed an algorithm to find these highscoring MTBs, based on some ideas from a previously method proposed for finding communities in partite networks [76]. First, we used the algorithm for type Rgen MTBs to find all maximal bicliques, and called each of them a bicluster. We then removed biclusters that are too similar to another one. After this step, we iteratively added extra rows or columns to each MTB in ways similar to the algorithms for type Lmi and type Lm MTBs, except that when a column/row was added to an MTB, it was not required to also add the rows/columns with 1’s in the adding column/row. For each bicluster, the best addition was kept. The process was repeated until no more mRNAs or miRNAs could be added without causing the density to drop below a threshold.
The pseudocode of the algorithm is given in Additional file 1: Algorithm 8.
Workflow for expression correlation analyses
We used a unified workflow for studying the negative correlations between miRNAs and mRNAs in an MTB (Figure 2 and Additional file 1: Figure S1a). Each time we considered one of the four integrated miRNAtarget interaction networks of expressed miRNAs and mRNAs as input (Table 1, Highconfidence/highcoverage expressed union with/without TarBase interactions). MTBs of the different types were identified from the network using the algorithms described above. For each MTB, we calculated the Pearson correlation between the expression levels of each pair of participating miRNA and mRNA across the human cell lines. We then summarized all these correlations by computing the fraction of them more negative than a correlation threshold t, multiple values of which (0.1 to 0.7 with a step size of 0.1) were tested. After collecting all these fractions from the MTBs of a particular type, we compared them with the fractions from two backgrounds. The first one involved 1,000 random sets of expressed miRNAs and mRNAs with sizes matching the size distribution of the actual MTBs. The second one involved the same miRNAs and their other targets not included in the same MTBs as them. To quantify the comparisons, we used Wilcoxon ranksum test to calculate a onesided pvalue for each MTB type at each value of t. A significant pvalue would mean the fractions from the MTBs were significantly higher than the set of fractions in comparison. As our goal was to compare the results in various parameter settings rather than emphasizing on the significance of one particular set of results, the reported pvalues were not corrected for multiple hypothesis testing. We remark that if one was to use the concept of MTB to identify one set of reliable miRNAtarget modules for downstream analyses, the statistical significance of such modules should be carefully corrected taking into account the number of hypothesis tests performed.
We also repeated the analysis when different miRNAs with the same miRNA numbers but different modifiers (such as hasmir121a and hsamir121b) were grouped together. The expression value of each group was defined as the average expression of the member miRNAs.
In the same way, we also tested the positive correlations between mRNAs in same MTBs, in which case we computed the fractions of pairs with expression correlation higher than a threshold t, where t took values from 0.1 to 0.7. The fractions obtained from mRNA pairs in same MTBs were first compared to fractions from random pairs of expressed mRNAs, and then to pairs of mRNAs targeted by the same miRNAs but were not in the same MTBs.
Predicting MTBs with strong miRNAmRNA expression anticorrelation
We developed a method for predicting MTBs with strong miRNAmRNA expression anticorrelation when expression data are unavailable. For each MTB, we constructed seven nonexpression features, namely 1) its number of mRNAs, 2) its number of miRNAs, 3) the density of 1’s in its submatrix, 4) the density of 1’s in other rows of the defining columns, 5) the density of 1’s in the other columns of the defining rows, 6) the density of 1’s in the other rows of the defining columns or the other columns of the defining rows, and 7) the MTB type. Each MTB was thus represented by a vector of seven numeric values. The goal was to identify the anticorrelation class of each MTB, where ten equalwidth classes were defined based on the distribution of average anticorrelation values of the MTBs. We then took 9/10 of the MTBs to train a Random Forest model using the implementation in Weka [77], and tested its accuracy using the remaining 1/10 of MTBs with their anticorrelation classes hidden. We repeated the process with 10 random sets of trainingtesting data, and reported their average area of the receiver operator characteristics (AUC).
Comparison with miRNA regulatory modules from Yoon and De Micheli
We compared the miRNAmRNA expression anticorrelation with the miRNA regulatory modules (MRMs) from Yoon and De Micheli [46]. We implemented this method and applied it to find MRMs from each of our input miRNAtarget networks. For each identified MRM, we computed the fraction of miRNAmRNA pairs with expression correlation more negative than a threshold t. We then compared these fraction values with the fraction values from our type Lgen MTBs using a onesided Wilcoxon ranksum test. A significant pvalue would indicate that the fraction values from the MTBs were significantly higher than the MRMs.
Workflow for testing whether correlated expression of mRNAs were more likely due to buffering than coregulation
To test if the correlated expression of two mRNAs in the same MTB is due to buffering or coregulation, we applied a method similar to the one in Sumazin et al. [14]. The idea is to compute d(R,T_{1},T_{2})=f(R,T_{1}T_{2})−f(R,T_{1}), where R is a regulating miRNA, T_{1} and T_{2} are two mRNA targets of it, f is the Pearson correlation function, and f(R,T_{1}T_{2}) is defined as the expected correlation between R and T_{1} after dividing the cell lines into two groups based on the expression value of T_{2} (above mean and below mean). If d(R,T_{1},T_{2}) is negative, it would mean that the expression relationship between R and T_{1} can be better explained when the expression of T_{2} is known, and thus T_{1} and T_{2} are not independently regulated by R, but they affect each other possibly due to buffering.
To globally test if the (R,T_{1},T_{2}) combinations in our MTBs have significantly more negative d(R,T_{1},T_{2}) values than combinations involving the same R but T_{1} and T_{2} outside our MTBs, we used a procedure similar to checking the anticorrelations between miRNAs and targets in MTBs, but with the distribution of anticorrelation values replaced by these d(R,T_{1},T_{2}) values. The fraction of (R,T_{1},T_{2}) combinations with a d(R,T_{1},T_{2}) value more negative than a threshold t was computed for each MTB, and the resulting distribution of fractions from all MTBs was compared to the background distribution with the same R’s but T_{1}’s and T_{2}’s outside the MTBs using a onesided Wilcoxon ranksum test.
Based on the above calculations, we also collected x MTBs with the most negative d(R,T_{1},T_{2}) values and the x with most positive d(R,T_{1},T_{2}) values. We called the former set of MTBs the “top” MTBs and the latter set the “bottom” MTBs as the former set was expected to exhibit stronger expression buffering among the mRNAs in each of them. We then used our correlation pipeline to test if the mRNAmRNA correlations were significantly stronger than other mRNA pairs targeted by the same miRNAs but were not in the same MTBs based on different values of the correlation threshold t. Considering the 4 input miRNAtarget networks, 4 values of x (100, 200, 500 and 1000), and 7 values of t (0.1 to 0.7), we compared the pvalues from the top MTBs and from the bottom MTBs under the 4×4×7=112 parameter settings.
Workflow for functional enrichment analyses
We also setup a workflow for evaluating the functional relationships between the genes in same MTBs (Additional file 1: Figure S1b). For each MTB, we collected the terms associated with each gene (mRNA) defined in Gene Ontology [47]. For each term, we then computed a pvalue using hypergeometric test, to indicate the enrichment of the term in this set of genes as compared to the background set of all genes. To ensure robustness of our results, we also computed EASE scores as defined on the DAVID Web site [48], which can be considered a more stringent version of the pvalues. The most significant pvalue from each MTB was then collected to form a distribution, and it was compared to the most significant pvalues from random sets of mRNAs of the same sizes of the MTBs. This comparison was quantified by a onesided Wilcoxon ranksum test, where a significant pvalue would indicate that the genes in the MTBs were more enriched in same functional terms than random gene sets.
We also repeated the same analysis for sets of random mRNAs with a similar size and a similar level of coexpression as the MTBs.
Availability
The source code and compiled programs we used for our analyses are available at http://yiplab.cse.cuhk.edu.hk/MTB/.
References
 1.
Carmell MA, Xuan Z, Zhang MQ, Hannon GJ: The Argonaute family: tentacles that reach into RNAi, developmental control, stem cell maintenance, and tumorigenesis. Genes Dev. 2002, 16 (21): 27332742. 10.1101/gad.1026102.
 2.
Bartel DP: MicroRNAs: Genomics, biogenesis, mechanism, and function. Cell. 2004, 116 (2): 281297. 10.1016/S00928674(04)000455.
 3.
ValenciaSanchez MA, Liu J, Hannon GJ, Parker R: Control of translation and mRNA degradation by miRNAs and siRNAs. Genes Dev. 2006, 20 (5): 515524. 10.1101/gad.1399806.
 4.
Bartel DP: MicroRNAs: target recognition and regulatory functions. Cell. 2009, 136: 215233. 10.1016/j.cell.2009.01.002.
 5.
Enright AJ, John B, Gaul U, Tuschl T, Sander C, Marks DS: MicroRNA targets in Drosophila. Genome Biol. 2003, 5 (R1):
 6.
John B, Enright AJ, Aravin A, Tuschl T, Sander C, Marks DS: Human MicroRNA targets. PLoS, Biol. 2004, 2 (e363):
 7.
FrancoZorrilla JM, Valli A, Todesco M, Mateos I, Puga MI, RubioSomoza I, Leyva A, Weigel D, García JA, PazAres J: Target mimicry provides a new mechanism for regulation of microRNA activity. Nat Genet. 2007, 39 (8): 10331037. 10.1038/ng2079.
 8.
Salmena L, Poliseno L, Tay Y, Kats L, Pandolfi PP: A ceRNA Hypothesis: the Rosetta stone of a hidden RNA language?. Cell. 2011, 146: 353358. 10.1016/j.cell.2011.07.014.
 9.
Arvey A, Larsson E, Sander C, Leslie CS, Marks DS: Target mRNA abundance dilutes MicroRNA and siRNA activity. Mol Syst Biol. 2010, 6 (363):
 10.
Ebert MS, Sharp PA: Roles for MicroRNAs in conferring robustness to biological processes. Cell. 2012, 149: 515524. 10.1016/j.cell.2012.04.005.
 11.
Poliseno L, Salmena L, Zhang J, Carver B, Havenman WJ, Pandolfi PP: A codingindependent function of gene and Pseudogene mRNAs regulates tumour biology. Nature. 2010, 465 (7301): 10331038. 10.1038/nature09144.
 12.
Cesana M, Cacchiarelli D, Legnini I, Santini T, Sthandier O, Chinappi M, Tramontano A, Bozzoni I: A Long Noncoding RNA controls muscle differentiation by functioning as a competing endogenous RNA. Cell. 2011, 147: 358369. 10.1016/j.cell.2011.09.028.
 13.
Karreth FA, Tay Y, Perna D, Ala U, Tan SM, Rust AG, DeNicola G, Webster KA, Weiss D, PerezMancera PA, Krauthammer M, Halaban R, Provero P, Adams DJ, Tuveson DA, Pandolfi PP: In vivo identification of tumorsuppressive PTEN ceRNAs in an oncogenic BRAFinduced mouse model of melanoma. Cell. 2011, 147: 382395. 10.1016/j.cell.2011.09.032.
 14.
Sumazin P, Yang X, Chiu HS, Chung WJ, Iyer A, LlobetNavas D, Rajbhandari P, Bansal M, Guarnieri P, Silva J, Califano A: An extensive microRNAmediated network of RNARNA interactions regulates established oncogenic pathways in glioblastoma. Cell. 2011, 147: 370381. 10.1016/j.cell.2011.09.041.
 15.
Tay Y, Kats L, Salmena L, Weiss D, Tan SM, Ala U, Karreth F, Poliseno L, Provero P, Cunto FD, Lieberman J, Rigoutsos I, Pandolfi PP: Codingindependent regulation of the tumor suppressor PTEN by competing endogenous mRNAs. Cell. 2011, 147: 344357. 10.1016/j.cell.2011.09.029.
 16.
RubioSomoza I, Weigel D, FrancoZorrilla JM, García JA, PazAres J: ceRNAs: miRNA target mimic mimics. Cell. 2011, 147: 14311432. 10.1016/j.cell.2011.12.003.
 17.
Joung JG, Hwang KB, Nam JW, Kim SJ, Zhang BT: Discovery of microRNAmRNA modules via populationbased probabilistic learning. Bioinformatics. 2007, 23 (9): 11411147. 10.1093/bioinformatics/btm045.
 18.
Tran DH, Satou K, Ho TB: Finding MicroRNA regulatory modules in human genome using rule induction. BMC Bioinformatics. 2008, 9 (S5):
 19.
Gennarino VA, D’Angelo G, Dharmalingam G, Fernandez S, Russolillo G, Sanges R, Mutarelli M, Belcastro V, Ballabio A, Verde P, Sardiello M, Banfi S: Identification of MicroRNAregulated gene networks by expression analysis of target genes. Genome Res. 2012, 22 (6): 11631172. 10.1101/gr.130435.111.
 20.
Alexiou P, Maragkakis M, Papadopoulos GL, Reczko M, Hatzigeorgiou AG: Lost in translation: an assessment and perspective for computational microRNA target identification. Bioinformatics. 2009, 25 (23): 30493055. 10.1093/bioinformatics/btp565.
 21.
Vergoulis T, Vlachos IS, Alexiou P, Georgakilas G, Maragkakis M, Reczko M, Gerangelos S, Koziris N, Dalamagas T, Hatzigeorgiou AG: TarBase 6.0: capturing the exponential growth of miRNA targets with experimental support. Nucleic Acids Res. 2012, 40: D222D229. 10.1093/nar/gkr1161.
 22.
Cheng Y, Church GM: Biclustering of expression data. Proceedings of the 8th International Conference on Intelligent Systems for Molecular Biology. 2000, 93103.
 23.
Djebali S, Davis CA, Merkel A, Dobin A, Lassmann T, Mortazavi A, Tanzer A, Lagarde J, Lin W, Schlesinger F, Xue C, Marinov GK, Khatun J, Williams BA, Zaleski C, Rozowsky J, Roder M, Kokocinski F, Abdelhamid RF, Alioto T, Antoshechkin I, Baer MT, Bar NS, Batut P, Bell K, Bell I, Chakrabortty S, Chen X, Chrast J, Curado J, et al: Landscape of Transcription in Human Cells. Nature. 2012, 489 (7414): 101108. 10.1038/nature11233.
 24.
The ENCODE Project Consortium: An integrated encyclopedia of DNA elements in the human genome. Nature. 2012, 489 (7414): 5774. 10.1038/nature11247.
 25.
Huang JC, Babak T, Corson TW, Chua G, Khan S, Gallie BL, Hughes TR, Blencowe BJ, Frey BJ, Morris QD: Using expression profiling data to identify human MicroRNA targets. Nat Methods. 2007, 4 (12): 10451049. 10.1038/nmeth1130.
 26.
Gennarino VA, Sardiello M, Avellino R, Meola N, Maselli V, Anand S, Cutillo L, Ballabio A, Banfi S: MicroRNA target prediction by expression analysis of host genes. Genome Res. 2009, 19 (3): 481490.
 27.
Lionetti M, Biasiolo M, Agnelli L, Todoerti K, Mosca L, Fabris S, Sales G, Deliliers GL, Bicciato S, Lombardi L, Bortoluzzi S, Neri A: Identification of microRNA expression patterns and definition of a microRNA/mRNA regulatory network in distinct molecular groups of multiple myeloma. Blood. 2009, 114 (25): e20e26. 10.1182/blood200908237495.
 28.
Liu B, Li J, Tsykin A: Discovery of functional miRNAmRNA regulatory modules with computational methods. J Biomed Inform. 2009, 42: 685691. 10.1016/j.jbi.2009.01.005.
 29.
Peng X, Li Y, Walters KA, Rosenzweig ER, Lederer SL, Aicher LD, Proll S, Katze MG: Computational identification of hepatitis C virus associated microRNAmRNA regulatory modules in human livers. BMC Genomics. 2009, 10 (373):
 30.
NunezIglesias J, Liu CC, Morgan TE, Finch CE, Zhou XJ: Joint genomewide profiling of miRNA and mRNA expression in Alzheimer’s disease cortex reveals altered miRNA regulation. PLOS ONE. 2010, 5 (e8898):
 31.
Ritchie W, Flamant S, Rasko JEJ: mimiRNA: a microRNA expression profiler and classification resource designed to identify functional correlations between microRNAs and their targets. Bioinformatics. 2010, 26 (2): 223227. 10.1093/bioinformatics/btp649.
 32.
Lu Y, Zhou Y, Qu W, Deng M, Zhang C: A Lasso regression model for the construction of microRNAtarget regulatory networks. Bioinformatics. 2011, 27 (17): 24062413. 10.1093/bioinformatics/btr410.
 33.
Dezulian T, Remmert M, Palatnik JF, Weigel D, Huson DH: Identification of plant microRNA Homologs. Bioinformatics. 2006, 22 (3): 359360. 10.1093/bioinformatics/bti802.
 34.
Artzi S, Kiezun A, Shomron N: miRNAminer: A tool for homologous microRNA gene search. BMC Bioinformatics. 2008, 9: 3910.1186/14712105939.
 35.
Friedländer MR, Chen W, Adamidi C, Maaskola J, Einspanier R, Knespel S, Rajewsky N: Discovering microRNAs from deep sequencing data using miRDeep. Nat Biotechnol. 2008, 26 (4): 407415. 10.1038/nbt1394.
 36.
Gorodkin J, Hofacker IL: From structure prediction to genomic screens for novel noncoding RNAs. PLoS Comput Biol. 2011, 7 (8): e100210010.1371/journal.pcbi.1002100.
 37.
Min H, Yoon S: Got Target?: computational methods for microRNA target prediction and their extension. Exp Mol Med. 2010, 42 (4): 233244. 10.3858/emm.2010.42.4.032.
 38.
Thomas M, Lieberman J, Lal A: Desperately seeking microRNA targets. Nat Struct Mol Biol. 2010, 17 (10): 11691174. 10.1038/nsmb.1921.
 39.
Joung JG, Fei Z: Identification of MicroRNA regulatory modules in arabidopsis via a probabilistic graphical model. Bioinformatics. 2009, 25 (3): 387393. 10.1093/bioinformatics/btn626.
 40.
Liu B, Li J, Tsykin A, Liu L, Gaur AB, Goodall GJ: Exploring complex miRNAmRNA interactions with Bayesian networks by splittingaveraging strategy. BMC Bioinformatics. 2009, 10 (408):
 41.
Bonnet E, Tatari M, Joshi A, Michoel T, Marchal K, Berx G, de Peer YV: Module network inference from a cancer gene expression data set identifies microRNA regulated modules. PLOS ONE. 2010, 5: e1016210.1371/journal.pone.0010162.
 42.
Liu B, Liu L, Tsykin A, Goodall GJ, Green JE, Zhu M, Kim CH, Li J: Identifying functional miRNAmRNA regulatory modules with correspondence latent dirichlet allocation. Bioinformatics. 2010, 26 (24): 31053111. 10.1093/bioinformatics/btq576.
 43.
Zhang S, Li Q, Liu J, Zhou XJ: A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNAgene regulatory modules. Bioinformatics. 2011, 27: i401i409. 10.1093/bioinformatics/btr206.
 44.
Liu B, Li J, Cairns MJ: Identifying miRNAs, targets and functions. Brief Bioinformatics. 2014, 15 (1): 119. 10.1093/bib/bbs075.
 45.
Breiman L: Random forests. Mach Learn. 2001, 45: 532. 10.1023/A:1010933404324.
 46.
Yoon S, De Micheli G: Prediction of regulatory modules comprising microRNAs and target genes. Bioinformatics. 2005, 21 (Suppl. 2): ii93ii100.
 47.
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, IsselTarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G: Gene ontology: tool for the unification of biology. Nat Genet. 2000, 25: 2529. 10.1038/75556.
 48.
Huang DW, Sherman BT, Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2008, 4: 4457. 10.1038/nprot.2008.211.
 49.
Cheng Z, Qiu S, Jiang L, Zhang A, Bao W, Liu P, Liu J: MiR320a is Downregulated in patients with myasthenia gravis and modulates inflammatory Cytokines production by targeting mitogenactivated protein kinase 1. J Clin Immunol. 2013, 33: 567576. 10.1007/s1087501298345.
 50.
Cloonan N, Brown MK, Steptoe AL, Wani S, Chan WL, Forrest AR, Kolle G, Gabrielli B, Grimmond SM: The miR175p microRNA is a key regulator of the G1/S phase cell cycle transition. Genome Biol. 2008, 9 (R127):
 51.
Wu N, Sulpice E, Obeid P, Benzina S, Kermarrec F, Combe S, Gidrol X: The miR17 family links p63 protein to MAPK signaling to promote the onset of human keratinocyte differentiation. PLOS ONE. 2012, 7 (e45761):
 52.
Fang L, Du WW, Yang W, Rutnam ZJ, Peng C, Li H, O’Malley YQ, Askeland RW, Sugg S, Liu M, Mehta T, Deng Z, Yang BB: MiR93 enhances angiogenesis and metastasis by targeting LATS2. Cell Cycle. 2012, 11 (23): 43524365. 10.4161/cc.22670.
 53.
Lee YS, Dutta A: The tumor suppressor microRNA let7 represses the HMGA2 oncogene. Genes Dev. 2007, 21 (9): 10251030. 10.1101/gad.1540407.
 54.
Mayr C, Hemann MT, Bartel DP: Disrupting the pairing between let7 and Hmga2 enhances oncogenic transformation. Science. 2008, 315 (5818): 15761579.
 55.
Kumar MS, ArmenterosMonterroso E, East P, Chakravorty P, Matthews N, Winslow MM, Downward J: HMGA2 functions as a competing endogenous RNA to promote lung cancer progression. Nature. 2014, 505 (7482): 212217.
 56.
Benhamed M, Herbig U, Ye T, Dejean A, Bischof O: Senescence is an endogenous trigger for MicroRNAdirected transcriptional gene silencing in human cells. Nat Cell Biol. 2012, 14 (3): 266275. 10.1038/ncb2443.
 57.
Sun F, Fu H, Liu Q, Tie Y, Zhu J, Xing R, Sun Z, Zheng X: Downregulation of CCND1 and CDK6 by miR34a induces cell cycle arrest. FEBS Lett. 2008, 582: 15641568. 10.1016/j.febslet.2008.03.057.
 58.
Joshi I, Minter LM, Telfer J, Demarest RM, Capobianco AJ, Aster JC, Sicinski P, Fauq A, Golde TE, Osborne BA: Notch signaling mediates G1/S cellcycle progression in T cells via Cyclin D3 and its dependent kinases. Blood. 2008, 113 (8): 16891698.
 59.
Furuta M, ichi Kozaki K, Tanimoto K, Tanaka S, Arii S, Shimamura T, Niida A, Miyano S, Inazawa J: The tumorsuppressive miR497195 cluster targets multiple cellcycle regulators in hepatocellular carcinoma. PLOS, ONE. 2013, 8 (e60155):
 60.
Capuano M, Iaffaldano L, Tinto N, Montanaro D, Capobianco V, Izzo V, Tucci F, Troncone G, Greco L, Sacchetti L: MicroRNA449a Overexpression, reduced NOTCH1 signals and scarce goblet cells characterize the small intestine of celiac patients. PLOS ONE. 2011, 6 (e29094):
 61.
Zhang C, Mo R, Yin B, Zhou L, Liu Y, Fan J: Tumor suppressor microRNA34a inhibits cell proliferation by targeting Notch1 in renal cell carcinoma. Oncol Lett. 2014, 7 (5): 16891694.
 62.
de Antonellis P, Medaglia C, Cusanelli E, Andolfo I, Liguori L, Vita GD, Carotenuto M, Bello A, Formiggini F, Galeone A, Rosa GD, Virgilio A, Scognamiglio I, Sciro M, Basso G, Schulte JH, Cinalli G, Iolascon A, Zollo M: MiR34a targeting of notch ligand deltalike 1 impairs CD15+/CD133+ tumorpropagating cells and supports neural differentiation in medulloblastoma. PLOS ONE. 2011, 6 (e24584):
 63.
Andersen DC, Jensen CH, Schneider M, Nossent AY, Eskildsen T, Hansen JL, Teisner B, Sheikh SP: MicroRNA15a finetunes the level of deltalike 1 homolog (DLK1) in proliferating 3T3L1 preadipocytes. Exp Cell Res. 2010, 316: 16811691. 10.1016/j.yexcr.2010.04.002.
 64.
Lu D, Davis MPA, AbreuGoodger C, Wang W, Campos LS, Siede J, Vigorito E, Skarnes WC, Dunham I, Enright AJ, Liu P: MiR25 Regulates Wwp2 and Fbxw7 and promotes reprogramming of mouse fibroblast cells to iPSCs. PLOS ONE. 2012, 7 (e40938):
 65.
Han H, Sun D, Li W, Shen H, Zhu Y, Li C, Chen Y, Lu L, Li W, Zhang J, Tian Y, Li Y: A cMycMicroRNA functional feedback loop affects hepatocarcinogenesis. Hepatology. 2013, 57: 23782389. 10.1002/hep.26302.
 66.
Savita U, Karunagaran D: MicroRNA106b25 cluster targetsβTRCP2, increases the expression of snail and enhances cell migration and invasion in H1299 (non small cell lung cancer) cells. Bioch Biophys Res Commun. 2013, 434: 841847. 10.1016/j.bbrc.2013.04.025.
 67.
Obayashi T, Okamura1 Y, Ito S, Tadaka S, Motoike IN, Kinoshita K: COXPRESdb: A database of comparative gene Coexpression networks of eleven species for mammals. Nucleic Acids Res. 2013, 41: D1014D1020. 10.1093/nar/gks1014.
 68.
Alexiou P, Vergoulis T, Gleditzsch M, Prekas G, Dalamagas T, Megraw M, Grosse I, Sellis T: Hatzigeorgiou AG: miRGen 2.0: A database of microRNA genomic information and regulation. Nucleic Acids Res. 2010, 38: D137D141. 10.1093/nar/gkp888.
 69.
andPeter T, Nelson MK, Kouranov A, Fitziev P, Bouyioukos C, Mourelatos Z, Hatzigeorgiou A: A combined computationalexperimental approach predicts human microRNA targets. Genes Dev. 2004, 18 (10): 11651178. 10.1101/gad.1184704.
 70.
Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E: The role of site accessibility in microRNA target recognition. Nat Genet. 2007, 39 (10): 12781284. 10.1038/ng2135.
 71.
Lewis BP, Burge CB, Bartel DP: Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell. 2005, 120: 1520. 10.1016/j.cell.2004.12.035.
 72.
Gray KA, Daugherty LC, Gordon SM, Seal RL, Wright MW, Bruford EA: Genenames.org: The HGNC resources in 2013. Nucleic Acids Res. 2013, 41: D545D552. 10.1093/nar/gks1066.
 73.
Peeters R: The maximum edge Biclique problem is NPcomplete. Discrete Appl Math. 2003, 131 (3): 651654. 10.1016/S0166218X(03)003330.
 74.
Agrawal R, Srikant R: Fast algorithms for mining association rules in large databases. Proceedings of the 20th International Conference on Very Large Data Bases. 1994, 487499.
 75.
Borgelt C, Kruse R: Induction of association rules: Apriori implementation. Proceedings of The 15th Conference on Computational Statistics. 2002, 395400.
 76.
Du N, Wang B, Wu B, Wang Y: Overlapping community detection in bipartite networks. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, Volume 1. 2008, 176179.
 77.
Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH: The WEKA data mining software: an upyear. SIGKDD Explorations. 2009, 11: 1018. 10.1145/1656274.1656278.
Acknowledgements
KYY is partially supported by the Hong Kong Research Grants Council Collaborative Research Fund CUHK8/CRF/11R. We thank Antonio Szeto and Kester Lee for helpful discussions.
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KYY conceived the study. DKSY and KYY designed the method. DKSY prepared the data and ran all the tests. KYY, DKSY and IKP interpreted the results. DKSY, IKP and KYY wrote the manuscript. All authors read and approved the final manuscript.
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Yip, D.K., Pang, I.K. & Yip, K.Y. Systematic exploration of autonomous modules in noisy microRNAtarget networks for testing the generality of the ceRNA hypothesis. BMC Genomics 15, 1178 (2014). https://doi.org/10.1186/14712164151178
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Keywords
 Competing endogeneous RNA
 MicroRNAtarget bicluster
 MicroRNA network