A Bipartite Network-based Method for Prediction of Long Non-coding RNA–protein Interactions *

摘要

As one large class of non-coding RNAs (ncRNAs), long ncRNAs ( lncRNAs ) have gained considerable attention in recent years. Mutations and dysfunction of lncRNAs have been implicated in human disorders. Many lncRNAs exert their effects through interactions with the corresponding RNA-binding proteins . Several computational approaches have been developed, but only few are able to perform the prediction of these interactions from a network-based point of view. Here, we introduce a computational method named lncRNA–protein bipartite network inference (LPBNI). LPBNI aims to identify potential lncRNA–interacting proteins , by making full use of the known lncRNA–protein interactions . Leave-one-out cross validation (LOOCV) test shows that LPBNI significantly outperforms other network-based methods, including random walk (RWR) and protein-based collaborative filtering (ProCF). Furthermore, a case study was performed to demonstrate the performance of LPBNI using real data in predicting potential lncRNA–interacting proteins. Keywords lncRNA ; Protein ; Interaction ; Bipartite network ; Propagation prs.rt("abs_end"); Introduction An increasing number of studies show that approximately 2% of the whole mammalian genome represents protein-coding genes, whereas the majority of the genome consists of non-coding RNA (ncRNA) genes. ncRNAs had long been regarded as transcriptional noise, but recent investigations demonstrate that ncRNAs play an important role in the regulation of diverse biological processes [1] , [2] , [3] , [4] and [5] . Long ncRNAs (lncRNAs), which consist of more than 200 nucleotides, constitute a large class of ncRNAs [6] and [7] . In the past several years, the number of identified lncRNAs has been increasing sharply because of the development of both bioinformatics tools and experimental techniques. Functional studies of lncRNAs show that mutated and dysfunctional lncRNAs are implicated in a range of cellular processes [8] , [9] , [10] , [11] and [12] and human diseases, ranging from neurodegeneration to cancer [13] , [14] , [15] , [16] , [17] and [18] . Although some lncRNAs, e.g. , Xist [19] and MALAT1 [20] , have been well studied, the functions of most lncRNAs remain unclear. Usually lncRNAs function through interacting with RNA-binding proteins (RBPs) [21] , [22] , [23] and [24] . Therefore, it is important to predict the potential lncRNA–protein interactions, in order to study the complex function of lncRNAs. Since the experimental identification of lncRNA–protein interactions remains costly, developing effective predictive approaches becomes essential. Recently, several computational methods have been reported for predicting potential lncRNA–protein interactions. For instance, Bellucci et al. developed catRAPID in 2011 [25] by taking into account secondary structure, hydrogen bonds, and van der Waals forces between lncRNAs and proteins. Next, Muppirala et al. [26] introduced a method named RPISeq, using only sequence information of lncRNAs and proteins. Support vector machine (SVM) classifiers [27] and random forest (RF) [28] are used to predict RBPs. In 2013, Lu et al. [29] developed a novel approach, named lncPro, which uses secondary structure, hydrogen bond, van der Waals force features, and yields the prediction score using Fisher’s linear discriminate method. Later on, an approach named RPI-Pred was developed by Suresh et al. [30] , they trained SVM-based approach, by extracting sequence and high-order 3D structure features of lncRNAs and proteins. All the aforementioned methods are based on the biological characteristics of ncRNAs and proteins. CatRAPID and lncPro combined sequence and structural features of lncRNAs and proteins. RPISeq was based on sequence features. RPI-Pred used the high-order structure features of lncRNAs and proteins. However some studies show that lncRNAs generally exhibit low sequence conservation [1] , which may make it difficult to predict interactions based on the intrinsic properties of lncRNAs. Biological network-based methods are widely used in many types of studies, such as disease gene prioritization [31] and drug-target interaction prediction [32] . The development of bioinformatics technologies such as CLIP-seq and cross-linking immunoprecipitation, has enabled us to construct lncRNA–protein interaction networks. We introduce here a novel computational method, lncRNA–protein bipartite network inference (LPBNI), for the prediction of lncRNA–protein interactions. LPBNI identifies novel lncRNA–protein pairs by efficiently using the lncRNA–protein bipartite network. In order to evaluate the performance of the proposed method, we compared LPBNI with other network-based methods, including random walk (RWR) [31] and protein-based collaborative filtering (ProCF) [33] . RWR [31] has been used to predict genes associated with potential diseases. ProCF is derived from the recommendation algorithms, similar to the item-based collaborative filtering method [33] . The