Predicting Long Noncoding RNA and Protein Interactions Using Heterogeneous Network Model

Fusing multiple protein-protein similarity networks to effectively predict lncRNA-protein interactions

BACKGROUND

Long non-coding RNA (lncRNA) plays important roles in many biological and pathological processes, including transcriptional regulation and gene regulation. As lncRNA interacts with multiple proteins, predicting lncRNA-protein interactions (lncRPIs) is an important way to study the functions of lncRNA. Up to now, there have been a few works that exploit protein-protein interactions (PPIs) to help the prediction of new lncRPIs.

RESULTS

In this paper, we propose to boost the prediction of lncRPIs by fusing multiple protein-protein similarity networks (PPSNs). Concretely, we first construct four PPSNs based on protein sequences, protein domains, protein GO terms and the STRING database respectively, then build a more informative PPSN by fusing these four constructed PPSNs. Finally, we predict new lncRPIs by a random walk method with the fused PPSN and known lncRPIs. Our experimental results show that the new approach outperforms the existing methods.

CONCLUSION

Fusing multiple protein-protein similarity networks can effectively boost the performance of predicting lncRPIs.

In the shadow: The emerging role of long non-coding RNAs in the immune response of Atlantic salmon

The genomic era has increased the research effort to uncover how the genome of an organism, and specifically the transcriptome, is modulated after interplaying with pathogenic microorganisms and ectoparasites. However, the ever-increasing accessibility of sequencing technology has also evidenced regulatory roles of long non-coding RNAs (lncRNAs) related to several biological processes including immune response. This study reports a high-confidence annotation and a comparative transcriptome analysis of lncRNAs from several tissues of Salmo salar infected with the most prevalent pathogens in the Chilean salmon aquaculture such as the infectious salmon anemia (ISA) virus, the intracellular bacterium Piscirickettsia salmonis and the ectoparasite copepod Caligus rogercresseyi. Our analyses showed that lncRNAs are widely modulated during infection. However, this modulation is pathogen-specific and highly correlated with immuno-related genes associated with innate immune response. These findings represent the first discovery for the widespread differential expression of lncRNAs in response to infections with different types of pathogens in Atlantic salmon, suggesting that lncRNAs are pivotal player during the fish immune response.

Prediction of lncRNA-protein interactions using HeteSim scores based on heterogeneous networks

Massive studies have indicated that long non-coding RNAs (lncRNAs) are critical for the regulation of cellular biological processes by binding with RNA-related proteins. However, only a few experimentally supported lncRNA-protein associations have been reported. Existing network-based methods are typically focused on intrinsic features of lncRNA and protein but ignore the information implicit in the topologies of biological networks associated with lncRNAs. Considering the limitations in previous methods, we propose PLPIHS, an effective computational method for Predicting lncRNA-Protein Interactions using HeteSim Scores. PLPIHS uses the HeteSim measure to calculate the relatedness score for each lncRNA-protein pair in the heterogeneous network, which consists of lncRNA-lncRNA similarity network, lncRNA-protein association network and protein-protein interaction network. An SVM classifier to predict lncRNA-protein interactions is built with the HeteSim scores. The results show that PLPIHS performs significantly better than the existing state-of-the-art approaches and achieves an AUC score of 0.97 in the leave-one-out validation test. We also compare the performances of networks with different connectivity density and find that PLPIHS performs well across all the networks. Furthermore, we use the proposed method to identify the related proteins for lncRNA MALAT1. Highly-ranked proteins are verified by the biological studies and demonstrate the effectiveness of our method.

Non-Coding RNAs in Lung Cancer: Contribution of Bioinformatics Analysis to the Development of Non-Invasive Diagnostic Tools

Lung cancer is currently the leading cause of cancer related mortality due to late diagnosis and limited treatment intervention. Non-coding RNAs are not translated into proteins and have emerged as fundamental regulators of gene expression. Recent studies reported that microRNAs and long non-coding RNAs are involved in lung cancer development and progression. Moreover, they appear as new promising non-invasive biomarkers for early lung cancer diagnosis. Here, we highlight their potential as biomarker in lung cancer and present how bioinformatics can contribute to the development of non-invasive diagnostic tools. For this, we discuss several bioinformatics algorithms and software tools for a comprehensive understanding and functional characterization of microRNAs and long non-coding RNAs.