Long Noncoding RNA and Protein Interactions: From Experimental Results to Computational Models Based on Network Methods

Abstract

Non-coding RNAs with a length of more than 200 nucleotides are long non-coding RNAs (lncRNAs), which have gained tremendous attention in recent decades. Many studies have confirmed that lncRNAs have important influence in post-transcriptional gene regulation; for example, lncRNAs affect the stability and translation of splicing factor proteins. The mutations and malfunctions of lncRNAs are closely related to human disorders. As lncRNAs interact with a variety of proteins, predicting the interaction between lncRNAs and proteins is a significant way to depth exploration functions and enrich annotations of lncRNAs. Experimental approaches for lncRNA–protein interactions are expensive and time-consuming. Computational approaches to predict lncRNA–protein interactions can be grouped into two broad categories. The first category is based on sequence, structural information and physicochemical property. The second category is based on network method through fusing heterogeneous data to construct lncRNA related heterogeneous network. The network-based methods can capture the implicit feature information in the topological structure of related biological heterogeneous networks containing lncRNAs, which is often ignored by sequence-based methods. In this paper, we summarize and discuss the materials, interaction score calculation algorithms, advantages and disadvantages of state-of-the-art algorithms of lncRNA–protein interaction prediction based on network methods to assist researchers in selecting a suitable method for acquiring more dependable results. All the related different network data are also collected and processed in convenience of users, and are available at https://github.com/HAN-Siyu/APINet/.

Long Non-coding RNAs in Myeloid Malignancies

“…LncRNAs are a class of transcripts with sequence lengths of more than 200 nucleotides (78, 79). They are involved in various molecular processes, such as chromatin interaction (80), modulating gene transcription through binding the promoter region (81), acting as a competing endogenous RNAs (ceRNAs) for microRNAs (82), interacting with the ribosome and thus interfering in translation as well as interacting with various proteins (83, 84) and determining cellular localization (84). Non-coding transcription across antisense strands of genes is a universal mechanism for both yeast and vertebrates.…”

Identification of hub genes and important pathways in determination of breast cancer survival using bioinformatics approaches

“…Bioinformatics methods for such purpose fall into two groups: strategies that use sequence, structural data and physicochemical features, and methods that are based on network construction. The latter can provide the inherent characteristics of topological configuration of associated biological networks which is often disregarded by the former strategies [6]. In the present work, we used GPL570 which is a good platform to evaluate the functional roles of lncRNAs in tumorigenesis [11,24].…”

“…However, several recent studies have indicated significance of other genes and signaling pathways in determination of overall survival (OS) of patients [2,5]. Among the recently appreciated genes in this regard are long non-coding RNAs (lncRNAs) [6]. These transcripts are involved in the regulation of fundamental cell survival pathways and have functional interactions with proteins and other non-coding RNAs that participate in the pathogenesis of breast cancer [7].…”

Computational Models in Non-Coding RNA and Human Disease

“…This Special Issue is composed of twelve papers, including ten original researches [35][36][37][38][39][40][41][42][43][44] and two review articles [45,46]. The topics of these manuscripts involve diverse ncRNAs, such as miRNA, lncRNA and circRNA, related to specific disease or different diseases.…”

“…In the review paper of Zhang et al [45], some important results about the lncRNA-protein interactions, including relevant databases, experimental approaches and computational models, especially those based on networks, were combed clearly. In addition, Zhao et al [46] summarized the kinetic modeling methods that investigated the association of miRNA-mediated signaling networks and diseases.…”