RAID: a comprehensive resource for human RNA-associated (RNA-RNA/RNA-protein) interaction

Downregulation of lncRNA SNHG1 in hypoxia and stem cells is associated with poor disease prognosis in gliomas

Gliomas are brain tumors associated with high morbidity, relapse and lethality despite improvement in therapeutic regimes. The hypoxic tumor microenvironment is a key feature associated with such poor outcomes in gliomas. The Hypoxia Inducible Factor (HIF) family of transcription factors are master regulators of cellular proliferation, high metabolic rates and angiogenesis via aberrant expression of downstream genes. Recent studies have implicated long non-coding RNAs (lncRNAs) as potential prognostic and diagnostic biomarkers. In this study, identification of hypoxia regulated lncRNA with a bioinformatic pipeline consisting of a newly developed tool "GenOx" was utilized for the identification of Hypoxia Response Element (HRE) and Hypoxia Ancillary Sequence (HAS) motifs in the promoter regions of lncRNAs. This was coupled with molecular, functional and interactome-based analyses of these hypoxia-relevant lncRNAs in primary tumors and cell-line models. We report on the identification of novel hypoxia regulated lncRNAs SNHG12, CASC7 and MF12-AS1. A strong association of RNA splicing mechanisms was observed with enriched lncRNAs. Several lncRNAs have emerged as prognostic biomarkers, of which TP53TG1 and SNHG1 were identified as highly relevant lncRNAs in glioma progression and validated in hypoxia cultured cells. Significantly, we determined that SNHG1 expression in tumor (vs. normal) is different from glioma stem cells, GSC (vs. tumors) and in hypoxia (vs. normoxia), positioning downregulation of SNHG1 to be associated with worsened prognosis.

Identifying Tumor-Associated Genes from Bilayer Networks of DNA Methylation Sites and RNAs

Network theory has attracted much attention from the biological community because of its high efficacy in identifying tumor-associated genes. However, most researchers have focused on single networks of single omics, which have less predictive power. With the available multiomics data, multilayer networks can now be used in molecular research. In this study, we achieved this with the construction of a bilayer network of DNA methylation sites and RNAs. We applied the network model to five types of tumor data to identify key genes associated with tumors. Compared with the single network, the proposed bilayer network resulted in more tumor-associated DNA methylation sites and genes, which we verified with prognostic and KEGG enrichment analyses.

Computational tools to study RNA-protein complexes

RNA is the key player in many cellular processes such as signal transduction, replication, transport, cell division, transcription, and translation. These diverse functions are accomplished through interactions of RNA with proteins. However, protein–RNA interactions are still poorly derstood in contrast to protein–protein and protein–DNA interactions. This knowledge gap can be attributed to the limited availability of protein-RNA structures along with the experimental difficulties in studying these complexes. Recent progress in computational resources has expanded the number of tools available for studying protein-RNA interactions at various molecular levels. These include tools for predicting interacting residues from primary sequences, modelling of protein-RNA complexes, predicting hotspots in these complexes and insights into derstanding in the dynamics of their interactions. Each of these tools has its strengths and limitations, which makes it significant to select an optimal approach for the question of interest. Here we present a mini review of computational tools to study different aspects of protein-RNA interactions, with focus on overall application, development of the field and the future perspectives.

RNAInter v4.0: RNA interactome repository with redefined confidence scoring system and improved accessibility

Establishing an RNA-associated interaction repository facilitates the system-level understanding of RNA functions. However, as these interactions are distributed throughout various resources, an essential prerequisite for effectively applying these data requires that they are deposited together and annotated with confidence scores. Hence, we have updated the RNA-associated interaction database RNAInter (RNA Interactome Database) to version 4.0, which is freely accessible at http://www.rnainter.org or http://www.rna-society.org/rnainter/. Compared with previous versions, the current RNAInter not only contains an enlarged data set, but also an updated confidence scoring system. The merits of this 4.0 version can be summarized in the following points: (i) a redefined confidence scoring system as achieved by integrating the trust of experimental evidence, the trust of the scientific community and the types of tissues/cells, (ii) a redesigned fully functional database that enables for a more rapid retrieval and browsing of interactions via an upgraded user-friendly interface and (iii) an update of entries to >47 million by manually mining the literature and integrating six database resources with evidence from experimental and computational sources. Overall, RNAInter will provide a more comprehensive and readily accessible RNA interactome platform to investigate the regulatory landscape of cellular RNAs.

Long non‑coding RNA profile revealed by microarray indicates that lncCUEDC1 serves a negative regulatory role in breast cancer stem cells

Previous studies have demonstrated that long non‑coding RNAs (lncRNAs) are involved in breast cancer development, progression and metastasis. However, the association between lncRNAs and breast cancer stem cells (BCSCs) has been poorly explored. To address this issue, microarray analyses were performed to detect the lncRNA profile of BCSCs. In addition, bioinformatics analyses, including Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes pathway analyses, were performed to explore the functional roles of lncRNAs in BCSCs. Lastly, loss of function assays were used to explore the potential function of lncRNA CUE domain containing 1 (lncCUEDC1). A total of 142 differentially expressed lncRNAs were identified. Among these, 25 were downregulated and 117 were upregulated in BCSCs compared with in non‑BCSCs. In addition, the present study revealed that the lncRNAs were largely associated with stemness‑related signaling pathways. Furthermore, it was demonstrated that lncCUEDC1 negatively regulated the phenotype and biological functions of BCSCs in vitro. Mechanistically, lncCUEDC1 could bind NANOG to inhibit the stemness. To the best of our knowledge, the present study was the first to established the lncRNA profile of BCSCs. These findings provided evidence for exploring the functions of lncRNAs in BCSCs and indicated that lncCUEDC1 is a prospective target in BCSCs.

Suppression of long noncoding RNA MALAT1 inhibits the development of uveal melanoma via microRNA-608-mediated inhibition of HOXC4

This study explored the effects of the metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) on the development of uveal melanoma. Moreover, the role of the MALAT1/microRNA-608 (miR-608)/homeobox C4 (HOXC4) axis was assessed by evaluating the proliferation, invasion, and migration, as well as the cell cycle distribution of uveal melanoma in vitro after knocking down MALAT1 or HOXC4 and/or overexpression of miR-608 in uveal melanoma cells (MUM-2B and C918). Moreover, the effects of the MALAT1/miR-608/HOXC4 axis in uveal melanoma in vivo were further evaluated by injecting the C918 cells into the NOD/SCID mice. HOXC4 was found to be a gene upregulated in uveal melanoma, while knockdown of its expression resulted in suppression of uveal melanoma cell migration, proliferation, and invasion, as well as cell cycle progression. In addition, the upregulation of miR-608 reduced the expression of HOXC4 in the uveal melanoma cells, which was rescued by overexpression of MALAT1. Hence, MALAT1 could upregulate the HOXC4 by binding to miR-608. The suppressed progression of uveal melanoma in vitro by miR-608 was rescued by overexpression of MALAT1. Additionally, in vivo assays demonstrated that downregulation of MALAT1 could suppress tumor growth through downregulation of HOXC4 expression via increasing miR-608 in uveal melanoma. In summary, MALAT1 downregulation functions to restrain the development of uveal melanoma via miR-608-mediated inhibition of HOXC4.

Multimodal Long Noncoding RNA Interaction Networks: Control Panels for Cell Fate Specification

Lineage specification in early development is the basis for the exquisitely precise body plan of multicellular organisms. It is therefore critical to understand cell fate decisions in early development. Moreover, for regenerative medicine, the accurate specification of cell types to replace damaged/diseased tissue is strongly dependent on identifying determinants of cell identity. Long noncoding RNAs (lncRNAs) have been shown to regulate cellular plasticity, including pluripotency establishment and maintenance, differentiation and development, yet broad phenotypic analysis and the mechanistic basis of their function remains lacking. As components of molecular condensates, lncRNAs interact with almost all classes of cellular biomolecules, including proteins, DNA, mRNAs, and microRNAs. With functions ranging from controlling alternative splicing of mRNAs, to providing scaffolding upon which chromatin modifiers are assembled, it is clear that at least a subset of lncRNAs are far from the transcriptional noise they were once deemed. This review highlights the diversity of lncRNA interactions in the context of cell fate specification, and provides examples of each type of interaction in relevant developmental contexts. Also highlighted are experimental and computational approaches to study lncRNAs.

Exploiting locational and topological overlap model to identify modules in protein interaction networks

Background

Clustering molecular network is a typical method in system biology, which is effective in predicting protein complexes or functional modules. However, few studies have realized that biological molecules are spatial-temporally regulated to form a dynamic cellular network and only a subset of interactions take place at the same location in cells.

Results

In this study, considering the subcellular localization of proteins, we first construct a co-localization human protein interaction network (PIN) and systematically investigate the relationship between subcellular localization and biological functions. After that, we propose a Locational and Topological Overlap Model (LTOM) to preprocess the co-localization PIN to identify functional modules. LTOM requires the topological overlaps, the common partners shared by two proteins, to be annotated in the same localization as the two proteins. We observed the model has better correspondence with the reference protein complexes and shows more relevance to cancers based on both human and yeast datasets and two clustering algorithms, ClusterONE and MCL.

Conclusion

Taking into consideration of protein localization and topological overlap can improve the performance of module detection from protein interaction networks.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2598-7) contains supplementary material, which is available to authorized users.

Comparison and analysis of lncRNA-mediated ceRNA regulation in different molecular subtypes of glioblastoma

LncRNA-mediated ceRNA regulation varies among different molecular subtypes in glioblastoma.

TP53LNC-DB, the database of lncRNAs in the p53 signalling network

The TP53 gene product, p53, is a pleiotropic transcription factor induced by stress, which functions to promote cell cycle arrest, apoptosis and senescence. Genome-wide profiling has revealed an extensive system of long noncoding RNAs (lncRNAs) that is integral to the p53 signalling network. As a research tool, we implemented a public access database called TP53LNC-DB that annotates currently available information relating lncRNAs to p53 signalling in humans.

Discovering Genetic Factors for psoriasis through exhaustively searching for significant second order SNP-SNP interactions

In this paper, we aim at discovering genetic factors of psoriasis through searching for statistically significant SNP-SNP interactions exhaustively from two real psoriasis genome-wide association study datasets (phs000019.v1.p1 and phs000982.v1.p1) downloaded from the database of Genotypes and Phenotypes. To deal with the enormous search space, our search algorithm is accelerated with eight biological plausible interaction patterns and a pre-computed look-up table. After our search, we have discovered several SNPs having a stronger association to psoriasis when they are in combination with another SNP and these combinations may be non-linear interactions. Among the top 20 SNP-SNP interactions being found in terms of pairwise p-value and improvement metric value, we have discovered 27 novel potential psoriasis-associated SNPs where most of them are reported to be eQTLs of a number of known psoriasis-associated genes. On the other hand, we have inferred a gene network after selecting the top 10000 SNP-SNP interactions in terms of improvement metric value and we have discovered a novel long distance interaction between XXbac-BPG154L12.4 and RNU6-283P which is not a long distance haplotype and may be a new discovery. Finally, our experiments with the synthetic datasets have shown that our pre-computed look-up table technique can significantly speed up the search process.

Identification and Analysis of Key Residues in Protein-RNA Complexes

Protein-RNA complexes play important roles in various biological processes. The functions of protein-RNA complexes are dictated by their interactions, binding, stability, and affinity. In this work, we have identified the key residues (KRs), which are involved in both stability and binding. We found that 42 percent of considered proteins share common binding and stabilizing residues, whereas these residues are distinct in 58 percent of the proteins. Overall, 5 percent of stabilizing and 3 percent of binding residues serve as key residues. These residues are enriched with the combination of polar, charged, aliphatic, and aromatic residues. Analysis on subclasses of protein-RNA complexes based on protein structural class, function and RNA type showed that regulatory proteins, and complexes with single stranded RNA and rRNA have appreciable number of key residues. Specifically, Arg, Tyr, and Thr are preferred in most of the subclasses of protein-RNA complexes. In addition, residues with similar chemical behavior have different preferences to be KRs, such that Arg, Tyr, Val, and Thr are preferred over Lys, Trp, Ile, and Ser, respectively. Atomic level contacts revealed that charged and polar-nonpolar contacts are dominant in enzymes, polar in structural, and nonpolar in regulatory proteins. On the other hand, polar-nonpolar contacts are enriched in all these classes of protein-RNA complexes. Further, the influence of sequence and structural features such as conservation score, surrounding hydrophobicity, solvent accessibility, secondary structure, and long-range order in key residues are also discussed. We envisage that the present study provides insights to understand the structural and functional aspects of protein-RNA complexes.

Computational approaches in design of nucleic acid-based therapeutics

Recent advances in computational and experimental methods have led to novel avenues for therapeutic development. Utilization of nucleic acids as therapeutic agents and/or targets has been recently gaining attention due to their potential as high-affinity, selective molecular building blocks for various therapies. Notably, development of computational algorithms for predicting accessible RNA binding sites, identifying therapeutic target sequences, modeling delivery into tissues, and designing binding aptamers have enhanced therapeutic potential for this new drug category. Here, we review trends in drug development within the pharmaceutical industry and ways by which nucleic acid-based drugs have arisen as effective therapeutic candidates. In particular, we focus on computational and experimental approaches to nucleic acid-based drug design, commenting on challenges and outlooks for future applications.

The Ever-Evolving Concept of the Gene: The Use of RNA/Protein Experimental Techniques to Understand Genome Functions

The completion of the human genome sequence together with advances in sequencing technologies have shifted the paradigm of the genome, as composed of discrete and hereditable coding entities, and have shown the abundance of functional noncoding DNA. This part of the genome, previously dismissed as “junk” DNA, increases proportionally with organismal complexity and contributes to gene regulation beyond the boundaries of known protein-coding genes. Different classes of functionally relevant nonprotein-coding RNAs are transcribed from noncoding DNA sequences. Among them are the long noncoding RNAs (lncRNAs), which are thought to participate in the basal regulation of protein-coding genes at both transcriptional and post-transcriptional levels. Although knowledge of this field is still limited, the ability of lncRNAs to localize in different cellular compartments, to fold into specific secondary structures and to interact with different molecules (RNA or proteins) endows them with multiple regulatory mechanisms. It is becoming evident that lncRNAs may play a crucial role in most biological processes such as the control of development, differentiation and cell growth. This review places the evolution of the concept of the gene in its historical context, from Darwin's hypothetical mechanism of heredity to the post-genomic era. We discuss how the original idea of protein-coding genes as unique determinants of phenotypic traits has been reconsidered in light of the existence of noncoding RNAs. We summarize the technological developments which have been made in the genome-wide identification and study of lncRNAs and emphasize the methodologies that have aided our understanding of the complexity of lncRNA-protein interactions in recent years.

Competing endogenous RNA networks in human cancer: hypothesis, validation, and perspectives

Non-coding RNAs represent a majority of the human transcriptome. However, less is known about the functions and regulatory mechanisms of most non-coding species. Moreover, little is known about the potential non-coding functions of coding RNAs. The competing endogenous RNAs (ceRNAs) hypothesis is proposed recently. This hypothesis describes potential communication networks among all transcript RNA species mediated by miRNAs and miRNA-recognizing elements (MREs) within RNA transcripts. Here we review the evolution of the ceRNA hypothesis, summarize the validation experiments and discusses the significance and perspectives of this hypothesis in human cancer.

Differently expressed long noncoding RNAs and mRNAs in TK6 cells exposed to low dose hydroquinone

Previous studies have shown that long noncoding RNAs (lncRNAs) were related to human carcinogenesis and might be designated as diagnosis and prognosis biomarkers. Hydroquinone (HQ), as one of the metabolites of benzene, was closely relevant to occupational benzene poisoning and occupational leukemia. Using high-throughput sequencing technology, we investigated differences in lncRNA and mRNA expression profiles between experimental group (HQ 20 μmol/L) and control group (PBS). Compared to control group, a total of 65 lncRNAs and 186 mRNAs were previously identified to be aberrantly expressed more than two fold change in experimental group. To validate the sequencing results, we selected 10 lncRNAs and 10 mRNAs for quantitative real-time PCR (qRT-PCR). Through GO annotation and KEGG pathway analysis, we obtained 3 mainly signaling pathways, including P53 signaling pathway, which plays an important role in tumorigenesis and progression. After that, 25 lncRNAs and 32 mRNAs formed the lncRNA-mRNA co-expression network were implemented to play biological functions of the dysregulated lncRNAs transcripts by regulating gene expression. The lncRNAs target genes prediction provided a new idea for the study of lncRNAs. Finally, we have another important discovery, which is screened out 11 new lncRNAs without annotated. All these results uncovered that lncRNA and mRNA expression profiles in TK6 cells exposed to low dose HQ were different from control group, helping to further study the toxicity mechanisms of HQ and providing a new direction for the therapy of leukemia.

Inferring microRNA-disease associations by random walk on a heterogeneous network with multiple data sources

Since the discovery of the regulatory function of microRNA (miRNA), increased attention has focused on identifying the relationship between miRNA and disease. It has been suggested that computational method are an efficient way to identify potential disease-related miRNAs for further confirmation using biological experiments. In this paper, we first highlighted three limitations commonly associated with previous computational methods. To resolve these limitations, we established disease similarity subnetwork and miRNA similarity subnetwork by integrating multiple data sources, where the disease similarity is composed of disease semantic similarity and disease functional similarity, and the miRNA similarity is calculated using the miRNA-target gene and miRNA-lncRNA (long non-coding RNA) associations. Then, a heterogeneous network was constructed by connecting the disease similarity subnetwork and the miRNA similarity subnetwork using the known miRNA-disease associations. We extended random walk with restart to predict miRNA-disease associations in the heterogeneous network. The leave-one-out cross-validation achieved an average area under the curve (AUC) of 0:8049 across 341 diseases and 476 miRNAs. For five-fold cross-validation, our method achieved an AUC from 0:7970 to 0:9249 for 15 human diseases. Case studies further demonstrated the feasibility of our method to discover potential miRNA-disease associations. An online service for prediction is freely available at http://ifmda.aliapp.com.

Detecting RNA-RNA interactions in E. coli using a modified CLASH method

Background

Bacterial small regulatory RNAs (sRNAs) play important roles in sensing environment changes through sRNA-target mRNA interactions. However, the current strategy for detecting sRNA-mRNA interactions usually combines bioinformatics prediction and experimental verification, which is hampered by low prediction accuracy and low-throughput. Additionally, among the 4736 sequenced bacterial genomes, only about 2164 sRNAs from 319 strains have been described. Furthermore, target mRNAs of only 157 sRNAs have been uncovered. Obviously, highly efficient methods were required to detect sRNA-mRNA interactions in the sequenced genomes. This study aimed to apply a modified CLASH (cross-linking, ligation and sequencing hybrids) method to detect RNA-RNA interactions in E. coli, a model bacterial organism.

Results

Statistically significant interactions were detected in 29 transcript pairs. To the best of our knowledge, 24 pairs were reported for the first time and were novel RNA interactions, including tRNA-tRNA, tRNA-ncRNA (non-coding RNA), tRNA-rRNA, rRNA-mRNA, rRNA-ncRNA, rRNA-rRNA, rRNA-IGT (intergenic transcript), and tRNA-IGT interactions.

Conclusions

Discovery of novel RNA-RNA interactions in the present study demonstrates that RNA-RNA interactions might be far more complicated than ever expected. New methods may be required to help discover more novel RNA-RNA interactions. The present work describes a high-throughput protocol not only for discovering new RNA interactions, but also directly obtaining base-pairing sequences, which should be useful in assessing RNA structure and interactions.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3725-3) contains supplementary material, which is available to authorized users.

Aberrantly expressed long noncoding RNAs in recurrent implantation failure: A microarray related study

Long noncoding RNAs (lncRNAs) are a class of noncoding RNAs longer than 200 nucleotides. They were long regarded as transcription noise for their low expression and non-protein coding features. Recent published reports indicate that lncRNAs are involved in virtually every aspect of human biology. We aimed to profile the endometrial lncRNA expression pattern in women with recurrent implantation failure (RIF) and predict the function of the genes of the dysregulated lncRNA transcripts. Endometrial samples (24) were collected during window of implantation (14 RIF women and 10 women who conceived after embryo transfer). For the microarray study, 7 RIF endometrium and 5 control endometrium were selected, and quantitative real-time PCR (RT-qPCR) was performed on the rest of the endometrial samples to validate the microarray results. After that, lncRNA-mRNA co-expression analysis, GO analysis, KEGG analysis, and lncRNA-transcript factor (TF) analysis were carried out to analyze the gene functions of the dysregulated lncRNA transcripts. We detected a total of 197 lncRNA transcripts that were dysregulated in RIF endometrium compared with the control group. The relative expression levels of eight selected lncRNA transcripts were validated by RT-qPCR and were in accordance with the microarray outcomes. GO and KEGG analyses revealed that the coexpressed mRNA transcripts were involved in pathways that may affect endometrial receptivity such as cell adhesion. The lncRNA target predictions provided potential TF targets of the dysregulated lncRNA transcripts. Our results indicate that lncRNA expression profiles of RIF endometrium were different from that of normal receptive endometrial, suggesting that lncRNAs may regulate endometrial receptivity.

ABBREVIATIONS

GO: Gene Oncology; GFs: growth factors; KEGG: Kyoto Encyclopedia of Genes and Genomes; lncRNAs: long noncoding RNAs; PCA3: prostate cancer antigen 3; RT-qPCR: quantitative real-time PCR; RIF: recurrent implantation failure; STK: serine/threonine kinase; TF: transcription factor; WOI: window of implantation.

RAIN: RNA-protein Association and Interaction Networks

Protein association networks can be inferred from a range of resources including experimental data, literature mining and computational predictions. These types of evidence are emerging for non-coding RNAs (ncRNAs) as well. However, integration of ncRNAs into protein association networks is challenging due to data heterogeneity. Here, we present a database of ncRNA-RNA and ncRNA-protein interactions and its integration with the STRING database of protein-protein interactions. These ncRNA associations cover four organisms and have been established from curated examples, experimental data, interaction predictions and automatic literature mining. RAIN uses an integrative scoring scheme to assign a confidence score to each interaction. We demonstrate that RAIN outperforms the underlying microRNA-target predictions in inferring ncRNA interactions. RAIN can be operated through an easily accessible web interface and all interaction data can be downloaded.Database URL: http://rth.dk/resources/rain.

RAID v2.0: an updated resource of RNA-associated interactions across organisms

With the development of biotechnologies and computational prediction algorithms, the number of experimental and computational prediction RNA-associated interactions has grown rapidly in recent years. However, diverse RNA-associated interactions are scattered over a wide variety of resources and organisms, whereas a fully comprehensive view of diverse RNA-associated interactions is still not available for any species. Hence, we have updated the RAID database to version 2.0 (RAID v2.0, www.rna-society.org/raid/) by integrating experimental and computational prediction interactions from manually reading literature and other database resources under one common framework. The new developments in RAID v2.0 include (i) over 850-fold RNA-associated interactions, an enhancement compared to the previous version; (ii) numerous resources integrated with experimental or computational prediction evidence for each RNA-associated interaction; (iii) a reliability assessment for each RNA-associated interaction based on an integrative confidence score; and (iv) an increase of species coverage to 60. Consequently, RAID v2.0 recruits more than 5.27 million RNA-associated interactions, including more than 4 million RNA-RNA interactions and more than 1.2 million RNA-protein interactions, referring to nearly 130 000 RNA/protein symbols across 60 species.

RNALocate: a resource for RNA subcellular localizations

Increasing evidence has revealed that RNA subcellular localization is a very important feature for deeply understanding RNA's biological functions after being transported into intra- or extra-cellular regions. RNALocate is a web-accessible database that aims to provide a high-quality RNA subcellular localization resource and facilitate future researches on RNA function or structure. The current version of RNALocate documents more than 37 700 manually curated RNA subcellular localization entries with experimental evidence, involving more than 21 800 RNAs with 42 subcellular localizations in 65 species, mainly including Homo sapiens, Mus musculus and Saccharomyces cerevisiae etc. Besides, RNA homology, sequence and interaction data have also been integrated into RNALocate. Users can access these data through online search, browse, blast and visualization tools. In conclusion, RNALocate will be of help in elucidating the entirety of RNA subcellular localization, and developing new prediction methods. The database is available at http://www.rna-society.org/rnalocate/.

Genome-wide survey of putative RNA-binding proteins encoded in the human proteome

RNA-binding proteins (RBPs) are involved in various post-transcriptional gene regulatory processes and are also functionally important members of the ribosome and the spliceosome. However, RBPs and their interactions with RNA are less well-studied in comparison to DNA-binding proteins. We have classified the existing RBP structures, available in complexes with RNA and RNA/DNA hybrids, into different structural families and created Hidden Markov Models (HMMs). These structure-centric family HMMs, along with the sequence-centric family HMMs, were used as a primary database to systematically search the human proteome for the presence of putative RBPs. We have found more than 2600 gene products with RBP signatures in humans, of which around 28% are likely to bind to RNA but not DNA, whereas 9% might bind to both RNA and DNA. 11% of them do not contain an explicit functional annotation yet. Nearly 30% of the putative RBPs are exclusively nuclear, 15% have known disease associations and around 30% are enzymes. Around 40% of the proteins identified in this study are novel and have not been reported by recent large-scale studies on human RBPs.

RStrucFam: a web server to associate structure and cognate RNA for RNA-binding proteins from sequence information

Background

RNA-binding proteins (RBPs) interact with their cognate RNA(s) to form large biomolecular assemblies. They are versatile in their functionality and are involved in a myriad of processes inside the cell. RBPs with similar structural features and common biological functions are grouped together into families and superfamilies. It will be useful to obtain an early understanding and association of RNA-binding property of sequences of gene products. Here, we report a web server, RStrucFam, to predict the structure, type of cognate RNA(s) and function(s) of proteins, where possible, from mere sequence information.

Results

The web server employs Hidden Markov Model scan (hmmscan) to enable association to a back-end database of structural and sequence families. The database (HMMRBP) comprises of 437 HMMs of RBP families of known structure that have been generated using structure-based sequence alignments and 746 sequence-centric RBP family HMMs. The input protein sequence is associated with structural or sequence domain families, if structure or sequence signatures exist. In case of association of the protein with a family of known structures, output features like, multiple structure-based sequence alignment (MSSA) of the query with all others members of that family is provided. Further, cognate RNA partner(s) for that protein, Gene Ontology (GO) annotations, if any and a homology model of the protein can be obtained. The users can also browse through the database for details pertaining to each family, protein or RNA and their related information based on keyword search or RNA motif search.

Conclusions

RStrucFam is a web server that exploits structurally conserved features of RBPs, derived from known family members and imprinted in mathematical profiles, to predict putative RBPs from sequence information. Proteins that fail to associate with such structure-centric families are further queried against the sequence-centric RBP family HMMs in the HMMRBP database. Further, all other essential information pertaining to an RBP, like overall function annotations, are provided. The web server can be accessed at the following link: http://caps.ncbs.res.in/rstrucfam.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1289-x) contains supplementary material, which is available to authorized users.

An integrative framework to identify cell death-related microRNAs in esophageal squamous cell carcinoma

Cell death is a critical biological process involved in many important functions, and defects in this system are usually linked with numerous human diseases including cancers. Esophageal squamous cell carcinoma is one of the most chemo- and biological therapy resistant cancers. Based on knowledge repository and four miRNAs profiling data, we proposed a general framework to hunt for cell death miRNAs in a context dependent manner. We predicted 12 candidate miRNAs from hundreds of others. Follow-up experimental verification of 7 miRNAs indicated at least 3 miRNAs (MIR20b, MIR498 and MIR196) were involved in both apoptosis and autophagy processes. These results indicated miRNAs intimately connected the two cell death modules in esophageal squamous cell carcinoma. This integrative framework can also be easily extended to identify miRNAs in other key cellular signaling pathways or may find conditional specific miRNAs in other cancer types.

BP Neural Network Could Help Improve Pre-miRNA Identification in Various Species

MicroRNAs (miRNAs) are a set of short (21–24 nt) noncoding RNAs that play significant regulatory roles in cells. In the past few years, research on miRNA-related problems has become a hot field of bioinformatics because of miRNAs' essential biological function. miRNA-related bioinformatics analysis is beneficial in several aspects, including the functions of miRNAs and other genes, the regulatory network between miRNAs and their target mRNAs, and even biological evolution. Distinguishing miRNA precursors from other hairpin-like sequences is important and is an essential procedure in detecting novel microRNAs. In this study, we employed backpropagation (BP) neural network together with 98-dimensional novel features for microRNA precursor identification. Results show that the precision and recall of our method are 95.53% and 96.67%, respectively. Results further demonstrate that the total prediction accuracy of our method is nearly 13.17% greater than the state-of-the-art microRNA precursor prediction software tools.

P2RX7-V3 is a novel oncogene that promotes tumorigenesis in uveal melanoma

Uveal melanoma (UM) has a high mortality rate for primary intraocular tumors. Approximately half of UM patients present with untreatable and fatal metastases. Long non-coding RNAs (lncRNAs) have emerged as potent regulatory RNAs that play key roles in various cellular processes and tumorigenesis. However, to date, their roles in UM are not well-known. Here, we identified a transcriptional variant transcribed from the P2RX7 gene locus, named P2RX7-V3 (P2RX7 variant 3), which was expressed at a high level in UM cells. P2RX7-V3 silencing revealed that this variant acts as a necessary UM oncoRNA. Knockdown of P2RX7-V3 expression significantly suppressed tumor growth in vitro and in vivo. A genome-wide cDNA array revealed that a variety of genes were dysregulated following P2RX7-V3 silencing. These observations identified P2RX7-V3 that plays a crucial role in UM tumorigenesis and may serve as a useful biomarker in the diagnosis and prognosis treatment of UM in the future.

A potential panel of six-long non-coding RNA signature to improve survival prediction of diffuse large-B-cell lymphoma

Long non-coding RNAs (lncRNAs) represent an emerging layer of cancer biology and have been implicated in the development and progression of cancers. However, the prognostic significance of lncRNAs in diffuse large-B-cell lymphoma (DLBCL) remains unclear and needs to be systematically investigated. In this study, we obtained and analyzed lncRNA expression profiles in three cohorts of 1043 DLBCL patients by repurposing the publicly available microarray datasets from the Gene Expression Omnibus (GEO) database. In the discovery series of 207 patients, we identified a set of six lncRNAs that was significantly associated with patients’ overall survival (OS) using univariate Cox regression analysis. The six prognostic lncRNAs were combined to form an expression-based six-lncRNA signature which classified patients of the discovery series into the high-risk group and low-risk group with significantly different survival outcome (HR = 2.31, 95% CI = 1.8 to 2.965, p < 0.001). The six-lncRNA signature was further confirmed in the internal testing series and two additional independent datasets with different array platform. Moreover, the prognostic value of the six-lncRNA signature is independent of conventional clinical factors. Functional analysis suggested that six-lncRNA signature may be involved with DLBCL through exerting their regulatory roles in known cancer-related pathways, immune system and signaling molecules interaction.

A Comprehensive Analysis of miRNA/isomiR Expression with Gender Difference

Although microRNAs (miRNAs) have been widely studied as epigenetic regulation molecules, fewer studies focus on the gender difference at the miRNA and isomiR expression levels. In this study, we aim to understand the potential relationships between gender difference and miRNA/isomiR expression through a comprehensive analysis of small RNA-sequencing datasets based on different human diseases and tissues. Based on specific samples from males and females, we determined that some miRNAs may be diversely expressed between different tissues and genders. Thus, these miRNAs may exhibit inconsistent and even opposite expression between males and females. According to deregulated miRNA expression profiles, some dominantly expressed miRNA loci were selected to analyze isomiR expression patterns using rates of dominant isomiRs. In some miRNA loci, isomiRs showed statistical significance between tumor and normal samples and between males and females samples, suggesting that isomiR expression patterns are not always invariable but may vary between males and females, as well as among different tissues, tumors, and normal samples. The divergence implicates the fluctuation in the expression of miRNA and its detailed expression at the isomiR levels. The divergence also indicates that gender difference may be an important factor that affects the screening of disease-associated miRNAs and isomiRs. This study suggests that miRNA/isomiR expression and gender difference may be more complex than previously assumed and should be further studied according to specific samples from males or females.

NPInter v3.0: an upgraded database of noncoding RNA-associated interactions

Despite the fact that a large quantity of noncoding RNAs (ncRNAs) have been identified, their functions remain unclear. To enable researchers to have a better understanding of ncRNAs’ functions, we updated the NPInter database to version 3.0, which contains experimentally verified interactions between ncRNAs (excluding tRNAs and rRNAs), especially long noncoding RNAs (lncRNAs) and other biomolecules (proteins, mRNAs, miRNAs and genomic DNAs). In NPInter v3.0, interactions pertaining to ncRNAs are not only manually curated from scientific literature but also curated from high-throughput technologies. In addition, we also curated lncRNA–miRNA interactions from in silico predictions supported by AGO CLIP-seq data. When compared with NPInter v2.0, the interactions are more informative (with additional information on tissues or cell lines, binding sites, conservation, co-expression values and other features) and more organized (with divisions on data sets by data sources, tissues or cell lines, experiments and other criteria). NPInter v3.0 expands the data set to 491,416 interactions in 188 tissues (or cell lines) from 68 kinds of experimental technologies. NPInter v3.0 also improves the user interface and adds new web services, including a local UCSC Genome Browser to visualize binding sites. Additionally, NPInter v3.0 defined a high-confidence set of interactions and predicted the functions of lncRNAs in human and mouse based on the interactions curated in the database. NPInter v3.0 is available at http://www.bioinfo.org/NPInter/.

Database URL: http://www.bioinfo.org/NPInter/

lncRNA-RNA Interactions across the Human Transcriptome

Long non-coding RNAs (lncRNAs) represent a numerous class of non-protein coding transcripts longer than 200 nucleotides. There is possibility that a fraction of lncRNAs are not functional and represent mere transcriptional noise but a growing body of evidence shows they are engaged in a plethora of molecular functions and contribute considerably to the observed diversification of eukaryotic transcriptomes and proteomes. Still, however, only ca. 1% of lncRNAs have well established functions and much remains to be done towards decipherment of their biological roles. One of the least studied aspects of lncRNAs biology is their engagement in gene expression regulation through RNA-RNA interactions. By hybridizing with mate RNA molecules, lncRNAs could potentially participate in modulation of pre-mRNA splicing, RNA editing, mRNA stability control, translation activation, or abrogation of miRNA-induced repression. Here, we implemented a similarity-search based method for transcriptome-wide identification of RNA-RNA interactions, which enabled us to find 18,871,097 lncRNA-RNA base-pairings in human. Further analyses showed that the interactions could be involved in processing, stability control and functions of 57,303 transcripts. An extensive use of RNA-Seq data provided support for approximately one third of the interactions, at least in terms of the two RNA components being co-expressed. The results suggest that lncRNA-RNA interactions are broadly used to regulate and diversify the human transcriptome.

iMiRNA-SSF: Improving the Identification of MicroRNA Precursors by Combining Negative Sets with Different Distributions

The identification of microRNA precursors (pre-miRNAs) helps in understanding regulator in biological processes. The performance of computational predictors depends on their training sets, in which the negative sets play an important role. In this regard, we investigated the influence of benchmark datasets on the predictive performance of computational predictors in the field of miRNA identification, and found that the negative samples have significant impact on the predictive results of various methods. We constructed a new benchmark set with different data distributions of negative samples. Trained with this high quality benchmark dataset, a new computational predictor called iMiRNA-SSF was proposed, which employed various features extracted from RNA sequences. Experimental results showed that iMiRNA-SSF outperforms three state-of-the-art computational methods. For practical applications, a web-server of iMiRNA-SSF was established at the website http://bioinformatics.hitsz.edu.cn/iMiRNA-SSF/.

LncRNAs and Esophageal Squamous Cell Carcinoma - Implications for Pathogenesis and Drug Development

LncRNAs are a group of ncRNA species longer than 200 nt, which have fundamental regulatory roles in diverse cellular processes and diseases progression. Esophageal cancer is a serious malignancy with respect to prognosis and mortality rate. It is among the five leading cancer types for the cancer deaths in males of middle age in the United States. In China, esophageal cancer is the fourth most frequently diagnosed cancer and the fourth leading cause of cancer death. The molecular mechanisms of esophageal cancer development are not fully understood, but emerging studies point out that lncRNAs may actively associate with the pathogenesis. In this review, we first provided an introduction of lncRNAs classifications. Then we focused on the recent findings on lncRNA expression and function in esophageal cancer development. Implications for pathogenesis and potential drug developments will also be discussed.

Introduction to Bioinformatics Resources for Post-transcriptional Regulation of Gene Expression

Untranslated regions (UTRs) and, to a lesser extent, coding sequences of mRNAs are involved in defining the fate of the mature transcripts through the modulation of three primary control processes, mRNA localization, degradation and translation; the action of trans-factors such as RNA-binding proteins (RBPs) and noncoding RNAs (ncRNAs) combined with the presence of defined sequence and structural cis-elements ultimately determines translation levels. Identifying functional regions in UTRs and uncovering post-transcriptional regulators acting upon these regions is thus of paramount importance to understand the spectrum of regulatory possibilities for any given mRNA. This tasks can now be approached computationally, to reduce the space of testable hypotheses and to drive experimental validation.This chapter focuses on presenting databases and tools allowing to study the various aspects of post-transcriptional regulation, including motif search (sequence and secondary structure), prediction of regulatory networks (e.g., RBP and ncRNA binding sites), profiling of the mRNAs translational state, and other aspects of this level of gene expression regulation. Two analysis pipelines are also presented as practical examples of how the described tools could be integrated and effectively employed.

Revealing potential molecular targets bridging colitis and colorectal cancer based on multidimensional integration strategy

Chronic inflammation may play a vital role in the pathogenesis of inflammation-associated tumors. However, the underlying mechanisms bridging ulcerative colitis (UC) and colorectal cancer (CRC) remain unclear. Here, we integrated multidimensional interaction resources, including gene expression profiling, protein-protein interactions (PPIs), transcriptional and post-transcriptional regulation data, and virus-host interactions, to tentatively explore potential molecular targets that functionally link UC and CRC at a systematic level. In this work, by deciphering the overlapping genes, crosstalking genes and pivotal regulators of both UC- and CRC-associated functional module pairs, we revealed a variety of genes (including FOS and DUSP1, etc.), transcription factors (including SMAD3 and ETS1, etc.) and miRNAs (including miR-155 and miR-196b, etc.) that may have the potential to complete the connections between UC and CRC. Interestingly, further analyses of the virus-host interaction network demonstrated that several virus proteins (including EBNA-LP of EBV and protein E7 of HPV) frequently inter-connected to UC- and CRC-associated module pairs with their validated targets significantly enriched in both modules of the host. Together, our results suggested that multidimensional integration strategy provides a novel approach to discover potential molecular targets that bridge the connections between UC and CRC, which could also be extensively applied to studies on other inflammation-related cancers.

ZFP36L1 promotes monocyte/macrophage differentiation by repressing CDK6

RNA binding proteins (RBPs)-mediated post-transcriptional control has been implicated in influencing various aspects of RNA metabolism and playing important roles in mammalian development and pathological diseases. However, the functions of specific RBPs and the molecular mechanisms through which they act in monocyte/macrophage differentiation remain to be determined. In this study, through bioinformatics analysis and experimental validation, we identify that ZFP36L1, a member of ZFP36 zinc finger protein family, exhibits significant decrease in acute myeloid leukemia (AML) patients compared with normal controls and remarkable time-course increase during monocyte/macrophage differentiation of PMA-induced THP-1 and HL-60 cells as well as induction culture of CD34⁺ hematopoietic stem/progenitor cells (HSPCs). Lentivirus-mediated gain and loss of function assays demonstrate that ZFP36L1 acts as a positive regulator to participate in monocyte/macrophage differentiation. Mechanistic investigation further reveals that ZFP36L1 binds to the CDK6 mRNA 3′untranslated region bearing adenine-uridine rich elements and negatively regulates the expression of CDK6 which is subsequently demonstrated to impede the in vitro monocyte/macrophage differentiation of CD34⁺ HSPCs. Collectively, our work unravels a ZFP36L1-mediated regulatory circuit through repressing CDK6 expression during monocyte/macrophage differentiation, which may also provide a therapeutic target for AML therapy.

A functional module-based exploration between inflammation and cancer in esophagus

Inflammation contributing to the underlying progression of diverse human cancers has been generally appreciated, however, explorations into the molecular links between inflammation and cancer in esophagus are still at its early stage. In our study, we presented a functional module-based approach, in combination with multiple data resource (gene expression, protein-protein interactions (PPI), transcriptional and post-transcriptional regulations) to decipher the underlying links. Via mapping differentially expressed disease genes, functional disease modules were identified. As indicated, those common genes and interactions tended to play important roles in linking inflammation and cancer. Based on crosstalk analysis, we demonstrated that, although most disease genes were not shared by both kinds of modules, they might act through participating in the same or similar functions to complete the molecular links. Additionally, we applied pivot analysis to extract significant regulators for per significant crosstalk module pair. As shown, pivot regulators might manipulate vital parts of the module subnetworks, and then work together to bridge inflammation and cancer in esophagus. Collectively, based on our functional module analysis, we demonstrated that shared genes or interactions, significant crosstalk modules, and those significant pivot regulators were served as different functional parts underlying the molecular links between inflammation and cancer in esophagus.

Survey of Natural Language Processing Techniques in Bioinformatics

Informatics methods, such as text mining and natural language processing, are always involved in bioinformatics research. In this study, we discuss text mining and natural language processing methods in bioinformatics from two perspectives. First, we aim to search for knowledge on biology, retrieve references using text mining methods, and reconstruct databases. For example, protein-protein interactions and gene-disease relationship can be mined from PubMed. Then, we analyze the applications of text mining and natural language processing techniques in bioinformatics, including predicting protein structure and function, detecting noncoding RNA. Finally, numerous methods and applications, as well as their contributions to bioinformatics, are discussed for future use by text mining and natural language processing researchers.

Genome wide discovery of long intergenic non-coding RNAs in Diamondback moth (Plutella xylostella) and their expression in insecticide resistant strains

Long non-coding RNAs (lncRNAs) play important roles in genomic imprinting, cancer, differentiation and regulation of gene expression. Here, we identified 3844 long intergenic ncRNAs (lincRNA) in Plutella xylostella, which is a notorious pest of cruciferous plants that has developed field resistance to all classes of insecticides, including Bacillus thuringiensis (Bt) endotoxins. Further, we found that some of those lincRNAs may potentially serve as precursors for the production of small ncRNAs. We found 280 and 350 lincRNAs that are differentially expressed in Chlorpyrifos and Fipronil resistant larvae. A survey on P. xylostella midgut transcriptome data from Bt-resistant populations revealed 59 altered lincRNA in two resistant strains compared with the susceptible population. We validated the transcript levels of a number of putative lincRNAs in deltamethrin-resistant larvae that were exposed to deltamethrin, which indicated that this group of lincRNAs might be involved in the response to xenobiotics in this insect. To functionally characterize DBM lincRNAs, gene ontology (GO) enrichment of their associated protein-coding genes was extracted and showed over representation of protein, DNA and RNA binding GO terms. The data presented here will facilitate future studies to unravel the function of lincRNAs in insecticide resistance or the response to xenobiotics of eukaryotic cells.

LncReg: a reference resource for lncRNA-associated regulatory networks

Long non-coding RNAs (lncRNAs) are critical in the regulation of various biological processes. In recent years, plethora of lncRNAs have been identified in mammalian genomes through different approaches, and the researchers are constantly reporting the regulatory roles of these lncRNAs, which leads to complexity of literature about particular lncRNAs. Therefore, for the convenience of the researchers, we collected regulatory relationships of the lncRNAs and built a database called ‘LncReg’. This database is developed by collecting 1081 validated lncRNA-associated regulatory entries, including 258 non-redundant lncRNAs and 571 non-redundant genes. With regulatory relationships information, LncReg can provide overall perspectives of regulatory networks of lncRNAs and comprehensive data for bioinformatics research, which is useful for understanding the functional roles of lncRNAs.

Database URL: http://bioinformatics.ustc.edu.cn/lncreg/

Similarity computation strategies in the microRNA-disease network: a survey

Various microRNAs have been demonstrated to play roles in a number of human diseases. Several microRNA-disease network reconstruction methods have been used to describe the association from a systems biology perspective. The key problem for the network is the similarity computation model. In this article, we reviewed the main similarity computation methods and discussed these methods and future works. This survey may prompt and guide systems biology and bioinformatics researchers to build more perfect microRNA-disease associations and may make the network relationship clear for medical researchers.

Network-based survival-associated module biomarker and its crosstalk with cell death genes in ovarian cancer

Ovarian cancer remains a dismal disease with diagnosing in the late, metastatic stages, therefore, there is a growing realization of the critical need to develop effective biomarkers for understanding underlying mechanisms. Although existing evidences demonstrate the important role of the single genetic abnormality in pathogenesis, the perturbations of interactors in the complex network are often ignored. Moreover, ovarian cancer diagnosis and treatment still exist a large gap that need to be bridged. In this work, we adopted a network-based survival-associated approach to capture a 12-gene network module based on differential co-expression PPI network in the advanced-stage, high-grade ovarian serous cystadenocarcinoma. Then, regulatory genes (protein-coding genes and non-coding genes) direct interacting with the module were found to be significantly overlapped with cell death genes. More importantly, these overlapping genes tightly clustered together pointing to the module, deciphering the crosstalk between network-based survival-associated module and cell death in ovarian cancer.

ncRDeathDB: A comprehensive bioinformatics resource for deciphering network organization of the ncRNA-mediated cell death system

Programmed cell death (PCD) is a critical biological process involved in many important processes, and defects in PCD have been linked with numerous human diseases. In recent years, the protein architecture in different PCD subroutines has been explored, but our understanding of the global network organization of the noncoding RNA (ncRNA)-mediated cell death system is limited and ambiguous. Hence, we developed the comprehensive bioinformatics resource (ncRDeathDB, www.rna-society.org/ncrdeathdb ) to archive ncRNA-associated cell death interactions. The current version of ncRDeathDB documents a total of more than 4600 ncRNA-mediated PCD entries in 12 species. ncRDeathDB provides a user-friendly interface to query, browse and manipulate these ncRNA-associated cell death interactions. Furthermore, this resource will help to visualize and navigate current knowledge of the noncoding RNA component of cell death and autophagy, to uncover the generic organizing principles of ncRNA-associated cell death systems, and to generate valuable biological hypotheses.

Identification of functionally methylated regions based on discriminant analysis through integrating methylation and gene expression data

DNA methylation is essential not only in cellular differentiation but also in diseases.

De-repressing LncRNA-Targeted Genes to Upregulate Gene Expression: Focus on Small Molecule Therapeutics

Non-protein coding RNAs (ncRNAs) make up the overwhelming majority of transcripts in the genome and have recently gained attention for their complex regulatory role in cells, including the regulation of protein-coding genes. Furthermore, ncRNAs play an important role in normal development and their expression levels are dysregulated in several diseases. Recently, several long noncoding RNAs (lncRNAs) have been shown to alter the epigenetic status of genomic loci and suppress the expression of target genes. This review will present examples of such a mechanism and focus on the potential to target lncRNAs for achieving therapeutic gene upregulation by de-repressing genes that are epigenetically silenced in various diseases. Finally, the potential to target lncRNAs, through their interactions with epigenetic enzymes, using various tools, such as small molecules, viral vectors and antisense oligonucleotides, will be discussed. We suggest that small molecule modulators of a novel class of drug targets, lncRNA-protein interactions, have great potential to treat some cancers, cardiovascular disease, and neurological disorders.