Abstract

In the last two decades, we have witnessed an impressive crescendo of non-coding RNA studies, due to both the development of high-throughput RNA-sequencing strategies and an ever-increasing awareness of the involvement of newly discovered ncRNA classes in complex regulatory networks. Together with excitement for the possibility to explore previously unknown layers of gene regulation, these advancements led to the realization of the need for shared criteria of data collection and analysis and for novel integrative perspectives and tools aimed at making biological sense of very large bodies of molecular information. In the last few years, efforts to respond to this need have been devoted mainly to the regulatory interactions involving ncRNAs as direct or indirect regulators of protein-coding mRNAs. Such efforts resulted in the development of new computational tools, allowing the exploitation of the information spread in numerous different ncRNA data sets to interpret transcriptome changes under physiological and pathological cell responses. While experimental validation remains essential to identify key RNA regulatory interactions, the integration of ncRNA big data, in combination with systematic literature mining, is proving to be invaluable in identifying potential new players, biomarkers and therapeutic targets in cancer and other diseases.

Introduction

Transcriptomic studies in recent years have shown that most of the mammalian genome is transcribed. Since only 2% of the genome sequence is of protein-coding type, most RNA represents either untranslated portions of pre-mRNA/mRNA or transcripts that, lacking any clear protein-coding function, are collectively referred to as non-coding RNAs (ncRNAs). The development of next-generation sequencing (NGS) and of new computational tools has allowed us to expand our knowledge of the non-coding transcriptome, contributing to the identification of several new ncRNA classes, with distinctive biogenesis pathways, sizes and modes of action. It is still an open question of whether the thorough transcription of the whole genome is functionally relevant, or if it reflects at least in part spurious RNA polymerase activity. However, many studies have clearly demonstrated the involvement of newly identified ncRNA classes in key gene regulatory networks at the transcriptional and post-transcriptional levels, resulting in their influence on core processes such as cell differentiation [1] and development [2], and their involvement in various diseases [3,4].

A large amount of new ncRNA data prompted the development of new specialized databases. This, in turn, sharpened the need to integrate available information in order to discover new functions of ncRNAs and shed light on their complex interactions in gene regulatory networks.

In this mini review, after briefly introducing the different ncRNA classes, we provide an overview of the functionally relevant interactions of ncRNA with DNA, RNA and proteins and describe the extent of information available in ncRNA databases. Lastly, we also provide a few examples of successful data integration to discover competing endogenous RNA (ceRNA) networks in cancer.

Overview of ncRNA classes

Besides the long-known housekeeping ncRNA classes represented by rRNAs, tRNAs and snoRNAs, many remarkable ncRNA classes have been put in light more recently in eukaryotes.

snoRNAs are a class of small (60–300 nt) ncRNAs that function as a guide for the post-transcriptional modification of rRNA and have recently been identified as potential cancer biomarkers [5]. In addition to their classical function, snoRNAs can also regulate splicing events through complementary base pairing to specific pre-mRNAs [6,7] or be processed to short RNAs playing microRNA-like roles [8].

MicroRNAs (miRNAs) are small ncRNAs (<22 nt) that can base pair with mRNA targets, hindering protein synthesis and promoting mRNA degradation. Each miRNA can recognize up to hundreds of target mRNAs, thus acting as a pleiotropic regulator of gene expression. Dysregulation of miRNA expression has been found to be involved in several pathological conditions including Alzheimer's disease [9] and cancer progression. The discovery that miRNA expression is specifically dysregulated in different types of cancer paved the way for the use of miRNA profiles as new clinical cancer biomarkers. Numerous oncomiRs (cancer-associated miRNAs) play direct roles in the regulation of oncogene expression. For example, the miRNAs of the let-7 family control the expression of the well-known Ras oncogene [10].

A more recently discovered class of small ncRNAs are the PIWI-interacting RNAs (piRNAs), which interact with PIWI proteins to form a complex that can silence transposons in germ cells [11,12] and potentially regulate gene expression in mammals [13,14]. Although piRNA biology is still largely unexplored, many studies revealed the existence of tumor-associated expression profiles, suggesting an involvement of piRNAs in cancer [15,16].

Circular RNAs (circRNAs) are a class of ncRNAs synthesized by backsplicing between a downstream 3′ splice site and an upstream 5′ splice site in a linear pre-mRNA. It has been suggested that circRNAs could regulate the rate of transcriptional elongation and thus potentially interfere with co-transcriptional splicing [17,18], influence translation by acting as ‘mRNA traps’ that sequester the translation start site [19], modulate ribosomal rRNA maturation [3] or act as sponges of specific RNA-binding proteins or miRNAs. For example, there is evidence that the circRNAs CDR1as and SRY function as miRNA sponges [20,21], thus tangling the landscape of gene regulation by ncRNAs.

Another newly discovered class of ncRNAs is represented by tRNA-derived fragments (tRFs), short RNA molecules originating from mature or precursor tRNAs. It has recently been suggested that tRFs have regulatory roles in translation [22], viral infection [23,24] and tumor development [25]. Very recently, rRNA-derived fragments (rRFs) were also suggested to influence gene expression through Argonaute (AGO)-dependent miRNA-like mechanisms [26].

Gene regulatory networks also involve another class of relatively small ncRNAs called enhancer RNAs (eRNAs), whose synthesis occurs at DNA sequences that bring distinctive epigenetic enhancer marks. eRNAs might participate in enhancer-dependent gene activation by regulating chromatin accessibility [27], Pol II binding [28], promoting loop chromatin formation [29,30] and modulating transcription factor binding [31].

The most functionally and structurally heterogeneous class of ncRNAs is represented by the so-called long ncRNAs (lncRNAs), which can derive from intergenic [32], intronic [33,34] or antisense transcription [35,36]. LncRNAs can influence gene expression through different mechanisms, such as the recruitment of epigenetic modifiers [37], the sequestration of miRNAs by ‘sponge’ activity [38] and the scaffolding of multiprotein complexes [39]. Given their regulatory versatility, lncRNAs are involved in various diseases, including mental disorders such as schizophrenia [40] and cancer [41].

A further element of complexity of the vast and intricate landscape of ncRNAs is represented by the ncRNAs deriving from the transcription of non-protein-coding repetitive elements like SINEs [4245].

ncRNA interactions with proteins and nucleic acids: overview and prediction tools

The different mechanisms through which ncRNAs exert their gene regulatory effects are based on ncRNA interaction with DNA, RNA and proteins.

The binding of RNA molecules to DNA sequences can rely on the formation of DNA–RNA triplexes, as in the case of the lncRNA MEG3 [46]. DNA–RNA triplexes are formed through a non-canonical base pairing of MEG3 with DNA distal regulatory elements that control the expression of TGF-β pathway genes. Bound MEG3 recruits PRC2, thereby modulating the transcription of TGFB2, TGFBR1 and SMAD2 genes [46]. The interaction between RNA and DNA is also important for gene regulation operated by small activating RNAs (saRNAs), which were shown to regulate the expression of the progesterone receptor (PR) through binding to PR promoter region [47]. One of the most used algorithms for in silico prediction of DNA–RNA triplex formation is represented by Triplexator, based on Hoogsteen and reverse-Hoogsteen base pairing [48]. Triplex domain finder (TDF), a more specific tool dedicated to the prediction of triplexes between DNA and lncRNAs, is based on Triplexator, but in addition, it also gives statistical information about the probability of triplex formation between DNA and lncRNA [49].

Essential to understanding the regulatory roles of ncRNAs is the comprehension of RNA–RNA interactions and their causative roles (Figure 1). This point is particularly relevant and increasingly well known for the interactions between miRNA and target mRNAs (or target ncRNAs). The most important high-throughput strategies to identify miRNA targets (both coding and non-coding) derive from CLIP-seq approaches, based on UV-cross-linking and subsequent immunoprecipitation of the interacting complexes using antibodies that target a given RNA-binding protein. The isolated RNA is reverse-transcribed to cDNA and deep-sequenced, in order to obtain a single-base resolution mapping of protein-binding sites on RNA [50]. A key application of this approach utilizes AGO-immunoprecipitation to purify AGO–miRNA–mRNA complexes, thus determining the miRNA targetomes [51]. A slight variation in CLIP-seq is represented by photoactivatable-ribonucleoside-enhanced cross-linking and immunoprecipitation (PAR-CLIP) [52]. In PAR-CLIP, the photoactivatable ribonucleoside 4-thiouridine (4sU) is used to label the RNA that is bound to the target protein, ensuring a more efficient cross-linking than that of CLIP-seq. AGO PAR-CLIP has been extensively used to identify miRNA targets with high sensitivity [53], and different tools, such as PAR-CLIP miRNA assignment (PARma) [54] and microMUMMIE [55], have been developed to analyze AGO PAR-CLIP data. These tools are based on the analysis of reading clusters that contain the T-to-C conversion (arising from incorporation of guanine instead of a cytosine during the reverse transcription step). The more recent microCLIP tool also incorporates the non-T-to-C clusters in the PAR-CLIP analysis, leading to a 14% increase in miRNA–target interaction (MTI) and to the discovery of previously unidentified miRNA targets [56].

Inhibition network between circRNAs/lncRNAs, miRNAs and mRNAs.

Figure 1.
Inhibition network between circRNAs/lncRNAs, miRNAs and mRNAs.

CircRNAs and lncRNAs can act as ceRNAs sponging miRNAs that target mRNAs. The interactions between ncRNAs in a ceRNA network are highly dynamic, depending on the relative abundance of each RNA species, the number of miRNAs that a ceRNA can sponge and the number of binding sites for a specific miRNA on the same ceRNA transcript. These reciprocal interactions influence gene expression and exert their role in cellular homeostasis, differentiation and disease. Target sites of miRNAs on ceRNAs or mRNAs are represented by colored RNA thickening; thickening color indicates miRNA specificity, created using BioRender.

Figure 1.
Inhibition network between circRNAs/lncRNAs, miRNAs and mRNAs.

CircRNAs and lncRNAs can act as ceRNAs sponging miRNAs that target mRNAs. The interactions between ncRNAs in a ceRNA network are highly dynamic, depending on the relative abundance of each RNA species, the number of miRNAs that a ceRNA can sponge and the number of binding sites for a specific miRNA on the same ceRNA transcript. These reciprocal interactions influence gene expression and exert their role in cellular homeostasis, differentiation and disease. Target sites of miRNAs on ceRNAs or mRNAs are represented by colored RNA thickening; thickening color indicates miRNA specificity, created using BioRender.

In addition to the key role of miRNA–mRNA interactions, studies in the last decade have put in evidence the importance of miRNA interactions with other ncRNAs. For example, according to the ceRNA hypothesis [57], any RNA presenting miRNA-responsive elements (MRE) can act as a competitor for miRNA-binding sites. Thus, RNAs that share the same MRE can cross-talk and indirectly regulate the abundance of each other by competing for miRNA binding. Therefore, ceRNAs can modulate mRNA expression by functioning as key regulators of miRNA action. Not surprisingly, given their regulatory potential, ceRNAs have been shown to play a significant role in cancer pathogenesis by altering the expression of tumorigenic [58] or tumor suppressor genes [59].

Lastly, there is increasing evidence that ncRNAs modulate various cellular processes through direct specific interaction with key regulatory proteins. For example, the circRNA circ-Foxo3 was found to inhibit cell cycle progression through direct binding to the cell cycle proteins CDK2 and p21 [60]. The ncRNA class with the most diversified mechanisms of action based on specific protein recognition is represented by lncRNAs. For example, a positive role in cell proliferation was reported for the lncRNA MALAT1, which through competitive binding to DBC1 protein can release complexed sirtuin 1 (SIRT1). SIRT1 is then free to deacetylate the tumor suppressor protein p53, reducing its transcriptional activity and therefore promoting cell proliferation [61]. A role in the stimulation of cell proliferation was also reported for the lncRNA ANRIL, which binds to and recruits polycomb repressive complex 2 (PRC2) to the p15 locus gene. The localization of PRC2 by ANRIL drives the deposition of the repressive mark H3K27me3, thereby stimulating cell proliferation [62]. The lncRNA growth arrest-specific-5 (GAS5) plays an opposite role in cell proliferation. GAS5 serves as a decoy for the glucocorticoid receptor (GR)-DNA binding at glucocorticoid response element (GRE), consequently modulating the arrest of cell proliferation during starvation [63].

Even though experimental techniques that identify RNA–protein binding, such as CLIP-based techniques, have the advantage to provide a strong functional evidence for existing interactions, the development of biocomputational tools to predict RNA–protein interactions can be of a great help. Well-known computational prediction methods are RPI-seq [64] and catRAPID [65]. RPI-seq is solely based on RNA and protein sequences. CATRapid graphic is instead based on the physicochemical properties of the single interacting polypeptide and nucleotide chains [65], while CATRapid omics predicts the transcriptome or the proteome that interact with a query protein of RNA, respectively, based on sequence and structural properties of RNA and protein domains [66]. For a more comprehensive list of bioinformatic tools that are available for the prediction of RNA–protein interactions, the reader can refer to the recent review by Pan et al. [67].

Challenges in the collection and organization of ncRNA data

The study of ncRNAs is challenged by computational and experimental limitations, and by the complexity to integrate data stored in different databases.

First of all, the in silico identification of new ncRNA sequences is hindered by the relatively weak evolutionary constraint and by the lack of easily identifiable common sequence features (such as the presence of ORFs, 5′-UTR or 3′-UTR of protein-coding genes). Even though different computational approaches were developed for the identification of specific classes of ncRNAs, the annotation of their genes has to be largely complemented with experimental transcriptomic evidence [6872].

The second major concern derives from the relatively low stability and/or expression of regulatory ncRNAs. As a consequence, ncRNA detection may not be comprehensive: some non-coding transcripts could be missed if the sequencing coverage is not uniform or if the low read number is masked by the high expression of protein-coding genes. Low transcript abundance in bulk samples could be mainly due to the specific expression pattern of single cells, resulting in a strong dilution of regulatory ncRNAs in RNA-seq data from tissue samples, or to the existence, especially in the case of lncRNAs, of multiple transcript isoforms due to complex alternative splicing patterns [73]. The issue of low-level RNA detection can be addressed by the use of RNA-seq technologies that allow the detection of rare/unstable RNA species. Global run-on sequencing (GRO-seq) [74], precision nuclear run-on sequencing (PRO-seq) [75], native elongating transcript sequencing (NET-seq) [76] and Start-seq [77] are methodologies that allowing to map the position of transcriptionally engaged RNA polymerases and transcription start sites. In GRO-seq, nuclear run-on conditions allow the nascent RNA to be labeled with 5-bromouridine 5′-triphosphate and then selected using immunopurification methods, reverse-transcribed into cDNA and deep-sequenced. In recent years, GRO-seq has been widely applied to study ncRNA species that are lowly expressed and/or highly unstable [78]. PRO-seq uses the four biotin-labeled NTPs during the run-on procedure to ensure a single-nucleotide resolution after the RNA purification step and deep sequencing. NET-seq allows the detection of nascent RNA that is still attached to the RNA polymerase through immunoprecipitation of FLAG-tagged RNA polymerase. While GRO-seq, PRO-seq and NET-seq are methods based on a metabolic or biochemical enrichment of the nascent RNA, small 5′-capped RNA sequencing (Start-seq) is based on the first step of gel size selection, followed by an enrichment for 5′-capped RNAs. Originally developed to detect the promoter-proximal RNA polymerase II (Pol II) pausing, this technique can also be used to detect divergent non-coding transcripts and non-genic regulatory elements, such as enhancers, that are transcribed by Pol II [79]. While GRO-seq, PRO-seq and NET-seq allow only the measure of transcription, techniques such as transient transcriptome sequencing (TT-seq) [80] and TimeLapse-seq [81] can determine the kinetics of both transcription and degradation. The above-mentioned challenges in biocomputational prediction and experimental detection of ncRNAs, together with the lack of standardized nomenclature, have brought to marked information heterogeneity among the high number of existing databases. This may result, for example, in a low annotation overlap in different ncRNA databases or in a different number of isoforms for the same transcript. Information heterogeneity is enhanced by the different sources from which each database retrieves annotation and sequencing data. Figure 2 shows how heterogeneous and interconnected are the database sources.

Network representation of ncRNA databases and their sources.

Figure 2.
Network representation of ncRNA databases and their sources.

Each database can retrieve information from a plethora of different databases, which brings to a big heterogeneity in ncRNA annotation. Filled circles represent databases that are restricted to ncRNAs, no more dated than five years and that include Homo sapiens sequences. Not filled circles represent the sources of RNA data. The color of the circles (light yellow–ochre–orange–brown) is correlated with the number of citations in PubMed (from 0 citations for CircFunBase and LncBook to 6548 citations for miRBase). The size of the filled circles represents the last update (small = 2014; big = 2019). The arrows end in ncRNA databases and start from their sources databases. For simplicity, we included sources databases that only collect RNA data. For a complete list of the sources of each database see Supplementary Table S1. The Cytoscape 3.7.1 software [114] was used to visualize the ncRNA databases network.

Figure 2.
Network representation of ncRNA databases and their sources.

Each database can retrieve information from a plethora of different databases, which brings to a big heterogeneity in ncRNA annotation. Filled circles represent databases that are restricted to ncRNAs, no more dated than five years and that include Homo sapiens sequences. Not filled circles represent the sources of RNA data. The color of the circles (light yellow–ochre–orange–brown) is correlated with the number of citations in PubMed (from 0 citations for CircFunBase and LncBook to 6548 citations for miRBase). The size of the filled circles represents the last update (small = 2014; big = 2019). The arrows end in ncRNA databases and start from their sources databases. For simplicity, we included sources databases that only collect RNA data. For a complete list of the sources of each database see Supplementary Table S1. The Cytoscape 3.7.1 software [114] was used to visualize the ncRNA databases network.

Another layer of complexity in data integration is encountered when combining sequencing data of different classes of ncRNAs and coding genes coming from a specific cell line/tissue. Indeed, each class of ncRNA requires the application of specific RNA-seq library protocols, such as a size selection step for small RNA sequencing, and specific algorithms for computational detection. This heterogeneity in sample preparation and in the biocomputational analysis are prone to biases in the comparison of expression profiles of different ncRNA classes, even if deriving from the same sample, which can contribute to a misinterpretation of the biological networks.

Main ncRNA databases

Since the advent of NGS, ncRNA information is stored in many databases which can differ according to: (i) the class of ncRNA, from databases exclusively dedicated to one specific class of ncRNA, to more comprehensive ones; (ii) the type of information: annotation, expression profiles in cell lines/tissues, RNA–target interactions, functional annotation, secondary structure, correlation with diseases; (iii) the different sources from where data are retrieved and the bioinformatic tools used to analyze them; (iv) the number of entries; (v) the maintenance of updated information. The most widely used databases are described in Table 1. For a more comprehensive information on the resources of each ncRNA database, we provide a list in Supplementary Table S1.

Table 1
List of the most updated and cited ncRNA databases that collect different types of information
Database Information Type of ncRNA Number of citations Last update Description References 
Rfam   All 2314 2019 Collection of ncRNA families represented by manually curated sequence alignments, consensus secondary structures and homology [82
miRDB   miRNA 296 2019 miRNA target prediction with MirTarget and functional annotations [107
HMDD  miRNA 466 2019 Catalog of human miRNA and disease associations supported by experimental evidences [93
LncRNADisease  lncRNA, circRNA 224 2019 Collection of experimentally supported lncRNA–disease associations [115
piRBase piRNA 36 2018 Collection of piRNA annotation and their functions [116
miRBase  miRNA 6548 2018 The primary resource for miRNAs nomenclature, annotation, sequence data and target prediction based on the analysis of small RNA deep sequencing data [85
miRWalk   miRNA 836 2018 Resource for predicted and validated microRNA-binding sites [117
starBase All 685 2018 Database of RNA–RNA and RNA–RBP interactions collected from CLIP-seq data that were analyzed using the tool PARalyzer; degradome-seq and RNA–RNA interactome data [94
TransmiR miRNA 153 2018 Database for transcription factors that regulate miRNA expression [118
Lnc2Cancer   lncRNA 69 2018 Experimentally supported associations between lncRNAs and human cancers; a collection of circulating, drug-resistant prognostic lncRNAs; catalog of lncRNAs regulated by miRNA, transcription factor, variant and methylation [119
NONCODE  All but tRNA and rRNA 585 2017 Annotation of ncRNAs, especially lncRNAs [120
SILVA   rRNA 1925 2017 Database of aligned small and large subunit rRNA sequences for Bacteria, Archaea and Eukarya [121
circBase   circRNA 185 2017 Annotation of circRNAs based on the analysis of six published data sets. The python scripts that were used for the identification of circRNAs are also provided. [122
miRTarBase  miRNA 1085 2017 Experimentally validated MTIs predicted with the bioinformatic tool miRTarCLIP [87
DIANA-TarBase  miRNA 867 2017 Collection of experimentally supported miRNA targets, derived from chimeric fragments, reporter gene assay and CLIP-seq data that are analyzed using the tool microCLIP; cell-type-specific miRNA-gene regulation; genomic co-ordinates of the miRNA-binding location [86
miRandola   All 67 2017 Database of extracellular ncRNAs [123
MNDR   All 48 2017 Experimentally and predicted associations between ncRNAs and diseases [124
CSCD  circRNA 21 2017 Catalog of cancer-specific circRNAs analyzed from eighty-seven ENCODE data sets samples [125
DbDEMC   miRNA 82 2016 Differentially expressed miRNAs in human cancers from microarray data [126
GtRNAdb   tRNA 423 2015 List of tRNA genes predicted by tRNAscan-SE tool on complete or nearly complete genomes [127
lncRNAdb   lncRNA 373 2015 Annotation of eukaryotic lncRNAs manually curated from referenced literature [128
DIANA-LncBase lncRNA 131 2015 Experimentally supported and in silico predicted miRNA–lncRNA interactions [108
TANRIC  lncRNA 106 2015 Expression profiles of lncRNAs in cancer samples from TCGA and CCLE [129
RADAR  All 134 2014 Collection of A-to-I RNA editing sites [130
Database Information Type of ncRNA Number of citations Last update Description References 
Rfam   All 2314 2019 Collection of ncRNA families represented by manually curated sequence alignments, consensus secondary structures and homology [82
miRDB   miRNA 296 2019 miRNA target prediction with MirTarget and functional annotations [107
HMDD  miRNA 466 2019 Catalog of human miRNA and disease associations supported by experimental evidences [93
LncRNADisease  lncRNA, circRNA 224 2019 Collection of experimentally supported lncRNA–disease associations [115
piRBase piRNA 36 2018 Collection of piRNA annotation and their functions [116
miRBase  miRNA 6548 2018 The primary resource for miRNAs nomenclature, annotation, sequence data and target prediction based on the analysis of small RNA deep sequencing data [85
miRWalk   miRNA 836 2018 Resource for predicted and validated microRNA-binding sites [117
starBase All 685 2018 Database of RNA–RNA and RNA–RBP interactions collected from CLIP-seq data that were analyzed using the tool PARalyzer; degradome-seq and RNA–RNA interactome data [94
TransmiR miRNA 153 2018 Database for transcription factors that regulate miRNA expression [118
Lnc2Cancer   lncRNA 69 2018 Experimentally supported associations between lncRNAs and human cancers; a collection of circulating, drug-resistant prognostic lncRNAs; catalog of lncRNAs regulated by miRNA, transcription factor, variant and methylation [119
NONCODE  All but tRNA and rRNA 585 2017 Annotation of ncRNAs, especially lncRNAs [120
SILVA   rRNA 1925 2017 Database of aligned small and large subunit rRNA sequences for Bacteria, Archaea and Eukarya [121
circBase   circRNA 185 2017 Annotation of circRNAs based on the analysis of six published data sets. The python scripts that were used for the identification of circRNAs are also provided. [122
miRTarBase  miRNA 1085 2017 Experimentally validated MTIs predicted with the bioinformatic tool miRTarCLIP [87
DIANA-TarBase  miRNA 867 2017 Collection of experimentally supported miRNA targets, derived from chimeric fragments, reporter gene assay and CLIP-seq data that are analyzed using the tool microCLIP; cell-type-specific miRNA-gene regulation; genomic co-ordinates of the miRNA-binding location [86
miRandola   All 67 2017 Database of extracellular ncRNAs [123
MNDR   All 48 2017 Experimentally and predicted associations between ncRNAs and diseases [124
CSCD  circRNA 21 2017 Catalog of cancer-specific circRNAs analyzed from eighty-seven ENCODE data sets samples [125
DbDEMC   miRNA 82 2016 Differentially expressed miRNAs in human cancers from microarray data [126
GtRNAdb   tRNA 423 2015 List of tRNA genes predicted by tRNAscan-SE tool on complete or nearly complete genomes [127
lncRNAdb   lncRNA 373 2015 Annotation of eukaryotic lncRNAs manually curated from referenced literature [128
DIANA-LncBase lncRNA 131 2015 Experimentally supported and in silico predicted miRNA–lncRNA interactions [108
TANRIC  lncRNA 106 2015 Expression profiles of lncRNAs in cancer samples from TCGA and CCLE [129
RADAR  All 134 2014 Collection of A-to-I RNA editing sites [130

A: annotation; I: interaction with targets; D: disease.

Among the databases that collect information about different ncRNA species, Rfam [82] is one of the most comprehensive ones. It collects RNA families based on sequence alignment, secondary structure and a covariance model. The aligned sequences are taken from generic sources such as the European nucleotide archive [83] or Ensembl [84] and from sources specialized in specific classes of ncRNAs, such as miRBase [85]. miRBase is the primary resource for miRNA nomenclature, annotation, sequence data and target prediction. miRNA annotation is validated by the analysis of small RNA deep sequencing experiments in gene expression omnibus (GEO) and sequence read archive (SRA) and a total of 1917 pri-miRNA and 2654 mature sequences are collected for Homo sapiens. Ongoing efforts aim at increasing functional information about specific miRNAs.

For a collection of experimentally validated miRNA targets, DIANA-TarBase [86] and miRTarBase [87] are available. DIANA-TarBase collects more than 1 million entries from more than 33 different methodologies, among which CLIP-seq, CLASH-seq (cross-linking, ligation and sequencing of hybrids) [88] and AGO CLIP-seq. However, information about the association of MTIs with various diseases is still lacking. This type of information is provided by miRTarBase, which reports considerably less MTIs than DIANA-TarBase, but gives information about their role in biological processes. MTIs are predicted using the bioinformatic tools miRTarCLIP [89], Piranha [90], TargetScan [91] and miRanda [92], while experimentally verified interactions are supported by methods such as CLIP-seq and reporter assays. Functional information for each miRNA is provided by (i) gene and miRNA expression profiling through a link to GEO and the cancer genome atlas (TCGA), (ii) gene set enrichment of miRNA target genes through analyses in Kyoto encyclopedia of genes and genomes (KEGG) and database for annotation, visualization and integrated discovery, (iii) disease information through a link to the human microRNA disease database (HMDD) [93]. In HMDD, the miRNA–disease association is supported by experimental evidence (genetics, epigenetics, circulating cell-free miRNAs, MTIs and expression in different tissues). It thus represents a powerful tool for investigating the role and patterns of miRNA in diseases, giving the possibility to develop novel miRNA–disease association prediction algorithms.

Lastly, a very useful tool that is used for the analysis of any kind of RNA–RNA and RNA–protein interactions (not necessarily involving a miRNA) is provided by starBase [94]. Only experimentally validated RNA interactions are included in this database, retrieving data from HITS-CLIP, PAR-CLIP, individual nucleotide resolution CLIP (iCLIP) [95] and CLASH-seq experiments. Based on the analysis of a total of 108 CLIP-seq data sets, starBase gives the most comprehensive list of MTIs, also helping in the detection of ceRNAs and in the construction of ceRNA regulatory networks.

Integration of ncRNA data in ceRNA networks

As mentioned earlier, the ability of miRNAs to reduce mRNA stability/translation through MREs binding [96] can be in turn fine-tuned by other RNAs, which compete for the binding of miRNAs (ceRNAs) [97]. Moreover, the same ceRNA can bear multiple binding sites for the same miRNA and the efficiency of a ceRNA depends on the number of miRNAs that it can sponge [21]. Ultimately, the robustness of ceRNA networks will be affected by the total number of ceRNAs that simultaneously share the same MREs [57]. In the past few years, ceRNAs have emerged as important modulators of gene expression that have been implied in cell differentiation [38], self-renewal of embryonic stem cells [98] and cancer progression. The role of ceRNAs in cancer has actively been investigated through biocomputational and experimental approaches, leading to the identification of the involvement of ceRNAs in solid [99,100] and liquid tumors [101]. The identification of a ceRNA is possible through algorithms that predict MTIs solely based on sequence information, with the obvious downside of many false-positive interactions. Therefore, the combination of both computational and experimental approaches and the integration with transcriptome profiles have been widely used in ceRNA studies, as exemplified by the prediction of ceRNA networks in lung squamous cell carcinoma (LUSC) [102], head and neck squamous cell carcinoma (HNSCC) [103] and laryngeal squamous cell carcinoma (LSCC) [104].

To construct ceRNA networks, these biocomputational studies performed data integration after an initial combined analysis of differentially expressed (DE) mRNAs, lncRNAs and miRNAs in TCGA and GEO data sets [102], mRNAs, lncRNAs and miRNAs in TCGA [103], and miRNAs, circRNAs, mRNAs and lncRNAs in five pairs of LSCC and matched non-carcinoma tissues using microarray [104]. miRNA–target pairs of the previously identified DE genes (coding and non-coding) were predicted using the computational algorithms miRcode [105], miRanda and PITA [106], or through the consultation of databases such as starBase, TargetScan, miRDB [107], miRTarBase, DIANA-LncBase [108] and DIANA-TarBase. The ceRNA networks were then constructed following different criteria: in the study by Li et al. [102], the GDCRNATools [109] was used for the construction of a lncRNA–miRNA–mRNA network, following restrictions based on (i) the number and hypergeometric probability of miRNAs shared by a lncRNA–mRNA pair, (ii) the strength of positive expression correlation between a lncRNA and a mRNA pair, and (iii) a similarity of roles shared by different miRNAs in regulating the expression of targets belonging to a single lncRNA–mRNA pair. Fang et al. [103], instead, manually constructed the ceRNA network retrieving DE miRNAs that were found to target DE lncRNA and then utilized the targeting miRNAs to detect DE mRNA targets, without imposing the limitations adopted by GDCRNATools. Zhao et al. [104] built a slightly more complex ceRNA network, including four different ncRNA species: miRNA, circRNA, lncRNA and mRNA. The ceRNA network was built based on the negative Pearson's correlation coefficient between the miRNA and their targets and a positive correlation between an mRNA/circRNA, mRNA/lncRNA pair or circRNA/lncRNA. Therefore, this ceRNA allows the prediction of dysregulated mRNAs by miRNA sequestration operated by both circRNAs and lncRNAs. Supported by GO, KEGG pathway and survival analysis, the construction of ceRNA networks allowed the identification of tumor suppressor genes in LUSC [102], the prediction of miRNAs and lncRNAs that could act as gene regulators in the development of HNSCC [103], and the identification of one lncRNA and one circRNA that could influence pathways that are known to be dysregulated in LSCC [104]. However, it has to be noted that a variety of factors can influence the establishment of a ceRNA cross-talk, such as the relative abundance of the two interacting species and the affinity of miRNA-binding sites. Therefore, ceRNA networks have to be considered as a tool to predict potential roles of ncRNAs in biological processes, but they cannot be reliable without experimental validation.

Concluding remarks

The study of the role of ncRNAs in normal cell functions and in disease is challenged by multiple factors, such as a difficult detection of non-coding RNAs in the whole-cell transcriptome. Bioinformatic techniques used in the analysis of RNA-seq data can be optimized, for example, by improving the automated annotation based on transcriptome assembly using short-reads or by developing methods that provide a statistical test and an appropriate threshold for high-confidence ncRNA detection, as recently exemplified for circRNAs [70].

Another challenge in the study of ncRNAs is the heterogeneity and dispersion of big amounts of data, requiring the existence of collective databases relying on a standardized nomenclature and collecting information from many different databases.

Lastly, functional validation of ncRNAs is challenged by their multitude of targets and by ill-defined phenotypes in knockout studies. Fundamental in ncRNA functional studies is the analysis of their interactions with other types of ncRNAs, mRNAs and RNA-binding proteins, taking into account the generation of networks through multiple interactions and the corresponding methodological challenges. As an exemplary case, the construction of ceRNA networks has been facilitated both by the advancement of bioinformatic tools to predict miRNA-binding sites and by the development of new sequencing techniques that capture RNA–RNA and RNA–protein interactions.

In order for ncRNA interaction networks to be correctly addressed, a key point deserving future efforts is that RNA interactions can be influenced by many different factors, such as RNA folding, RNA modifications, the relative concentration of the interacting species and the presence of other ceRNAs. Active fields of research that take into account these issues aim for the global characterization of RNA structuromes [110], epistructuromes [111] and interactomes [112,113].

Summary

  • ncRNAs regulate gene expression at the transcriptional and post-transcriptional level, exerting a central role in cell homeostasis and in disease

  • The study of non-coding gene structure, transcripts and function has to face both computational and experimental challenges

  • Big amounts of ncRNA data are stored in a plethora of different databases; while this increases the specificity of information that can be retrieved, it also makes integration difficult due to heterogeneity and dispersion of ncRNA data

  • The function of ncRNAs in regulating gene expression can be studied with the construction of ceRNA networks through the usage of databases and bioinformatic tools centered on sequence-specific RNA–RNA interactions

  • The study of ncRNAs will be helped by the development of new algorithms and sequencing techniques that are methodologically less biased and heterogeneous and by the development of more unified and standardized databases

Abbreviations

     
  • AGO

    Argonaute

  •  
  • ceRNA

    competing endogenous RNA

  •  
  • circRNAs

    circular RNAs

  •  
  • DE

    differentially expressed

  •  
  • GAS5

    growth arrest-specific-5

  •  
  • GEO

    gene expression omnibus

  •  
  • GR

    glucocorticoid receptor

  •  
  • GRO-seq

    global run-on sequencing

  •  
  • HMDD

    human microRNA disease database

  •  
  • HNSCC

    head and neck squamous cell carcinoma

  •  
  • KEGG

    Kyoto encyclopedia of genes and genomes

  •  
  • lncRNAs

    long ncRNAs

  •  
  • LSCC

    laryngeal squamous cell carcinoma

  •  
  • LUSC

    lung squamous cell carcinoma

  •  
  • MRE

    miRNA-responsive elements

  •  
  • MTI

    miRNA–target interaction

  •  
  • ncRNAs

    non-coding RNAs

  •  
  • NET-seq

    native elongating transcript sequencing

  •  
  • NGS

    next-generation sequencing

  •  
  • PAR-CLIP

    photoactivatable-ribonucleoside-enhanced cross-linking and immunoprecipitation

  •  
  • PARma

    PAR-CLIP miRNA assignment

  •  
  • PR

    progesterone receptor

  •  
  • PRO-seq

    precision nuclear run-on sequencing

  •  
  • SIRT1

    sirtuin 1

Author Contribution

S.C. and E.D.N. wrote the manuscript; D.C., G.D. and B.M. conceived the review; B.M. supervised the redaction.

Acknowledgements

This work was supported by the Italian Association for Cancer Research [AIRC, Grant IG16877 to G.D.]. E.D.N. thanks Regione Lazio for the ‘Torno Subito’ mobility Programme.

Competing Interests

The Authors declare that there are no competing interests associated with the manuscript.

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Author notes

*

Present address: Centre de Regulació Genòmica (CRG), The Barcelona Institute for Science and Technology (BIST), 08003 Barcelona, Spain.

Supplementary data