Cancer Letters 407 (2017) 1e8
Contents lists available at ScienceDirect
Cancer Letters journal homepage: www.elsevier.com/locate/canlet
Mini-review
A comprehensive review of web-based non-coding RNA resources for cancer research Yun Zheng a, *, Li Liu b, Girish C. Shukla c a
Yunnan Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, Yunnan, 650500, China b Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, Yunnan, 650500, China c Center for Gene Regulation in Health and Disease, Cleveland State University, Cleveland, OH, 44115, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 14 June 2017 Received in revised form 2 August 2017 Accepted 8 August 2017
Non-coding RNAs include many kinds of RNAs that did not encode proteins. Recent evidences reveal that ncRNAs play critical roles in initiation and progression of cancers. But it is not easy for cancer biologists and medical doctors to easily know the potential roles of ncRNAs in cancer and retrieve the information of ncRNAs under their investigations. To make the available web-based resources more accessible and understandable, we made a comprehensive review for 49 web-based resources of three types of ncRNAs, i.e., microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs). We also listed some preferred resources for 6 different types of analyses related to ncRNAs. © 2017 Elsevier B.V. All rights reserved.
Keywords: Non-coding RNA (ncRNA) microRNA (miRNA) Long non-coding RNA (lncRNA) Web-base resources Cancer
Introduction Non-coding RNAs have been reported to be key players in cancers. Based on the size of the RNAs, non-coding RNAs are generally categorized as small RNAs with less than 50 nucleotides (nt), such as microRNAs (miRNAs), small interfering RNAs (siRNAs), and piwiacting RNAs (piRNAs) and long non-coding RNAs (lncRNAs), with more than 200 nt. Long non-coding RNAs could further be classified into several types, such as long intergenic non-coding RNAs (lincRNAs), long intronic non-coding RNAs, circular RNAs (circRNAs), and circular intronic RNAs (ciRNAs) [1]. MiRNAs are a class of small non-coding RNA molecules that can regulate gene expression by specifically recognizing their complementary sites on their target mRNAs [2]. By either repressing mRNA translation or inducing mRNA degradation [3], miRNAs are involved in many biological processes including cell cycle, differentiation, development, metabolism, and so on [4,5]. As miRNAs are critical regulators, their deregulation may lead to deregulated genes, which subsequently results in severe diseases, such as cancers. LncRNAs are noticed from the pervasive transcription in mammals [6e8]. Accumulated evidences demonstrated that lncRNAs
* Corresponding author. E-mail address:
[email protected] (Y. Zheng). http://dx.doi.org/10.1016/j.canlet.2017.08.015 0304-3835/© 2017 Elsevier B.V. All rights reserved.
have functional roles in many biological processes [9,10] and human diseases including cancers [11e15] and neurological diseases [16,17]. Circular RNAs (circRNAs) are a class of non-coding RNAs that are formed by back-spliced exons [18e23] and lariats produced in splicing process [24,25]. Accumulating evidences show that circRNAs may play roles in diseases, such as cancer [26e29], heart failure [30] and Alzheimers disease (AD) [31]. In the following parts of the work, we will introduce resources of miRNAs, lncRNAs and circRNAs. We then discussed the resources that could be used in 6 different types of analyses of ncRNAs, and recommended some preferred resources for the related analyses. Finally, we briefly introduce the UCSC Genome Browser [110] and the non-coding RNA track within it, and Integrated Genome Viewer (IGV) [112].
Resources of miRNAs The web-based resources for miRNAs are listed in Table 1. The first database, miRBase, officially reports the miRNAs in all species, including human [32]. The sequences of pre-miRNAs and mature miRNAs, the secondary structures of pre-miRNAs, and related literature could be obtained from the miRBase. EVpedia is an integrated and comprehensive proteome, transcriptome, and lipidome database of extracellular vesicles (EVs) in
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Table 1 Web-based resources for miRNAs. Database
URL
Description
Ref.
miRBase EVpedia deepBase miRGator ChIPBase GTRD DIANA-TarBase miRTarBase miRCode starBase miRWalk
http://www.mirbase.org/ http://evpedia.info/ http://rna.sysu.edu.cn/deepBase/ http://mirgator.kobic.re.kr/ http://rna.sysu.edu.cn/chipbase/ http://gtrd.biouml.org/ http://www.microrna.gr/tarbase/ http://mirtarbase.mbc.nctu.edu.tw/ http://www.mircode.org/ http://starbase.sysu.edu.cn/ http://zmf.umm.uni-heidelberg.de/apps/ zmf/mirwalk2/ http://compbio.uthsc.edu/miRSNP/ http://compbio.uthsc.edu/SomamiR/ http://www.oncomir.umn.edu/ http://bioinfo.au.tsinghua.edu.cn/oncomirdb/ http://mircancer.ecu.edu/ http://www.cuilab.cn/hmdd/ http://www.mir2disease.org/
The official database of miRNAs in different species Vesicular mRNAs, miRNAs, and lipids Evolution and expression patterns of diverse ncRNAs Expression profiles, diversities and targets of miRNAs Transcription factor binding sites on miRNAs and lncRNA from ChIP-Seq data Transcription factor binding sites for human and mouse from ChIP-Seq data Experimentally validated miRNA targets Experimentally validated miRNA targets miRNA target sites on mRNAs and lncRNAs miRNA:mRNA, miRNA:lncRNA, miRNA:ceRNA relations obtained A comprehensive resources for predicted and validated miRNA targets
[32] [33,34] [35,36] [37e39] [40,41] [42] [43] [44e46] [47] [48,49] [50,51]
Variations in miRNA seed regions and their target sites Cancer somatic mutations in miRNAs and their target sites miRNA expression in Sarcoma and colon cancer Experimentally verified oncogenic and tumor-suppressive miRNAs miRNA expression profiles in various human cancers Curated human miRNA and disease associations miRNAs deregulated in diseases
[52,53] [54,55] [56,57] [58] [59] [60] [61]
PolymiRTS SomamiR Oncomir OncomiRDB miRCancer HMDD2.0 miR2Disease
many species, including human [33,34]. EVpedia provides databases of vesicular mRNAs, miRNAs, and lipids. Users could search miRNAs in EVs originated from different cells, including some cancer cell lines, in EVpedia. deepBase annotates various small RNAs (miRNAs, siRNAs and piRNAs), lncRNAs and circRNAs [35,36]. In addition to expression of sRNAs, deepBase also provides conservation, expression and prediction functions of lncRNAs. miRGator provides diversities or isoforms, expression profiles, targets of miRNAs, and expression relations between miRNAs and targets [37e39]. ChIPBase collects transcription factor (TF) binding sites and histone modifications and motifs for lncRNAs, miRNAs, and protein-coding genes from 10,200 ChIP-seq data [40,41]. ChIPBase includes a tool to explore the co-expression patterns between TFs and genes by integrating around 10,000 tumor and 9100 normal samples, respectively. ChIPBase also provides a tool to find enriched GO terms of a given TF. GTRD is has processed 8828 ChIP-seq data for 713 TFs for human and mouse with four different peak-calling algorithms. The gene regulated by a given TF or the potential TFs for a given gene could be searched in GTRD [42]. GTRD visualized the putative TF binding sites on a genome browser. DIANA-TarBase collected over 500,000 miRNA:target relations with experimental validations from 356 different cell types from 24 species [43]. miRTarBase includes around 360,000 experimentally verified miRNA:target relations obtained by text mining and manual surveying [46]. miRTarBase offers various ways of query, such as by targets, pathways and disease, to find miRNAs' relations to diseases. miRCode reports putative miRNA target sites across the complete GENCODE annotated transcriptome, including 10,419 lncRNA genes [47]. starBase reports interactions between miRNAs and various molecules, such as mRNAs, lncRNAs, and circRNAs by analyzing CLIP-seq data [48,49]. starBase provides a useful tool to analyze the networks of miRNAs, a specific target of interests, and competitive endogenous RNAs (ceRNAs) for The Cancer Genome Atlas data [49]. miRWalk provides predicted and experimentally verified miRNA:target interactions within the complete sequence of a gene, and combines this information with a comparison of binding sites from 12 existing miRNA-target prediction programs [50,51]. miRNA:target pairs could be searched for specific GO terms, diseases and OMIM disorders in miRWalk.
Mutations in miRNAs or miRNA target sites may change the specificities between miRNAs and their targets. Thus, some mutations in miRNAs or miRNA target sites may have played roles in cancers [62]. PolymiRTS is a database of mutations in miRNAs and miRNA target sites [52,53]. SomamiR further provides somatic mutations in miRNAs, or miRNA complementary sites in multiple kinds of target RNAs, including mRNAs, circRNAs and lncRNAs [54,55]. Oncomir provides miRNA expression in Sarcoma and colon cancer [56,57]. OncomiRDB collects experimentally verified oncogenic and tumor-suppressive miRNAs using text mining [58]. miRCancer provides miRNA expression profiles in various human cancers obtained by text mining techniques and manual revision [59]. HMDD (v2.0) [60] is a comprehensive database of miRNAs and diseases associations that are experimentally supported. HMDD supports the search of a miRNA's role in different diseases or miRNAs related to a specific diseases. In addition to miRNA:target relations, circulation, genetic and epigenetic relations between miRNAs and diseases are also collected in HMDD. miR2Disease [61] is a another database of curated relations of miRNA and diseases. miR2Disease supports queries based on miRNAs, targets and diseases. miR2Disease includes deregulated expression patterns of miRNAs in various human diseases, experimentally verified miRNA targets, and related reference. Resources of lncRNAs Recently, more and more resources of lncRNAs are being developed and some of them are shown in Table 2. By integrating lncRNAs in GENCODE, NONCODE, and LNCipedia, lncRNAWiki stores 105,255 non-redundant lncRNA transcripts [63]. Among 719 community-curated lncRNAs in lncRNAWiki, 289 have been experimentally proved to be associated with cancer and other diseases [63]. Although hundreds of thousands of lncRNAs have been reported, but only a small port of them have been demonstrated to have clear biological functions. lncRNAdb includes 287 eukaryotic lncRNAs that have biological functions [65]. NONCODE is an integrated database of 527,336 lncRNAs in 16 species [66e69]. Diverse information of lncRNAs, including expression pattern in different tissues, putative functions, relation to diseases, and conservation, is provided in NONCODE. C-It-Loci integrates three types of conserved regions in three species, i.e., human, mouse and zebrafish [70]. Of the 11,725 regions
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Table 2 Web-based resources for lncRNAs. Database/tool
URL
Description
Ref.
lncRNAWiki
http://lncrna.big.ac.cn/
[63]
lncRNAdb
http://www.lncrnadb.org/
NONCODE C-It-Loci NPInter lncRNAtor
http://www.noncode.org/ http://c-it-loci.uni-frankfurt.de/ http://www.bioinfo.org/NPInter/ http://lncrnator.ewha.ac.kr/
lncPro
http://bioinfo.bjmu.edu.cn/lncpro/
LNCipedia
http://www.lncipedia.org/
LncRBase TF2LncRNA LongTarget AnnoLnc lncRNA2Target
http://bicresources.jcbose.ac.in/zhumur/lncrbase/ http://mlg.hit.edu.cn/tf2lncrna/ http://lncrna.smu.edu.cn/ http://annolnc.cbi.pku.edu.cn/ http://www.lncrna2target.org/
lncRNA2Function
http://mlg.hit.edu.cn/lncrna2function/
Co-LncRNA
http://www.bio-bigdata.com/Co-LncRNA/
ncFANs
http://www.ebiomed.org/ncFANs/
Linc2GO
http://www.bioinfo.tsinghua.edu.cn/~liuke/Linc2GO
lncRNASNP LincSNP LNCediting TANRIC LncRNADisease Lnc2Cancer
http://bioinfo.life.hust.edu.cn/lncRNASNP/ http://bioinfo.hrbmu.edu.cn/LincSNP/ http://bioinfo.life.hust.edu.cn/LNCediting/ http://ibl.mdanderson.org/tanric/_design/ basic/index.html http://www.cuilab.cn/lncrnadisease/ http://www.bio-bigdata.net/lnc2cancer/
oncoNcRNA
http://rna.sysu.edu.cn/onconcrna/
CPC ViennaRNA
http://cpc2.cbi.pku.edu.cn/ http://rna.tbi.univie.ac.at/
A wiki-based platform on lncRNAs that are curated and edited by the community A database of lncRNAs that have biological functions in eukaryotes A database of expression and biological functions of lncRNAs Identifying tissue-specific transcripts across three species Functional interactions between ncRNAs and biomolecules Expression patterns, functional annotations, coding potential, and conservation of lncRNAs A tool for predicting the interaction between lncRNAs and proteins A database for annotated lncRNAs and their structures, coding potential, and miRNA binding sites A comprehensive database for lncRNA in human and mouse Franscription factor binding sites on lncRNAs Identification of lncRNA DNA-binding motifs and binding sites Annotate novel human lncRNAs by inputting sequences A database of differentially expressed genes after lncRNA knockdown or overexpression A database of potential functions of lncRNAs predicted by coexpression between lncRNAs and coding genes Co-expressed protein-coding genes of lncRNAs and their GO terms and KEGG pathways A web-based tool for functional annotation of human and mouse lncRNAs Functional annotation of human lncRNAs using ceRNA hypothesis SNPs in human and mouse lncRNAs Disease-associated SNPs in human lncRNAs A database of A-to-I editing sites in lncRNAs Interactive analysis of lncRNAs in the context of clinical and other molecular data A database of relations between lncRNAs and diseases A database of cancer-related lncRNAs that are experimentally validated A tool for exploring the functions and clinical relevance of ncRNAs in cancers Calculate coding capacities of lncRNAs and other RNAs Multiple tools for prediction of RNA secondary structure
containing lncRNAs, 8409 regions share a lncRNA among more than one tissue, and 511 within all three species. C-It-Loci allows complex search criteria to identify transcripts enriched in specific tissues. NPInter stores functional and experimentally verified interactions between ncRNAs (except tRNAs and rRNAs) and biomolecules (proteins, RNAs and DNAs) [71,72]. NPInter can be used to search interactions for a specified lncRNA, protein or miRNA. lncRNAtor includes diverse information of lncRNAs, including expression profile, interacting (binding) protein, integrated sequence curation, evolutionary scores, and coding potential [73]. lncRNAtor can be used to search co-expressed genes for a given lncRNA or vice versa. lncRNAtor also provides interacting lncRNA for specified proteins. lncPro predicts the interaction between lncRNAs and proteins with machine learning method [74]. Only the sequences of a lncRNA and several proteins are needed to predict potential interactions between the lncRNA and proteins. LNCipedia (v4.1) contains 146,742 human annotated lncRNAs, and their sequences, secondary structures, protein coding potential, and predicted miRNA binding sites [75]. lncRNABase stores 216,562 lncRNAs in human and mouse [76]. lncRNABase could be used to search lncRNAs based on diseases, tissues, positions relative to coding genes, and associated miRNAs/ piRNAs. TF2LncRNA provides TF binding sites on lncRNAs [77]. TF2LncRNA can find common TFs for a set of co-expressed lncRNAs or find putative lncRNAs that are regulated by a given TF.
[64,65] [66e69] [70] [71,72] [73] [74] [75] [76] [77] [78] [79] [80] [81] [82] [83] [84] [85] [86] [87] [88] [89] [90] [91] [92,93] [94]
Increasing evidences show that many lncRNAs contain DNAbinding motifs and can bind to DNA to induce methylation. Thus, to know the functions of these lncRNAs, it is critical to predict putative lncRNA binding sites on DNA or to find target targets of lncRNAs. LongTarget provides lncRNA DNA-binding motifs and binding sites [78]. AnnoLnc is a tool to annotate novel human lncRNAs [79]. AnnoLnc generates sequence and structure features, regulation, expression, protein interaction, genetic association and evolution for input lncRNA sequences. lncRNA2Target lists differentially expressed genes after lncRNA knockdown or overexpression for over 200 lncRNAs in either human or mouse [80]. Based on correlation of expression levels of over 9000 lncRNAs and protein coding genes, lncRNA2Function provides putative annotations of lncRNAs in terms of Gene Ontology (GO) and pathways [81]. Co-LncRNA is another database that compute GO and KEGG pathway of a single or multiple lncRNAs using the co-expressed coding genes of lncRNAs [82]. ncFANs provides functional annotations of human and mouse lncRNAs by using co-expression network of coding and non-coding genes [83]. In comparison, Linc2GO integrates miRNA:mRNA and miRNA:lincRNA interaction data to generate lincRNA functional annotations [84]. lncRNASNP collects 495,729 and 777,095 SNPs in more than 30,000 lncRNAs in human and mouse, respectively [85]. LincSNP provides disease-associated SNPs in human lncRNAs, as well as SNPs in TF binding sites of lncRNAs [86].
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LNCediting reports A-to-I editing sites that may affect secondary structures of lncRNAs and lncRNA:miRNA interactions [87]. The Atlas of Noncoding RNAs in Cancer, TANRIC, is an interactive tool to explore lncRNAs with genomic, proteomic, epigenomic and clinical data in cancers and provides lncRNA expression profiles of 20 cancer types in The Cancer Genome Atlas (TCGA) and over 8000 independent samples [88]. TANRIC could be used to investigate the biological significance of lncRNAs and lncRNA expression on drug sensitivity. TANRIC also allows users to query expression profiles of user-defined lncRNAs quickly. LncRNADisease integrates more than 1000 lncRNA and disease relations curated from literature, and also provides predicted associated diseases for over 1500 lncRNAs [89]. Lnc2Cancer includes 1488 associations between 666 human lncRNAs and 97 human cancers [90]. oncoNcRNA provides the somatic copy number alternations for over 58,000 lncRNAs, 34,000 piwi-interacting RNAs (piRNAs), 2700 miRNAs, 600 tRNAs and 400 small nucleolar RNAs (snoRNAs) in 64 human cancer types [91]. In addition to those databases in Table 2, ChIPBase [40,41] in Table 1 includes TF binding sites on lncRNAs. deepBase [35,36] in Table 1 provides expression patterns, annotations of lncRNAs and putative functions of lncRNAs from co-expression networks. Two other issues for lncRNAs are coding potential and structure. CPC1 is an online system which will calculate the coding ability of RNA sequences input by users [92]. The updated version of CPC1, CPC2, is much faster and more accurate [93]. CPC2 is also available on mobile devices and as download package. The ViennaRNA Web Services include many tools related to prediction of RNA structures [94]. RNAfold is a tool to predict secondary structure of RNA sequences. RNAz Server can be used if users want to predict thermodynamically stable and evolutionarily conserved RNA secondary structures in multiple sequence alignments.
Resources of circRNAs As listed in Table 3, there have been some databases for circRNAs although they are just being noticed recently. circBase collects circRNAs from human, mouse, C. elegans and D. melanogaster [95]. circBase provides search tools based on sequences, gene description, and genomic positions. circNet provides expression profiles of known and newly predicted circRNAs in 464 RNA-seq profiles [96]. After integrating the predicted and verified miRNA:mRNA relations and the predicted miRNA:circRNA relations, networks including miRNA, mRNA, and circRNAs are also provided in circNet. CircInteractome is another database the provide predicted miRNA binding sites on circRNAs by using CLIP (cross-linking immunoprecipitation) sequencing profiles [97]. Furthermore, CircInteractome also provides junction-spanning primers for specific detection of circRNAs of interests and siRNAs that target the backsplicing junction sites for possible silencing of circRNAs [97].
circRNAs were recently reported to encode proteins [100]. circRNADb include 32,914 human circRNAs, of which 16,328 may have coding capacities by containing ORFs of at least 100 amino acids [98]. In addition, CircInteractome also could be used to identify potential internal ribosomal entry sites in circRNAs [97]. Circ2Traits includes 1951 human circRNAs potentially associated with 105 different diseases in two ways [99]. First, like circNet, the miRNA binding sites on circRNAs are analyzed to organize a network of miRNAs, mRNAs and circRNAs. Second, disease associated SNPs are mapped on circRNA loci, and Argonaute (Ago) interaction sites on circular RNAs are identified. Furthermore, starBase [48,49] in Table 1 provides miRNA binding sites on circRNAs based on CLIP-seq profiles. deepBase [35,36] in Table 1 includes annotations of circRNAs. CPC [92,93] listed in 2 could be used to calculate coding capacities in circRNAs.
A usage guide of web-based ncRNA resources There are many online-based resources for ncRNAs. Thus, it is convenient to know how to use these resources effectively. Based on the expected analyses shown in Fig. 1, we introduce the resources related to different types of analyses and suggest some preferred resources in the following. As shown in Fig. 1, the first type of analysis is to explore ncRNAs with differential expression levels in different tissues or groups of disease conditions. Several databases provide the expression levels of miRNAs, such as miRGator, deepBase, Oncomir, and miRCancer. miRGator is a preferred tool, since it visualizes the expression of miRNAs in different diseases and organs and provides search tools based on miRNAs and diseases. miRGator could also be used to investigate and visualize the abundances of isomirs for selected small RNA sequencing profiles. Four databases, i.e., deepBase, AnnoLnc, lncRNAtor and NONCODE, provide visualized expression levels of lncRNAs in normal samples. AnnoLnc and lncRNAtor also include expression levels of lncRNAs in cancer samples, only lncRNAtor provides comparisons between normal and tumor samples. lncRNAWiki has statements of expression levels for some lncRNAs. Therefore, lncRNAtor is preferred for exploring lncRNA expression levels. CircNet is the only database that has expression levels of circRNAs in different tissues or cells. When deregulated ncRNAs were identified, the next question is which transcription factors (TFs) drive the ncRNAs. Therefore, the second type of analysis is to find putative regulators of the ncRNAs. As shown in Fig. 1, ChIPBase is a comprehensive database to explore putative TFs for miRNAs, lncRNAs and coding genes. Furthermore, ChIPBase provides a tool to explore the co-expression of TFs and targets in around 20,000 samples representing diverse tissue types, including 32 cancer types and cell lines of 20 cancer types. There are no databases dedicated to regulators of circRNAs till now. Since circRNA were originated from exons of genes, thus it is feasible to search the TFs of the host genes of circRNAs in TF binding site databases such as GTRD [42] and ChIPBase. In summary, ChIPBase is a preferred tool to investigate the regulations of ncRNAs.
Table 3 Web-based resources for circRNAs. Database
URL
Description
Ref.
circBase CircNet CircInteractome
http://www.circbase.org/ http://circnet.mbc.nctu.edu.tw/ https://circinteractome.nia.nih.gov/
[95] [96] [97]
circRNADb Circ2Traits
http://reprod.njmu.edu.cn/circrnadb/ http://gyanxet-beta.com/circdb/
A database of circRNAs in human, mouse, C. elegans and D. melanogaster Regulatory networks of miRNAs, mRNAs and circRNAs miRNA binding sites on circRNAs, primers for validation, and siRNAs for silencing circRNAs circRNAs and their coding capacities 1951 human circRNAs potentially associated with 105 different diseases
[98] [99]
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Fig. 1. Different types of analyses and related resources of ncRNAs. The arrows indicate the suggested steps when performing different types of analyses for ncRNAs. The resources in bold face are recommended for the corresponding analysis in the same line and the ncRNAs in the same column.
The proteins binding to ncRNAs are important for the biogenesis or functions of ncRNAs. Thus, the third type of analysis is to investigate the binding proteins of ncRNAs (see Fig. 1). miRWalk summarizes proteins involved in biogenesis of miRNAs and proteins linked to Ago proteins in human and mouse [50,51]. Among four resources, NPInter, lncRNAtor, lncPro, AnnoLnc, that provide interacting proteins of lncRNAs, NPInter and lncRNAtor could used to search binding proteins for a specified lncRNA or vice versa and are recommended. Only CircInteractome could be used to search circRNAs that are bound by specified proteins. The deregulation of ncRNAs might be resulted from mutations [62,101,102] and editing [103e105]. SNPs and editing in miRNAs or lncRNAs may also affect their complementarities with other RNAs or proteins [62,101,102,105,106]. Thus, as shown in Fig. 1, the fourth type of analysis is to find mutation and editing sites in ncRNAs. PolymiRTS has comprehensive information of SNPs in miRNAs and in miRNA complementary sites on targets [52,53] with search tools based on miRNAs and target genes. Furthermore, PolymiRTS can be used to search SNPs associated with diseases or traits. Therefore, PolymiRTS is a recommended tool for exploring SNPs in miRNAs and/or their complementary sites. LincSNP could be used to search SNPs in lncRNAs using disease, name of lncRNA or genomic position, which makes it a preferred tool for analyzing SNPs in lncRNAs. Both DARNED [107,108] and RADAR [109] have A-to-I editing information for human, mouse and Drosophila for the whole genome. Thus, DARNED and RADAR could be explore editing sites in miRNAs, lncRNAs and circRNAs. But RADAR provides the conservation and editing levels for some editing sites, and is recommended. As shown in Fig. 1, the fifth type of analysis is to explore potential functions of ncRNAs. Some databases include predicted or validated targets for miRNAs, such as miRWalk, starBase, miRTarBase, DIANA-TarBase, miRCode, and miRGator. miRWalk is recommended since it provides very comprehensive search options for both predicted and experimentally validated miRNA targets,
including miRNA, gene, GO terms, diseases, disease ontologies and OMIM disorders. miRTarBase is also recommended for exploring miRNA targets since miRTarBase could be used to search experimentally validated miRNA targets using miRNA, target gene, pathway, validation method, disease and literature. Many databases, such as AnnoLnc, lncRNA2Function, lncRNA2Target, CoLncRNA, ncFANs, Linc2GO, lncRNAtor, NONCODE, and LongTarget, include functional annotations for lncRNAs. AnnoLnc is a preferred tool to search potential functions of lncRNAs because diverse information of lncRNA is provided, such as expression, secondary structure, transcriptional regulation, miRNA interaction, protein interaction, conservation, genetic association, co-expression with coding genes in both normal and cancer samples, and putative GO terms. Because the names of lncRNAs are largely not unified thus using lncRNA sequence as input might be another advantage of AnnoLnc. Both CircNet and CircInteractome provide putative miRNA binding sites on circRNAs, but CircNet has a visualized interface and is a preferred tool. The sixth type of analysis is to find the associations between ncRNAs and diseases. Both miR2Disease and HMDD2.0 have search functions based on miRNAs and diseases, but miR2Disease has additional search based on miRNA targets. OncomiRDB is a more dedicated and preferred tool for investigating miRNAs associated with cancers. In addition to search options of miRNA, disease, and target, users could search cancer-related miRNAs with cancerrelated phenotype or cellular process, such as proliferation and apoptosis in OncomiRDB. miRWalk is also recommended to search disease-associated miRNAs and miRNA:target relations with options of diseases, disease ontology and OMIM disorders. TANRIC is recommended to investigate the cancer-associated lncRNAs since it provides diverse analysis for specified lncRNAs, such as differential analysis for different stages or subtypes of cancer, survival analysis, co-expression analysis and differential analysis for somatic mutations in lncRNAs. LncRNADisease and Lnc2Cancer are also very good
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choices for exploring experimentally verified cancer-related lncRNAs. Circ2Traits is the only database that has association information between circRNAs and diseases. Although it is suggested to follow the arrows in Fig. 1 to perform the analysis tasks, researchers could always choose the resources directly related to the analysis that they want to perform in their studies. Genome browser UCSC Genome Browser is the most comprehensive genome browser for various purposes [110]. There are nine categories of features that the users choose and set, i.e., Mapping and Sequencing, Genes and Gene Predictions, Phenotype and Literature, mRNA and EST, Expression, Regulation, Comparative Genomics, Variation and Repeats. Furthermore, the users could add custom tracks for their own data. UCSC Genome Browser is an online system which will be updated regularly. In the panel of “Genes and Gene Predictions”, there is a track of “Non-coding RNA”. This track include the annotation of lncRNAs, miRNAs, snoRNAs and tRNAs, along with RNA-Seq reads expression abundances for lncRNAs across 22 human tissues and cell lines reported in Ref. [111]. Because the high-throughput sequencing data could be very large, it might be more convenient to have a genome-scale tool to view these data off-line. Integrated Genomics Viewer (IGV) [112] is a visualization tool for integrated exploration and analysis of various types of data on a genome-wide scale. IGV is developed in the Java programming language which could be installed on all platforms that support Java. Because IGV is an off-line tool, the users need to prepare their own data to be integrated into an analysis. Conclusions Non-coding RNAs play essential roles in cancers and other human diseases. The fast progresses of sequencing technologies and the rapid increases of results reported in literature have generated abundant information of ncRNAs than that could be handled and analyzed manually. The web-based resources of ncRNAs summarized here could be served as primer for cancer biologists and clinical doctors, as well as other researchers, who are doing researches of ncRNAs. Acknowledgements The research was supported in part by two grants (No. 31460295 and 31760314) of National Natural Science Foundation of China (http://www.nsfc.gov.cn/) and a grant (No. SKLGE-1511) of the Open Research Funds of the State Key Laboratory of Genetic Engineering, Fudan University, China, to YZ. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Conflict of interest There are no conflicts of interests in the work. References [1] G.S. Laurent, C. Wahlestedt, P. Kapranov, The Landscape of long noncoding RNA classification, Trends Genet. 31 (5) (2015) 239e251. [2] V. Ambros, The functions of animal microRNAs, Nature 431 (2004) 350e355. [3] S. Jonas, E. Izaurralde, Towards a molecular understanding of microRNAmediated gene silencing, Nat. Rev. Genet. 16 (7) (2015) 421e433. [4] D. Bartel, MicroRNAs: genomics, biogenesis, mechanism, and function, Cell 116 (2) (2004) 281e297.
[5] D.P. Bartel, MicroRNAs: target recognition and regulatory functions, Cell 136 (2) (2009) 215e233. [6] P. Carninci, T. Kasukawa, S. Katayama, J. Gough, M. Frith, N. Maeda, et al., The transcriptional landscape of the mammalian genome, Science 309 (5740) (2005) 1559e1563. , T.R. Gingeras, [7] E. Birney, J.A. Stamatoyannopoulos, A. Dutta, R. Guigo E.H. Margulies, et al., Identification and analysis of functional elements in 1% of the human genome by the encode pilot project, Nature 447 (7146) (2007) 799e816. [8] S. Djebali, C.A. Davis, A. Merkel, A. Dobin, T. Lassmann, A. Mortazavi, et al., Landscape of transcription in human cells, Nature 489 (7414) (2012) 101e108. [9] M.B. Clark, J.S. Mattick, Long noncoding RNAs in cell biology, Seminars Cell Dev. Biol. 22 (4) (2011) 366e376. [10] C.P. Ponting, P.L. Oliver, W. Reik, Evolution and functions of long noncoding RNAs, Cell 136 (4) (2009) 629e641. [11] D.S. Perez, T.R. Hoage, J.R. Pritchett, A.L. Ducharme-Smith, M.L. Halling, S.C. Ganapathiraju, et al., Long, abundantly expressed non-coding transcripts are altered in cancer, Hum. Mol. Genet. 17 (5) (2008) 642e655. [12] M. Huarte, J.L. Rinn, Large non-coding RNAs: missing links in cancer? Hum. Mol. Genet. 19 (R2) (2010) R152eR161. [13] J.R. Prensner, A.M. Chinnaiyan, The emergence of lncRNAs in cancer biology, Cancer Discov. 1 (5) (2011) 391e407. [14] R. Spizzo, M.I. Almeida, A. Colombatti, G.A. Calin, Long non-coding RNAs and cancer: a new frontier of translational research? Oncogene 31 (43) (2012) 4577e4587. [15] M. Huarte, The emerging role of lncRNAs in cancer, Nat. Med. 21 (11) (2015) 1253e1261. [16] S.-Y. Ng, L. Lin, B.S. Soh, L.W. Stanton, Long noncoding RNAs in development and disease of the central nervous system, Trends Genet. 29 (8) (2013) 461e468. [17] I.A. Qureshi, M.F. Mehler, Long non-coding RNAs: novel targets for nervous system disease diagnosis and therapy, Neurotherapeutics 10 (4) (2013) 632e646. [18] J. Salzman, C. Gawad, P.L. Wang, N. Lacayo, P.O. Brown, Circular RNAs are the predominant transcript isoform from hundreds of human genes in diverse cell types, PLoS One 7 (2) (2012) 1e12. [19] S. Memczak, M. Jens, A. Elefsinioti, F. Torti, J. Krueger, A. Rybak, et al., Circular RNAs are a large class of animal RNAs with regulatory potency, Nature 495 (7441) (2013) 333e338. [20] J. Salzman, R.E. Chen, M.N. Olsen, P.L. Wang, P.O. Brown, Cell-type specific features of circular RNA expression, PLoS Genet. 9 (9) (2013) 1e15. [21] T.B. Hansen, T.I. Jensen, B.H. Clausen, J.B. Bramsen, B. Finsen, C.K. Damgaard, et al., Natural RNA circles function as efficient microRNA sponges, Nature 495 (7441) (2013) 384e388. [22] X.-O. Zhang, H.-B. Wang, Y. Zhang, X. Lu, L.-L. Chen, L. Yang, Complementary sequence-mediated exon circularization, Cell 159 (1) (2014) 134e147. [23] J.U. Guo, V. Agarwal, H. Guo, D.P. Bartel, Expanded identification and characterization of mammalian circular RNAs, Genome Biol. 15 (7) (2014) 409. [24] Y. Zhang, X.-O. Zhang, T. Chen, J.-F. Xiang, Q.-F. Yin, Y.-H. Xing, et al., Circular intronic long noncoding RNAs, Mol. Cell 51 (6) (2013) 792e806. [25] Z. Li, S. Wang, J. Cheng, C. Su, S. Zhong, Q. Liu, et al., Intron lariat RNA inhibits microRNA biogenesis by sequestering the dicing complex in Arabidopsis, PLoS Genet. 12 (11) (2016), e1006422. [26] S. Qu, X. Yang, X. Li, J. Wang, Y. Gao, R. Shang, et al., Circular RNA: a new star of noncoding RNAs, Cancer Lett. 365 (2) (2015) 141e148. [27] J. Li, J. Yang, P. Zhou, Y. Le, C. Zhou, S. Wang, et al., Circular RNAs in cancer: novel insights into origins, properties, functions and implications, Am. J. Cancer Res. 5 (2) (2015) 472e480. [28] Y. Dong, D. He, Z. Peng, W. Peng, W. Shi, J. Wang, et al., Circular RNAs in cancer: an emerging key player, J. Hematol. Oncol. 10 (1) (2017) 2. [29] L.-D. Hou, J. Zhang, Circular RNAs: an emerging type of RNA in cancer, Int. J. Immunopathol. Pharmacol. 30 (1) (2017) 1e6. [30] Y. Devaux, E.E. Creemers, R.A. Boon, S. Werfel, T. Thum, S. Engelhardt, et al., Circular RNAs in heart failure, Eur. J. Heart Fail. 19 (2017) 701e709. [31] W.J. Lukiw, Circular RNA (circRNA) in Alzheimer's disease (AD), Front. Genet. 4 (2013) 307. [32] A. Kozomara, S. Griffiths-Jones, miRBase: annotating high confidence microRNAs using deep sequencing data, Nucleic Acids Res. 42 (D1) (2014) D68eD73. [33] D.-K. Kim, J. Lee, R.J. Simpson, J. Ltvall, Y.S. Gho, Evpedia: a community web resource for prokaryotic and eukaryotic extracellular vesicles research, Seminars Cell Dev. Biol. 40 (2015) 4e7. [34] D.-K. Kim, J. Lee, S.R. Kim, D.-S. Choi, Y.J. Yoon, J.H. Kim, et al., Evpedia: a community web portal for extracellular vesicles research, Bioinformatics 31 (6) (2015) 933e939. [35] J.-H. Yang, P. Shao, H. Zhou, Y.-Q. Chen, L.-H. Qu, deepBase: a database for deeply annotating and mining deep sequencing data, Nucleic Acids Res. 38 (Suppl. 1) (2010) D123eD130. [36] L.-L. Zheng, J.-H. Li, J. Wu, W.-J. Sun, S. Liu, Z.-L. Wang, et al., deepBase v2.0: identification, expression, evolution and function of small RNAs, lncRNAs and circular RNAs from deep-sequencing data, Nucleic Acids Res. 44 (D1) (2016) D196eD202. [37] S. Nam, B. Kim, S. Shin, S. Lee, miRGator: an integrated system for functional annotation of microRNAs, Nucleic Acids Res. 36 (Suppl. 1) (2008) D159eD164.
Y. Zheng et al. / Cancer Letters 407 (2017) 1e8 [38] S. Cho, Y. Jun, S. Lee, H.-S. Choi, S. Jung, Y. Jang, et al., miRGator v2. 0: an integrated system for functional investigation of microRNAs, Nucleic Acids Res. 39 (Suppl. 1) (2011) D158eD162. [39] S. Cho, I. Jang, Y. Jun, S. Yoon, M. Ko, Y. Kwon, et al., Mirgator v3. 0: a microRNA portal for deep sequencing, expression profiling and mRNA targeting, Nucleic Acids Res. 41 (D1) (2013) D252eD257. [40] J.-H. Yang, J.-H. Li, S. Jiang, H. Zhou, L.-H. Qu, Chipbase: a database for decoding the transcriptional regulation of long non-coding RNA and microRNA genes from chip-seq data, Nucleic Acids Res. 41 (D1) (2013) D177eD187. [41] K.-R. Zhou, S. Liu, W.-J. Sun, L.-L. Zheng, H. Zhou, J.-H. Yang, et al., Chipbase v2.0: decoding transcriptional regulatory networks of non-coding RNAs and protein-coding genes from chip-seq data, Nucleic Acids Res. 45 (D1) (2017) D43eD50. [42] I. Yevshin, R. Sharipov, T. Valeev, A. Kel, F. Kolpakov, GTRD: a database of transcription factor binding sites identified by ChIP-seq experiments, Nucleic Acids Res. 45 (D1) (2017) D61eD67. [43] I.S. Vlachos, M.D. Paraskevopoulou, D. Karagkouni, G. Georgakilas, T. Vergoulis, I. Kanellos, et al., DIANA-TarBase v7. 0: indexing more than half a million experimentally supported miRNA: mRNA interactions, Nucleic Acids Res. 43 (D1) (2015) D153eD159. [44] S.-D. Hsu, F.-M. Lin, W.-Y. Wu, C. Liang, W.-C. Huang, W.-L. Chan, et al., miRTarBase: a database curates experimentally validated microRNAetarget interactions, Nucleic Acids Res. 39 (Suppl. 1) (2011) D163eD169. [45] S.-D. Hsu, Y.-T. Tseng, S. Shrestha, Y.-L. Lin, A. Khaleel, C.-H. Chou, et al., miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions, Nucleic Acids Res. 42 (D1) (2014) D78eD85. [46] C.-H. Chou, N.-W. Chang, S. Shrestha, S.-D. Hsu, Y.-L. Lin, W.-H. Lee, et al., miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database, Nucleic Acids Res. 44 (D1) (2016) D239eD247. [47] A. Jeggari, D.S. Marks, E. Larsson, miRcode: a map of putative microRNA target sites in the long non-coding transcriptome, Bioinformatics 28 (15) (2012) 2062e2063. [48] J.-H. Yang, J.-H. Li, P. Shao, H. Zhou, Y.-Q. Chen, L.-H. Qu, starBase: a database for exploring microRNA-RNA interaction maps from Argonaute CLIP-Seq and Degradome-Seq data, Nucleic Acids Res. 39 (Suppl. 1) (2011) D202eD209. [49] J.-H. Li, S. Liu, H. Zhou, L.-H. Qu, J.-H. Yang, starBase v2. 0: decoding miRNAceRNA, miRNA-ncRNA and proteineRNA interaction networks from largescale CLIP-Seq data, Nucleic Acids Res. 42 (D1) (2014) D92eD97. [50] H. Dweep, C. Sticht, P. Pandey, N. Gretz, miRWalkedatabase: prediction of possible mirna binding sites by “walking” the genes of three genomes, J. Biomed. Inf. 44 (5) (2011) 839e847. [51] H. Dweep, N. Gretz, miRWalk2. 0: a comprehensive atlas of microRNA-target interactions, Nat. Methods 12 (8) (2015), 697e697. [52] J.D. Ziebarth, A. Bhattacharya, A. Chen, Y. Cui, PolymiRTS Database 2.0: linking polymorphisms in microRNA target sites with human diseases and complex traits, Nucleic Acids Res. 40 (D1) (2012) D216eD221. [53] A. Bhattacharya, J.D. Ziebarth, Y. Cui, PolymiRTS Database 3.0: linking polymorphisms in microRNAs and their target sites with human diseases and biological pathways, Nucleic Acids Res. 42 (D1) (2014) D86eD91. [54] A. Bhattacharya, J.D. Ziebarth, Y. Cui, SomamiR: a database for somatic mutations impacting microRNA function in cancer, Nucleic Acids Res. 41 (D1) (2013) D977eD982. [55] A. Bhattacharya, Y. Cui, SomamiR 2.0: a database of cancer somatic mutations altering microRNA-ceRNA interactions, Nucleic Acids Res. 44 (D1) (2016) D1005eD1010. [56] A.L. Sarver, A.J. French, P.M. Borralho, V. Thayanithy, A.L. Oberg, K.A. Silverstein, et al., Human colon cancer profiles show differential microRNA expression depending on mismatch repair status and are characteristic of undifferentiated proliferative states, BMC Cancer 9 (1) (2009) 401. [57] A.L. Sarver, R. Phalak, V. Thayanithy, S. Subramanian, S-MED: sarcoma microRNA expression database, Lab. Investig. 90 (5) (2010) 753e761. [58] D. Wang, J. Gu, T. Wang, Z. Ding, OncomiRDB: a database for the experimentally verified oncogenic and tumor-suppressive microRNAs, Bioinformatics 30 (15) (2014) 2237e2238. [59] B. Xie, Q. Ding, H. Han, D. Wu, miRCancer: a microRNA-cancer association database constructed by text mining on literature, Bioinformatics 29 (5) (2013) 638e644. [60] Y. Li, C. Qiu, J. Tu, B. Geng, J. Yang, T. Jiang, et al., HMDD v2. 0: a database for experimentally supported human microRNA and disease associations, Nucleic Acids Res. 42 (D1) (2014) D1070eD1074. [61] Q. Jiang, Y. Wang, Y. Hao, L. Juan, M. Teng, X. Zhang, et al., miR2Disease: a manually curated database for microRNA deregulation in human disease, Nucleic Acids Res. 37 (Suppl. 1) (2009) D98eD104. [62] M. Han, Y. Zheng, Comprehensive analysis of single nucleotide polymorphisms in human MicroRNAs, PLoS One 8 (11) (2013), e78028. [63] L. Ma, A. Li, D. Zou, X. Xu, L. Xia, J. Yu, et al., LncRNAWiki: harnessing community knowledge in collaborative curation of human long non-coding RNAs, Nucleic Acids Res. 43 (D1) (2015) D187eD192. [64] P.P. Amaral, M.B. Clark, D.K. Gascoigne, M.E. Dinger, J.S. Mattick, lncRNAdb: a reference database for long noncoding RNAs, Nucleic Acids Res. 39 (Suppl. 1) (2011) D146eD151.
7
[65] X.C. Quek, D.W. Thomson, J. Maag, N. Bartonicek, B. Signal, M.B. Clark, et al., lncRNAdb v2.0: expanding the reference database for functional long noncoding RNAs, Nucleic Acids Res. 43 (D1) (2015) D168eD173. [66] C. Liu, B. Bai, G. Skogerb, L. Cai, W. Deng, Y. Zhang, et al., NONCODE: an integrated knowledge database of non-coding RNAs, Nucleic Acids Res. 33 (Suppl. 1) (2005) D112eD115. [67] S. He, C. Liu, G. Skogerb, H. Zhao, J. Wang, T. Liu, et al., NONCODE v2.0: decoding the non-coding, Nucleic Acids Res. 36 (Suppl. 1) (2008) D170eD172. [68] D. Bu, K. Yu, S. Sun, C. Xie, G. Skogerb, R. Miao, et al., NONCODE v3.0: integrative annotation of long noncoding RNAs, Nucleic Acids Res. 40 (D1) (2012) D210eD215. [69] Y. Zhao, H. Li, S. Fang, Y. Kang, W. Wu, Y. Hao, et al., NONCODE 2016: an informative and valuable data source of long non-coding RNAs, Nucleic Acids Res. 44 (D1) (2016) D203eD208. [70] T. Weirick, D. John, S. Dimmeler, S. Uchida, C-It-Loci: a knowledge database for tissue-enriched loci, Bioinformatics 31 (21) (2015) 3537e3543. [71] J. Yuan, W. Wu, C. Xie, G. Zhao, Y. Zhao, R. Chen, NPInter v2. 0: an updated database of ncRNA interactions, Nucleic Acids Res. 42 (D1) (2013) D104eD108. [72] Y. Hao, W. Wu, H. Li, J. Yuan, J. Luo, Y. Zhao, et al., NPInter v3.0: an upgraded database of noncoding RNA-associated interactions, Database 2016 (2016) baw057. [73] C. Park, N. Yu, I. Choi, W. Kim, S. Lee, lncRNAtor: a comprehensive resource for functional investigation of long non-coding RNAs, Bioinformatics 30 (17) (2014) 2480e2485. [74] Q. Lu, S. Ren, M. Lu, Y. Zhang, D. Zhu, X. Zhang, et al., Computational prediction of associations between long non-coding RNAs and proteins, BMC Genomics 14 (1) (2013) 651. [75] P.-J. Volders, K. Verheggen, G. Menschaert, K. Vandepoele, L. Martens, J. Vandesompele, et al., An update on LNCipedia: a database for annotated human lncRNA sequences, Nucleic Acids Res. 43 (D1) (2015) D174eD180. [76] S. Chakraborty, A. Deb, R.K. Maji, S. Saha, Z. Ghosh, LncRBase: an enriched resource for lncRNA information, PLoS One 9 (9) (2014) 1e11. [77] Qinghua Jiang, Jixuan Wang, Yadong Wang, Rui Ma, Xiaoliang Wu, Yu Li, TF2LncRNA: identifying common transcription factors for a list of lncRNA genes from ChIP-seq data, BioMed Res. Int. (2014), 317642, 5 pages. [78] S. He, H. Zhang, H. Liu, H. Zhu, LongTarget: a tool to predict lncRNA DNAbinding motifs and binding sites via hoogsteen base-pairing analysis, Bioinformatics 31 (2) (2015) 178e186. [79] M. Hou, X. Tang, F. Tian, F. Shi, F. Liu, G. Gao, AnnoLnc: a web server for systematically annotating novel human lncRNAs, BMC genomics 17 (1) (2016) 931. [80] Q. Jiang, J. Wang, X. Wu, R. Ma, T. Zhang, S. Jin, et al., LncRNA2Target: a database for differentially expressed genes after lncRNA knockdown or overexpression, Nucleic Acids Res. 43 (D1) (2015) D193eD196. [81] Q. Jiang, R. Ma, J. Wang, X. Wu, S. Jin, J. Peng, et al., LncRNA2Function: a comprehensive resource for functional investigation of human lncRNAs based on RNA-seq data, BMC Genomics 16 (3) (2015) S2. [82] Z. Zhao, J. Bai, A. Wu, Y. Wang, J. Zhang, Z. Wang, et al., Co-LncRNA: investigating the lncRNA combinatorial effects in GO annotations and KEGG pathways based on human RNA-Seq data, Database 2015 (2015) bav082. [83] Q. Liao, H. Xiao, D. Bu, C. Xie, R. Miao, H. Luo, et al., ncFANs: a web server for functional annotation of long non-coding RNAs, Nucleic Acids Res. 39 (Suppl. 2) (2011) W118eW124. [84] K. Liu, Z. Yan, Y. Li, Z. Sun, Linc2GO: a human lincRNA function annotation resource based on ceRNA hypothesis, Bioinformatics 29 (17) (2013) 2221e2222. [85] J. Gong, W. Liu, J. Zhang, X. Miao, A.-Y. Guo, lncRNASNP: a database of snps in lncRNAs and their potential functions in human and mouse, Nucleic Acids Res. 43 (D1) (2015) D181eD186. [86] S. Ning, M. Yue, P. Wang, Y. Liu, H. Zhi, Y. Zhang, et al., LincSNP 2.0: an updated database for linking disease-associated SNPs to human long noncoding RNAs and their TFBSs, Nucleic Acids Res. 45 (D1) (2017) D74eD78. [87] J. Gong, C. Liu, W. Liu, Y. Xiang, L. Diao, A.-Y. Guo, et al., LNCediting: a database for functional effects of RNA editing in lncRNAs, Nucleic Acids Res. 45 (D1) (2017) D79eD84. [88] J. Li, L. Han, P. Roebuck, L. Diao, L. Liu, Y. Yuan, et al., TANRIC: an interactive open platform to explore the function of lncRNAs in cancer, Cancer Res. 75 (18) (2015) 3728e3737. [89] G. Chen, Z. Wang, D. Wang, C. Qiu, M. Liu, X. Chen, et al., LncRNADisease: a database for long-non-coding RNA-associated diseases, Nucleic Acids Res. 41 (D1) (2013) D983eD986. [90] S. Ning, J. Zhang, P. Wang, H. Zhi, J. Wang, Y. Liu, et al., Lnc2Cancer: a manually curated database of experimentally supported lncRNAs associated with various human cancers, Nucleic Acids Res. 44 (D1) (2016) D980eD985. [91] Z.-L. Wang, X.-Q. Zhang, H. Zhou, J.-H. Yang, L.-H. Qu, oncoNcRNA: a web portal for exploring the non-coding RNAs with oncogenic potentials in human cancers, Non Coding RNA 3 (1) (2017) 7. [92] L. Kong, Y. Zhang, Z.-Q. Ye, X.-Q. Liu, S.-Q. Zhao, L. Wei, et al., CPC: assess the protein-coding potential of transcripts using sequence features and support vector machine, Nucleic Acids Res. 35 (Suppl. 2) (2007) W345eW349. [93] Y.-J. Kang, D.-C. Yang, L. Kong, M. Hou, Y.-Q. Meng, L. Wei, et al., CPC2: a fast and accurate coding potential calculator based on sequence intrinsic features, Nucleic Acids Res. 45 (W1) (2017) W12eW16.
8
Y. Zheng et al. / Cancer Letters 407 (2017) 1e8
€ ck, I.L. Hofacker, The Vienna [94] A.R. Gruber, R. Lorenz, S.H. Bernhart, R. Neubo RNA websuite, Nucleic Acids Res. 36 (Suppl. 2) (2008) W70eW74. [95] P. Glaar, P. Papavasileiou, N. Rajewsky, circBase: a database for circular RNAs, RNA 20 (11) (2014) 1666e1670. [96] Y.-C. Liu, J.-R. Li, C.-H. Sun, E. Andrews, R.-F. Chao, F.-M. Lin, et al., CircNet: a database of circular RNAs derived from transcriptome sequencing data, Nucleic Acids Res. 44 (D1) (2015) D209eD215. [97] D.B. Dudekula, A.C. Panda, I. Grammatikakis, S. De, K. Abdelmohsen, M. Gorospe, CircInteractome: a web tool for exploring circular RNAs and their interacting proteins and microRNAs, RNA Biol. 13 (1) (2016) 34e42. [98] X. Chen, P. Han, T. Zhou, X. Guo, X. Song, Y. Li, circRNADb: a comprehensive database for human circular RNAs with protein-coding annotations, Sci. Rep. 6 (2016). [99] S. Ghosal, S. Das, R. Sen, P. Basak, J. Chakrabarti, Circ2Traits: a comprehensive database for circular RNA potentially associated with disease and traits, Front. Genet. 4 (2013) 283. [100] K. Servick, Circular RNAs hint at new realm of genetics, Science 355 (2017), 1363e1363. [101] R. Duan, C. Pak, P. Jin, Single nucleotide polymorphism associated with mature miR-125a alters the processing of pri-miRNA, Hum. Mol. Genet. 16 (9) (2007) 1124e1131. [102] B. Ryan, A. Robles, C. Harris, Genetic variation in microRNA networks: the implications for cancer research, Nat. Rev. Cancer 10 (6) (2010) 389e402. [103] Y.-K. Kim, I. Heo, V.N. Kim, Modifications of small RNAs and their associated proteins, Cell 143 (5) (2010) 703e709.
[104] I. Heo, M. Ha, J. Lim, M.-J.J. Yoon, J.-E.E. Park, S.C. Kwon, et al., Mono-Uridylation of Pre-MicroRNA as a key step in the biogenesis of group II let-7 MicroRNAs, Cell 151 (3) (2012) 521e532. [105] Y. Zheng, B. Ji, R. Song, S. Wang, T. Li, X. Zhang, et al., Accurate detection for a wide range of mutation and editing sites of micrornas from small rna highthroughput sequencing profiles, Nucleic Acids Res. 44 (14) (2016), e123. [106] Y. Kawahara, B. Zinshteyn, P. Sethupathy, H. Iizasa, A.G. Hatzigeorgiou, K. Nishikura, Redirection of silencing targets by Adenosine-to-Inosine editing of miRNAs, Science 315 (5815) (2007) 1137e1140. [107] A. Kiran, P.V. Baranov, DARNED: a DAtabase of RNa EDiting in humans, Bioinformatics 26 (14) (2010) 1772e1776. [108] A.M. Kiran, J.J. O'Mahony, K. Sanjeev, P.V. Baranov, Darned in 2013: inclusion of model organisms and linking with wikipedia, Nucleic Acids Res. 41 (D1) (2013) D258eD261. [109] G. Ramaswami, J.B. Li, RADAR: a rigorously annotated database of A-to-I RNA editing, Nucleic Acids Res. 42 (D1) (2014) D109eD113. [110] P.A. Fujita, B. Rhead, A.S. Zweig, A.S. Hinrichs, D. Karolchik, M.S. Cline, et al., The ucsc genome browser database: update 2011, Nucleic Acids Res. 39 (Suppl. 1) (2011) D876eD882. [111] M.N. Cabili, C. Trapnell, L. Goff, M. Koziol, B. Tazon-Vega, A. Regev, et al., Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses, Genes Dev. 25 (18) (2011) 1915e1927. [112] J.T. Robinson, H. Thorvaldsdottir, W. Winckler, M. Guttman, E.S. Lander, G. Getz, et al., Integrative genomics viewer, Nat. Biotechnol. 29 (1) (2011) 24e26.
