- Software review
- Open Access
Databases and resources for human small non-coding RNAs
© Henry Stewart Publications 2011
Received: 21 November 2010
Accepted: 21 November 2010
Published: 1 March 2011
Recent advances in high-throughput sequencing have facilitated the genome-wide studies of small non-coding RNAs (sRNAs). Numerous studies have highlighted the role of various classes of sRNAs at different levels of gene regulation and disease. The fast growth of sequence data and the diversity of sRNA species have prompted the need to organise them in annotation databases. There are currently several databases that collect sRNA data. Various tools are provided for access, with special emphasis on the well-characterised family of micro-RNAs. The striking heterogeneity of the new classes of sRNAs and the lack of sufficient functional annotation, however, make integration of these datasets a difficult task. This review describes the currently available databases for human sRNAs that are accessible via the internet, and some of the large datasets for human sRNAs from high-throughput sequencing experiments that are so far only available as supplementary data in publications. Some of the main issues related to the integration and annotation of sRNA datasets are also discussed.
In 2001, three groups published independent reports on the discovery of a new class of small non-coding RNAs (sRNAs), which were named micro-RNAs (miRNAs) [1–3]. These comprise a large family of small, ~22 nucleotide-long, non-coding RNAs that have emerged as key players in post-transcriptional gene regulation . Subsequent years have witnessed the discovery of many new types of sRNAs. In humans, apart from the hundreds of miRNAs detected so far, there are also many endogenous small interfering RNAs (endo-siRNAs) and piwi-interacting RNAs (piRNAs)[6, 7]. These and other short non-coding RNA molecules collectively are called 'sRNAs'. They are generally short (~18-30 nucleotides [nt]); do not code for proteins; exert their function as RNA molecules generally combined with protein factors; and represent a substantial portion of the RNA output of cells. Moreover, sRNAs encompass a diverse, widespread and basal regulatory system: they are known to regulate genes and genomes at different levels, including chromatin structure, transcription, RNA stability and translation [8–10]. Furthermore, they can act as activators or inhibitors and their disruption has been linked to disease . The explosion of information on sRNAs makes necessary its organisation--in terms of their biogenesis, expression properties and functional characteristics--into public databases.
Sequence, genomic location and
predicted targets of published
miRNAs with links to references
expression profiles and predicted
miRNAs and isomiRs
Reads and isomiRs assigned to
miRNAs from human 293T cells,
with miRNA annotation from
Experimentally verified and
predicted siRNAs. Sequence
information and links to the
Sequence information, clusters and
homology searches for piRNAs
Sequence, expression information
and predicted targets with
miRNAs, other structural
Sequence families of structural
RNAs. Families represented by a
multiple sequence alignment and a
Sequence information with links to
GenBank and functional information
piRNAs, other ncRNAs
Sequence information with links to
literature and other databases
Sequences and clusters for sRNAs
from different tissues and for
computationally predicted sRNAs
ncRNAs from various
sources (see text)
Annotation of known and predicted
non-coding RNAs of various
lengths with visualisation in
Human sRNA datasets from deep sequencing
20-90nt sRNAs, localised within -250 to +50 of TSSs.
Similar to PASRs. Dataset included in deepBase
Promoter-associated small RNAs. 20-200 nt long, with 5'
ends coinciding with the TSSs
18 nt length sRNAs, localised downstream of TSS
Nuclear sRNAs, enriched at splice sites
Termini-associated sRNAs from the antisense strand
sRNA sequences from libraries from different human
organ systems and cell types. Included in MirZ
miRNAs from HeLa cells. Dataset included in deepBase
miRNAs expressed in human leucocytes
sRNAs associated with AGO1 and AGO2, derived from
snoRNAs and with miRNA-like functions. Dataset included
Derived from tRNAs, in competition with miRNAs
Derived from snoRNAs, with miRNA-like functions
Although it is generally assumed that a single precursor miRNA molecule leads to a single functional miRNA, there is evidence that precursors can be processed with heterogeneous ends, giving rise to isomiRs . A recently published database collects isomiR sequences from high-throughput sequencing of miRNAs from human 293T cells  (Table 1). This database allows one to retrieve all reads and isomiRs assigned to a specific miRNA, with results linked to miRBase and Ensembl .
The endo-siRNAs, which were first observed in plants, are also a very abundant class of sRNAs . They share some properties regarding biogenesis and function with miRNAs [36, 37]. More recently, other sources of endo-siRNAs have been identified, such as convergent mRNA transcripts and sense-antisense pairs . To date, there is only one database specific for siRNAs, siRNAdb, which contains the collections of endogenous and exogenous siRNA molecules from the literature that have been experimentally verified. Moreover, siRNAdb also includes predicted siRNAs based on a combination of computational prediction methods [39–41]. Additionally, the database includes information about targets and experimental sources. A set of target predictions is also available for the non-experimentally verified siRNAs.
In 2006, a new abundant sRNA species of approximately 30 nt was observed in extracts of total RNA from mouse testes . These somewhat larger types of sRNA, called piRNAs, were found to exert their function most clearly in the germline  and possibly in cancer cell lines [44, 45]. In contrast to miRNAs and endo-siRNAs, they derive from single-stranded precursors . The only database exclusively dedicated to piRNAs is piRNAbank, which contains piRNA sequences collected from Genbank  and published data, indexed through unique identifiers linked to NCBI and with additional information on gene name and genomic position.
Small nucleolar RNAs (snoRNAs) are longer types of non-coding RNA (ncRNA; 60-300 nt in length), but are also regarded as sRNAs. These are a highly evolutionarily conserved class of RNAs which function mostly in the nucleolus and participate in the chemical modification of other RNAs, mainly ribosomal RNAs (rRNAs) . The snoRNA-LBME-db database  was created to collect the available information on human snoRNAs. Besides sequence information, it also includes predicted RNA targets and potential base-pairing interactions with these targets. The data have been collected from the literature and can be accessed by the name of the snoRNA or can be downloaded in their entirety. This database includes the Cajal body-specific RNAs (scaRNAs). This is a class of nuclear sRNAs similar to snoRNAs which accumulate specifically in Cajal bodies and guide modifications of small nuclear RNAs (snRNAs). The latter are also considered to be sRNAs and are part of the spliceosome . All of these nuclear sRNAs and other structural RNAs are present in the Rfam database (Table 1), which is a general collection of structured RNA families  represented by their sequences and a structural model. Rfam also includes models for miRNAs.
Recent advances in deep-sequencing technologies have yielded a large number of short RNA sequences,[16, 37, 52, 53] leading to an exhaustive detection of both known and novel sRNAs. In order to facilitate access to deep-sequencing data, the Short Read Archive (SRA) of NCBI  and the European Nucleotide Archive  (ENA) http://www.ebi.ac.uk/ena/ have been created. They provide centralised access to published sequencing data, including sRNAs, and, more notably, all the data are accessible through powerful search tools. Many of these sRNAs, however, still await classification. They vary significantly in origin and structure and their function is often unknown, which makes it difficult to develop databases for their storage and analysis.
A growing number of public databases are now available for sRNAs obtained from deep-sequencing experiments that classify them in terms of the experimental origin, function (if known) and genomic localisation. These databases try to integrate novel heterogeneous data and sometimes include curated data. The NONCODE database  (Table 1) provides organised information for snoRNAs, piRNAs, miRNAs, scaRNAs and other ncRNA classes present in GenBank, classified according to cellular process. Similarly, RNAdb  (Table 1) provides access to data from snoRNA-LBME-db, miRBase, the FANTOM project  and the H-invitational project . All the datasets are divided according to the main sRNA classes. Additionally, RNAdb contains predictions based on comparative methods. All RNAdb data are available for downloading.
The deepBase  database (Table 1) contains the most heterogeneous collection of sRNAs from deep-sequencing data from different libraries (Table 2) [19, 25, 27]. The deep-sequencing data have been mapped to the human genome assembly and annotated according to various sRNA classes, which are defined by the location of the mapped sRNAs: non-coding RNA-associated small RNAs (nasRNAs); sRNAs that overlap promoter regions (pasRNAs), which overlap with the transcription start site (TSS) of genes; exon-associated small RNAs (easRNAs), which overlap with exons from RefSeq  genes; and repeat-associated small RNAs (rasRNAs), which overlap with repeat elements from the University of California, Santa Cruz (UCSC) genome browser . Additionally, deepBase includes: all known miRNAs from miRBase and snoRNAs from snoRNA-LBME; novel predicted miRNAs and snoRNAs; and RNA clusters built from the sRNAs that are proximal in the genomic sequence.
Finally, fRNAdb  (Table 1) is a searchable database, with functional and genomic annotation for ncRNAs of various lengths from snoRNA-LBME-db, miRBase, the FANTOM and the H-invitational projects, NONCODE and RNAdb. At the same URL, the authors provide visualisation of the annotated and predicted ncRNAs with a mirror of the UCSC browser.
The sRNA world is expanding: it includes many new sRNA sequences that share many features but also differ in others, which makes it difficult to integrate them into the same resource. Despite the current wealth of public databases, there are many recently discovered sRNA classes that are not present in any specific or general database. Below, we enumerate some of the datasets that we cannot find in the previously mentioned databases, and which are only available directly from the publications.
Deep sequencing has led to the detection of at least three new classes of sRNAs linked to the region proximal to the promoter and TSS of genes: promoter-associated sRNAs, which are hypothesised to result from the transcription of independent capped short transcripts or as cleavage products of longer RNAs and which were specifically called 'PASRs'; TSS-associated RNAs (TSSa RNAs), included in deepBase and similar to PASRs (Table 2); and transcription initiation RNAs (tiRNAs) (Table 2), which are predominantly 18 nt in length and originate mostly from the region downstream of the TSS, possibly from the backtracking of RNA polymerase II (RNAPII) at the start of transcription . Interestingly, a new class of nuclear sRNAs, also of about 18 nt in length, recently has been found to be associated with the 5' splice sites of genes (spliRNAs) (Table 2). PASRs, TSSaRNAs and tiRNAs datasets are available at NCBI GEO, and spliRNAs at the FANTOM web page . A different class of sRNAs, also with positional biases, are the termini-associated short RNAs (TASRs) (Table 2) and their antisense counterparts (aTASRs), obtained via a change of the sequencing protocol. These are found to be located antisense of the 3'-untranslated regions of genes. They are only available from the SRA at NCBI (Table 2).
Interestingly, some authors have proposed that some miRNAs and other sRNAs could stem from the processing of snoRNAs by a still unknown mechanism [66–68]. Moreover, there is also some evidence from deep-sequencing experiments that other sRNAs also could originate from transfer RNAs (tRNAs) [28–30]. The sRNAs derived from snoRNAs [31, 68] and tRNAs [28–30] (see Table 2) are not available in any of the databases mentioned above.
Additionally, there are datasets of sRNAs expressed in specific cell lines, like novel miRNAs obtained from human leucocytes  (Table 2), which have not yet been integrated into any database. These examples of new classes of sRNAs display diverse positional and length biases and their function is mostly uncharacterised. They are therefore difficult to integrate with each other and within the existing sRNA databases.
The sRNA data currently available--especially those obtained from deep-sequencing experiments--are very heterogeneous. Ideally, these sRNA datasets should be compiled and integrated together, thereby facilitating functional and computational downstream analyses. One of the main problems with the integration of sRNA data is the lack of functional information. While miRNAs and siRNAs are well characterised in terms of how they interact with their targets,[10, 36, 69] nothing is known so far about the mode of action of many of the new classes of sRNAs. This is further complicated by the fact that sRNAs may have multiple functions. For instance, exogenous siRNAs may affect gene expression  and also splicing  by inducing chromatin changes. Likewise, miRNAs can also affect gene expression through a similar pathway affecting chromatin, and other sRNAs originating from sense-antisense transcription can also trigger similar mechanisms .
For many of the new sRNA classes, then, we still lack a function and a target definition. Interestingly, the Argonaute (AGO) family of proteins is known to be essential for the function of miRNAs, piRNAs and endo-siRNAs . It may be possible, therefore, that some of the new classes of sRNAs exert their function through one or more members of the AGO family as well, and may interact with DNA or RNA in a similar way. Further evidence on the proteins that interact with the new classes of sRNAs will help in defining their function and possible targets.
Another important issue for the classification of sRNAs is the characterisation of their biogenesis. Interestingly, some of the new sRNAs have similar positional biases. The sRNAs localised near the TSS of genes have been linked to transcription activity . Likewise, TSSa RNAs and PASRs have been associated with transcription and processing of other RNAs . These similarities may be indicative of a common biogenesis mechanism. Furthermore, many snRNAs are found to be independent of the processing machinery responsible for the biogenesis of miRNAs . Thus, there must be other factors involved in the generation of these sRNAs. Recent experiments have shown that there is considerable endonucleolytic activity associated with still undetermined proteins . These proteins may be the key factors responsible for the biogenesis of some of the novel classes of sRNAs.
In summary, high-throughput methods--and especially deep-sequencing technologies--provide a unique opportunity to explore the wealth of RNA species in diverse cellular contexts. Different types of RNA have been characterised using these technologies, giving rise to new sRNA categories. Further experiments will be necessary to determine the function and biogenesis of these new molecules. Considering the large amount of data that are being generated, we are just touching the tip of the iceberg regarding the sRNA world. The coming years will witness new and exciting developments in molecular and computational biology in the area of sRNAs.
- Lagos-Quintana M, Rauhut R, Lendeckel W, Tuschl T: Identification of novel genes coding for small expressed RNAs. Science. 2001, 294: 853-858. 10.1126/science.1064921.View ArticlePubMedGoogle Scholar
- Lau NC, Lim LP, Weinstein EG, Bartel DP: An abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegans. Science. 2001, 294: 858-862. 10.1126/science.1065062.View ArticlePubMedGoogle Scholar
- Lee RC, Ambros V: An extensive class of small RNAs in Caenorhabditis elegans. Science. 2001, 294: 862-864. 10.1126/science.1065329.View ArticlePubMedGoogle Scholar
- Fabian MR, Sonenberg N, Filipowicz W: Regulation of mRNA translation and stability by microRNAs. Annu Rev Biochem. 2010, 79: 351-379. 10.1146/annurev-biochem-060308-103103.View ArticlePubMedGoogle Scholar
- Okamura K, Balla S, Martin R, Liu N, et al: Two distinct mechanisms generate endogenous siRNAs from bidirectional transcription in Drosophila melanogaster. Nat Struct Mol Biol. 2008, 15: 581-590. 10.1038/nsmb.1438.PubMed CentralView ArticlePubMedGoogle Scholar
- Aravin A, Gaidatzis D, Pfeffer S, Lagos-Quintana M, et al: A novel class of small RNAs bind to MILI protein in mouse testes. Nature. 2006, 442: 203-207.PubMedGoogle Scholar
- Girard A, Sachidanandam R, Hannon GJ, Carmell MA: A germline-specific class of small RNAs binds mammalian Piwi proteins. Nature. 2006, 442: 199-202.PubMedGoogle Scholar
- Van Wolfswinkel JC, Ketting RF: The role of small non-coding RNAs in genome stability and chromatin organization. J Cell Sci. 2010, 123: 1825-1839. 10.1242/jcs.061713.View ArticlePubMedGoogle Scholar
- Bühler M, Moazed D: Transcription and RNAi in heterochromatic gene silencing. Nat Struct Mol Biol. 2007, 14: 1041-1048. 10.1038/nsmb1315.View ArticlePubMedGoogle Scholar
- Carthew RW, Sontheimer EJ: Origins and mechanisms of miRNAs and siRNAs. Cell. 2009, 136: 642-655. 10.1016/j.cell.2009.01.035.PubMed CentralView ArticlePubMedGoogle Scholar
- Taft RJ, Pang KC, Mercer TR, Dinger M, et al: Non-coding RNAs: Regulators of disease. J Pathol. 2010, 220: 126-139. 10.1002/path.2638.View ArticlePubMedGoogle Scholar
- Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, et al: GenBank: Update. Nucleic Acids Res. 2004, 32: D23-D26. 10.1093/nar/gkh045.PubMed CentralView ArticlePubMedGoogle Scholar
- Stoesser G, Baker W, van der Broek A, Camon E, et al: The EMBL Nucleotide Sequence Database. Nucleic Acids Res. 2002, 30: 21-26. 10.1093/nar/30.1.21.PubMed CentralView ArticlePubMedGoogle Scholar
- Kaminuma E, Kosuge T, Kodama Y, Aono H, et al: DDBJ progress report. Nucleic Acids Res. 2010, D22-D27. 39 DatabaseGoogle Scholar
- Barret T, Troup DB, Wilhite SE, Ledoux P, et al: NCBI GEO: Archive for high-throughput functional genomic data. Nucleic Acids Res. 2009, 37: D885-D890. 10.1093/nar/gkn764.View ArticleGoogle Scholar
- Griffiths-Jones S: miRbase: MicroRNA sequences and annotation. Curr Protoc Bioinformatics. 2010, Chapter 12: Unit 12.9.1-12.9.10, Wiley online at: http://onlinelibrary.wiley.com/doi/10.1002/0471250953. bil209s29/abstract;jsessionid = 8A10CDA0953FDEE1C0A384F3FF7AC1A6. d03t02Google Scholar
- Hausser J, Berninger P, Rodak C, Jantscher Y, et al: MirZ: An integrated microRNA expression atlas and target prediction resource. Nucleic Acids Res. 2009, 37: 266-272. 10.1093/nar/gkp412.View ArticleGoogle Scholar
- Landgraf P, Rusu M, Sheridan R, Sewer A, et al: A mammalian microRNA expression atlas based on small RNA library sequencing. Cell. 2007, 129: 1401-1414. 10.1016/j.cell.2007.04.040.PubMed CentralView ArticlePubMedGoogle Scholar
- Seila AC, Calabrese JM, Levine SS, Yeo GW, et al: Divergent transcription from active promoters. Science. 2008, 322: 1849-1851. 10.1126/science.1162253.PubMed CentralView ArticlePubMedGoogle Scholar
- Fejes-Toth K, Sotirova V, Sachidanandam R, Assaf G, et al: Affymetrix ENCODE Transcriptome Project: Cold Spring Harbor Laboratory ENCODE Transcriptome Project. Post-transcriptional processing generates a diversity of 5'-modified long and short RNAs. Nature. 2009, 457: 1028-1032. 10.1038/nature07759.PubMed CentralView ArticleGoogle Scholar
- Taft RJ, Glazov EA, Cloonan N, Simons C, et al: Tiny RNAs associated with transcription start sites in animals. Nat Genet. 2009, 41: 572-578. 10.1038/ng.312.View ArticlePubMedGoogle Scholar
- Taft RJ, Simons C, Nahkuri S, Oey H, et al: Nuclear-localized tiny RNAs are associated with transcription initiation and splice sites in metazoans. Nat Struct Mol Biol. 2010, 17: 1030-1034. 10.1038/nsmb.1841.View ArticlePubMedGoogle Scholar
- Kapranov P, Cheng J, Dike S, Nix DA, et al: RNA maps reveal new RNA classes and a possible function for pervasive transcription. Science. 2007, 316: 1484-1488. 10.1126/science.1138341.View ArticlePubMedGoogle Scholar
- Kapranov P, Ozsolak F, Kim SW, Foissac S, et al: New class of gene-termini-associated human RNAs suggests a novel RNA copying mechanism. Nature. 2010, 466: 642-646. 10.1038/nature09190.PubMed CentralView ArticlePubMedGoogle Scholar
- Friedländer MR, Chen W, Adamidi C, Maaskola J, et al: Discovering microRNAs from deep sequencing data using miRDeep. Nat Biotechnol. 2008, 26: 407-415. 10.1038/nbt1394.View ArticlePubMedGoogle Scholar
- Vaz C, Ahmad HM, Sharma P, Gupta R, et al: Analysis of microRNA transcriptome by deep sequencing of small RNA libraries of peripheral blood. BMC Genomics. 2010, 11: 288-10.1186/1471-2164-11-288.PubMed CentralView ArticlePubMedGoogle Scholar
- Ender C, Krek A, Friedländer MR, Beitzinger M, et al: A human snoRNA with microRNA-like functions. Mol Cell. 2008, 32: 519-528. 10.1016/j.molcel.2008.10.017.View ArticlePubMedGoogle Scholar
- Lee YS, Shibata Y, Malhotra A, Dutta A: A novel class of small RNAs: tRNA-derived RNA fragments (tRFs). Genes Dev. 2009, 23: 2639-2649. 10.1101/gad.1837609.PubMed CentralView ArticlePubMedGoogle Scholar
- Cole C, Sobala A, Lu C, Thatcher SR, et al: Filtering of deep sequencing data reveals the existence of abundant Dicer-dependet small RNAs derived from tRNAs. RNA. 2009, 15: 2147-2160. 10.1261/rna.1738409.PubMed CentralView ArticlePubMedGoogle Scholar
- Haussecker D, Huang Y, Lau A, Parameswaran P, et al: Human tRNA-derived small RNAs in the global regulation of RNA silencing. RNA. 2010, 16: 673-695. 10.1261/rna.2000810.PubMed CentralView ArticlePubMedGoogle Scholar
- Brameier M, Herwig A, Reinhardt R, Walter L, et al: Human box C/D snoRNAs with miRNA like functions: Expanding the range of regulatory RNAs. Nucleic Acids Res. 2010, 39: D675-D686.View ArticleGoogle Scholar
- Gaidatzis D, van Nimwegen E, Hausser J, Zavolan M: Inference of miRNA targets using evolutionary conservation and pathway analysis. BMC Bioinformatics. 2007, 8: 69-10.1186/1471-2105-8-69.PubMed CentralView ArticlePubMedGoogle Scholar
- Morin RD, O'Connor MD, Griffith M, Kuchenbauer F, et al: Application of massively parallel sequencing to microRNA pro-filing and discovery in human embryonic stem cells. Genome Res. 2008, 18: 610-621. 10.1101/gr.7179508.PubMed CentralView ArticlePubMedGoogle Scholar
- Lee LW, Zhang S, Etheridge A, Ma L, et al: Complexity of the microRNA repertoire revealed by next generation sequencing. RNA. 2010, 16: 2170-2180. 10.1261/rna.2225110.PubMed CentralView ArticlePubMedGoogle Scholar
- Hubbard TJ, Aken BL, Ayling S, Ballester B, et al: Ensembl 2009. Nucleic Acids Res. 2009, 37: D690-D697. 10.1093/nar/gkn828.PubMed CentralView ArticlePubMedGoogle Scholar
- Bartel DP: MicroRNAs: Target recognition and regulatory functions. Cell. 2009, 136: 215-230. 10.1016/j.cell.2009.01.002.PubMed CentralView ArticlePubMedGoogle Scholar
- Mattick JS, Makunin IV: Small regulatory RNAs in mammals. Hum Mol Genet. 2005, 1: R121-R132.View ArticleGoogle Scholar
- Chalk AM, Wahlestedt C, Sonnhammer EL: Improved and automated prediction of effective siRNA. Biochem Biophys Res Commun. 2004, 319: 264-274. 10.1016/j.bbrc.2004.04.181.View ArticlePubMedGoogle Scholar
- Chalk AM, Warfinge RE, Georgii-Hemming P, Sonnhammer EL: SiRNAdb: A database of siRNA sequences. Nucleic Acids Res. 2005, 33: D131-D134. 10.1093/nar/gni130.PubMed CentralView ArticlePubMedGoogle Scholar
- Reynolds A, Leake D, Boese Q, Scaringe S, et al: Rational siRNA design for RNA interference. Nat Biotechnol. 2004, 22: 326-330. 10.1038/nbt936.View ArticlePubMedGoogle Scholar
- Ui-Tei K, Naito Y, Takahashi F, Haraguchi T, et al: Guidelines for the selection of highly effective siRNA sequence for mammalian and chick RNA interference. Nucleic Acids Res. 2004, 32: 936-948. 10.1093/nar/gkh247.PubMed CentralView ArticlePubMedGoogle Scholar
- Grivna ST, Beyret E, Wang Z, Lin H: A novel class of small RNAs in mouse spermatogenic cells. Genes Dev. 2006, 20: 1709-1714. 10.1101/gad.1434406.PubMed CentralView ArticlePubMedGoogle Scholar
- Thomson T, Lin H: The biogenesis and function of PIWI proteins and piRNAs: Progress and prospect. Annu Rev Cell Dev Biol. 2009, 25: 355-376. 10.1146/annurev.cellbio.24.110707.175327.PubMed CentralView ArticlePubMedGoogle Scholar
- Lee HJ, Schütte D, Wulf G, Füzesi L, et al: Stem-cell protein PIWIL2 is widely expressed in tumors and inhibits apoptosis through activation of Stat3/Bcl-XL pathway. Hum Mol Genet. 2006, 15: 201-211.View ArticlePubMedGoogle Scholar
- Lu Y, Li C, Zhang K, Sun H, et al: Identification of piRNAs in Hela cells by massive parallel sequencing. BMB Reports. 2010, 43: 635-641. 10.5483/BMBRep.2010.43.9.635.View ArticlePubMedGoogle Scholar
- Lakshmi SS, Agrawal S: piRNABank: A web resource on classified and clustered Piwi-interacting RNAs. Nucleic Acids Res. 2008, 36: D173-D177.View ArticleGoogle Scholar
- Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, et al: GenBank. 2009. Nucleic Acids Res. 2009, 37: D26-D31. 10.1093/nar/gkn723.PubMed CentralView ArticlePubMedGoogle Scholar
- Matera AG, Terns RM, Terns MP: Non-coding RNAs: Lessons from the small nuclear and small nucleolar RNAs. Nat Rev Mol Cell Biol. 2007, 8: 209-220. 10.1038/nrm2124.View ArticlePubMedGoogle Scholar
- Lestrade L, Weber MJ: SnoRNA-LBME-db, a compren-hensive database of human H/ACA and C/D box snoRNAs. Nucleic Acids Res. 2006, 34: D158-D162. 10.1093/nar/gkj002.PubMed CentralView ArticlePubMedGoogle Scholar
- Darzacq X, Jády BE, Verheggen C, Kiss AM, et al: Cajal body-specific small nuclear RNAs: A novel class of 2'-O-methylation and pseudoridylation guide RNAs. EMBO J. 2002, 21: 2746-2756. 10.1093/emboj/21.11.2746.PubMed CentralView ArticlePubMedGoogle Scholar
- Gardner PP, Daub J, Tate JG, Nawrocki EP, et al: Rfam: Updates to the RNA families database. Nucleic Acids Res. 2009, 37: D136-D140. 10.1093/nar/gkn766.PubMed CentralView ArticlePubMedGoogle Scholar
- Malone C, Hannon G: Small RNAs as guardians of the genome. Cell. 2009, 136: 656-668. 10.1016/j.cell.2009.01.045.PubMed CentralView ArticlePubMedGoogle Scholar
- Ghildiyal M, Zamore PD: Small silencing RNAs: An expanding universe. Nat Rev Genet. 2009, 10: 94-108. 10.1038/nrg2504.PubMed CentralView ArticlePubMedGoogle Scholar
- Leinonen R, Sugawara H, Shumway M, on behalf of the International Nucleotide Sequence Database Collaboration: The sequence read archive. Nucleic Acids Res. 2010, D19-D21. 39 DatabaseGoogle Scholar
- Leinonen R, Akhtar R, Birney E, Bower L, et al: The European Nucleotide Archive. Nucleic Acids Res. 2010, D28-D31. 39 DatabaseGoogle Scholar
- He S, Liu C, Skogerbø G, Zhao H, et al: NONCODE v2.0: Decoding the non-coding. Nucleic Acids Res. 2008, 36: D170-D172. 10.1093/nar/gkn294.PubMed CentralView ArticlePubMedGoogle Scholar
- Pang KC, Stephen S, Dinger ME, Engström PG, et al: RNAdb 2.0: An expanded database of mammalian non-coding RNAs. Nucleic Acids Res. 2007, 35: D178-D182. 10.1093/nar/gkl926.PubMed CentralView ArticlePubMedGoogle Scholar
- Ajioka I, Maeda T, Nakajima K: Large-scale correlation of DNA accession numbers to the cDNAs in the FANTOM full-length mouse cDNA clone set. Keio J Med. 2006, 55: 107-110. 10.2302/kjm.55.107.View ArticlePubMedGoogle Scholar
- Yamasaki C, Murakami K, Takeda J, Sato Y, et al: H-InvDB in 2009: Extended database and data mining resources for human genes and transcripts. Nucleic Acids Res. 2010, 38: D626-D632. 10.1093/nar/gkp1020.PubMed CentralView ArticlePubMedGoogle Scholar
- Yang J-H, Shao P, Zhou H, Chen Y-Q, et al: deepBase: A database for deeply annotating and mining deep sequencing data. Nucleic Acids Res. 2009, 38: D123-D130.PubMed CentralView ArticlePubMedGoogle Scholar
- Pruitt KD, Tatusova T, Klimke W, Maglott DR: NCBI Reference Sequences: Current status, policy and new initiatives. Nucleic Acids Res. 2009, 37: D32-D36. 10.1093/nar/gkn721.PubMed CentralView ArticlePubMedGoogle Scholar
- Rhead B, Karolchik D, Kuhn RM, Hinrichs AS, et al: The UCSC Genome Browser database: Update 2010. Nucleic Acids Res. 2010, 38: D613-619. 10.1093/nar/gkp939.PubMed CentralView ArticlePubMedGoogle Scholar
- Mituyama T, Yamada K, Hattori E, Okida H, et al: The functional RNA Database 3.0: Databases to support mining and annotation of functional RNAs. Nucleic Acids Res. 2009, 37: D89-D92. 10.1093/nar/gkn805.PubMed CentralView ArticlePubMedGoogle Scholar
- Taft RJ, Kaplan CD, Simons C, Mattick JS: Evolution, biogenesis and function of promoter-associated RNAs. Cell Cycle. 2009, 8: 2332-2338. 10.4161/cc.8.15.9154.View ArticlePubMedGoogle Scholar
- Kawaji H, Severin J, Lizio M, Waterhouse A, et al: The FANTOM web resource: From mammalian transcriptional landscape to its dynamic regulation. Genome Biol. 2009, 10: R40-10.1186/gb-2009-10-4-r40.PubMed CentralView ArticlePubMedGoogle Scholar
- Kishore S, Khanna A, Zhang Z, Hui J, et al: The snoRNA MBII-52 (SNORD 115) is processed into smaller RNAs and regulates alternative splicing. Hum Mol Genet. 2010, 19: 1153-1164. 10.1093/hmg/ddp585.PubMed CentralView ArticlePubMedGoogle Scholar
- Khanna A, Stamm S: Regulation of alternative splicing by short non-coding nuclear RNAs. RNA Biol. 2010, 7: 4-View ArticleGoogle Scholar
- Taft RJ, Glazov EA, Lassmann T, Hayashizaki Y, et al: Small RNAs derived from snoRNAs. RNA. 2009, 15: 1233-1240. 10.1261/rna.1528909.PubMed CentralView ArticlePubMedGoogle Scholar
- Shin C, Nam J-W, Farh KK-H, Chiang HR, et al: Expanding the microRNA targeting code: Functional sites with centered pairing. Mol Cell. 2010, 38: 789-802. 10.1016/j.molcel.2010.06.005.PubMed CentralView ArticlePubMedGoogle Scholar
- Morris KV, Chan SW, Jacobsen SE, Looney DJ: Small interfering RNA-induced transcriptional gene silencing in human cells. Science. 2004, 305: 1289-1292. 10.1126/science.1101372.View ArticlePubMedGoogle Scholar
- Alló M, Buggiano V, Fededa JP, Petrillo E, et al: Control of alternative splicing through siRNA-mediated transcriptional gene silencing. Nat Struct Mol Biol. 2009, 16: 717-724. 10.1038/nsmb.1620.View ArticlePubMedGoogle Scholar
- Kim DH, Saetrom P, Snove O, Rossi JJ: MicroRNA-directed transcriptional gene silencing in mammalian cells. Proc Natl Acad Sci USA. 2008, 105: 16230-16235. 10.1073/pnas.0808830105.PubMed CentralView ArticlePubMedGoogle Scholar
- Yu W, Gius D, Onyango P, Muldoon-Jacobs K, et al: Epigenetic silencing of tumour suppressor gene p15 by its antisense RNA. Nature. 2008, 451: 202-206. 10.1038/nature06468.PubMed CentralView ArticlePubMedGoogle Scholar
- Kim VN, Han J, Siomi MC: Biogenesis of small RNAs in animals. Nature. 2009, 12: 126-139.Google Scholar
- Babiarz JE, Ruby JG, Wang Y, Bartel DP, et al: Mouse ES cells express endogenous shRNAs, siRNAs, and other microprocessor-independent, Dicer-dependent small RNAs. Genes Dev. 2008, 22: 2773-2785. 10.1101/gad.1705308.PubMed CentralView ArticlePubMedGoogle Scholar
- Karginov FV, Cheloufi S, Chong MM, Stark A, et al: Diverse endonucleolytic cleavage sites in the mammalian transcriptome depend upon microRNAs, Drosha, and additional nucleases. Mol Cell. 2010, 38: 781-788. 10.1016/j.molcel.2010.06.001.PubMed CentralView ArticlePubMedGoogle Scholar