RNA22

Last updated April 18, 2020

Rna22 is a pattern-based algorithm for the discovery of microRNA target sites and the corresponding heteroduplexes.^[1]

The algorithm is conceptually distinct from other methods for predicting microRNA:mRNA heteroduplexes in that it does not use experimentally validated heteroduplexes for training, instead relying only on the sequences of known mature miRNAs that are found in the public databases. The key idea of rna22 is that the reverse complement of any salient sequence features that one can identify in mature microRNA sequences (using pattern discovery techniques) should allow one to identify candidate microRNA target sites in a sequence of interest: rna22 makes use of the Teiresias algorithm to discover such salient features. Once a candidate microRNA target site has been located, the targeting microRNA can be identified with the help of any of several algorithms able to compute RNA:RNA heteroduplexes. A new version (v2.0) of the algorithm is now available: v2.0-beta adds probability estimates to each prediction, gives users the ability to choose the sensitivity/specificity settings on-the-fly, is significantly faster than the original, and can be accessed through http://cm.jefferson.edu/rna22/Interactive/.

Rna22 neither relies on nor imposes any cross-organism conservation constraints to filter out unlikely candidates; this gives it the ability to discover microRNA binding sites that may not be conserved in phylogenetically proximal organisms. Also, as mentioned above, rna22 can identify putative microRNA binding sites without needing to know the identity of the targeting microRNA. A notable property of rna22 is that it does not require the presence of the exact reverse complement of a microRNA's seed in a putative target permitting bulges and G:U wobbles in the seed region of the heteroduplex. Lastly, the algorithm has been shown to achieve high signal-to-noise ratio.^[2]

Use of rna22 led to the discovery of "non-canonical" microRNA targets in the coding regions of the mouse Nanog, Oct4 and Sox2.^[3] Most of these targets are not conserved in the human orthologues of these three transcription factors even though they reside in the coding region of the corresponding mRNAs. Moreover, most of these targets contain G:U wobbles, one or more bulges, or both, in the seed region of the heteroduplex. In addition to coding regions, rna22 has helped discover non-canonical targets in 3'UTRs.^[4]

A recent study^[5] examined the problem of non-canonical miRNA targets using molecular dynamics simulations of the crystal structure of the Argonaute-miRNA:mRNA ternary complex. The study found that several kinds of modifications, including combinations of multiple G:U wobbles and mismatches in the seed region, are admissible and result in only minor structural fluctuations that do not affect the stability of the ternary complex. The study also showed that the findings of the molecular dynamics simulation are supported by HITS-CLIP (CLIP-seq) data. These results suggest that bona fide miRNA targets transcend the canonical seed-model in turn making target prediction tools like rna22 an ideal choice for exploring the newly augmented spectrum of miRNA targets.

Name	Description	type	Link	References
RNA22 version 2.0	The first web-site link (interactive & dynamic) permits the user to find on-the-fly putative miRNA binding sites for any sequence of interest (i.e. a protein-coding mRNA, or long non-coding RNA) and for any miRNA (publicly known or novel). The second link^[6] (precomputed & static) provides access to RNA22 v2 predictions for all protein coding transcripts in human, mouse, roundworm, and fruit fly. It allows the user to visualize the predictions within a cDNA map and also find transcripts where multiple miRNA's of interest target.	microRNA target predictions	interactive predictions precomputed predictions	TBD
RNA22	The link^[6] (precomputed & static) provides access to RNA22 predictions for all protein coding transcripts in human, mouse, roundworm, and fruit fly. It allows you to visualize the predictions within a cDNA map and also find transcripts where multiple miRNA's of interest target.	microRNA target predictions	precomputed predictions	^[1]

Related Research Articles

Messenger RNA RNA that is read by the ribosome to produce a protein

Messenger RNA (mRNA) is a single-stranded RNA molecule that corresponds to the genetic sequence of a gene and is read by the ribosome in the process of producing a protein. mRNA is created during the process of transcription, where the enzyme RNA polymerase converts genes into primary transcript mRNA. This pre-mRNA usually still contains introns, regions that will not go on to code for the final amino acid sequence. These are removed in the process of RNA splicing, leaving only exons, regions that will encode the protein. This exon sequence constitutes mature mRNA. Mature mRNA is then read by the ribosome, and, utilising amino acids carried by transfer RNA (tRNA), the ribosome creates the protein. This process is known as translation. All of these processes form part of the central dogma of molecular biology, which describes the flow of genetic information in a biological system.

microRNA Small non-coding ribonucleic acid molecule

A microRNA is a small non-coding RNA molecule found in plants, animals and some viruses, that functions in RNA silencing and post-transcriptional regulation of gene expression. miRNAs function via base-pairing with complementary sequences within mRNA molecules. As a result, these mRNA molecules are silenced, by one or more of the following processes: (1) Cleavage of the mRNA strand into two pieces, (2) Destabilization of the mRNA through shortening of its poly(A) tail, and (3) Less efficient translation of the mRNA into proteins by ribosomes.

In molecular genetics, the three prime untranslated region (3'-UTR) is the section of messenger RNA (mRNA) that immediately follows the translation termination codon. The 3'-UTR often contains regulatory regions that post-transcriptionally influence gene expression.

A transfer RNA is an adaptor molecule composed of RNA, typically 76 to 90 nucleotides in length, that serves as the physical link between the mRNA and the amino acid sequence of proteins. tRNA does this by carrying an amino acid to the protein synthetic machinery of a cell (ribosome) as directed by a 3-nucleotide sequence (codon) in a messenger RNA (mRNA). As such, tRNAs are a necessary component of translation, the biological synthesis of new proteins in accordance with the genetic code.

Regulation of gene expression, or gene regulation, includes a wide range of mechanisms that are used by cells to increase or decrease the production of specific gene products. Sophisticated programs of gene expression are widely observed in biology, for example to trigger developmental pathways, respond to environmental stimuli, or adapt to new food sources. Virtually any step of gene expression can be modulated, from transcriptional initiation, to RNA processing, and to the post-translational modification of a protein. Often, one gene regulator controls another, and so on, in a gene regulatory network.

In molecular biology lin-4 is a microRNA (miRNA) that was identified from a study of developmental timing in the nematode Caenorhabditis elegans. It was the first to be discovered of the miRNAs, a class of non-coding RNAs involved in gene regulation. miRNAs are transcribed as ~70 nucleotide precursors and subsequently processed by the Dicer enzyme to give a 21 nucleotide product. The extents of the hairpin precursors are not generally known and are estimated based on hairpin prediction. The products are thought to have regulatory roles through complete or partial complementarity to mRNA. The lin-4 gene has been found to lie within a 4.11kb intron of a separate host gene.

The miR-103 microRNA precursor, is a short non-coding RNA gene involved in gene regulation. miR-103 and miR-107 have now been predicted or experimentally confirmed in human.

There are 89 known sequences today in the microRNA 19 (miR-19) family but it will change quickly. They are found in a large number of vertebrate species. The miR-19 microRNA precursor is a small non-coding RNA molecule that regulates gene expression. Within the human and mouse genome there are three copies of this microRNA that are processed from multiple predicted precursor hairpins:

The miR-24 microRNA precursor is a small non-coding RNA molecule that regulates gene expression. microRNAs are transcribed as ~70 nucleotide precursors and subsequently processed by the Dicer enzyme to give a mature ~22 nucleotide product. In this case the mature sequence comes from the 3' arm of the precursor. The mature products are thought to have regulatory roles through complementarity to mRNA. miR-24 is conserved in various species, and is clustered with miR-23 and miR-27, on human chromosome 9 and 19. Recently, miR-24 has been shown to suppress expression of two crucial cell cycle control genes, E2F2 and Myc in hematopoietic differentiation and also to promote keratinocyte differentiation by repressing actin-cytoskeleton regulators PAK4, Tsk5 and ArhGAP19.

The mir-2 microRNA family includes the microRNA genes mir-2 and mir-13. Mir-2 is widespread in invertebrates, and it is the largest family of microRNAs in the model species Drosophila melanogaster. MicroRNAs from this family are produced from the 3' arm of the precursor hairpin. Leaman et al. showed that the miR-2 family regulates cell survival by translational repression of proapoptotic factors. Based on computational prediction of targets, a role in neural development and maintenance has been suggested.

In molecular biology, miR-137 is a short non-coding RNA molecule that functions to regulate the expression levels of other genes by various mechanisms. miR-137 is located on human chromosome 1p22 and has been implicated to act as a tumor suppressor in several cancer types including colorectal cancer, squamous cell carcinoma and melanoma via cell cycle control.

This microRNA database and microRNA targets databases is a compilation of databases and web portals and servers used for microRNAs and their targets. MicroRNAs (miRNAs) represent an important class of small non-coding RNAs (ncRNAs) that regulate gene expression by targeting messenger RNAs.

PAR-CLIP is a biochemical method for identifying the binding sites of cellular RNA-binding proteins (RBPs) and microRNA-containing ribonucleoprotein complexes (miRNPs). The method relies on the incorporation of ribonucleoside analogs that are photoreactive, such as 4-thiouridine (4-SU) and 6-thioguanosine (6-SG), into nascent RNA transcripts by living cells. Irradiation of the cells by ultraviolet light of 365 nm wavelength induces efficient crosslinking of photoreactive nucleoside–labeled cellular RNAs to interacting RBPs. Immunoprecipitation of the RBP of interest is followed by isolation of the crosslinked and coimmunoprecipitated RNA. The isolated RNA is converted into a cDNA library and is deep sequenced using next-generation sequencing technology.

High-throughput sequencing of RNA isolated by crosslinking immunoprecipitation is a genome-wide means of mapping protein–RNA binding sites or RNA modification sites in vivo. HITS-CLIP was originally used to generate genome-wide protein-RNA interaction maps for the neuron-specific RNA-binding protein and splicing factor NOVA1 and NOVA2; since then a number of other splicing factor maps have been generated, including those for PTB, RbFox2, SFRS1, hnRNP C, and even N6-Methyladenosine (m6A) mRNA modifications.

miR-296 is a family of microRNA precursors found in mammals, including humans. The ~22 nucleotide mature miRNA sequence is excised from the precursor hairpin by the enzyme Dicer. This sequence then associates with RISC which effects RNA interference.

isomiRs are miRNA sequences that have variations with respect to the reference sequence. The term was coined by Morin et al in 2008. It has been found that isomiR expression profiles can also exhibit race, population, and gender dependencies.

In molecular biology, competing endogenous RNAs regulate other RNA transcripts by competing for shared microRNAs (miRNAs). Models for ceRNA regulation describe how changes in the expression of one or multiple miRNA targets alter the number of unbound miRNAs and lead to observable changes in miRNA activity - i.e., the abundance of other miRNA targets. Models of ceRNA regulation differ greatly. Some describe the kinetics of target-miRNA-target interactions, where changes in the expression of one target species sequester one miRNA species and lead to changes in the dysregulation of the other target species. Others attempt to model more realistic cellular scenarios, where multiple RNA targets are affecting multiple miRNAs and where each target pair is co-regulated by multiple miRNA species. Some models focus on mRNA 3' UTRs as targets, and others consider long non-coding RNA targets as well. It's evident that our molecular-biochemical understanding of ceRNA regulation remains incomplete.

MicroRNA sequencing (miRNA-seq), a type of RNA-Seq, is the use of next-generation sequencing or massively parallel high-throughput DNA sequencing to sequence microRNAs, also called miRNAs. miRNA-seq differs from other forms of RNA-seq in that input material is often enriched for small RNAs. miRNA-seq allows researchers to examine tissue-specific expression patterns, disease associations, and isoforms of miRNAs, and to discover previously uncharacterized miRNAs. Evidence that dysregulated miRNAs play a role in diseases such as cancer has positioned miRNA-seq to potentially become an important tool in the future for diagnostics and prognostics as costs continue to decrease. Like other miRNA profiling technologies, miRNA-Seq has both advantages and disadvantages.

In bioinformatics, TargetScan is a web server that predicts biological targets of microRNAs (miRNAs) by searching for the presence of sites that match the seed region of each miRNA. For many species, other types of sites, known as 3'-compensatory sites are also identified. These miRNA target predictions are regularly updated and improved by the laboratory of David Bartel in conjunction with the Whitehead Institute Bioinformatics and Research Computing Group.

References

1 2 Miranda KC, Huynh T, Tay Y, Ang YS, Tam WL, Thomson AM, Lim B, Rigoutsos I (2006). "A pattern-based method for the identification of MicroRNA binding sites and their corresponding heteroduplexes". Cell. 126 (6): 1203–17. doi:10.1016/j.cell.2006.07.031. PMID 16990141.
↑ Ritchie W, Flamant S, Rasko JE (2009). "Predicting microRNA targets and functions: traps for the unwary". Nature Methods. 6 (6): 397–8. doi:10.1038/nmeth0609-397. PMID 19478799.
↑ Tay Y, Zhang J, Thomson AM, Lim B, Rigoutsos I (2008). "MicroRNAs to Nanog, Oct4 and Sox2 coding regions modulate embryonic stem cell differentiation". Nature. 455 (7126): 1124–8. doi:10.1038/nature07299. PMID 18806776.
↑ Lal A, Navarro F, Maher CA, Maliszewski LE, Yan N, O'Day E, Chowdhury D, Dykxhoorn DM, Tsai P, Hofmann O, Becker KG, Gorospe M, Hide W, Lieberman J (2009). "miR-24 inhibits cell proliferation by targeting E2F2, MYC, and other cell-cycle genes via binding to "seedless" 3'UTR microRNA recognition elements". Mol Cell. 35 (5): 610–25. doi:10.1016/j.molcel.2009.08.020. PMC 2757794 . PMID 19748357.
↑ Xia Z, Clark P, Huynh T, Loher P, Zhao Y, Chen HW, Rigoutsos I, Zhou R (2012). "Molecular dynamics simulations of Ago silencing complexes reveal a large repertoire of admissible 'seed-less' targets". Scientific Reports. 2: 569. doi:10.1038/srep00569. PMC 3415692 . PMID 22888400.
1 2 Loher P, Rigoutsos I (2012). "Interactive exploration of RNA22 microRNA target predictions". Bioinformatics. 28 (24): 3322–3323. doi: 10.1093/bioinformatics/bts615 . PMID 23074262.

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[pmid16990141-1] 1 2 Miranda KC, Huynh T, Tay Y, Ang YS, Tam WL, Thomson AM, Lim B, Rigoutsos I (2006). "A pattern-based method for the identification of MicroRNA binding sites and their corresponding heteroduplexes". Cell. 126 (6): 1203–17. doi:10.1016/j.cell.2006.07.031. PMID 16990141.

[pmid19478799-2] Ritchie W, Flamant S, Rasko JE (2009). "Predicting microRNA targets and functions: traps for the unwary". Nature Methods. 6 (6): 397–8. doi:10.1038/nmeth0609-397. PMID 19478799.

[pmid18806776-3] Tay Y, Zhang J, Thomson AM, Lim B, Rigoutsos I (2008). "MicroRNAs to Nanog, Oct4 and Sox2 coding regions modulate embryonic stem cell differentiation". Nature. 455 (7126): 1124–8. doi:10.1038/nature07299. PMID 18806776.

[pmid19748357-4] Lal A, Navarro F, Maher CA, Maliszewski LE, Yan N, O'Day E, Chowdhury D, Dykxhoorn DM, Tsai P, Hofmann O, Becker KG, Gorospe M, Hide W, Lieberman J (2009). "miR-24 inhibits cell proliferation by targeting E2F2, MYC, and other cell-cycle genes via binding to "seedless" 3'UTR microRNA recognition elements". Mol Cell. 35 (5): 610–25. doi:10.1016/j.molcel.2009.08.020. PMC 2757794 . PMID 19748357.

[srep00569-5] Xia Z, Clark P, Huynh T, Loher P, Zhao Y, Chen HW, Rigoutsos I, Zhou R (2012). "Molecular dynamics simulations of Ago silencing complexes reveal a large repertoire of admissible 'seed-less' targets". Scientific Reports. 2: 569. doi:10.1038/srep00569. PMC 3415692 . PMID 22888400.

[bts615-6] 1 2 Loher P, Rigoutsos I (2012). "Interactive exploration of RNA22 microRNA target predictions". Bioinformatics. 28 (24): 3322–3323. doi: 10.1093/bioinformatics/bts615 . PMID 23074262.