View source for Bioinformatics discovery of non-coding RNAs

[[Non-coding RNA]]s have been discovered using both experimental and [[bioinformatic]] approaches.  Bioinformatic approaches can be divided into three main categories.  The first involves [[homology search]], although these techniques are by definition unable to find new classes of ncRNAs.  The second category includes [[algorithm]]s designed to discover specific types of ncRNAs that have similar properties.  Finally, some discovery methods are based on very general properties of [[RNA]], and are thus able to discover entirely new kinds of ncRNAs.
==Discovery by homology search==
Homology search refers to the process of searching a [[sequence database]] for RNAs that are similar to already known RNA sequences.  Any algorithm that is designed for homology search of nucleic acid sequences can be used, e.g., [[BLAST (biotechnology)|BLAST]].<ref>{{cite journal |vauthors=Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ |title=Gapped BLAST and PSI-BLAST: a new generation of protein database search programs |journal=Nucleic Acids Res. |volume=25 |issue=17 |pages=3389–3402 |date=September 1997 |pmid=9254694 |pmc=146917 |doi= 10.1093/nar/25.17.3389}}</ref>  However, such algorithms typically are not as sensitive or accurate as algorithms specifically designed for RNA.
Of particular importance for RNA is its conservation of a [[Nucleic acid secondary structure|secondary structure]], which can be modeled to achieve additional accuracy in searches.  For example, [[Covariance model (RNA)|Covariance model]]s<ref>{{cite journal |vauthors=Eddy SR, Durbin R |title=RNA sequence analysis using covariance models |journal=Nucleic Acids Res. |volume=22 |issue=11 |pages=2079–2088 |date=June 1994 |pmid=8029015 |pmc=308124 |doi= 10.1093/nar/22.11.2079}}</ref> can be viewed as an extension to a [[profile hidden Markov model]] that also reflects conserved secondary structure.  Covariance models are implemented in the Infernal software package.<ref>{{cite journal |vauthors=Nawrocki EP, Eddy SR |title=Infernal 1.1: 100-fold faster RNA homology searches |journal=Bioinformatics |volume=29 |issue=22 |pages=2933–2935 |date=November 2013 |pmid=24008419 |pmc=3810854 |doi=10.1093/bioinformatics/btt509 }}</ref>
==Discovery of specific types of ncRNAs==
Some types of RNAs have shared properties that algorithms can exploit.  For example, tRNAscan-SE<ref>{{cite journal |vauthors=Lowe TM, Eddy SR |title=tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence |journal=Nucleic Acids Res. |volume=25 |issue=5 |pages=955–964 |date=March 1997 |pmid=9023104 |pmc=146525 |doi= 10.1093/nar/25.5.955}}</ref> is specialized to finding [[tRNA]]s.  The heart of this program is a tRNA homology search based on covariance models, but other tRNA-specific search programs are used to accelerate searches.
The properties of [[snoRNA]]s have enabled the development of programs to detect new examples of snoRNAs, including those that might be only distantly related to previously known examples.  Computer programs implementing such approaches include snoscan<ref>{{cite journal |vauthors=Lowe TM, Eddy SR |s2cid=8084145 |title=A computational screen for methylation guide snoRNAs in yeast |journal=Science |volume=283 |issue=5405 |pages=1168–1171 |date=February 1999 |pmid=10024243 |doi= 10.1126/science.283.5405.1168|bibcode=1999Sci...283.1168L }}</ref> and snoReport.<ref>{{cite journal |vauthors=Hertel J, Hofacker IL, Stadler PF |title=SnoReport: computational identification of snoRNAs with unknown targets |journal=Bioinformatics |volume=24 |issue=2 |pages=158–164 |date=January 2008 |pmid=17895272 |doi=10.1093/bioinformatics/btm464 |doi-access=free }}</ref>
Similarly, several algorithms have been developed to detect [[microRNA]]s.  Examples include miRNAFold<ref>{{cite journal |vauthors=Tempel S, Tahi F |title=A fast ab-initio method for predicting miRNA precursors in genomes. |journal=Nucleic Acids Res. |volume=40 |issue=11 |pages=955–964 |year=2012 |pmid=22362754 |doi= 10.1093/nar/gks146 |pmc=3367186}}</ref> and miRNAminer.<ref>{{cite journal |vauthors=Artzi S, Kiezun A, Shomron N |title=miRNAminer: a tool for homologous microRNA gene search. |journal=BMC Bioinformatics |volume=9|page=39 |year=2008 |pmid=18215311 |doi=10.1186/1471-2105-9-39 |pmc=2258288 |issue=1 |doi-access=free }}</ref>
==Discovery by general properties==
Some properties are shared by multiple unrelated classes of ncRNA, and these properties can be targeted to discover new classes.  Chief among them is the conservation of an RNA secondary structure.  To measure conservation of secondary structure, it is necessary to somehow find homologous sequences that might exhibit a common structure.  Strategies to do this have included the use of BLAST between two sequences <ref name="QRNA">{{cite journal |vauthors=Rivas E, Eddy SR |title=Noncoding RNA gene detection using comparative sequence analysis |journal=BMC Bioinformatics |volume=2 |pages=8 |date=2001 |pmid=11801179 |pmc=64605 |doi= 10.1186/1471-2105-2-8 |doi-access=free }}</ref> or multiple sequences,<ref>{{cite journal |vauthors=Tseng HH, Weinberg Z, Gore J, Breaker RR, Ruzzo WL |title=Finding non-coding RNAs through genome-scale clustering |journal=J Bioinform Comput Biol |volume=7 |issue=2 |pages=373–388 |date=April 2009 |pmid=19340921 |pmc=3417115 |doi= 10.1142/s0219720009004126}}</ref> exploited synteny via orthologous genes<ref name="22motifs">{{cite journal |vauthors=Weinberg Z, Barrick JE, Yao Z, Roth A, Kim JN, Gore J, Wang JX, Lee ER, Block KF, Sudarsan N, Neph S, Tompa M, Ruzzo WL, Breaker RR |title=Identification of 22 candidate structured RNAs in bacteria using the CMfinder comparative genomics pipeline |journal=Nucleic Acids Res. |volume=35 |issue=14 |pages=4809–4819 |date=2007 |pmid=17621584 |pmc=1950547 |doi=10.1093/nar/gkm487 }}</ref><ref name="P5SM">{{cite journal |vauthors=Hammond MC, Wachter A, Breaker RR |title=A plant 5S ribosomal RNA mimic regulates alternative splicing of transcription factor IIIA pre-mRNAs |journal=Nat. Struct. Mol. Biol. |volume=16 |issue=5 |pages=541–549 |date=May 2009 |pmid=19377483 |pmc=2680232 |doi=10.1038/nsmb.1588 }}</ref>  or used [[locality sensitive hashing]] in combination with sequence and structural features.<ref>{{cite journal |vauthors=Heyne S, Costa F, Rose D, Backofen R |title=GraphClust: alignment-free structural clustering of local RNA secondary structures |journal=Bioinformatics |volume=28 |issue=12 |pages=i224–32 |date=June 2012 |pmid=22689765 |pmc=3371856 |doi=10.1093/bioinformatics/bts224 }}</ref>
Mutations that change the [[nucleotide]] sequence, but preserve secondary structure are called [[covariation (RNA)|covariation]], and can provide evidence of conservation.  Other statistics and probabilistic models can be used to measure such conservation.  The first ncRNA discovery method to use structural conservation was QRNA,<ref name="QRNA"/> which compared the probabilities of an alignment of two sequences based on either an RNA model or a model in which only the primary sequence conserved.  Work in this direction has allowed for more than two sequences and included phylogenetic models, e.g., with EvoFold.<ref>{{cite journal |vauthors=Pedersen JS, Bejerano G, Siepel A, Rosenbloom K, Lindblad-Toh K, Lander ES, Kent J, Miller W, Haussler D |title=Identification and classification of conserved RNA secondary structures in the human genome |journal=PLOS Comput. Biol. |volume=2 |issue=4 |pages=e33 |date=April 2006 |pmid=16628248 |pmc=1440920 |doi=10.1371/journal.pcbi.0020033 |bibcode=2006PLSCB...2...33P |doi-access=free }}</ref>  An approach taken in RNAz<ref>{{cite journal |vauthors=Washietl S, Hofacker IL, Stadler PF |title=Fast and reliable prediction of noncoding RNAs |journal=Proc. Natl. Acad. Sci. U.S.A. |volume=102 |issue=7 |pages=2454–2459 |date=February 2005 |pmid=15665081 |pmc=548974 |doi=10.1073/pnas.0409169102 |doi-access=free }}</ref> involved computing statistics on an input multiple-sequence alignment.  Some of these statistics relate to structural conservation, while others measure general properties of the alignment that could affect the expected ranges of the structural statistics.  These statistics were combined using a [[support vector machine]].
Other properties include the appearance of a [[Promoter (genetics)|promoter]] to transcribe the RNA.  ncRNAs are also often followed by a [[Intrinsic termination|Rho-independent transcription terminator]].
Using a combination of these approaches, multiple studies have enumerated candidate RNAs, e.g., <ref name="QRNA"/><ref name="P5SM"/>
Some studies have proceeded to manual analysis of the predictions to find a details structural and functional prediction.<ref name="22motifs"/><ref>{{cite journal |vauthors=Weinberg Z, Wang JX, Bogue J, Yang J, Corbino K, Moy RH, Breaker RR |title=Comparative genomics reveals 104 candidate structured RNAs from bacteria, archaea, and their metagenomes |journal=Genome Biol. |volume=11 |issue=3 |pages=R31 |date=2010 |pmid=20230605 |pmc=2864571 |doi=10.1186/gb-2010-11-3-r31 |doi-access=free }}</ref><ref>{{cite journal |vauthors=Weinberg Z, Lünse CE, Corbino KA, Ames TD, Nelson JW, Roth A, Perkins KR, Sherlock ME, Breaker RR |title=Detection of 224 candidate structured RNAs by comparative analysis of specific subsets of intergenic regions |journal=Nucleic Acids Res. |volume=45 |issue=18 |pages=10811–10823 |date=October 2017 |pmid=28977401 |pmc=5737381 |doi=10.1093/nar/gkx699 }}</ref>
==See also==
* [[6A RNA motif]]
* [[AbiF RNA motif]]
* [[ARRPOF RNA motif]]
* [[CyVA-1 RNA motif]]
* [[List of RNA structure prediction software]]
==References==
{{Reflist|2}}
{{Bioinformatics}}
[[Category:Non-coding RNA]]
[[Category:Bioinformatics]]