Bioinformatics discovery of non-coding RNAs

Non-coding RNAs 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 algorithms 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.

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[1]. 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 secondary structure, which can be modeled to achieve additional accuracy in searches. For example, Covariance models[2] 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.[3]

Discovery of specific types of ncRNAs

Some types of RNAs have shared properties that algorithms can exploit. For example, tRNAscan-SE[4] is specialized to finding tRNAs. 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 snoRNAs 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[5] and snoReport.[6]

Similarly, several algorithms have been developed to detect microRNAs. Examples include miRNAFold[7] and miRNAminer[8]

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 [9] or multiple sequences,[10] exploited synteny via orthologous genes[11][12] or used locality sensitive hashing in combination with sequence and structural features[13].

Mutations that change the nucleotide sequence, but preserve secondary structure are called 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[9], 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.[14] An approach taken in RNAz[15] 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 to transcribe the RNA. ncRNAs are also often followed by a Rho-independent transcription terminator.

Using a combination of these approaches, multiple studies have enumerated candidate RNAs, e.g., [9][12] Some studies have proceeded to manual analysis of the predictions to find a details structural and functional prediction.[11][16][17]

gollark: `load("return _HOST")()` returns the `_HOST` from outside the sandbox in mine. Replace `return _HOST` with `doEvilOutOfSandboxStuff()` and it's a problem.
gollark: I have no idea how, but <@!111572502722920448>'s old `sBox` thing seems to not experience the `load` issue despite not overriding it anywhere.
gollark: Now I just need to find the other ~5 million sandboxbreaking bugs!
gollark: Also, load is limited to taking `t` as that third mode arg, right?
gollark: Amazing!

References
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  2. Eddy SR, Durbin R (June 1994). "RNA sequence analysis using covariance models". Nucleic Acids Res. 22 (11): 2079–2088. doi:10.1093/nar/22.11.2079. PMC 308124. PMID 8029015.
  3. Nawrocki EP, Eddy SR (November 2013). "Infernal 1.1: 100-fold faster RNA homology searches". Bioinformatics. 29 (22): 2933–2935. doi:10.1093/bioinformatics/btt509. PMC 3810854. PMID 24008419.
  4. Lowe TM, Eddy SR (March 1997). "tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence". Nucleic Acids Res. 25 (5): 955–964. doi:10.1093/nar/25.5.955. PMC 146525. PMID 9023104.
  5. Lowe TM, Eddy SR (February 1999). "A computational screen for methylation guide snoRNAs in yeast". Science. 283 (5405): 1168–1171. doi:10.1126/science.283.5405.1168. PMID 10024243. S2CID 8084145.
  6. Hertel J, Hofacker IL, Stadler PF (January 2008). "SnoReport: computational identification of snoRNAs with unknown targets". Bioinformatics. 24 (2): 158–164. doi:10.1093/bioinformatics/btm464. PMID 17895272.
  7. Tempel S, Tahi F (2012). "A fast ab-initio method for predicting miRNA precursors in genomes". Nucleic Acids Res. 40 (11): 955–964. doi:10.1093/nar/gks146. PMC 3367186. PMID 22362754.
  8. Artzi S, Kiezun A, Shomron N (2008). "miRNAminer: a tool for homologous microRNA gene search". BMC Bioinformatics. 9 (1): 39. doi:10.1186/1471-2105-9-39. PMC 2258288. PMID 18215311.
  9. Rivas E, Eddy SR (2001). "Noncoding RNA gene detection using comparative sequence analysis". BMC Bioinformatics. 2: 8. doi:10.1186/1471-2105-2-8. PMC 64605. PMID 11801179.
  10. Tseng HH, Weinberg Z, Gore J, Breaker RR, Ruzzo WL (April 2009). "Finding non-coding RNAs through genome-scale clustering". J Bioinform Comput Biol. 7 (2): 373–388. doi:10.1142/s0219720009004126. PMC 3417115. PMID 19340921.
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  12. Hammond MC, Wachter A, Breaker RR (May 2009). "A plant 5S ribosomal RNA mimic regulates alternative splicing of transcription factor IIIA pre-mRNAs". Nat. Struct. Mol. Biol. 16 (5): 541–549. doi:10.1038/nsmb.1588. PMC 2680232. PMID 19377483.
  13. Heyne S, Costa F, Rose D, Backofen R (June 2012). "GraphClust: alignment-free structural clustering of local RNA secondary structures". Bioinformatics. 28 (12): i224–32. doi:10.1093/bioinformatics/bts224. PMC 3371856. PMID 22689765.
  14. Pedersen JS, Bejerano G, Siepel A, Rosenbloom K, Lindblad-Toh K, Lander ES, Kent J, Miller W, Haussler D (April 2006). "Identification and classification of conserved RNA secondary structures in the human genome". PLOS Comput. Biol. 2 (4): e33. doi:10.1371/journal.pcbi.0020033. PMC 1440920. PMID 16628248.
  15. Washietl S, Hofacker IL, Stadler PF (February 2005). "Fast and reliable prediction of noncoding RNAs". Proc. Natl. Acad. Sci. U.S.A. 102 (7): 2454–2459. doi:10.1073/pnas.0409169102. PMC 548974. PMID 15665081.
  16. Weinberg Z, Wang JX, Bogue J, Yang J, Corbino K, Moy RH, Breaker RR (2010). "Comparative genomics reveals 104 candidate structured RNAs from bacteria, archaea, and their metagenomes". Genome Biol. 11 (3): R31. doi:10.1186/gb-2010-11-3-r31. PMC 2864571. PMID 20230605.
  17. Weinberg Z, Lünse CE, Corbino KA, Ames TD, Nelson JW, Roth A, Perkins KR, Sherlock ME, Breaker RR (October 2017). "Detection of 224 candidate structured RNAs by comparative analysis of specific subsets of intergenic regions". Nucleic Acids Res. 45 (18): 10811–10823. doi:10.1093/nar/gkx699. PMC 5737381. PMID 28977401.

See also
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