Sfold Explained

Logo Alt:Sfold Logo
Author:Ye Ding and Charles E. Lawrence
Developer:Dang Long and Chaochun Liu (application modeling); Clarence Chan, Adam Wolenc, William A. Rennie and Charles S. Carmack (software development)
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Operating System:Linux
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Sfold is a software program developed to predict probable RNA secondary structures through structure ensemble sampling and centroid predictions[1] [2] with a focus on assessment of RNA target accessibility,[3] for major applications to the rational design of siRNAs[4] in the suppression of gene expressions, and to the identification of targets for regulatory RNAs particularly microRNAs.[5] [6]

Development

The core RNA secondary structure prediction algorithm is based on rigorous statistical (stochastic) sampling of Boltzmann ensemble of RNA secondary structures, enabling statistical characterization of any local structural features of potential interest to experimental investigators. In a review on nucleic acid structure and prediction,[7] the potential of structure sampling described in a prototype algorithm[8] was highlighted. With the publication of the mature algorithms for Sfold, the sampling approach became the focus of a review[9] Both the sampling approach and the centroid predictions were discussed in a comprehensive review. As an application module of the Sfold package, the STarMir program[10] has been widely used for its capability in modeling target accessibility. STarMir was described in an independent study on microRNA target prediction[11] STarMir predictions have been used in an attempt to derive improved predictions.[12] Predictions by Sfold have led to new biological insights.[13] The novel ideas of ensemble sampling and centroids have been adopted by others not only for RNA problems, but also for other fundamental problems in computational biology and genomics.[14] [15] [16] [17] [18]

An implementation of stochastic sampling has been included in two widely used RNA software packages, RNA Structure[19] and the ViennaRNA Package,[20] which are also based on the Turner RNA thermodynamic parameters.[21] Sfold was featured on a Nucleic Acids Research cover,[22] and was highlighted in Science NetWatch.[23] The underlying novel model for STarMir was featured in the Cell Biology section of Nature Research Highlights.[24]

Distribution

Sfold runs on Linux, and is freely available to the scientific community for non-commercial applications, and is available under license for commercial applications. Both the source code and the executables are available at GitHub.

External links

Notes and References

  1. Ding . Y . Lawrence . CE . Charles Lawrence (mathematician) . 2003 . A statistical sampling algorithm for RNA secondary structure prediction. . Nucleic Acids Res. . 15;31 . 24 . 7280–301 . 10.1093/nar/gkg938 . 297010 . 14654704.
  2. Ding . Y . Chan . CY . Lawrence . CE . Charles Lawrence (mathematician) . 2005 . RNA secondary structure prediction by centroids in a Bolzmann weighed ensemble . RNA . 11 . 8 . 1157–1166 . 10.1261/rna.2500605 . 1370799 . 16043502 . free.
  3. Ding . Y . Lawrence . CE . Statistical Prediction of single stranded regions in RNA secondary structure and application to predicting effective antisense target sites and beyond . Nucleic Acids Research . 2001 . 1, 29 . 5 . 1035–46 . 10.1093/nar/29.5.1034 . 11222752. free . 29728 .
  4. Elbashir . SM . Harborth . J . Lendeckel . W . Yalcin . A . Weber . K . Tuschi . T . "Duplexes of 21-nucleotide RNAs mediate RNA interference in cultured mammalian cells . Nature . 2001 . 411 . 6836 . 494–8 . 10.1038/35078107. 11373684 . 710341 .
  5. Lee . RC . Feinbaum . RL . Ambros . V . The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14 . Cell . 1993 . 75 . 5 . 843–54 . 10.1016/0092-8674(93)90529-y. 8252621 . 205020975 . free .
  6. Long . D . Lee . R . William . P . Chan . CY . Ambros . V . Ding . Y . Potent effect of target secondary structure on microRNA function . Nat Struct Mol Biol . 2007 . 14 . 4 . 287–94 . 10.1038/nsmb1226 . 17401373. 650349 .
  7. Zucker . M. . 2000 . Calculating nucleic acid secondary structure . Curr. Opin. Struct. Biol. . 10 . 3 . 303–310 . 10.1016/s0959-440x(00)00088-9 . 10851192.
  8. Ding . Y. . Lawrence . C. E. . 1999 . A Bayesian Statistical Algorithm for RNA Secondary Structure Prediction . Computers & Chemistry . 23 . 3–4 . 387–400 . 10.1016/S0097-8485(99)00010-8. 10404626 .
  9. Mathews . David H. . 2006 . Revolutions in RNA Secondary Structure Prediction . Journal of Molecular Biology . 359 . 3 . 526–532 . 10.1016/j.jmb.2006.01.067 . 16500677 . 0022-2836.
  10. Rennie . William . Liu . Chaochun . Carmack . C. Steven . Wolenc . Adam . Kanoria . Shaveta . Lu . Jun . Long . Dang . Ding . Ye . 2014-05-06 . STarMir: a web server for prediction of microRNA binding sites . Nucleic Acids Research . 42 . W1 . W114–W118 . 10.1093/nar/gku376 . 1362-4962. free . 24803672 . 4086099 .
  11. Wong . Leon . You . Zhu-Hong . Guo . Zhen-Hao . Yi . Hai-Cheng . Chen . Zhan-Heng . Cao . Mei-Yuan . 2020-07-09 . MIPDH: A Novel Computational Model for Predicting microRNA–mRNA Interactions by DeepWalk on a Heterogeneous Network . ACS Omega . 5 . 28 . 17022–17032 . 10.1021/acsomega.9b04195 . 2470-1343. free . 32715187 . 7376568 .
  12. Ullah . Abu Z.M. Dayem . Sahoo . Sudhakar . Steinhöfel . Kathleen . Albrecht . Andreas A. . 2012 . Derivative scores from site accessibility and ranking of miRNA target predictions . International Journal of Bioinformatics Research and Applications . 8 . 3/4 . 171–191 . 10.1504/ijbra.2012.048966 . 22961450 . 1744-5485.
  13. Adams . L. . Pri-miRNA processing: structure is the key. . Nature Reviews Genetics . 2017 . 18 . 3 . 145 . 10.1038/nrg.2017.6 . 28138147. 30513706 .
  14. Huang . F. W. . Qin . Jing . Reidys . Christian M . Stadler . Peter F . Target prediction and a statistical sampling algorithm for RNA-RNA interaction. . Bioinformatics . 2009 . 26 . 2 . 175–181 . 10.1093/bioinformatics/btp635 . 19910305. 2804298 .
  15. Harmanchi . Arif Ozgun . Gaurav . Sharma . Mathews . David H . Stochastic sampling of the RNA structural alignment space . Nucleic Acids Research . 2009 . 37 . 12 . 4063–4075 . 10.1093/nar/gkp276 . 19429694. 2709569 .
  16. Hamada . M . Kiryu . H . Mituyama . T . Asai . K . Prediction of RNA secondary structure using generalized centroid estimators . Bioinformatics . 2009 . 25 . 4 . 465–473 . 10.1093/bioinformatics/btn601 . 19095700. free .
  17. Carvalho . L. E. . Lawrence . C. E. . Centroid estimation in discrete high- dimensional spaces with applications in biology. . Proc Natl Acad Sci . 2008 . 105 . 9 . 3209–14 . 10.1073/pnas.0712329105 . 18305160 . 2265131 . 2008PNAS..105.3209C . free .
  18. Newberg . L. A. . Thompson . W. A. . Colan . S . Smith . T. M. . McCue . L. A. . Lawrence . C. E. . Centroid estimation in discrete high- dimensional spaces with applications in biology. . Bioinformatics . 2007 . 23 . 14 . 1718–27 . 10.1093/bioinformatics/btm241 . 17488758. 2268014 .
  19. Bellaousov . S . Reuter . Js . Seetin . MG . Mathews . DH . RNAstructure: Web servers for RNA secondary structure prediction and analysis . Nucleic Acids Research . 2013 . 41 . (Web Server Issue) . W471-4 . 10.1093/nar/gkt290 . 23620284. free . 3692136 .
  20. Gruber . AR . Lorenz . R . Bernhart . SH . Neuböck . R . Hofacker . IL . The Vienna RNA websuite . Nucleic Acids Research . 2008 . 36 . Web Server Issue . W70-4 . 10.1093/nar/gkn188 . 18424795. free . 2447809 .
  21. Mathews . DH . Sabina . J . Turner . DH . Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure . J. Mol. Biol. . 1999 . 288 . 5 . 911–40 . 10.1006/jmbi.1999.2700 . 10329189. free .
  22. https://academic.oup.com/nar/article/31/24/7280/2904423
  23. TOOLS: Nucleic Acid Origami . Science . 2003 . 300 . 5621 . 873 . 10.1126/science.300.5621.873d. 220109027 .
  24. 2007 . Research highlights . Nature . en . 446 . 7136 . 586–587 . 10.1038/446586a . 0028-0836. free . 2007Natur.446..586. .

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