The effect of regions flanking target site on siRNA potency

Genomics

Volumn 102, Issue 4, 2013, Pages 215-222

  Liu, Li ^a   Li, Qian Zhong ^a   Lin, Hao ^b   Zuo, Yong Chun ^a  

^a INNER MONGOLIA UNIVERSITY   (China)

^b UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

Flanking sequence; Regression analysis; SiRNA; Support vector machine

Indexed keywords

SMALL INTERFERING RNA;

ACCURACY; AREA UNDER THE CURVE; ARTICLE; CONTROLLED STUDY; CORRELATION COEFFICIENT; DNA BASE COMPOSITION; DNA FLANKING REGION; GC RICH SEQUENCE; GENE INTERACTION; GENETIC ALGORITHM; MATHEMATICAL COMPUTING; PREDICTIVE VALUE; PRIORITY JOURNAL; RANDOM FOREST; RECEIVER OPERATING CHARACTERISTIC; RNA INTERFERENCE; SCORING SYSTEM; SENSITIVITY AND SPECIFICITY; SEQUENCE ANALYSIS; SUPPORT VECTOR MACHINE; THERMODYNAMICS; THERMOSTABILITY;

FLANKING SEQUENCE; REGRESSION ANALYSIS; SIRNA; SUPPORT VECTOR MACHINE;

ALGORITHMS; BASE SEQUENCE; COMPUTATIONAL BIOLOGY; DATABASES, GENETIC; MODELS, MOLECULAR; REGRESSION ANALYSIS; RNA INTERFERENCE; RNA, SMALL INTERFERING; SEQUENCE ANALYSIS, RNA; SUPPORT VECTOR MACHINES;

EID: 84886254200     PISSN: 08887543     EISSN: 10898646     Source Type: Journal    
DOI: 10.1016/j.ygeno.2013.07.009     Document Type: Article

References (50)

1. Fire A., Xu S., Montgomery M.K., Kostas S.A., Driver S.E., Mello C.C. Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans. Nature 1998, 391:806-811.
2. Hannon G.J. RNA interference. Nature 2002, 418:244-251.
3. McManus M.T., Sharp P.A. Gene silencing in mammals by small interfering RNAs. Nat. Rev. Genet. 2002, 3:737-747.
4. Elbashir S.M., Lendeckel W., Tuschl T. RNA interference is mediated by 21- and 22-nucleotide RNAs. Genes Dev. 2001, 15:188-200.
5. Meister G., Tuschl T. Mechanisms of gene silencing by double-stranded RNA. Nature 2004, 431:343-349.
6. Tijsterman M., Plasterk R.H. Dicers at RISC; the mechanism of RNAi. Cell 2004, 117(1):1-3.
7. Elbashir S.M., Martinez J., Patkaniowska A., Lendeckel W., Tuschl T. Functional anatomy of siRNAs for mediating efficient RNAi in Drosophila melanogaster embryo lysate. EMBO J. 2001, 20(23):6877-6888.
8. Reynolds A., Leake D., Boese Q., Scaringe S., Marshall W.S., Khvorova A. Rational siRNA design for RNA interference. Nat. Biotechnol. 2004, 22(3):326-330.
9. Ui-Tei K., Naito Y., Takahashi F., Haraguchi T., Ohki-Hamazaki H., Juni A., Ueda R., Saigo K. Guidelines for the selection of highly effective siRNA sequences for mammalian and chick RNA interference. Nucleic Acids Res. 2004, 32(3):936-948.
10. Amarzguioui M., Prydz H. An algorithm for selection of functional siRNA sequences. Biochem. Biophys. Res. Commun. 2004, 316:1050-1058.
11. Hsieh A.C., Bo R., Monola J., Vazquez F., Bare O., Khvorova A., Scaringe S., Sellers W.R. A library of siRNA duplexes targeting the phosphoinositide 3-kinase pathway: determinants of gene silencing for use in cell-based screens. Nucleic Acids Res. 2004, 32(3):893-901.
12. Jagla B., Aulner N., Kelly P.D., Song D., Volchuk A., Zatorski A., Shum D., Mayer T., De Angelis D.A., Ouerfelli O., Rutishauser U., Rothman J.E. Sequence characteristics of functional siRNAs. RNA 2005, 11(6):864-872.
13. Schwarz D.S., Hutvagner G., Du T., Xu Z., Aronin N., Zamore P.D. Asymmetry in the assembly of the RNAi enzyme complex. Cell 2003, 115:199-208.
14. Khvorova A., Reynolds A., Jayasena S.D. Functional siRNAs and miRNAs exhibit strand bias. Cell 2003, 115:209-216.
15. Luo K.Q., Chang D.C. The gene-silencing efficiency of siRNA is strongly dependent on the local structure of mRNA at the targeted region. Biochem. Biophys. Res. Commun. 2004, 318:303-310.
16. Shao Y., Chan C.Y., Maliyekkel A., Lawrence C.E., Roninson I.B., Ding Y. Effect of target secondary structure on RNAi efficiency. RNA 2007, 13(10):1631-1640.
17. Lu Z.J., Mathews D.H. Efficient siRNA selection using hybridization thermodynamics. Nucleic Acids Res. 2008, 36(2):640-647.
18. Bohula E.A., Salisbury A.J., Sohail M., Playford M.P., Riedemann J., Southern E.M., Macaulay V.M. The efficacy of small interfering RNAs targeted to the type 1 insulin-like growth factor receptor (IGF1R) is influenced by secondary structure in the IGF1R transcript. J. Biol. Chem. 2003, 278:15991-15997.
19. Far R.K., Sczakiel G. The activity of siRNA in mammalian cells is related to structural target accessibility: a comparison with antisense oligonucleotides. Nucleic Acids Res. 2003, 31:4417-4424.
20. Overhoff M., Alken M., Far R.K., Lemaitre M., Lebleu B., Sczakiel G., Robbins I. Local RNA target structure influences siRNA efficacy: a systematic global analysis. J. Mol. Biol. 2005, 348:871-881.
21. Schubert S., Grunweller A., Erdmann V.A., Kurreck J. Local RNA target structure influences siRNA efficacy: systematic analysis of intentionally designed binding regions. J. Mol. Biol. 2005, 348:883-893.
22. Westerhout E.M., Berkhout B. A systematic analysis of the effect of target RNA structure on RNA interference. Nucleic Acids Res. 2007, 35(13):4322-4330.
23. Robins H., Li Y., Padgett R.W. Incorporating structure to predict microRNA targets. Proc. Natl. Acad. Sci. U.S.A. 2005, 102:4006-4009.
24. Long D., Lee R., Williams P., Chan C.Y., Ambros V., Ding Y. Potent effect of target structure on microRNA function. Nat. Struct. Mol. Biol. 2007, 14(4):287-294.
25. Kertesz M., Iovino N., Unnerstall U., Gaul U., Segal E. The role of site accessibility in microRNA target recognition. Nat. Genet. 2007, 39:1278-1284.
26. Hofacker I.L. How microRNAs choose their targets. Nat. Genet. 2007, 39:1191-1192.
27. Grimson A., Farh K.K., Johnston W.K., Garrett-Engele P., Lim L.P., Bartel D.P. MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol. Cell. 2007, 27(1):91-105.
28. Sun G., Li H., Rossi J.J. Sequence context outside the target region influences the effectiveness of miR-223 target sites in the RhoB 3'UTR. Nucleic Acids Res. 2010, 38(1):239-252.
29. Jacobsen A., Wen J., Marks D.S., Krogh A. Signatures of RNA binding proteins globally coupled to effective microRNA target sites. Genome Res. 2010, 20(8):1010-1019.
30. Huesken D., Lange J., Mickanin C., Weiler J., Asselbergs F., Warner J., Meloon B., Engel S., Rosenberg A., Cohen D., Labow M., Reinhardt M., Natt F., Hall J. Design of a genome-wide siRNA library using an artificial neural network. Nat. Biotechnol. 2005, 23:995-1001.
31. Shabalina S.A., Spiridonov A.N., Ogurtsov A.Y. Computational models with thermodynamic and composition features improve siRNA design. BMC Bioinforma. 2006, 7:65.
32. Vert J.P., Foveau N., Lajaunie C., Vandenbrouck Y. An accurate and interpretable model for siRNA efficacy prediction. BMC Bioinforma. 2006, 7:520.
33. Ladunga I. More complete gene silencing by fewer siRNAs: transparent optimized design and biophysical signature. Nucleic Acids Res. 2007, 35:433-440.
34. Ichihara M., Murakumo Y., Masuda A., Matsuura T., Asai N., Jijiwa M., Ishida M., Shinmi J., Yatsuya H., Qiao S., Takahashi M., Ohno K. Thermodynamic instability of siRNA duplex is a prerequisite for dependable prediction of siRNA activities. Nucleic Acids Res. 2007, 35(18):e123.
35. Matveeva O., Nechipurenko Y., Rossi L., Moore B., Saetrom P., Ogurtsov A.Y., Atkins J.F., Shabalina S.A. Comparison of approaches for rational siRNA design leading to a new efficient and transparent method. Nucleic Acids Res. 2007, 35:e63.
36. Peek A.S. Improving model predictions for RNA interference activities that use support vector machine regression by combining and filtering features. BMC Bioinforma. 2007, 8:182.
37. Wang X., Wang X., Varma R.K., Beauchamp L., Magdaleno S., Sendera T.J. Selection of hyperfunctional siRNAs with improved potency and specificity. Nucleic Acids Res. 2009, 37(22):e152.
38. Klingelhoefer J.W., Moutsianas L., Holmes C. Approximate Bayesian feature selection on a large meta-dataset offers novel insights on factors that effect siRNA potency. Bioinformatics 2009, 25(13):1594-1601.
39. HU W., HU J. Prediction of siRNA potency using sparse logistic regression. J. Comput. Biol. 2011, 18:1-8.
40. Pan W.J., Chen C.W., Chu Y.W. siPRED: predicting siRNA efficacy using various characteristic methods. PLoS One 2011, 6(11):e27602.
41. Chan C.Y., Carmack C.S., Long D.D., Maliyekkel A., Shao Y., Roninson I.B., Ding Y. A structural interpretation of the effect of GC-content on efficiency of RNA interference. BMC Bioinforma. 2009, 10(Suppl. 1):S33.
42. Turner D.H., Mathews D.H. NNDB: the nearest neighbor parameter database for predicting stability of nucleic acid secondary structure. Nucleic Acids Res. 2010, 38:D280-D282. (Database issue).
43. Feng Y., Luo L. Use of tetrapeptide signals for protein secondary-structure prediction. Amino Acids 2008, 35(3):607-614.
44. Saeys Y., Inza I., Larranaga P. A review of feature selection techniques in bioinformatics. Bioinformatics 2007, 23:2507-2517.
45. Ebina T., Toh H., Kuroda Y. DROP: an SVM domain linker predictor trained with optimal features selected by random forest. Bioinformatics 2011, 27(4):487-494.
46. Breiman L. Random forests. Mach. Learn. 2001, 45(1):5-32.
47. Cortes C., Vapnik V. Support vector networks. Mach. Learn. 1995, 20:273-297.
48. Chang C.C., Lin C.J. LIBSVM: a library for support vector machines. ACM Trans.Intell. Syst. Technol. 2011, 2. (27:1-27:27).
49. Barreau C., Paillard L., Osborne H.B. AU-rich elements and associated factors: are there unifying principles?. Nucleic Acids Res. 2005, 33:7138-7150.
50. Larsson E., Sander C., Marks D. mRNA turnover rate limits siRNA and microRNA efficacy. Mol. Syst. Biol. 2010, 6:433.