In-Silico Algorithms for the Screening of Possible microRNA Binding Sites and Their Interactions
MicroRNAs (miRNAs) comprise a recently discovered class of small, non-coding RNA molecules of 21-25 nucleotides in length that regulate the gene expression by base-pairing with the transcripts of their targets i.e. protein-coding genes, leading to down-regulation or repression of the target genes. H...
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| Vydáno v: | Current genomics Ročník 14; číslo 2; s. 127 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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United Arab Emirates
01.04.2013
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| ISSN: | 1389-2029 |
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| Abstract | MicroRNAs (miRNAs) comprise a recently discovered class of small, non-coding RNA molecules of 21-25 nucleotides in length that regulate the gene expression by base-pairing with the transcripts of their targets i.e. protein-coding genes, leading to down-regulation or repression of the target genes. However, target gene activation has also been described. miRNAs are involved in diverse regulatory pathways, including control of developmental timing, apoptosis, cell proliferation, cell differentiation, modulation of immune response to macrophages, and organ development and are associated with many diseases, such as cancer. Computational prediction of miRNA targets is much more challenging in animals than in plants, because animal miRNAs often perform imperfect base-pairing with their target sites, unlike plant miRNAs which almost always bind their targets with near perfect complementarity. In the past years, a large number of target prediction programs and databases on experimentally validated information have been developed for animal miRNAs to fulfil the need of experimental scientists conducting miRNA research. In this review we first succinctly describe the prediction criteria (rules or principles) adapted by prediction algorithms to generate possible miRNA binding site interactions and introduce most relevant algorithms, and databases. We then summarize their applications with the help of some previously published studies. We further provide experimentally validated functional binding sites outside 3'-UTR region of target mRNAs and the resources which offer such predictions. Finally, the issue of experimental validation of miRNA binding sites will be briefly discussed. |
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| AbstractList | MicroRNAs (miRNAs) comprise a recently discovered class of small, non-coding RNA molecules of 21-25 nucleotides in length that regulate the gene expression by base-pairing with the transcripts of their targets i.e. protein-coding genes, leading to down-regulation or repression of the target genes. However, target gene activation has also been described. miRNAs are involved in diverse regulatory pathways, including control of developmental timing, apoptosis, cell proliferation, cell differentiation, modulation of immune response to macrophages, and organ development and are associated with many diseases, such as cancer. Computational prediction of miRNA targets is much more challenging in animals than in plants, because animal miRNAs often perform imperfect base-pairing with their target sites, unlike plant miRNAs which almost always bind their targets with near perfect complementarity. In the past years, a large number of target prediction programs and databases on experimentally validated information have been developed for animal miRNAs to fulfil the need of experimental scientists conducting miRNA research. In this review we first succinctly describe the prediction criteria (rules or principles) adapted by prediction algorithms to generate possible miRNA binding site interactions and introduce most relevant algorithms, and databases. We then summarize their applications with the help of some previously published studies. We further provide experimentally validated functional binding sites outside 3'-UTR region of target mRNAs and the resources which offer such predictions. Finally, the issue of experimental validation of miRNA binding sites will be briefly discussed.MicroRNAs (miRNAs) comprise a recently discovered class of small, non-coding RNA molecules of 21-25 nucleotides in length that regulate the gene expression by base-pairing with the transcripts of their targets i.e. protein-coding genes, leading to down-regulation or repression of the target genes. However, target gene activation has also been described. miRNAs are involved in diverse regulatory pathways, including control of developmental timing, apoptosis, cell proliferation, cell differentiation, modulation of immune response to macrophages, and organ development and are associated with many diseases, such as cancer. Computational prediction of miRNA targets is much more challenging in animals than in plants, because animal miRNAs often perform imperfect base-pairing with their target sites, unlike plant miRNAs which almost always bind their targets with near perfect complementarity. In the past years, a large number of target prediction programs and databases on experimentally validated information have been developed for animal miRNAs to fulfil the need of experimental scientists conducting miRNA research. In this review we first succinctly describe the prediction criteria (rules or principles) adapted by prediction algorithms to generate possible miRNA binding site interactions and introduce most relevant algorithms, and databases. We then summarize their applications with the help of some previously published studies. We further provide experimentally validated functional binding sites outside 3'-UTR region of target mRNAs and the resources which offer such predictions. Finally, the issue of experimental validation of miRNA binding sites will be briefly discussed. MicroRNAs (miRNAs) comprise a recently discovered class of small, non-coding RNA molecules of 21-25 nucleotides in length that regulate the gene expression by base-pairing with the transcripts of their targets i.e. protein-coding genes, leading to down-regulation or repression of the target genes. However, target gene activation has also been described. miRNAs are involved in diverse regulatory pathways, including control of developmental timing, apoptosis, cell proliferation, cell differentiation, modulation of immune response to macrophages, and organ development and are associated with many diseases, such as cancer. Computational prediction of miRNA targets is much more challenging in animals than in plants, because animal miRNAs often perform imperfect base-pairing with their target sites, unlike plant miRNAs which almost always bind their targets with near perfect complementarity. In the past years, a large number of target prediction programs and databases on experimentally validated information have been developed for animal miRNAs to fulfil the need of experimental scientists conducting miRNA research. In this review we first succinctly describe the prediction criteria (rules or principles) adapted by prediction algorithms to generate possible miRNA binding site interactions and introduce most relevant algorithms, and databases. We then summarize their applications with the help of some previously published studies. We further provide experimentally validated functional binding sites outside 3'-UTR region of target mRNAs and the resources which offer such predictions. Finally, the issue of experimental validation of miRNA binding sites will be briefly discussed. |
| Author | Dweep, Harsh Gretz, Norbert Sticht, Carsten |
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| References | 18344688 - Cell Cycle. 2008 Mar 15;7(6):759-64 18542052 - Nat Biotechnol. 2008 Aug;26(8):941-6 18158296 - Nucleic Acids Res. 2008 Jan;36(Database issue):D149-53 16736023 - Nat Genet. 2006 Jun;38 Suppl:S8-13 16978421 - BMC Bioinformatics. 2006 Sep 18;7:411 17612493 - Mol Cell. 2007 Jul 6;27(1):91-105 22135297 - Nucleic Acids Res. 2012 Jan;40(Database issue):D222-9 21286309 - Curr Genomics. 2010 Aug;11(5):311-25 19536157 - Nature. 2009 Jul 23;460(7254):479-86 18653886 - Science. 2008 Jul 25;321(5888):537-41 21037258 - Nucleic Acids Res. 2011 Jan;39(Database issue):D152-7 11896390 - Nat Genet. 2002 Apr;30(4):363-4 16141076 - Science. 2005 Sep 2;309(5740):1577-81 17360662 - Proc Natl Acad Sci U S A. 2007 Feb 27;104(9):3432-7 15738385 - Proc Natl Acad Sci U S A. 2005 Mar 15;102(11):4006-9 15652478 - Cell. 2005 Jan 14;120(1):21-4 17923084 - Cell. 2007 Oct 5;131(1):25-8 12824340 - Nucleic Acids Res. 2003 Jul 1;31(13):3429-31 15372042 - Nature. 2004 Sep 16;431(7006):350-5 18426918 - RNA. 2008 Jun;14(6):1012-7 17893677 - Nat Genet. 2007 Oct;39(10):1278-84 21984948 - PLoS One. 2011;6(9):e25787 17204650 - Science. 2007 Jan 5;315(5808):97-100 19765283 - BMC Bioinformatics. 2009 Sep 18;10:295 21695135 - PLoS One. 2011;6(6):e20746 19336450 - Genome Res. 2009 Jul;19(7):1175-83 15014042 - Genes Dev. 2004 Mar 1;18(5):504-11 17085592 - Proc Natl Acad Sci U S A. 2006 Nov 14;103(46):17337-42 15131085 - Genes Dev. 2004 May 15;18(10):1165-78 21441354 - Mol Biol Evol. 2011 Sep;28(9):2421-4 17108354 - Nucleic Acids Res. 2007 Jan;35(Database issue):D149-55 15685193 - Nature. 2005 Feb 17;433(7027):769-73 22139918 - Nucleic Acids Res. 2012 Jan;40(Database issue):D1016-22 15610730 - Mol Cell. 2004 Dec 22;16(6):861-5 23326503 - PLoS One. 2013;8(1):e53780 21605702 - J Biomed Inform. 2011 Oct;44(5):839-47 20620952 - Mol Cell. 2010 Jun 25;38(6):789-802 16458514 - Curr Biol. 2006 Mar 7;16(5):460-71 10329189 - J Mol Biol. 1999 May 21;288(5):911-40 12824337 - Nucleic Acids Res. 2003 Jul 1;31(13):3406-15 20888440 - N Biotechnol. 2010 Dec 31;27(6):734-8 19721809 - Curr Genomics. 2009 Mar;10(1):35-41 14744438 - Cell. 2004 Jan 23;116(2):281-97 22333591 - Cell Cycle. 2012 Mar 1;11(5):922-33 20799968 - Genome Biol. 2010;11(8):R90 18232104 - Pac Symp Biocomput. 2008;:64-74 18996891 - Nucleic Acids Res. 2009 Jan;37(Database issue):D105-10 18645597 - Curr Genomics. 2007 Jun;8(4):229-33 17535905 - Proc Natl Acad Sci U S A. 2007 Jun 5;104(23):9667-72 18852463 - Proc Natl Acad Sci U S A. 2008 Oct 21;105(42):16230-5 17592038 - RNA. 2007 Aug;13(8):1198-204 17135348 - Proc Natl Acad Sci U S A. 2006 Dec 12;103(50):18957-62 20547158 - FEBS Lett. 2010 Jul 16;584(14):3198-202 21966251 - Curr Genomics. 2011 Apr;12(2):130-7 21062822 - Nucleic Acids Res. 2011 Jan;39(Database issue):D158-62 14709173 - Genome Biol. 2003;5(1):R1 22321448 - BMC Res Notes. 2012 Feb 09;5:91 16141061 - Science. 2005 Sep 2;309(5740):1519-24 22223877 - J Am Soc Nephrol. 2012 Mar;23 (3):458-69 22343717 - Nat Struct Mol Biol. 2012 Feb 12;19(3):321-7 15383676 - RNA. 2004 Oct;10(10):1507-17 8252621 - Cell. 1993 Dec 3;75(5):843-54 15854907 - Curr Biol. 2005 Apr 26;15(8):743-9 18806776 - Nature. 2008 Oct 23;455(7216):1124-8 16337999 - Cell. 2005 Dec 16;123(6):1133-46 18227514 - Proc Natl Acad Sci U S A. 2008 Feb 5;105(5):1608-13 14697198 - Cell. 2003 Dec 26;115(7):787-98 21532838 - Curr Genomics. 2010 Nov;11(7):537-61 18927107 - Nucleic Acids Res. 2009 Jan;37(Database issue):D98-104 20371350 - Cell. 2010 Apr 2;141(1):129-41 15806104 - Nat Genet. 2005 May;37(5):495-500 18955434 - Genome Res. 2009 Jan;19(1):92-105 17532529 - Drug Discov Today. 2007 Jun;12(11-12):452-8 15035981 - Cell. 2004 Mar 19;116(6):779-93 15652477 - Cell. 2005 Jan 14;120(1):15-20 18923704 - PLoS One. 2008;3(10):e3420 22549745 - Mol Neurobiol. 2012 Jun;45(3):520-35 15735639 - Nature. 2005 Mar 17;434(7031):338-45 21448463 - PLoS One. 2011 Mar 23;6(3):e18115 18472421 - Curr Biol. 2008 May 20;18(10):758-62 21071411 - Nucleic Acids Res. 2011 Jan;39(Database issue):D163-9 22371183 - Haematologica. 2012 Aug;97(8):1218-24 18197166 - Nat Rev Genet. 2008 Feb;9(2):102-14 15502875 - PLoS Biol. 2004 Nov;2(11):e363 16845041 - Nucleic Acids Res. 2006 Jul 1;34(Web Server issue):W429-34 16009126 - Cell. 2005 Jul 15;122(1):6-7 22319602 - PLoS One. 2012;7(2):e31021 19440450 - Curr Genomics. 2008 Apr;9(2):97-109 20089154 - Genome Biol. 2010 Jan 20;11(1):R6 18029362 - Nucleic Acids Res. 2008 Jan;36(Database issue):D165-9 18668040 - Nature. 2008 Sep 4;455(7209):58-63 16990141 - Cell. 2006 Sep 22;126(6):1203-17 |
| References_xml | – reference: 16141061 - Science. 2005 Sep 2;309(5740):1519-24 – reference: 18029362 - Nucleic Acids Res. 2008 Jan;36(Database issue):D165-9 – reference: 16978421 - BMC Bioinformatics. 2006 Sep 18;7:411 – reference: 21062822 - Nucleic Acids Res. 2011 Jan;39(Database issue):D158-62 – reference: 22223877 - J Am Soc Nephrol. 2012 Mar;23 (3):458-69 – reference: 14709173 - Genome Biol. 2003;5(1):R1 – reference: 18653886 - Science. 2008 Jul 25;321(5888):537-41 – reference: 15014042 - Genes Dev. 2004 Mar 1;18(5):504-11 – reference: 10329189 - J Mol Biol. 1999 May 21;288(5):911-40 – reference: 18542052 - Nat Biotechnol. 2008 Aug;26(8):941-6 – reference: 18158296 - Nucleic Acids Res. 2008 Jan;36(Database issue):D149-53 – reference: 22549745 - Mol Neurobiol. 2012 Jun;45(3):520-35 – reference: 21605702 - J Biomed Inform. 2011 Oct;44(5):839-47 – reference: 17204650 - Science. 2007 Jan 5;315(5808):97-100 – reference: 15652478 - Cell. 2005 Jan 14;120(1):21-4 – reference: 15383676 - RNA. 2004 Oct;10(10):1507-17 – reference: 18227514 - Proc Natl Acad Sci U S A. 2008 Feb 5;105(5):1608-13 – reference: 19440450 - Curr Genomics. 2008 Apr;9(2):97-109 – reference: 20799968 - Genome Biol. 2010;11(8):R90 – reference: 22319602 - PLoS One. 2012;7(2):e31021 – reference: 19721809 - Curr Genomics. 2009 Mar;10(1):35-41 – reference: 21037258 - Nucleic Acids Res. 2011 Jan;39(Database issue):D152-7 – reference: 17923084 - Cell. 2007 Oct 5;131(1):25-8 – reference: 19536157 - Nature. 2009 Jul 23;460(7254):479-86 – reference: 15372042 - Nature. 2004 Sep 16;431(7006):350-5 – reference: 16736023 - Nat Genet. 2006 Jun;38 Suppl:S8-13 – reference: 20371350 - Cell. 2010 Apr 2;141(1):129-41 – reference: 20620952 - Mol Cell. 2010 Jun 25;38(6):789-802 – reference: 22321448 - BMC Res Notes. 2012 Feb 09;5:91 – reference: 18232104 - Pac Symp Biocomput. 2008;:64-74 – reference: 21695135 - PLoS One. 2011;6(6):e20746 – reference: 18923704 - PLoS One. 2008;3(10):e3420 – reference: 22343717 - Nat Struct Mol Biol. 2012 Feb 12;19(3):321-7 – reference: 16337999 - Cell. 2005 Dec 16;123(6):1133-46 – reference: 22135297 - Nucleic Acids Res. 2012 Jan;40(Database issue):D222-9 – reference: 21071411 - Nucleic Acids Res. 2011 Jan;39(Database issue):D163-9 – reference: 20547158 - FEBS Lett. 2010 Jul 16;584(14):3198-202 – reference: 20089154 - Genome Biol. 2010 Jan 20;11(1):R6 – reference: 17108354 - Nucleic Acids Res. 2007 Jan;35(Database issue):D149-55 – reference: 15610730 - Mol Cell. 2004 Dec 22;16(6):861-5 – reference: 20888440 - N Biotechnol. 2010 Dec 31;27(6):734-8 – reference: 18472421 - Curr Biol. 2008 May 20;18(10):758-62 – reference: 17612493 - Mol Cell. 2007 Jul 6;27(1):91-105 – reference: 19336450 - Genome Res. 2009 Jul;19(7):1175-83 – reference: 15652477 - Cell. 2005 Jan 14;120(1):15-20 – reference: 14697198 - Cell. 2003 Dec 26;115(7):787-98 – reference: 16845041 - Nucleic Acids Res. 2006 Jul 1;34(Web Server issue):W429-34 – reference: 18668040 - Nature. 2008 Sep 4;455(7209):58-63 – reference: 21448463 - PLoS One. 2011 Mar 23;6(3):e18115 – reference: 22371183 - Haematologica. 2012 Aug;97(8):1218-24 – reference: 17532529 - Drug Discov Today. 2007 Jun;12(11-12):452-8 – reference: 15131085 - Genes Dev. 2004 May 15;18(10):1165-78 – reference: 21532838 - Curr Genomics. 2010 Nov;11(7):537-61 – reference: 18955434 - Genome Res. 2009 Jan;19(1):92-105 – reference: 16990141 - Cell. 2006 Sep 22;126(6):1203-17 – reference: 21286309 - Curr Genomics. 2010 Aug;11(5):311-25 – reference: 18927107 - Nucleic Acids Res. 2009 Jan;37(Database issue):D98-104 – reference: 17135348 - Proc Natl Acad Sci U S A. 2006 Dec 12;103(50):18957-62 – reference: 15035981 - Cell. 2004 Mar 19;116(6):779-93 – reference: 16141076 - Science. 2005 Sep 2;309(5740):1577-81 – reference: 18996891 - Nucleic Acids Res. 2009 Jan;37(Database issue):D105-10 – reference: 22333591 - Cell Cycle. 2012 Mar 1;11(5):922-33 – reference: 17893677 - Nat Genet. 2007 Oct;39(10):1278-84 – reference: 15502875 - PLoS Biol. 2004 Nov;2(11):e363 – reference: 12824337 - Nucleic Acids Res. 2003 Jul 1;31(13):3406-15 – reference: 16458514 - Curr Biol. 2006 Mar 7;16(5):460-71 – reference: 14744438 - Cell. 2004 Jan 23;116(2):281-97 – reference: 22139918 - Nucleic Acids Res. 2012 Jan;40(Database issue):D1016-22 – reference: 21984948 - PLoS One. 2011;6(9):e25787 – reference: 18426918 - RNA. 2008 Jun;14(6):1012-7 – reference: 17360662 - Proc Natl Acad Sci U S A. 2007 Feb 27;104(9):3432-7 – reference: 18645597 - Curr Genomics. 2007 Jun;8(4):229-33 – reference: 18852463 - Proc Natl Acad Sci U S A. 2008 Oct 21;105(42):16230-5 – reference: 15735639 - Nature. 2005 Mar 17;434(7031):338-45 – reference: 17085592 - Proc Natl Acad Sci U S A. 2006 Nov 14;103(46):17337-42 – reference: 15738385 - Proc Natl Acad Sci U S A. 2005 Mar 15;102(11):4006-9 – reference: 15854907 - Curr Biol. 2005 Apr 26;15(8):743-9 – reference: 21966251 - Curr Genomics. 2011 Apr;12(2):130-7 – reference: 16009126 - Cell. 2005 Jul 15;122(1):6-7 – reference: 21441354 - Mol Biol Evol. 2011 Sep;28(9):2421-4 – reference: 23326503 - PLoS One. 2013;8(1):e53780 – reference: 19765283 - BMC Bioinformatics. 2009 Sep 18;10:295 – reference: 17535905 - Proc Natl Acad Sci U S A. 2007 Jun 5;104(23):9667-72 – reference: 15685193 - Nature. 2005 Feb 17;433(7027):769-73 – reference: 18806776 - Nature. 2008 Oct 23;455(7216):1124-8 – reference: 8252621 - Cell. 1993 Dec 3;75(5):843-54 – reference: 18344688 - Cell Cycle. 2008 Mar 15;7(6):759-64 – reference: 15806104 - Nat Genet. 2005 May;37(5):495-500 – reference: 18197166 - Nat Rev Genet. 2008 Feb;9(2):102-14 – reference: 17592038 - RNA. 2007 Aug;13(8):1198-204 – reference: 12824340 - Nucleic Acids Res. 2003 Jul 1;31(13):3429-31 – reference: 11896390 - Nat Genet. 2002 Apr;30(4):363-4 |
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