In silico tools for splicing defect prediction: a survey from the viewpoint of end users
RNA splicing is the process during which introns are excised and exons are spliced. The precise recognition of splicing signals is critical to this process, and mutations affecting splicing comprise a considerable proportion of genetic disease etiology. Analysis of RNA samples from the patient is th...
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| Vydáno v: | Genetics in medicine Ročník 16; číslo 7; s. 497 - 503 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Nature Publishing Group US
01.07.2014
Elsevier Limited |
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| ISSN: | 1098-3600, 1530-0366, 1530-0366 |
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| Abstract | RNA splicing is the process during which introns are excised and exons are spliced. The precise recognition of splicing signals is critical to this process, and mutations affecting splicing comprise a considerable proportion of genetic disease etiology. Analysis of RNA samples from the patient is the most straightforward and reliable method to detect splicing defects. However, currently, the technical limitation prohibits its use in routine clinical practice. In silico tools that predict potential consequences of splicing mutations may be useful in daily diagnostic activities. In this review, we provide medical geneticists with some basic insights into some of the most popular in silico tools for splicing defect prediction, from the viewpoint of end users. Bioinformaticians in relevant areas who are working on huge data sets may also benefit from this review. Specifically, we focus on those tools whose primary goal is to predict the impact of mutations within the 5′ and 3′ splicing consensus regions: the algorithms used by different tools as well as their major advantages and disadvantages are briefly introduced; the formats of their input and output are summarized; and the interpretation, evaluation, and prospection are also discussed.
Genet Med
16
7, 497–503. |
|---|---|
| AbstractList | RNA splicing is the process during which introns are excised and exons are spliced. The precise recognition of splicing signals is critical to this process, and mutations affecting splicing comprise a considerable proportion of genetic disease etiology. Analysis of RNA samples from the patient is the most straightforward and reliable method to detect splicing defects. However, currently, the technical limitation prohibits its use in routine clinical practice. In silico tools that predict potential consequences of splicing mutations may be useful in daily diagnostic activities. In this review, we provide medical geneticists with some basic insights into some of the most popular in silico tools for splicing defect prediction, from the viewpoint of end users. Bioinformaticians in relevant areas who are working on huge data sets may also benefit from this review. Specifically, we focus on those tools whose primary goal is to predict the impact of mutations within the 5[OElig and 3[OElig splicing consensus regions: the algorithms used by different tools as well as their major advantages and disadvantages are briefly introduced; the formats of their input and output are summarized; and the interpretation, evaluation, and prospection are also discussed. RNA splicing is the process during which introns are excised and exons are spliced. The precise recognition of splicing signals is critical to this process, and mutations affecting splicing comprise a considerable proportion of genetic disease etiology. Analysis of RNA samples from the patient is the most straightforward and reliable method to detect splicing defects. However, currently, the technical limitation prohibits its use in routine clinical practice. In silico tools that predict potential consequences of splicing mutations may be useful in daily diagnostic activities. In this review, we provide medical geneticists with some basic insights into some of the most popular in silico tools for splicing defect prediction, from the viewpoint of end users. Bioinformaticians in relevant areas who are working on huge data sets may also benefit from this review. Specifically, we focus on those tools whose primary goal is to predict the impact of mutations within the 5′ and 3′ splicing consensus regions: the algorithms used by different tools as well as their major advantages and disadvantages are briefly introduced; the formats of their input and output are summarized; and the interpretation, evaluation, and prospection are also discussed. Genet Med 16 7, 497–503. RNA splicing is the process during which introns are excised and exons are spliced. The precise recognition of splicing signals is critical to this process, and mutations affecting splicing comprise a considerable proportion of genetic disease etiology. Analysis of RNA samples from the patient is the most straightforward and reliable method to detect splicing defects. However, currently, the technical limitation prohibits its use in routine clinical practice. In silico tools that predict potential consequences of splicing mutations may be useful in daily diagnostic activities. In this review, we provide medical geneticists with some basic insights into some of the most popular in silico tools for splicing defect prediction, from the viewpoint of end users. Bioinformaticians in relevant areas who are working on huge data sets may also benefit from this review. Specifically, we focus on those tools whose primary goal is to predict the impact of mutations within the 5′ and 3′ splicing consensus regions: the algorithms used by different tools as well as their major advantages and disadvantages are briefly introduced; the formats of their input and output are summarized; and the interpretation, evaluation, and prospection are also discussed.Genet Med16 7, 497–503. RNA splicing is the process during which introns are excised and exons are spliced. The precise recognition of splicing signals is critical to this process and mutations affecting splicing comprise a considerable proportion of genetic disease etiology. Analysis of RNA samples from the patient is the most straightforward and reliable method to detect splicing defects. However, currently the technical limitation prohibits its use in routine clinical practice. In silico tools that predict potential consequences of splicing mutations may be useful in daily diagnostic activities. In this review, we provide medical geneticists with some basic insights into some of the most popular in silico tools for splicing defect prediction, from the viewpoint of end-users. Bioinformaticians in relevant areas who are working on huge datasets may also benefit from this review. Specifically, we focus on those tools whose primary goal is to predict the impact of mutations within the 5′ and 3′ splicing consensus regions: the algorithms used by different tools as well as their major advantages and disadvantages are briefly introduced; the formats of their input and output are summarized; and the interpretation, evaluation, and prospection are also discussed. RNA splicing is the process during which introns are excised and exons are spliced. The precise recognition of splicing signals is critical to this process, and mutations affecting splicing comprise a considerable proportion of genetic disease etiology. Analysis of RNA samples from the patient is the most straightforward and reliable method to detect splicing defects. However, currently, the technical limitation prohibits its use in routine clinical practice. In silico tools that predict potential consequences of splicing mutations may be useful in daily diagnostic activities. In this review, we provide medical geneticists with some basic insights into some of the most popular in silico tools for splicing defect prediction, from the viewpoint of end users. Bioinformaticians in relevant areas who are working on huge data sets may also benefit from this review. Specifically, we focus on those tools whose primary goal is to predict the impact of mutations within the 5' and 3' splicing consensus regions: the algorithms used by different tools as well as their major advantages and disadvantages are briefly introduced; the formats of their input and output are summarized; and the interpretation, evaluation, and prospection are also discussed.RNA splicing is the process during which introns are excised and exons are spliced. The precise recognition of splicing signals is critical to this process, and mutations affecting splicing comprise a considerable proportion of genetic disease etiology. Analysis of RNA samples from the patient is the most straightforward and reliable method to detect splicing defects. However, currently, the technical limitation prohibits its use in routine clinical practice. In silico tools that predict potential consequences of splicing mutations may be useful in daily diagnostic activities. In this review, we provide medical geneticists with some basic insights into some of the most popular in silico tools for splicing defect prediction, from the viewpoint of end users. Bioinformaticians in relevant areas who are working on huge data sets may also benefit from this review. Specifically, we focus on those tools whose primary goal is to predict the impact of mutations within the 5' and 3' splicing consensus regions: the algorithms used by different tools as well as their major advantages and disadvantages are briefly introduced; the formats of their input and output are summarized; and the interpretation, evaluation, and prospection are also discussed. RNA splicing is the process during which introns are excised and exons are spliced. The precise recognition of splicing signals is critical to this process, and mutations affecting splicing comprise a considerable proportion of genetic disease etiology. Analysis of RNA samples from the patient is the most straightforward and reliable method to detect splicing defects. However, currently, the technical limitation prohibits its use in routine clinical practice. In silico tools that predict potential consequences of splicing mutations may be useful in daily diagnostic activities. In this review, we provide medical geneticists with some basic insights into some of the most popular in silico tools for splicing defect prediction, from the viewpoint of end users. Bioinformaticians in relevant areas who are working on huge data sets may also benefit from this review. Specifically, we focus on those tools whose primary goal is to predict the impact of mutations within the 5' and 3' splicing consensus regions: the algorithms used by different tools as well as their major advantages and disadvantages are briefly introduced; the formats of their input and output are summarized; and the interpretation, evaluation, and prospection are also discussed. |
| Author | Liu, Xiaoming Jian, Xueqiu Boerwinkle, Eric |
| AuthorAffiliation | 2 Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, USA 1 Division of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA |
| AuthorAffiliation_xml | – name: 2 Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, USA – name: 1 Division of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA |
| Author_xml | – sequence: 1 givenname: Xueqiu surname: Jian fullname: Jian, Xueqiu organization: Division of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston – sequence: 2 givenname: Eric surname: Boerwinkle fullname: Boerwinkle, Eric organization: Division of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Human Genome Sequencing Center, Baylor College of Medicine – sequence: 3 givenname: Xiaoming surname: Liu fullname: Liu, Xiaoming email: Xiaoming.Liu@uth.tmc.edu organization: Division of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/24263461$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1101/gr.118638.110 10.1089/cmb.1997.4.311 10.1161/CIRCGENETICS.108.829747 10.1093/nar/gkg616 10.1073/pnas.1101135108 10.1371/journal.pone.0057173 10.1093/nar/15.17.7155 10.1093/nar/29.5.1185 10.1126/science.278.5341.1315 10.1089/1066527041410418 10.1093/bioinformatics/bth058 10.1038/nrg775 10.1038/ejhg.2008.257 10.1038/ng854 10.1093/nar/gkm407 10.1016/j.ymgme.2008.12.014 10.1086/498853 10.1016/j.cell.2009.02.011 10.1002/humu.20151 10.1038/ejhg.2011.100 10.1073/pnas.74.8.3171 10.1002/humu.20765 10.1038/nrg2164 10.1016/0092-8674(95)90460-3 10.1002/humu.22101 10.1002/humu.20811 10.1186/1471-2105-12-S4-S2 10.1007/PL00006200 10.1016/0092-8674(77)90180-5 10.1093/nar/gkg901 10.1006/jmbi.1997.0951 10.1016/0022-2836(91)90380-O 10.1002/humu.10295 10.1007/BF00210743 10.1038/embor.2009.170 10.1093/nar/gkp215 10.1146/annurev.bi.50.070181.002025 10.1093/bioinformatics/bts074 10.1093/nar/gkp320 |
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| References | Sterne-Weiler, Howard, Mort, Cooper, Sanford (CR15) 2011; 21 Baralle, Lucassen, Buratti (CR16) 2009; 10 Houdayer, Dehainault, Mattler (CR38) 2008; 29 Psaty, O’Donnell, Gudnason (CR36) 2009; 2 Holla, Nakken, Mattingsdal (CR39) 2009; 96 Freund, Asang, Kammler (CR27) 2003; 31 CR37 Chow, Gelinas, Broker, Roberts (CR2) 1977; 12 Rogozin, Milanesi (CR18) 1997; 45 Schwartz, Hall, Ast (CR32) 2009; 37 Cartegni, Hastings, Calarco, de Stanchina, Krainer (CR10) 2006; 78 CR34 Cartegni, Chew, Krainer (CR6) 2002; 3 Wang, Cooper (CR11) 2007; 8 Brunak, Engelbrecht, Knudsen (CR21) 1991; 220 Divina, Kvitkovicova, Buratti, Vorechovsky (CR29) 2009; 17 Shapiro, Senapathy (CR4) 1987; 15 Desmet, Hamroun, Lalande, Collod-Béroud, Claustres, Béroud (CR31) 2009; 37 Houdayer, Caux-Moncoutier, Krieger (CR35) 2012; 33 Cartegni, Krainer (CR9) 2002; 30 Krawczak, Reiss, Cooper (CR13) 1992; 90 Lim, Fairbrother (CR30) 2012; 28 Cartegni, Wang, Zhu, Zhang, Krainer (CR17) 2003; 31 Lim, Ferraris, Filloux, Raphael, Fairbrother (CR14) 2011; 108 Lefebvre, Bürglen, Reboullet (CR8) 1995; 80 Berget, Moore, Sharp (CR1) 1977; 74 Eng, Coutinho, Nahas (CR26) 2004; 23 Yeo, Burge (CR25) 2004; 11 Pertea, Lin, Salzberg (CR20) 2001; 29 Cooper, Wan, Dreyfuss (CR12) 2009; 136 Théry, Krieger, Gaildrat (CR41) 2011; 19 Colombo, De Vecchi, Caleca (CR42) 2013; 8 Burge, Tuschl, Sharp, Gesteland, Cech, Atkins (CR5) 1999 Faber, Glatting, Mueller, Risch, Hotz-Wagenblatt (CR33) 2011; 12 Vreeswijk, Kraan, van der Klift (CR40) 2009; 30 Burge, Karlin (CR19) 1997; 268 Reese, Eeckman, Kulp, Haussler (CR22) 1997; 4 Nalla, Rogan (CR28) 2005; 25 Breathnach, Chambon (CR3) 1981; 50 Dogan, Getoor, Wilbur, Mount (CR23) 2007; 35 Brendel, Xing, Zhu (CR24) 2004; 20 Lynch, Lee, Morrow, Welcsh, León, King (CR7) 1997; 278 Faber (10.1038/gim.2013.176_bb0170) Lefebvre (10.1038/gim.2013.176_bb0045) Cartegni (10.1038/gim.2013.176_bb0055) 10.1038/gim.2013.176_bb0190 Yeo (10.1038/gim.2013.176_bb0130) Desmet (10.1038/gim.2013.176_bb0160) Houdayer (10.1038/gim.2013.176_bb0195) Rogozin (10.1038/gim.2013.176_bb0095) 10.1038/gim.2013.176_bb0175 Sterne-Weiler (10.1038/gim.2013.176_bb0080) Shapiro (10.1038/gim.2013.176_bb0025) Lim (10.1038/gim.2013.176_bb0075) Eng (10.1038/gim.2013.176_bb0135) Chow (10.1038/gim.2013.176_bb0015) Cooper (10.1038/gim.2013.176_bb0065) Dogan (10.1038/gim.2013.176_bb0120) Vreeswijk (10.1038/gim.2013.176_bb0205) Wang (10.1038/gim.2013.176_bb0060) Freund (10.1038/gim.2013.176_bb0140) Cartegni (10.1038/gim.2013.176_bb0035) Reese (10.1038/gim.2013.176_bb0115) Lynch (10.1038/gim.2013.176_bb0040) Burge (10.1038/gim.2013.176_bb0100) Cartegni (10.1038/gim.2013.176_bb0050) Pertea (10.1038/gim.2013.176_bb0105) Nalla (10.1038/gim.2013.176_bb0145) Berget (10.1038/gim.2013.176_bb0010) Lim (10.1038/gim.2013.176_bb0155) CHARGE Consortium (10.1038/gim.2013.176_bb0185) Cartegni (10.1038/gim.2013.176_bb0090) Holla (10.1038/gim.2013.176_bb0200) Houdayer (10.1038/gim.2013.176_bb0180) Krawczak (10.1038/gim.2013.176_bb0070) Brendel (10.1038/gim.2013.176_bb0125) Brunak (10.1038/gim.2013.176_bb0110) Breathnach (10.1038/gim.2013.176_bb0020) Théry (10.1038/gim.2013.176_bb0210) Schwartz (10.1038/gim.2013.176_bb0165) Baralle (10.1038/gim.2013.176_bb0085) Divina (10.1038/gim.2013.176_bb0150) Burge (10.1038/gim.2013.176_bb0030) 1999 Colombo (10.1038/gim.2013.176_bb0215) |
| References_xml | – start-page: 525 year: 1999 end-page: 560 ident: CR5 article-title: Splicing of precursors to mRNAs by the spliceosomes. publication-title: The RNA World – volume: 21 start-page: 1563 year: 2011 end-page: 1571 ident: CR15 article-title: Loss of exon identity is a common mechanism of human inherited disease. publication-title: Genome Res doi: 10.1101/gr.118638.110 – volume: 4 start-page: 311 year: 1997 end-page: 323 ident: CR22 article-title: Improved splice site detection in Genie. publication-title: J Comput Biol doi: 10.1089/cmb.1997.4.311 – volume: 2 start-page: 73 year: 2009 end-page: 80 ident: CR36 article-title: Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium: Design of prospective meta-analyses of genome-wide association studies from 5 cohorts. publication-title: Circ Cardiovasc Genet doi: 10.1161/CIRCGENETICS.108.829747 – volume: 31 start-page: 3568 year: 2003 end-page: 3571 ident: CR17 article-title: ESEfinder: A web resource to identify exonic splicing enhancers. publication-title: Nucleic Acids Res doi: 10.1093/nar/gkg616 – ident: CR37 – volume: 108 start-page: 11093 year: 2011 end-page: 11098 ident: CR14 article-title: Using positional distribution to identify splicing elements and predict pre-mRNA processing defects in human genes. publication-title: Proc Natl Acad Sci USA doi: 10.1073/pnas.1101135108 – volume: 8 start-page: e57173 year: 2013 ident: CR42 article-title: Comparative and in silico analyses of variants in splicing regions of BRCA1 and BRCA2 genes and characterization of novel pathogenic mutations. publication-title: PLoS ONE doi: 10.1371/journal.pone.0057173 – volume: 15 start-page: 7155 year: 1987 end-page: 7174 ident: CR4 article-title: RNA splice junctions of different classes of eukaryotes: sequence statistics and functional implications in gene expression. publication-title: Nucleic Acids Res doi: 10.1093/nar/15.17.7155 – volume: 29 start-page: 1185 year: 2001 end-page: 1190 ident: CR20 article-title: GeneSplicer: a new computational method for splice site prediction. publication-title: Nucleic Acids Res doi: 10.1093/nar/29.5.1185 – volume: 278 start-page: 1315 year: 1997 end-page: 1318 ident: CR7 article-title: Nonsyndromic deafness DFNA1 associated with mutation of a human homolog of the Drosophila gene diaphanous. publication-title: Science doi: 10.1126/science.278.5341.1315 – volume: 11 start-page: 377 year: 2004 end-page: 394 ident: CR25 article-title: Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals. publication-title: J Comput Biol doi: 10.1089/1066527041410418 – volume: 20 start-page: 1157 year: 2004 end-page: 1169 ident: CR24 article-title: Gene structure prediction from consensus spliced alignment of multiple ESTs matching the same genomic locus. publication-title: Bioinformatics doi: 10.1093/bioinformatics/bth058 – volume: 3 start-page: 285 year: 2002 end-page: 298 ident: CR6 article-title: Listening to silence and understanding nonsense: exonic mutations that affect splicing. publication-title: Nat Rev Genet doi: 10.1038/nrg775 – volume: 17 start-page: 759 year: 2009 end-page: 765 ident: CR29 article-title: Ab initio prediction of mutation-induced cryptic splice-site activation and exon skipping. publication-title: Eur J Hum Genet doi: 10.1038/ejhg.2008.257 – volume: 30 start-page: 377 year: 2002 end-page: 384 ident: CR9 article-title: Disruption of an SF2/ASF-dependent exonic splicing enhancer in SMN2 causes spinal muscular atrophy in the absence of SMN1. publication-title: Nat Genet doi: 10.1038/ng854 – volume: 35 start-page: W285 year: 2007 end-page: W291 ident: CR23 article-title: SplicePort–an interactive splice-site analysis tool. publication-title: Nucleic Acids Res doi: 10.1093/nar/gkm407 – volume: 96 start-page: 245 year: 2009 end-page: 252 ident: CR39 article-title: Effects of intronic mutations in the LDLR gene on pre-mRNA splicing: Comparison of wet-lab and bioinformatics analyses. publication-title: Mol Genet Metab doi: 10.1016/j.ymgme.2008.12.014 – volume: 78 start-page: 63 year: 2006 end-page: 77 ident: CR10 article-title: Determinants of exon 7 splicing in the spinal muscular atrophy genes, SMN1 and SMN2. publication-title: Am J Hum Genet doi: 10.1086/498853 – volume: 136 start-page: 777 year: 2009 end-page: 793 ident: CR12 article-title: RNA and disease. publication-title: Cell doi: 10.1016/j.cell.2009.02.011 – volume: 25 start-page: 334 year: 2005 end-page: 342 ident: CR28 article-title: Automated splicing mutation analysis by information theory. publication-title: Hum Mutat doi: 10.1002/humu.20151 – volume: 19 start-page: 1052 year: 2011 end-page: 1058 ident: CR41 article-title: Contribution of bioinformatics predictions and functional splicing assays to the interpretation of unclassified variants of the BRCA genes. publication-title: Eur J Hum Genet doi: 10.1038/ejhg.2011.100 – volume: 74 start-page: 3171 year: 1977 end-page: 3175 ident: CR1 article-title: Spliced segments at the 5’ terminus of adenovirus 2 late mRNA. publication-title: Proc Natl Acad Sci USA doi: 10.1073/pnas.74.8.3171 – volume: 29 start-page: 975 year: 2008 end-page: 982 ident: CR38 article-title: Evaluation of in silico splice tools for decision-making in molecular diagnosis. publication-title: Hum Mutat doi: 10.1002/humu.20765 – volume: 8 start-page: 749 year: 2007 end-page: 761 ident: CR11 article-title: Splicing in disease: disruption of the splicing code and the decoding machinery. publication-title: Nat Rev Genet doi: 10.1038/nrg2164 – volume: 80 start-page: 155 year: 1995 end-page: 165 ident: CR8 article-title: Identification and characterization of a spinal muscular atrophy-determining gene. publication-title: Cell doi: 10.1016/0092-8674(95)90460-3 – volume: 33 start-page: 1228 year: 2012 end-page: 1238 ident: CR35 article-title: Guidelines for splicing analysis in molecular diagnosis derived from a set of 327 combined in silico/in vitro studies on BRCA1 and BRCA2 variants. publication-title: Hum Mutat doi: 10.1002/humu.22101 – volume: 30 start-page: 107 year: 2009 end-page: 114 ident: CR40 article-title: Intronic variants in BRCA1 and BRCA2 that affect RNA splicing can be reliably selected by splice-site prediction programs. publication-title: Hum Mutat doi: 10.1002/humu.20811 – volume: 12 start-page: S2 issue: suppl 4 year: 2011 ident: CR33 article-title: Genome-wide prediction of splice-modifying SNPs in human genes using a new analysis pipeline called AASsites. publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-12-S4-S2 – volume: 45 start-page: 50 year: 1997 end-page: 59 ident: CR18 article-title: Analysis of donor splice sites in different eukaryotic organisms. publication-title: J Mol Evol doi: 10.1007/PL00006200 – volume: 12 start-page: 1 year: 1977 end-page: 8 ident: CR2 article-title: An amazing sequence arrangement at the 5’ ends of adenovirus 2 messenger RNA. publication-title: Cell doi: 10.1016/0092-8674(77)90180-5 – volume: 31 start-page: 6963 year: 2003 end-page: 6975 ident: CR27 article-title: A novel approach to describe a U1 snRNA binding site. publication-title: Nucleic Acids Res doi: 10.1093/nar/gkg901 – volume: 268 start-page: 78 year: 1997 end-page: 94 ident: CR19 article-title: Prediction of complete gene structures in human genomic DNA. publication-title: J Mol Biol doi: 10.1006/jmbi.1997.0951 – volume: 220 start-page: 49 year: 1991 end-page: 65 ident: CR21 article-title: Prediction of human mRNA donor and acceptor sites from the DNA sequence. publication-title: J Mol Biol doi: 10.1016/0022-2836(91)90380-O – volume: 23 start-page: 67 year: 2004 end-page: 76 ident: CR26 article-title: Nonclassical splicing mutations in the coding and noncoding regions of the ATM Gene: maximum entropy estimates of splice junction strengths. publication-title: Hum Mutat doi: 10.1002/humu.10295 – ident: CR34 – volume: 90 start-page: 41 year: 1992 end-page: 54 ident: CR13 article-title: The mutational spectrum of single base-pair substitutions in mRNA splice junctions of human genes: causes and consequences. publication-title: Hum Genet doi: 10.1007/BF00210743 – volume: 10 start-page: 810 year: 2009 end-page: 816 ident: CR16 article-title: Missed threads. The impact of pre-mRNA splicing defects on clinical practice. publication-title: EMBO Rep doi: 10.1038/embor.2009.170 – volume: 37 start-page: e67 year: 2009 ident: CR31 article-title: Human Splicing Finder: an online bioinformatics tool to predict splicing signals. publication-title: Nucleic Acids Res doi: 10.1093/nar/gkp215 – volume: 50 start-page: 349 year: 1981 end-page: 383 ident: CR3 article-title: Organization and expression of eucaryotic split genes coding for proteins. publication-title: Annu Rev Biochem doi: 10.1146/annurev.bi.50.070181.002025 – volume: 28 start-page: 1031 year: 2012 end-page: 1032 ident: CR30 article-title: Spliceman–a computational web server that predicts sequence variations in pre-mRNA splicing. publication-title: Bioinformatics doi: 10.1093/bioinformatics/bts074 – volume: 37 start-page: W189 issue: Web Server issue year: 2009 end-page: W192 ident: CR32 article-title: SROOGLE: webserver for integrative, user-friendly visualization of splicing signals. publication-title: Nucleic Acids Res doi: 10.1093/nar/gkp320 – ident: 10.1038/gim.2013.176_bb0155 – start-page: 525 year: 1999 ident: 10.1038/gim.2013.176_bb0030 article-title: Splicing of precursors to mRNAs by the spliceosomes. – ident: 10.1038/gim.2013.176_bb0125 – ident: 10.1038/gim.2013.176_bb0145 – ident: 10.1038/gim.2013.176_bb0200 – ident: 10.1038/gim.2013.176_bb0070 – ident: 10.1038/gim.2013.176_bb0080 – ident: 10.1038/gim.2013.176_bb0095 – ident: 10.1038/gim.2013.176_bb0160 – ident: 10.1038/gim.2013.176_bb0210 – ident: 10.1038/gim.2013.176_bb0140 – ident: 10.1038/gim.2013.176_bb0050 – ident: 10.1038/gim.2013.176_bb0135 – ident: 10.1038/gim.2013.176_bb0055 – ident: 10.1038/gim.2013.176_bb0060 – ident: 10.1038/gim.2013.176_bb0165 – ident: 10.1038/gim.2013.176_bb0035 – ident: 10.1038/gim.2013.176_bb0085 – ident: 10.1038/gim.2013.176_bb0170 – ident: 10.1038/gim.2013.176_bb0020 – ident: 10.1038/gim.2013.176_bb0110 – ident: 10.1038/gim.2013.176_bb0010 – ident: 10.1038/gim.2013.176_bb0150 – ident: 10.1038/gim.2013.176_bb0185 – ident: 10.1038/gim.2013.176_bb0175 – ident: 10.1038/gim.2013.176_bb0190 – ident: 10.1038/gim.2013.176_bb0130 – ident: 10.1038/gim.2013.176_bb0100 – ident: 10.1038/gim.2013.176_bb0015 – ident: 10.1038/gim.2013.176_bb0195 – ident: 10.1038/gim.2013.176_bb0115 – ident: 10.1038/gim.2013.176_bb0065 – ident: 10.1038/gim.2013.176_bb0025 – ident: 10.1038/gim.2013.176_bb0180 – ident: 10.1038/gim.2013.176_bb0040 – ident: 10.1038/gim.2013.176_bb0120 – ident: 10.1038/gim.2013.176_bb0045 – ident: 10.1038/gim.2013.176_bb0215 – ident: 10.1038/gim.2013.176_bb0090 – ident: 10.1038/gim.2013.176_bb0205 – ident: 10.1038/gim.2013.176_bb0105 – ident: 10.1038/gim.2013.176_bb0075 |
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| Snippet | RNA splicing is the process during which introns are excised and exons are spliced. The precise recognition of splicing signals is critical to this process,... RNA splicing is the process during which introns are excised and exons are spliced. The precise recognition of splicing signals is critical to this process and... |
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| SubjectTerms | 631/114 631/208/1516 631/208/1792 Biomedical and Life Sciences Biomedicine Computational Biology - methods Computer Simulation Genetic Variation - genetics Human Genetics Humans Laboratory Medicine Mutation review RNA Precursors - genetics RNA Splicing - genetics Software |
| Title | In silico tools for splicing defect prediction: a survey from the viewpoint of end users |
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