MicroRNAs (miRNAs) are short endogenous noncoding RNAs that bind to target mRNAs, usually resulting in degradation and translational repression. Identification of miRNA targets is crucial for deciphering functional roles of the numerous miRNAs that are rapidly generated by sequencing efforts. Computational prediction methods are widely used for high-throughput generation of putative miRNA targets.
Researcher from the University of Alberta review a comprehensive collection of 38 miRNA sequence-based computational target predictors in animals that were developed over the past decade. Their in-depth analysis considers all significant perspectives including the underlying predictive methodologies with focus on how they draw from the mechanistic basis of the miRNA-mRNA interaction. They also discuss ease of use, availability, impact of the considered predictors and the evaluation protocols that were used to assess them. The gene-level evaluation is based on three benchmark data sets that rely on different ways to annotate targets including biochemical assays, microarrays and pSILAC. The researchers offer practical advice on selection of appropriate predictors according to certain properties of miRNA sequences, characteristics of a specific application and desired levels of predictive quality. Finally, they discuss future work related to the design of new models, data quality, improved usability, need for standardized evaluation and ability to predict mRNA expression changes.
Methodologies and the corresponding mechanistic basis of miRNA–mRNA interaction used by the miRNA target predictors
|
Predictor |
Reference |
Year published |
Model type |
Complementarity |
Site accessibility |
Conservation |
||
|
Seed |
Nonseed |
Free energy |
AU % |
|||||
|
Stark et al. |
[20] |
2003 |
Screening |
1–8 |
miRNA size+5 |
mFold |
✕ |
a d |
|
TargetScan |
[44] |
2003 |
Score |
7mer-m8 |
to 1st mismatch |
Vienna RNA |
✓ |
m r z |
|
DIANA-microT |
[54] |
2004 |
Score |
✕ |
38 nt |
✓ |
m |
|
|
RNAhybrid |
[47] |
2004 |
Score |
6mer |
✕ |
RNAhybrid |
✕ |
a d |
|
miRanda |
[55] |
2004 |
Score |
7mer-m8 |
✕ |
Vienna RNA |
✕ |
f m r |
|
Rajewsky’s |
[23] |
2004 |
Score |
1–8 |
✕ |
mFold |
✕ |
d |
|
TargetScanS |
[56] |
2005 |
Score |
6mer |
✕ |
✕ |
✕ |
c g h m r |
|
Robins |
[57] |
2005 |
Score |
2–8 |
✕ |
Vienna RNA |
✕ |
✕ |
|
Xie et al. |
[58] |
2005 |
Score |
8mer |
✕ |
✕ |
✕ |
g h m r |
|
PicTar |
[46] |
2005 |
Score |
7mer-A1, 7mer-m8 |
Remaining |
mFold |
✕ |
d |
|
MovingTarget |
[59] |
2005 |
Screening |
1–8 |
50 nt |
DINAMelt |
✕ |
d |
|
Microlnspector |
[60] |
2005 |
Score |
7mer-A1, 7mer-m8 |
✕ |
Vienna RNA |
✕ |
✕ |
|
TargetBoost |
[61] |
2005 |
GP |
pattern |
30 nt |
mFold |
✕ |
✕ |
|
Stark et al. |
[62] |
2005 |
Score |
6mer |
10th nt to end |
RNAhybrid |
✕ |
d |
|
miTarget |
[63] |
2006 |
SVM |
2–7 |
20 nt |
Vienna RNA |
✓ |
✕ |
|
RNA22 |
[15] |
2006 |
Score |
✕ |
Pattern |
✕ |
✕ |
✕ |
|
MicroTar |
[64] |
2006 |
Score |
7mer-A1, 7mer-m8 |
✕ |
Vienna RNA |
✕ |
✕ |
|
EIMMo |
[65] |
2007 |
Bayesian |
7mer-A1, 7mer-m8 |
✕ |
✕ |
✕ |
✓ |
|
STarMir |
[66] |
2007 |
Score |
✕ |
miRNA size |
sFold |
✕ |
✕ |
|
PITA |
[67] |
2007 |
Score |
6mer |
✕ |
Vienna RNA |
✕ |
✕ |
|
TargetRank |
[68] |
2007 |
Score |
6mer |
✕ |
✕ |
✓ |
✓ |
|
MirTarget2 |
[43] |
2008 |
SVM |
6mer |
✕ |
Vienna RNA |
✕ |
✕ |
|
HuMiTar |
[48] |
2008 |
Score |
6mer |
9–13, 14–20 nt |
✕ |
✕ |
✕ |
|
TargetMiner |
[49] |
2009 |
SVM |
6mer |
13–16 nt |
✓ |
✓ |
✓ |
|
TargetSpy |
[52] |
2010 |
DS |
✕ |
All |
Vienna RNA |
✕ |
✕ |
|
Mtar |
[53] |
2010 |
ANN |
6mer |
Remaining |
Vienna RNA |
✕ |
✕ |
|
mirSVR |
[40] |
2010 |
Score |
2–7 |
✕ |
miRNAbind |
✓ |
✓ |
|
SVMicrO |
[50] |
2010 |
SVM |
5 patterns |
Remaining |
Vienna RNA |
✓ |
✓ |
|
RepTar |
[69] |
2010 |
Screening |
6mer |
Remaining |
Vienna RNA |
✕ |
✓ |
|
PACMIT |
[51] |
2011 |
Screening |
✕ |
Remaining |
Vienna RNA |
✕ |
✕ |
|
MultiMiTar |
[70] |
2011 |
SVM |
6mer |
13–16 nt |
✕ |
✓ |
✕ |
|
miREE |
[71] |
2011 |
SVM |
1–8 |
13–16 nt, remain |
Vienna RNA |
✓ |
✕ |
|
miRcode |
[72] |
2012 |
Screening |
7mer-A1, 7mer-m8 |
✕ |
✕ |
✕ |
P M other V |
|
miRmap |
[41] |
2012 |
Regression |
6mer |
Remaining |
Vienna RNA |
✓ |
M |
|
HomoTarget |
[73] |
2012 |
ANN |
1–8 |
Remaining |
✓ |
✕ |
✕ |
|
SuperMirTar |
[74] |
2013 |
Graph |
6mer |
12–17 nt |
RNAhybrid |
✓ |
✕ |
|
Fujiwara’s |
[75] |
2013 |
Cis-element |
✕ |
✕ |
✕ |
✕ |
✕ |
|
MIRZA |
[76] |
2013 |
Bayesian |
1–8 |
Remaining |
✓ |
✕ |
✕ |
[box type=”shadow” align=”alignleft” ]Fan X, Kurgan L. (2014) Comprehensive overview and assessment of computational prediction of microRNA targets in animals. Brief Bioinform [Epub ahead of print]. [abstract][/fusion_text][/fusion_builder_column_inner][/fusion_builder_row_inner][/fusion_builder_column]
Related Posts
MicroRNAs (miRNAs) are short endogenous noncoding RNAs that bind to target mRNAs, usually resulting in degradation and translational repression. Identification of miRNA targets is crucial for deciphering functional roles of the numerous miRNAs that are rapidly generated by sequencing efforts. Computational prediction methods are widely used for high-throughput generation of putative miRNA targets.
Researcher from the University of Alberta review a comprehensive collection of 38 miRNA sequence-based computational target predictors in animals that were developed over the past decade. Their in-depth analysis considers all significant perspectives including the underlying predictive methodologies with focus on how they draw from the mechanistic basis of the miRNA-mRNA interaction. They also discuss ease of use, availability, impact of the considered predictors and the evaluation protocols that were used to assess them. The gene-level evaluation is based on three benchmark data sets that rely on different ways to annotate targets including biochemical assays, microarrays and pSILAC. The researchers offer practical advice on selection of appropriate predictors according to certain properties of miRNA sequences, characteristics of a specific application and desired levels of predictive quality. Finally, they discuss future work related to the design of new models, data quality, improved usability, need for standardized evaluation and ability to predict mRNA expression changes.
Methodologies and the corresponding mechanistic basis of miRNA–mRNA interaction used by the miRNA target predictors
|
Predictor |
Reference |
Year published |
Model type |
Complementarity |
Site accessibility |
Conservation |
||
|
Seed |
Nonseed |
Free energy |
AU % |
|||||
|
Stark et al. |
[20] |
2003 |
Screening |
1–8 |
miRNA size+5 |
mFold |
✕ |
a d |
|
TargetScan |
[44] |
2003 |
Score |
7mer-m8 |
to 1st mismatch |
Vienna RNA |
✓ |
m r z |
|
DIANA-microT |
[54] |
2004 |
Score |
✕ |
38 nt |
✓ |
m |
|
|
RNAhybrid |
[47] |
2004 |
Score |
6mer |
✕ |
RNAhybrid |
✕ |
a d |
|
miRanda |
[55] |
2004 |
Score |
7mer-m8 |
✕ |
Vienna RNA |
✕ |
f m r |
|
Rajewsky’s |
[23] |
2004 |
Score |
1–8 |
✕ |
mFold |
✕ |
d |
|
TargetScanS |
[56] |
2005 |
Score |
6mer |
✕ |
✕ |
✕ |
c g h m r |
|
Robins |
[57] |
2005 |
Score |
2–8 |
✕ |
Vienna RNA |
✕ |
✕ |
|
Xie et al. |
[58] |
2005 |
Score |
8mer |
✕ |
✕ |
✕ |
g h m r |
|
PicTar |
[46] |
2005 |
Score |
7mer-A1, 7mer-m8 |
Remaining |
mFold |
✕ |
d |
|
MovingTarget |
[59] |
2005 |
Screening |
1–8 |
50 nt |
DINAMelt |
✕ |
d |
|
Microlnspector |
[60] |
2005 |
Score |
7mer-A1, 7mer-m8 |
✕ |
Vienna RNA |
✕ |
✕ |
|
TargetBoost |
[61] |
2005 |
GP |
pattern |
30 nt |
mFold |
✕ |
✕ |
|
Stark et al. |
[62] |
2005 |
Score |
6mer |
10th nt to end |
RNAhybrid |
✕ |
d |
|
miTarget |
[63] |
2006 |
SVM |
2–7 |
20 nt |
Vienna RNA |
✓ |
✕ |
|
RNA22 |
[15] |
2006 |
Score |
✕ |
Pattern |
✕ |
✕ |
✕ |
|
MicroTar |
[64] |
2006 |
Score |
7mer-A1, 7mer-m8 |
✕ |
Vienna RNA |
✕ |
✕ |
|
EIMMo |
[65] |
2007 |
Bayesian |
7mer-A1, 7mer-m8 |
✕ |
✕ |
✕ |
✓ |
|
STarMir |
[66] |
2007 |
Score |
✕ |
miRNA size |
sFold |
✕ |
✕ |
|
PITA |
[67] |
2007 |
Score |
6mer |
✕ |
Vienna RNA |
✕ |
✕ |
|
TargetRank |
[68] |
2007 |
Score |
6mer |
✕ |
✕ |
✓ |
✓ |
|
MirTarget2 |
[43] |
2008 |
SVM |
6mer |
✕ |
Vienna RNA |
✕ |
✕ |
|
HuMiTar |
[48] |
2008 |
Score |
6mer |
9–13, 14–20 nt |
✕ |
✕ |
✕ |
|
TargetMiner |
[49] |
2009 |
SVM |
6mer |
13–16 nt |
✓ |
✓ |
✓ |
|
TargetSpy |
[52] |
2010 |
DS |
✕ |
All |
Vienna RNA |
✕ |
✕ |
|
Mtar |
[53] |
2010 |
ANN |
6mer |
Remaining |
Vienna RNA |
✕ |
✕ |
|
mirSVR |
[40] |
2010 |
Score |
2–7 |
✕ |
miRNAbind |
✓ |
✓ |
|
SVMicrO |
[50] |
2010 |
SVM |
5 patterns |
Remaining |
Vienna RNA |
✓ |
✓ |
|
RepTar |
[69] |
2010 |
Screening |
6mer |
Remaining |
Vienna RNA |
✕ |
✓ |
|
PACMIT |
[51] |
2011 |
Screening |
✕ |
Remaining |
Vienna RNA |
✕ |
✕ |
|
MultiMiTar |
[70] |
2011 |
SVM |
6mer |
13–16 nt |
✕ |
✓ |
✕ |
|
miREE |
[71] |
2011 |
SVM |
1–8 |
13–16 nt, remain |
Vienna RNA |
✓ |
✕ |
|
miRcode |
[72] |
2012 |
Screening |
7mer-A1, 7mer-m8 |
✕ |
✕ |
✕ |
P M other V |
|
miRmap |
[41] |
2012 |
Regression |
6mer |
Remaining |
Vienna RNA |
✓ |
M |
|
HomoTarget |
[73] |
2012 |
ANN |
1–8 |
Remaining |
✓ |
✕ |
✕ |
|
SuperMirTar |
[74] |
2013 |
Graph |
6mer |
12–17 nt |
RNAhybrid |
✓ |
✕ |
|
Fujiwara’s |
[75] |
2013 |
Cis-element |
✕ |
✕ |
✕ |
✕ |
✕ |
|
MIRZA |
[76] |
2013 |
Bayesian |
1–8 |
Remaining |
✓ |
✕ |
✕ |
[box type=”shadow” align=”alignleft” ]Fan X, Kurgan L. (2014) Comprehensive overview and assessment of computational prediction of microRNA targets in animals. Brief Bioinform [Epub ahead of print]. [abstract][/fusion_text][/fusion_builder_column_inner][/fusion_builder_row_inner][/fusion_builder_column]
Related Posts
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