ArticleBMC bioinformatics2007
Identification of sequence motifs significantly associated with antisense activity.
Article in BMC bioinformatics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Knockdown of LdMC1 and Hsp70 by antisense oligonucleotides causes cell-cycle defects and programmed cell death in Leishmania donovani.Molecular and cellular biochemistry · 2012Article
- Increased in vivo inhibition of gene expression by combining RNA interference and U1 inhibition.Nucleic acids research · 2012Article
- Antisense DNA parameters derived from next-nearest-neighbor analysis of experimental data.BMC bioinformatics · 2010Article
- Gene silencing by synthetic U1 adaptors.Nature biotechnology · 2009Article
- IDT SciTools: a suite for analysis and design of nucleic acid oligomers.Nucleic acids research · 2008Article
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundPredicting the suppression activity of antisense oligonucleotide sequences is the main goal of the rational design of nucleic acids. To create an effective predictive model, it is important to know what properties of an oligonucleotide sequence associate significantly with antisense activity. Also, for the model to be efficient we must know what properties do not associate significantly and can be omitted from the model. This paper will discuss the results of a randomization procedure to find motifs that associate significantly with either high or low antisense suppression activity, analysis of their properties, as well as the results of support vector machine modelling using these significant motifs as features.
resultsWe discovered 155 motifs that associate significantly with high antisense suppression activity and 202 motifs that associate significantly with low suppression activity. The motifs range in length from 2 to 5 bases, contain several motifs that have been previously discovered as associating highly with antisense activity, and have thermodynamic properties consistent with previous work associating thermodynamic properties of sequences with their antisense activity. Statistical analysis revealed no correlation between a motif's position within an antisense sequence and that sequences antisense activity. Also, many significant motifs existed as subwords of other significant motifs. Support vector regression experiments indicated that the feature set of significant motifs increased correlation compared to all possible motifs as well as several subsets of the significant motifs.
conclusionThe thermodynamic properties of the significantly associated motifs support existing data correlating the thermodynamic properties of the antisense oligonucleotide with antisense efficiency, reinforcing our hypothesis that antisense suppression is strongly associated with probe/target thermodynamics, as there are no enzymatic mediators to speed the process along like the RNA Induced Silencing Complex (RISC) in RNAi. The independence of motif position and antisense activity also allows us to bypass consideration of this feature in the modelling process, promoting model efficiency and reducing the chance of overfitting when predicting antisense activity. The increase in SVR correlation with significant features compared to nearest-neighbour features indicates that thermodynamics alone is likely not the only factor in determining antisense efficiency.
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