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Filtering of Ineffective siRNAs and Improved siRNA Design Tool

Authors: Siu-Ming Yiu; Prudence W. H. Wong; Tak Wah Lam; Y. C. Mui; Hsiang-fu Kung; Marie C. M. Lin; Y. T. Cheung;

Filtering of Ineffective siRNAs and Improved siRNA Design Tool

Abstract

Abstract Motivation: Short interfering RNAs (siRNAs) can be used to suppress gene expression and possess many potential applications in therapy, but how to design an effective siRNA is still not clear. Based on the MPI (Max-Planck-Institute) basic principles, a number of siRNA design tools have been developed recently. The set of candidates reported by these tools is usually large and often contains ineffective siRNAs. In view of this, we initiate the study of filtering ineffective siRNAs. Results: The contribution of this paper is 2-fold. First, we propose a fair scheme to compare existing design tools based on real data in the literature. Second, we attempt to improve the MPI principles and existing tools by an algorithm that can filter ineffective siRNAs. The algorithm is based on some new observations on the secondary structure, which we have verified by AI techniques (decision trees and support vector machines). We have tested our algorithm together with the MPI principles and the existing tools. The results show that our filtering algorithm is effective. Availability: The siRNA design software tool can be found in the website http://www.cs.hku.hk/~sirna/ Contact: smyiu@cs.hku.hk

Country
China (People's Republic of)
Related Organizations
Keywords

Models, Molecular, Artificial intelligence, Sequence analysis, rna, Molecular Sequence Data, Computer-aided design, Models, Artificial Intelligence, rna, molecular, RNA, Small Interfering, Base Sequence, Sequence Analysis, RNA, Sequence analysis, 006, Benchmarking, Models, Chemical, Computer-Aided Design, Genetic Engineering, Sequence Alignment, Models, molecular, Algorithms, Software

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    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    68
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
68
Top 10%
Top 10%
Top 10%
Green
gold