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An efficient algorithm for systematic analysis of nucleotide strings suitable for siRNA design.

Baranova A, Bode J, Manyam G, Emelianenko M - BMC Res Notes (2011)

Bottom Line: The "off-target" silencing effect hinders the development of siRNA-based therapeutic and research applications.We propose a new approach that reduces the computational time as compared to existing techniques.These strings could then be used as siRNA seats, as specific probes for gene expression studies by oligonucleotide-based microarrays, for the design of molecular beacon probes for Real-Time PCR and, generally, any type of PCR primers.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Systems Biology, George Mason University, Fairfax VA, USA. abaranov@gmu.edu.

ABSTRACT

Background: The "off-target" silencing effect hinders the development of siRNA-based therapeutic and research applications. Existing solutions for finding possible locations of siRNA seats within a large database of genes are either too slow, miss a portion of the targets, or are simply not designed to handle a very large number of queries. We propose a new approach that reduces the computational time as compared to existing techniques.

Findings: The proposed method employs tree-based storage in a form of a modified truncated suffix tree to sort all possible short string substrings within given set of strings (i.e. transcriptome). Using the new algorithm, we pre-computed a list of the best siRNA locations within each human gene ("siRNA seats"). siRNAs designed to reside within siRNA seats are less likely to hybridize off-target. These siRNA seats could be used as an input for the traditional "set-of-rules" type of siRNA designing software. The list of siRNA seats is available through a publicly available database located at http://web.cos.gmu.edu/~gmanyam/siRNA_db/search.php

Conclusions: In attempt to perform top-down prediction of the human siRNA with minimized off-target hybridization, we developed an efficient algorithm that employs suffix tree based storage of the substrings. Applications of this approach are not limited to optimal siRNA design, but can also be useful for other tasks involving selection of the characteristic strings specific to individual genes. These strings could then be used as siRNA seats, as specific probes for gene expression studies by oligonucleotide-based microarrays, for the design of molecular beacon probes for Real-Time PCR and, generally, any type of PCR primers.

No MeSH data available.


Related in: MedlinePlus

Algorithm efficiency comparison. Time taken to retrieve all unique strings of length n for the new algorithm (crosses) and the previously suggested CRM algorithm (circles) on a logarithmic scale.
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Figure 4: Algorithm efficiency comparison. Time taken to retrieve all unique strings of length n for the new algorithm (crosses) and the previously suggested CRM algorithm (circles) on a logarithmic scale.

Mentions: The new algorithm was implemented in C and compared to the CRM algorithm described in [18]. Both codes were tested on a 640-core SGI Altix cluster. The comparative review of the performance is presented in Figure 4. The new algorithm was able to retrieve the list of unique substrings of length n, where 9 <n < 15 on average 100 times faster than the CRM. The time required for the generation of CRM input, the string pre-sorting time, is not reflected in the efficiency comparison, while the new algorithm has a built-in sorting routine whose execution time was taken into account. The advantage of the new method would be even more visible had the time to pre-sort the list been counted as part of the CRM execution time too.


An efficient algorithm for systematic analysis of nucleotide strings suitable for siRNA design.

Baranova A, Bode J, Manyam G, Emelianenko M - BMC Res Notes (2011)

Algorithm efficiency comparison. Time taken to retrieve all unique strings of length n for the new algorithm (crosses) and the previously suggested CRM algorithm (circles) on a logarithmic scale.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117723&req=5

Figure 4: Algorithm efficiency comparison. Time taken to retrieve all unique strings of length n for the new algorithm (crosses) and the previously suggested CRM algorithm (circles) on a logarithmic scale.
Mentions: The new algorithm was implemented in C and compared to the CRM algorithm described in [18]. Both codes were tested on a 640-core SGI Altix cluster. The comparative review of the performance is presented in Figure 4. The new algorithm was able to retrieve the list of unique substrings of length n, where 9 <n < 15 on average 100 times faster than the CRM. The time required for the generation of CRM input, the string pre-sorting time, is not reflected in the efficiency comparison, while the new algorithm has a built-in sorting routine whose execution time was taken into account. The advantage of the new method would be even more visible had the time to pre-sort the list been counted as part of the CRM execution time too.

Bottom Line: The "off-target" silencing effect hinders the development of siRNA-based therapeutic and research applications.We propose a new approach that reduces the computational time as compared to existing techniques.These strings could then be used as siRNA seats, as specific probes for gene expression studies by oligonucleotide-based microarrays, for the design of molecular beacon probes for Real-Time PCR and, generally, any type of PCR primers.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Systems Biology, George Mason University, Fairfax VA, USA. abaranov@gmu.edu.

ABSTRACT

Background: The "off-target" silencing effect hinders the development of siRNA-based therapeutic and research applications. Existing solutions for finding possible locations of siRNA seats within a large database of genes are either too slow, miss a portion of the targets, or are simply not designed to handle a very large number of queries. We propose a new approach that reduces the computational time as compared to existing techniques.

Findings: The proposed method employs tree-based storage in a form of a modified truncated suffix tree to sort all possible short string substrings within given set of strings (i.e. transcriptome). Using the new algorithm, we pre-computed a list of the best siRNA locations within each human gene ("siRNA seats"). siRNAs designed to reside within siRNA seats are less likely to hybridize off-target. These siRNA seats could be used as an input for the traditional "set-of-rules" type of siRNA designing software. The list of siRNA seats is available through a publicly available database located at http://web.cos.gmu.edu/~gmanyam/siRNA_db/search.php

Conclusions: In attempt to perform top-down prediction of the human siRNA with minimized off-target hybridization, we developed an efficient algorithm that employs suffix tree based storage of the substrings. Applications of this approach are not limited to optimal siRNA design, but can also be useful for other tasks involving selection of the characteristic strings specific to individual genes. These strings could then be used as siRNA seats, as specific probes for gene expression studies by oligonucleotide-based microarrays, for the design of molecular beacon probes for Real-Time PCR and, generally, any type of PCR primers.

No MeSH data available.


Related in: MedlinePlus