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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

Generation of the storage of all substrings. This example illustrates the steps of Algorithm 1 for the input consisting of 3 genes AGAGAGGC, TCAATCCC and AATAAATC. All of the corresponding n-strings are identified with the number of occurrences stored in the leaf. The list of unique n-strings is provided, and the "siRNA seats" resulting from this computation are specified.
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Figure 3: Generation of the storage of all substrings. This example illustrates the steps of Algorithm 1 for the input consisting of 3 genes AGAGAGGC, TCAATCCC and AATAAATC. All of the corresponding n-strings are identified with the number of occurrences stored in the leaf. The list of unique n-strings is provided, and the "siRNA seats" resulting from this computation are specified.

Mentions: The flowchart description of this algorithm is given in Figure 2. In Figure 3 we provide an illustration of the steps the algorithm performs to store each of the input nucleotide strings and to identify the corresponding siRNA seats for some sample data. In this simple example with only 3 genes present, the suffix tree generated at Step 3 has 13 leaves with 11 of them correspond to unique n-strings recorded in the list UNIQUE_n. The output is given in a form of 7 unique N-strings which we refer to as ''siRNA seats''.


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)

Generation of the storage of all substrings. This example illustrates the steps of Algorithm 1 for the input consisting of 3 genes AGAGAGGC, TCAATCCC and AATAAATC. All of the corresponding n-strings are identified with the number of occurrences stored in the leaf. The list of unique n-strings is provided, and the "siRNA seats" resulting from this computation are specified.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Generation of the storage of all substrings. This example illustrates the steps of Algorithm 1 for the input consisting of 3 genes AGAGAGGC, TCAATCCC and AATAAATC. All of the corresponding n-strings are identified with the number of occurrences stored in the leaf. The list of unique n-strings is provided, and the "siRNA seats" resulting from this computation are specified.
Mentions: The flowchart description of this algorithm is given in Figure 2. In Figure 3 we provide an illustration of the steps the algorithm performs to store each of the input nucleotide strings and to identify the corresponding siRNA seats for some sample data. In this simple example with only 3 genes present, the suffix tree generated at Step 3 has 13 leaves with 11 of them correspond to unique n-strings recorded in the list UNIQUE_n. The output is given in a form of 7 unique N-strings which we refer to as ''siRNA seats''.

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