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A Practical and Scalable Tool to Find Overlaps between Sequences.

Rachid MH, Malluhi Q - Biomed Res Int (2015)

Bottom Line: The paper demonstrates an efficient construction of this time-efficient and space-economical tree data structure.Experimental evaluation indicates superior results in terms of space and time over existing solutions.Results also show that the proposed technique is highly scalable in a parallel execution environment.

View Article: PubMed Central - PubMed

Affiliation: KINDI Lab for Computing Research, Qatar University, P.O. Box 2713, Doha, Qatar.

ABSTRACT
The evolution of the next generation sequencing technology increases the demand for efficient solutions, in terms of space and time, for several bioinformatics problems. This paper presents a practical and easy-to-implement solution for one of these problems, namely, the all-pairs suffix-prefix problem, using a compact prefix tree. The paper demonstrates an efficient construction of this time-efficient and space-economical tree data structure. The paper presents techniques for parallel implementations of the proposed solution. Experimental evaluation indicates superior results in terms of space and time over existing solutions. Results also show that the proposed technique is highly scalable in a parallel execution environment.

No MeSH data available.


Pseudocode for the parallel algorithm.
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Related In: Results  -  Collection


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alg4: Pseudocode for the parallel algorithm.

Mentions: To illustrate the concept, a simple example is shown. Let G = {ACC, AATC, CGTC, TTA, TGA, CCAT} be a group of strings that 3 processors are working on. The number of steps to process these strings is 6, 10, 10, 6, 6, and 10. Accordingly, the share for each processor is 16. Processor 1 gets strings 1 and 2, processor 2 gets strings 3 and 4, and processor 3 gets strings 5 and 6. However, this may not be the case in practice. The pseudocode is shown in Algorithm 4.


A Practical and Scalable Tool to Find Overlaps between Sequences.

Rachid MH, Malluhi Q - Biomed Res Int (2015)

Pseudocode for the parallel algorithm.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4417569&req=5

alg4: Pseudocode for the parallel algorithm.
Mentions: To illustrate the concept, a simple example is shown. Let G = {ACC, AATC, CGTC, TTA, TGA, CCAT} be a group of strings that 3 processors are working on. The number of steps to process these strings is 6, 10, 10, 6, 6, and 10. Accordingly, the share for each processor is 16. Processor 1 gets strings 1 and 2, processor 2 gets strings 3 and 4, and processor 3 gets strings 5 and 6. However, this may not be the case in practice. The pseudocode is shown in Algorithm 4.

Bottom Line: The paper demonstrates an efficient construction of this time-efficient and space-economical tree data structure.Experimental evaluation indicates superior results in terms of space and time over existing solutions.Results also show that the proposed technique is highly scalable in a parallel execution environment.

View Article: PubMed Central - PubMed

Affiliation: KINDI Lab for Computing Research, Qatar University, P.O. Box 2713, Doha, Qatar.

ABSTRACT
The evolution of the next generation sequencing technology increases the demand for efficient solutions, in terms of space and time, for several bioinformatics problems. This paper presents a practical and easy-to-implement solution for one of these problems, namely, the all-pairs suffix-prefix problem, using a compact prefix tree. The paper demonstrates an efficient construction of this time-efficient and space-economical tree data structure. The paper presents techniques for parallel implementations of the proposed solution. Experimental evaluation indicates superior results in terms of space and time over existing solutions. Results also show that the proposed technique is highly scalable in a parallel execution environment.

No MeSH data available.