compPknots: A Framework for Parallel Prediction and Comparison of RNA Secondary Structures with Pseudoknots

Estrada, Trilce; Licon, Abel; Taufer, Michela

doi:10.1007/11942634_70

Cited by 10 publications

(20 citation statements)

References 14 publications

(11 reference statements)

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“…( ii ) An implementing computer algorithms to run on the graphics hardware like GPU 16. ( iii ) A parallel implementation on the Beowulf cluster by using Message Passing Interface (MPI) library 17. ( iv ) The fine-grained hardware implemented on FPGA 18–20.…”

Section: Parallel Methods and Schemesmentioning

confidence: 99%

“…Many researchers exploited this parallel architecture to compute the traditional RNA secondary structure detection methods. The parallel implementation on the Beowulf cluster was utilized for re-implementing the original RNA prediction methods 17. This work pointed out that there were good results with a higher accuracy and a faster execution time in the RNA structural algorithms, comparing to the original RNA detection methods 25,34.…”

Section: Parallel Methods and Schemesmentioning

confidence: 99%

“…( ii ) A one solitary RNA parallel method on the GPU 16. ( iii ) One work of the RNA parallel algorithm on the Beowulf cluster 17. ( iv ) Three different parallel RNA algorithms were implemented on the FPGA 18–20.…”

Section: Schematic Studymentioning

confidence: 99%

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A Comparative Taxonomy of Parallel Algorithms for RNA Secondary Structure Prediction

Al-Khatib

Abdullah

Rashid

2010

Evol Bioinform Online

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RNA molecules have been discovered playing crucial roles in numerous biological and medical procedures and processes. RNA structures determination have become a major problem in the biology context. Recently, computer scientists have empowered the biologists with RNA secondary structures that ease an understanding of the RNA functions and roles. Detecting RNA secondary structure is an NP-hard problem, especially in pseudoknotted RNA structures. The detection process is also time-consuming; as a result, an alternative approach such as using parallel architectures is a desirable option. The main goal in this paper is to do an intensive investigation of parallel methods used in the literature to solve the demanding issues, related to the RNA secondary structure prediction methods. Then, we introduce a new taxonomy for the parallel RNA folding methods. Based on this proposed taxonomy, a systematic and scientific comparison is performed among these existing methods.

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Section: Parallel Methods and Schemesmentioning

confidence: 99%

Section: Parallel Methods and Schemesmentioning

confidence: 99%

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A Comparative Taxonomy of Parallel Algorithms for RNA Secondary Structure Prediction

Al-Khatib

Abdullah

Rashid

2010

Evol Bioinform Online

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“…Figure 1 shows the time and memory (in logarithmic scale) allocated for the prediction of RNA pseudoknots with various lengths using one of the most accurate prediction programs, Pknots-RE [11]. The algorithm underlying Pknots-RE has a run time and memory demand in the order of n 6 and n 4 respectively, where n is the length of the input sequence [11]. The program conducts an exhaustive search for the optimal structure with the lowest free energy and has the capability to predict rather complex structures, even some non-planar structures for short RNA segments of up to 200 nucleotides.…”

Section: Figure 1 Time and Memory Usage By Pknots-re For Pseudobase mentioning

confidence: 99%

“…As shown in [6], predictions can significantly benefit from the combined prediction capability of different codes as oppose to using single codes separately. We are also working on developing an intelligent strategy for generating chunks.…”

Section: Future Work and Conclusionmentioning

confidence: 99%

On the Effectiveness of Rebuilding RNA Secondary Structures from Sequence Chunks

Taufer

Solorio

Licon

et al. 2008

2008 IEEE International Symposium on Parallel and Distributed Processing

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Despite the computing power of emerging technologies, predicting Background and SignificanceRNA secondary structure prediction can provide insights in the reconstruction of 3D RNA structures and their functionality. Study of RNA secondary structures is a field that is raising the attention of the scientific community; new insights in the field point out the need for supporting experimental research with computational results. The latter can help to narrow down the space of the experiments and therefore the cost to obtain results. Figure 1. Time and memory usage by Pknots-RE for PseudoBase sequences with different lengthsDespite the computing power of supercomputers and emerging advanced technologies, e.g., multi-core architectures, the prediction of secondary structures for long RNA using thermodynamics-based methods e.g., Zuker and Turner [14], is still infeasible, especially if the structures include complex secondary structures such as pseudoknots. The space and time required for accurate predictions of pseudoknots based on free energy minimization algorithms grow very rapidly with the sequence length. Figure 1 shows the time and memory (in logarithmic scale) allocated for the prediction of RNA pseudoknots with various lengths using one of the most accurate prediction programs, Pknots-RE [11]. The algorithm underlying Pknots-RE has a run time and memory demand in the order of n 6 and n 4 re-

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