Rose: generating sequence families.

Stoye, Jens; Evers, Dirk; Meyer, Folker

doi:10.1093/bioinformatics/14.2.157

Cited by 241 publications

(190 citation statements)

References 15 publications

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“…Here instances (besides the first instance) were generated with an artificial evolution tool Rose (cf. Stoye et al 1998). Both tests showed good (in some cases superlinear) speedup of the algorithms and good quality of the obtained results outperforming those obtained by the existing software packages.…”

mentioning

confidence: 68%

“…Here, the instances (besides the first instance) were generated with the use of an artificial evolution tool Rose (cf. Stoye et al 1998). In the third set of experiments, the comparison with other known methods based on Maximum Parsimony local search procedures was performed.…”

Section: Computational Experimentsmentioning

confidence: 99%

“…Stoye et al 1998), which provides a probabilistic model of the evolution of RNA-, DNA-, or protein-like sequences. Rose is commonly used, and its results are useful for the evaluation of methods for multiple sequence alignment construction and the prediction of phylogenetic relationships (cf.…”

Section: Computational Experimentsmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive memory programming: local search parallel algorithms for phylogenetic tree construction

et al. 2010

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One of the most important aspect of molecular and computational biology is the reconstruction of evolutionary relationships. The area is well explored after decades of intensive research. Despite this fact there remains a need for good and efficient algorithms that are capable of reconstructing the evolutionary relationship in reasonable time.Since the problem is computationally intractable, exact algorithms are used only for small groups of species. In the Maximum Parsimony approach the time of computation grows so fast when number of sequences increases, that in practice it is possible to find the optimal solution for instances containing about 20 sequences only.It is this reason that in practical applications, heuristic methods are used. In this paper, parallel adaptive memory programming algorithms based on Maximum Parsimony and some known neighborhood search methods for phylogenetic tree construction are proposed, and the results of computational experiments are presented. The proposed algorithms achieve a superlinear speedup and find solutions of good quality.

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confidence: 68%

Section: Computational Experimentsmentioning

confidence: 99%

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Adaptive memory programming: local search parallel algorithms for phylogenetic tree construction

et al. 2010

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“…To focus on the algorithmic efficiency in our study, we made two simplifications in our experiments, one is that we use a pseudo count method to train the HMM, and another is that we neglected the accuracy comparison with other methods and our parallel method has the same accuracy with the serial method. We employ the automatic sequence-generating program ROSE [25] to generate different test cases.…”

Section: Resultsmentioning

confidence: 99%

A tile-based parallel Viterbi algorithm for biological sequence alignment on GPU with CUDA

Yin

Bader

2010

2010 IEEE International Symposium on Parallel &Amp; Distributed Processing, Workshops and PHD Forum (IPDPSW)

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The Viterbi algorithm is the compute-intensive kernel in Hidden Markov Model (HMM) based sequence alignment applications. In this paper, we investigate extending several parallel methods, such as the wave-front and streaming methods for the Smith-Waterman algorithm, to achieve a significant speed-up on a GPU. The wave-front method can take advantage of the computing power of the GPU but it cannot handle long sequences because of the physical GPU memory limit. On the other hand, the streaming method can process long sequences but with increased overhead due to the increased data transmission between CPU and GPU. To further improve the performance on GPU, we propose a new tile-based parallel algorithm. We take advantage of the homological segments to divide long sequences into many short pieces and each piece pair (tile) can be fully held in the GPU's memory. By reorganizing the computational kernel of the Viterbi algorithm, the basic computing unit can be divided into two parts: independent and dependent parts. All of the independent parts are executed with a balanced load in an optimized coalesced memory-accessing manner, which significantly improves the Viterbi algorithm's performance on GPU. The experimental results show that our new tile-based parallel Viterbi algorithm can outperform the wave-front and the streaming methods. Especially for the long sequence alignment problem, the best performance of tile-based algorithm is on average about an order magnitude faster than the serial Viterbi algorithm.

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“…No indels were introduced in the simulation. We used rose [24] to generate the sequences and r8s [25] to generate the trees. We see that the accuracy of both algorithms improves as more sequence data is available.…”

Section: Methodsmentioning

confidence: 99%

Towards a Practical O(n logn) Phylogeny Algorithm

Brown

Truszkowski

2011

Lecture Notes in Computer Science

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Recently, we have identified a randomized quartet phylogeny algorithm that has O(n log n) runtime with high probability, which is asymptotically optimal. Our algorithm has high probability of returning the correct phylogeny when quartet errors are independent and occur with known probability, and when the algorithm uses a guide tree on O(log log n) taxa that is correct with high probability. In practice, none of these assumptions is correct: quartet errors are positively correlated and occur with unknown probability, and the guide tree is often error prone. Here, we bring our work out of the purely theoretical setting. We present a variety of extensions which, while only slowing the algorithm down by a constant factor, make its performance nearly comparable to that of Neighbour Joining , which requires (n 3 ) runtime in existing implementations. Our results suggest a new direction for quartet-based phylogenetic reconstruction that may yield striking speed improvements at minimal accuracy cost. An early prototype implementation of our software is available at

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Rose: generating sequence families.

Cited by 241 publications

References 15 publications

Adaptive memory programming: local search parallel algorithms for phylogenetic tree construction

Adaptive memory programming: local search parallel algorithms for phylogenetic tree construction

A tile-based parallel Viterbi algorithm for biological sequence alignment on GPU with CUDA

Towards a Practical O(n logn) Phylogeny Algorithm

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