Faster Algorithms for Optimal Multiple Sequence Alignment Based on Pairwise Comparisons

Bilu, Yonatan; Agarwal, Pankaj K.; Kolodny, Rachel

doi:10.1109/tcbb.2006.53

Cited by 10 publications

(10 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this case the synchronization process was not satisfactory: some selected mean postures were completely outliers or nonsimilar to any meaningful posture. It means that the smoothness factor μ in (12) and (16) plays an important role. To find an optimal value of this parameter a visual criterion has been used (the manual synchronization that had been done before yields such a visual estimation technique).…”

Section: Results Of Synchronizationmentioning

confidence: 99%

“…It is obvious that an arbitrary choice among the training set is not a reasonable solution, and now we aim to find a statistically proven rule that is able to make an optimal choice according to some appropriate criterion. Note that each synchronized pair of sequences (n,m) has its own synchronization distance calculated by (12). Then the full synchronization of all the sequences relative to the pattern sequences n has its own global distance:…”

Section: Synchronization Algorithmmentioning

confidence: 99%

“…The DP approach has been widely used in literature for stereo matching and image processing applications [12][13][14]. Such applications often demand fast calculations in real-time, robustness against image discontinuities, and unambiguous matching.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Nonlinear Synchronization for Automatic Learning of 3D Pose Variability in Human Motion Sequences

Mozerov

Rius

Roca

et al. 2009

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

A dense matching algorithm that solves the problem of synchronizing prerecorded human motion sequences, which show different speeds and accelerations, is proposed. The approach is based on minimization of MRF energy and solves the problem by using Dynamic Programming. Additionally, an optimal sequence is automatically selected from the input dataset to be a timescale pattern for all other sequences. The paper utilizes an action specific model which automatically learns the variability of 3D human postures observed in a set of training sequences. The model is trained using the public CMU motion capture dataset for the walking action, and a mean walking performance is automatically learnt. Additionally, statistics about the observed variability of the postures and motion direction are also computed at each time step. The synchronized motion sequences are used to learn a model of human motion for action recognition and full-body tracking purposes.

show abstract

Section: Results Of Synchronizationmentioning

confidence: 99%

Section: Synchronization Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Nonlinear Synchronization for Automatic Learning of 3D Pose Variability in Human Motion Sequences

Mozerov

Rius

Roca

et al. 2009

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

show abstract

“…Some methods to improve the execution time of MSA algorithm based on pairwise comparison, where the goal is to find an optimal alignment with some restrictions, were proposed [9]. However, they did not achieve the efficiency of parallel solutions [10][11][12].…”

Section: Sequence Alignmentmentioning

confidence: 99%

Improvements in the score matrix calculation method using parallel score estimating algorithm

Zafalon¹,

Marucci²,

Momente³

et al. 2013

JBPC

View full text Add to dashboard Cite

The increasing amount of sequences stored in genomic databases has become unfeasible to the sequential analysis. Then, the parallel computing brought its power to the Bioinformatics through parallel algorithms to align and analyze the sequences, providing improvements mainly in the running time of these algorithms. In many situations, the parallel strategy contributes to reducing the computational complexity of the big problems. This work shows some results obtained by an implementation of a parallel score estimating technique for the score matrix calculation stage, which is the first stage of a progressive multiple sequence alignment. The performance and quality of the parallel score estimating are compared with the results of a dynamic programming approach also implemented in parallel. This comparison shows a significant reduction of running time. Moreover, the quality of the final alignment, using the new strategy, is analyzed and compared with the quality of the approach with dynamic programming.

show abstract

“…A number of strategies have been applied to obtain MSAs, such as progressive alignment methods [3][4], iterative methods [5][6], dynamic programming [7], genetic algorithms [8], greedy algorithms [9], Markov chain processes [10], and even simulated annealing methods [11]. Currently, MSAs are obtained via two main approaches.…”

Section: Introductionmentioning

confidence: 99%

PaMSA: A Parallel Algorithm for the Global Alignment of Multiple Protein Sequences

Andalon-Garcia¹,

Chavoya²

2017

ijacsa

View full text Add to dashboard Cite

Abstract-Multiple sequence alignment (MSA) is a well-known problem in bioinformatics whose main goal is the identification of evolutionary, structural or functional similarities in a set of three or more related genes or proteins. We present a parallel approach for the global alignment of multiple protein sequences that combines dynamic programming, heuristics, and parallel programming techniques in an iterative process. In the proposed algorithm, the longest common subsequence technique is used to generate a first MSA by aligning identical residues. An iterative process improves the MSA by applying a number of operators that were defined in the present work, in order to produce more accurate alignments. The accuracy of the alignment was evaluated through the application of optimization functions. In the proposed algorithm, a number of processes work independently at the same time searching for the best MSA of a set of sequences. There exists a process that acts as a coordinator, whereas the rest of the processes are considered slave processes. The resulting algorithm was called PaMSA, which stands for Parallel MSA. The MSA accuracy and response time of PaMSA were compared against those of Clustal W, T-Coffee, MUSCLE, and Parallel T-Coffee on 40 datasets of protein sequences. When run as a sequential application, PaMSA turned out to be the second fastest when compared against the nonparallel MSA methods tested (Clustal W, T-Coffee, and MUSCLE). However, PaMSA was designed to be executed in parallel. When run as a parallel application, PaMSA presented better response times than Parallel T-Cofffee under the conditions tested. Furthermore, the sum-of-pairs scores achieved by PaMSA when aligning groups of sequences with an identity percentage score from approximately 70% to 100%, were the highest in all cases. PaMSA was implemented on a cluster platform using the C++ language through the application of the standard Message Passing Interface (MPI) library.

show abstract

Faster Algorithms for Optimal Multiple Sequence Alignment Based on Pairwise Comparisons

Cited by 10 publications

References 43 publications

Nonlinear Synchronization for Automatic Learning of 3D Pose Variability in Human Motion Sequences

Nonlinear Synchronization for Automatic Learning of 3D Pose Variability in Human Motion Sequences

Improvements in the score matrix calculation method using parallel score estimating algorithm

PaMSA: A Parallel Algorithm for the Global Alignment of Multiple Protein Sequences

Contact Info

Product

Resources

About