80,020 research outputs found
Parallel progressive multiple sequence alignment on reconfigurable meshes
<p>Abstract</p> <p>Background</p> <p>One of the most fundamental and challenging tasks in bio-informatics is to identify related sequences and their hidden biological significance. The most popular and proven best practice method to accomplish this task is aligning multiple sequences together. However, multiple sequence alignment is a computing extensive task. In addition, the advancement in DNA/RNA and Protein sequencing techniques has created a vast amount of sequences to be analyzed that exceeding the capability of traditional computing models. Therefore, an effective parallel multiple sequence alignment model capable of resolving these issues is in a great demand.</p> <p>Results</p> <p>We design <it>O</it>(1) run-time solutions for both local and global dynamic programming pair-wise alignment algorithms on reconfigurable mesh computing model. To align <it>m </it>sequences with max length <it>n</it>, we combining the parallel pair-wise dynamic programming solutions with newly designed parallel components. We successfully reduce the progressive multiple sequence alignment algorithm's run-time complexity from <it>O</it>(<it>m </it>× <it>n</it><sup>4</sup>) to <it>O</it>(<it>m</it>) using <it>O</it>(<it>m </it>× <it>n</it><sup>3</sup>) processing units for scoring schemes that use three distinct values for match/mismatch/gap-extension. The general solution to multiple sequence alignment algorithm takes <it>O</it>(<it>m </it>× <it>n</it><sup>4</sup>) processing units and completes in <it>O</it>(<it>m</it>) time.</p> <p>Conclusions</p> <p>To our knowledge, this is the first time the progressive multiple sequence alignment algorithm is completely parallelized with <it>O</it>(<it>m</it>) run-time. We also provide a new parallel algorithm for the Longest Common Subsequence (LCS) with <it>O</it>(1) run-time using <it>O</it>(<it>n</it><sup>3</sup>) processing units. This is a big improvement over the current best constant-time algorithm that uses <it>O</it>(<it>n</it><sup>4</sup>) processing units.</p
Multiple Biolgical Sequence Alignment: Scoring Functions, Algorithms, and Evaluations
Aligning multiple biological sequences such as protein sequences or DNA/RNA sequences is a fundamental task in bioinformatics and sequence analysis. These alignments may contain invaluable information that scientists need to predict the sequences\u27 structures, determine the evolutionary relationships between them, or discover drug-like compounds that can bind to the sequences. Unfortunately, multiple sequence alignment (MSA) is NP-Complete. In addition, the lack of a reliable scoring method makes it very hard to align the sequences reliably and to evaluate the alignment outcomes.
In this dissertation, we have designed a new scoring method for use in multiple sequence alignment. Our scoring method encapsulates stereo-chemical properties of sequence residues and their substitution probabilities into a tree-structure scoring scheme. This new technique provides a reliable scoring scheme with low computational complexity.
In addition to the new scoring scheme, we have designed an overlapping sequence clustering algorithm to use in our new three multiple sequence alignment algorithms. One of our alignment algorithms uses a dynamic weighted guidance tree to perform multiple sequence alignment in progressive fashion. The use of dynamic weighted tree allows errors in the early alignment stages to be corrected in the subsequence stages. Other two algorithms utilize sequence knowledge-bases and sequence consistency to produce biological meaningful sequence alignments. To improve the speed of the multiple sequence alignment, we have developed a parallel algorithm that can be deployed on reconfigurable computer models. Analytically, our parallel algorithm is the fastest progressive multiple sequence alignment algorithm
Accelerated large-scale multiple sequence alignment
<p>Abstract</p> <p>Background</p> <p>Multiple sequence alignment (MSA) is a fundamental analysis method used in bioinformatics and many comparative genomic applications. Prior MSA acceleration attempts with reconfigurable computing have only addressed the first stage of progressive alignment and consequently exhibit performance limitations according to Amdahl's Law. This work is the first known to accelerate the third stage of progressive alignment on reconfigurable hardware.</p> <p>Results</p> <p>We reduce subgroups of aligned sequences into discrete profiles before they are pairwise aligned on the accelerator. Using an FPGA accelerator, an overall speedup of up to 150 has been demonstrated on a large data set when compared to a 2.4 GHz Core2 processor.</p> <p>Conclusions</p> <p>Our parallel algorithm and architecture accelerates large-scale MSA with reconfigurable computing and allows researchers to solve the larger problems that confront biologists today. Program source is available from <url>http://dna.cs.byu.edu/msa/</url>.</p
Simultaneous identification of specifically interacting paralogs and inter-protein contacts by Direct-Coupling Analysis
Understanding protein-protein interactions is central to our understanding of
almost all complex biological processes. Computational tools exploiting rapidly
growing genomic databases to characterize protein-protein interactions are
urgently needed. Such methods should connect multiple scales from evolutionary
conserved interactions between families of homologous proteins, over the
identification of specifically interacting proteins in the case of multiple
paralogs inside a species, down to the prediction of residues being in physical
contact across interaction interfaces. Statistical inference methods detecting
residue-residue coevolution have recently triggered considerable progress in
using sequence data for quaternary protein structure prediction; they require,
however, large joint alignments of homologous protein pairs known to interact.
The generation of such alignments is a complex computational task on its own;
application of coevolutionary modeling has in turn been restricted to proteins
without paralogs, or to bacterial systems with the corresponding coding genes
being co-localized in operons. Here we show that the Direct-Coupling Analysis
of residue coevolution can be extended to connect the different scales, and
simultaneously to match interacting paralogs, to identify inter-protein
residue-residue contacts and to discriminate interacting from noninteracting
families in a multiprotein system. Our results extend the potential
applications of coevolutionary analysis far beyond cases treatable so far.Comment: Main Text 19 pages Supp. Inf. 16 page
A New Simulated Annealing Algorithm for the Multiple Sequence Alignment Problem: The approach of Polymers in a Random Media
We proposed a probabilistic algorithm to solve the Multiple Sequence
Alignment problem. The algorithm is a Simulated Annealing (SA) that exploits
the representation of the Multiple Alignment between sequences as a
directed polymer in dimensions. Within this representation we can easily
track the evolution in the configuration space of the alignment through local
moves of low computational cost. At variance with other probabilistic
algorithms proposed to solve this problem, our approach allows for the creation
and deletion of gaps without extra computational cost. The algorithm was tested
aligning proteins from the kinases family. When D=3 the results are consistent
with those obtained using a complete algorithm. For where the complete
algorithm fails, we show that our algorithm still converges to reasonable
alignments. Moreover, we study the space of solutions obtained and show that
depending on the number of sequences aligned the solutions are organized in
different ways, suggesting a possible source of errors for progressive
algorithms.Comment: 7 pages and 11 figure
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