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259
MEGA5: Molecular evolutionary genetics analysis using maximum . . .
, 2011
"... Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version ..."
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Cited by 7284 (25 self)
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Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version 5 (MEGA5), which is a user-friendly software for mining online databases, building sequence alignments and phylogenetic trees, and using methods of evolutionary bioinformatics in basic biology, biomedicine, and evolution. The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models (nucleotide or amino acid), inferring ancestral states and sequences (along with probabilities), and estimating evolutionary rates site-by-site. In computer simulation analyses, ML tree inference algorithms in MEGA5 compared favorably with other software packages in terms of computational efficiency and the accuracy of the estimates of phylogenetic trees, substitution parameters, and rate variation among sites. The MEGA user interface has now been enhanced to be activity driven to make it easier for the use of both beginners and experienced scientists. This version of MEGA is intended for the Windows platform, and it has been configured for effective use on Mac OS X and Linux desktops. It is available free of charge from
Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with
, 2006
"... Updated information and services can be found at: ..."
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Cited by 492 (25 self)
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Updated information and services can be found at:
Phylogenetic models of rate heterogeneity: A high performance computing perspective
- In Proceedings of the 20th Internationational Parallel and Distributed Processing Symposium (IPDPS
, 2006
"... Inference of phylogenetic trees using the maximum likelihood (ML) method is NP-hard. Furthermore, the computation of the likelihood function for huge trees of more than 1,000 organisms is computationally intensive due to a large amount of floating point operations and high memory consumption. Within ..."
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Cited by 43 (9 self)
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Inference of phylogenetic trees using the maximum likelihood (ML) method is NP-hard. Furthermore, the computation of the likelihood function for huge trees of more than 1,000 organisms is computationally intensive due to a large amount of floating point operations and high memory consumption. Within this context, the present paper compares two competing mathematical models that account for evolutionary rate heterogeneity: the Γ and CAT models. The intention of this paper is to show that—from a purely empirical point of view—CAT can be used instead of Γ. The main advantage of CAT over Γ consists in significantly lower memory consumption and faster inference times. An experimental study using RAxML has been performed on 19 real-world datasets comprising 73 up to 1,663 DNA sequences. Results show that CAT is on average 5.5 times faster than Γ and—surprisingly enough—also yields trees with slightly superior Γ likelihood values. The usage of the CAT model decreases the amount of average L2 and L3 cache misses by factor 8.55. 1.
A.: Initial Experiences Porting a Bioinformatics Application to a Graphics
- Processor. Lecture Notes in Computer Science 3746
, 2005
"... Abstract. Bioinformatics applications are one of the most relevant and compute-demanding applications today. While normally these applica-tions are executed on clusters or dedicated parallel systems, in this work we explore the use of an alternative architecture. We focus on exploiting the compute-i ..."
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Cited by 41 (3 self)
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Abstract. Bioinformatics applications are one of the most relevant and compute-demanding applications today. While normally these applica-tions are executed on clusters or dedicated parallel systems, in this work we explore the use of an alternative architecture. We focus on exploiting the compute-intensive characteristics offered by the graphics processors (GPU) in order to accelerate a bioinformatics application. The GPU is a good match for these applications as it is an inexpensive, high-performance SIMD architecture. In our initial experiments we evaluate the use of a regular graphics card to improve the performance of RAxML, a bioinformatics program for phylogenetic tree inference. In this paper we focus on porting to the GPU the most time-consuming loop, which accounts for nearly 50 % of the total execution time. The preliminary results show that the loop code achieves a speedup of 3x while the whole application with a single loop optimization, achieves a speedup of 1.2x. 1
RAxML-OMP: An efficient program for phylogenetic inference on SMPs
- In Proc. of PaCT05
, 2005
"... Abstract. Inference of phylogenetic trees comprising hundreds or even thousands of organisms based on the Maximum Likelihood (ML) method is computationally extremely intensive. In order to accelerate computations we implemented RAxML-OMP, an efficient OpenMP-parallelization for Symmetric Multi-Proce ..."
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Cited by 40 (12 self)
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Abstract. Inference of phylogenetic trees comprising hundreds or even thousands of organisms based on the Maximum Likelihood (ML) method is computationally extremely intensive. In order to accelerate computations we implemented RAxML-OMP, an efficient OpenMP-parallelization for Symmetric Multi-Processing machines (SMPs) based on the sequential program RAxML-V (Randomized Axelerated Maximum Likelihood). RAxML-V is a program for inference of evolutionary trees based upon the ML method and incorporates several advanced search algorithms like fast hill-climbing and simulated annealing. We assess performance of RAxML-OMP on the widely used Intel Xeon, Intel Itanium, and AMD Opteron architectures. RAxML-OMP scales particularly well on the AMD Opteron architecture and achieves even super-linear speedups for large datasets (with a length ≥ 5.000 base pairs) due to improved cache-efficiency and data locality. RAxML-OMP is freely available as open source code. 1
Ancient Protostome Origin of Chemosensory Ionotropic Glutamate Receptors and the Evolution of Insect Taste
"... Ionotropic glutamate receptors (iGluRs) are a highly conserved family of ligand-gated ion channels present in animals, plants, and bacteria, which are best characterized for their roles in synaptic communication in vertebrate nervous systems. A variant subfamily of iGluRs, the Ionotropic Receptors ( ..."
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Cited by 32 (0 self)
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Ionotropic glutamate receptors (iGluRs) are a highly conserved family of ligand-gated ion channels present in animals, plants, and bacteria, which are best characterized for their roles in synaptic communication in vertebrate nervous systems. A variant subfamily of iGluRs, the Ionotropic Receptors (IRs), was recently identified as a new class of olfactory receptors in the fruit fly, Drosophila melanogaster, hinting at a broader function of this ion channel family in detection of environmental, as well as intercellular, chemical signals. Here, we investigate the origin and evolution of IRs by comprehensive evolutionary genomics and in situ expression analysis. In marked contrast to the insect-specific Odorant Receptor family, we show that IRs are expressed in olfactory organs across Protostomia—a major branch of the animal kingdom that encompasses arthropods, nematodes, and molluscs—indicating that they represent an ancestral protostome chemosensory receptor family. Two subfamilies of IRs are distinguished: conserved ‘‘antennal IRs,’ ’ which likely define the first olfactory receptor family of insects, and species-specific ‘‘divergent IRs,’ ’ which are expressed in peripheral and internal gustatory neurons, implicating this family in taste and food assessment. Comparative analysis of drosophilid IRs reveals the selective forces that have shaped the repertoires in flies with distinct chemosensory preferences. Examination of IR gene structure and genomic distribution suggests both non-allelic homologous recombination and retroposition contributed to the expansion of this multigene family. Together, these findings lay a foundation for functional analysis of these receptors in both neurobiological
Improving the efficiency of SPR moves in phylogenetic tree search methods based on maximum likelihood
- BIOINFORMATICS
, 2005
"... Motivation: Maximum likelihood methods have become very popular for constructing phylogenetic trees from sequence data. However, despite noticeable recent progress, with large and difficult data sets (e.g. multiple genes with conflicting signals) current ML programs still require huge computing time ..."
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Cited by 31 (9 self)
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Motivation: Maximum likelihood methods have become very popular for constructing phylogenetic trees from sequence data. However, despite noticeable recent progress, with large and difficult data sets (e.g. multiple genes with conflicting signals) current ML programs still require huge computing times and can become trapped in bad local optima of the likelihood function. When this occurs, the resulting trees may still show some of the defects (e.g. long branch attraction) of starting trees obtained using fast distance or parsimony programs. Methods: Subtree Pruning and Regrafting (SPR) topological rearrangements are usually sufficient to intensively search the tree space. Here, we propose two new methods to make SPR moves more efficient. The first method uses a fast distance-based approach to detect the least promising candidate SPR moves, which are then simply discarded. The second method locally estimates the change in likelihood for any remaining potential SPRs, as opposed to globally evaluating the entire tree for each possible move. These two methods are implemented in a new algorithm with a sophisticated filtering strategy, which efficiently selects potential SPRs and concentrates most of the likelihood computation on the promising moves. Results: Experiments with real data sets comprising 35 to 250 taxa show that, while indeed greatly reducing the amount of computation, our approach provides likelihood values at least as good as those of the best known ML methods so far, and is very robust to poor starting trees. Furthermore, combining our new SPR algorithm with local moves such as PHYML’s nearest neighbor interchanges, the time needed to find good solutions can sometimes be reduced even more. Availability: Executables of our SPR program and the used data sets are available for download at
Dynamic multigrain parallelization on the cell broadband engine. Pages 90–100
- in Proceedings of the 12th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
, 2007
"... This paper addresses the problem of orchestrating and scheduling parallelism at multiple levels of granularity on heterogeneous multicore processors. We present policies and mechanisms for adaptive exploitation and scheduling of multiple layers of parallelism on the Cell Broadband Engine. Our polici ..."
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Cited by 31 (10 self)
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This paper addresses the problem of orchestrating and scheduling parallelism at multiple levels of granularity on heterogeneous multicore processors. We present policies and mechanisms for adaptive exploitation and scheduling of multiple layers of parallelism on the Cell Broadband Engine. Our policies combine event-driven task scheduling with malleable loop-level parallelism, which is exposed from the runtime system whenever task-level parallelism leaves cores idle. We present a runtime system for scheduling applications with layered parallelism on Cell and investigate its potential with RAxML, a computational biology application which infers large phylogenetic trees, using the Maximum Likelihood (ML) method. Our experiments show that the Cell benefits significantly from dynamic parallelization methods, that selectively exploit the layers of parallelism in the system, in response to workload characteristics. Our runtime environment outperforms naive parallelization and scheduling based on MPI and Linux by up to a factor of 2.6. We are able to execute RAxML on one Cell four times faster than on a dual-processor system with Hyperthreaded Xeon processors, and 5–10 % faster than on a single-processor system with a dualcore, quad-thread IBM Power5 processor. 1
