1918620
MEGA: Molecular Evolutionary Genetics Analysis software for microcomputers
A computer program package called MEGA has been developed for estimating evolutionary distances, reconstructing phylogenetic trees and computing basic statistical quantities from molecular data. It is written in C++ and is intended to be used on IBM and IBM-compatible personal computers. In this program, various methods for estimating evolutionary distances from nucleotide and amino acid sequence data, three different methods of phylogenetic inference (UPGMA, neighbor-joining and maximum parsimony) and two statistical tests of topological differences are included. For the maximum parsimony method, new algorithms of branch-and-bound and heuristic searches are implemented. In addition, MEGA computes statistical quantities such as nucleotide and amino acid frequencies, transition/transversion biases, codon frequencies (codon usage tables), and the number of variable sites in specified segments in nucleotide and amino acid sequences. Advanced on-screen sequence data and phylogenetic-tree editors facilitate publication-quality outputs with a wide range of printers. Integrated and interactive designs, on-line context-sensitive helps, and a text-file editor make MEGA easy to use.
6010311
MEGA3: Integrated software for Molecular Evolutionary Genetics Analysis and sequence alignment
With its theoretical basis firmly established in molecular evolutionary and population genetics, the comparative DNA and protein sequence analysis plays a central role in reconstructing the evolutionary histories of species and multigene families, estimating rates of molecular evolution, and inferring the nature and extent of selective forces shaping the evolution of genes and genomes. The scope of these investigations has now expanded greatly owing to the development of high-throughput sequencing techniques and novel statistical and computational methods. These methods require easy-to-use computer programs. One such effort has been to produce Molecular Evolutionary Genetics Analysis (MEGA) software, with its focus on facilitating the exploration and analysis of the DNA and protein sequence variation from an evolutionary perspective. Currently in its third major release, MEGA3 contains facilities for automatic and manual sequence alignment, web-based mining of databases, inference of the phylogenetic trees, estimation of evolutionary distances and testing evolutionary hypotheses. This paper provides an overview of the statistical methods, computational tools, and visual exploration modules for data input and the results obtainable in MEGA.
1061170
Identifying spatially similar gene expression patterns in early stage fruit fly embryo images: binary feature versus invariant moment digital representations
BackgroundModern developmental biology relies heavily on the analysis of embryonic gene expression patterns. Investigators manually inspect hundreds or thousands of expression patterns to identify those that are spatially similar and to ultimately infer potential gene interactions. However, the rapid accumulation of gene expression pattern data over the last two decades, facilitated by high-throughput techniques, has produced a need for the development of efficient approaches for direct comparison of images, rather than their textual descriptions, to identify spatially similar expression patterns.ResultsThe effectiveness of the Binary Feature Vector (BFV) and Invariant Moment Vector (IMV) based digital representations of the gene expression patterns in finding biologically meaningful patterns was compared for a small (226 images) and a large (1819 images) dataset. For each dataset, an ordered list of images, with respect to a query image, was generated to identify overlapping and similar gene expression patterns, in a manner comparable to what a developmental biologist might do. The results showed that the BFV representation consistently outperforms the IMV representation in finding biologically meaningful matches when spatial overlap of the gene expression pattern and the genes involved are considered. Furthermore, we explored the value of conducting image-content based searches in a dataset where individual expression components (or domains) of multi-domain expression patterns were also included separately. We found that this technique improves performance of both IMV and BFV based searches.ConclusionsWe conclude that the BFV representation consistently produces a more extensive and better list of biologically useful patterns than the IMV representation. The high quality of results obtained scales well as the search database becomes larger, which encourages efforts to build automated image query and retrieval systems for spatial gene expression patterns.
13160483
MEGA2: molecular evolutionary genetics analysis software
UNLABELLED
We have developed a new software package, Molecular Evolutionary Genetics Analysis version 2 (MEGA2), for exploring and analyzing aligned DNA or protein sequences from an evolutionary perspective. MEGA2 vastly extends the capabilities of MEGA version 1 by: (1) facilitating analyses of large datasets; (2) enabling creation and analyses of groups of sequences; (3) enabling specification of domains and genes; (4) expanding the repertoire of statistical methods for molecular evolutionary studies; and (5) adding new modules for visual representation of input data and output results on the Microsoft Windows platform.


AVAILABILITY
http://www.megasoftware.net.


CONTACT
s.kumar@asu.edu
25064321
TimeTree: a public knowledge-base of divergence times among organisms
UNLABELLED
Biologists and other scientists routinely need to know times of divergence between species and to construct phylogenies calibrated to time (timetrees). Published studies reporting time estimates from molecular data have been increasing rapidly, but the data have been largely inaccessible to the greater community of scientists because of their complexity. TimeTree brings these data together in a consistent format and uses a hierarchical structure, corresponding to the tree of life, to maximize their utility. Results are presented and summarized, allowing users to quickly determine the range and robustness of time estimates and the degree of consensus from the published literature.


AVAILABILITY
TimeTree is available at http://www.timetree.net
2904394
Physical simulation for animation and visual effects: parallelization and characterization for chip multiprocessors
We explore the emerging application area of physics-based simulation for computer animation and visual special effects. In particular, we examine its parallelization potential and characterize its behavior on a chip multiprocessor (CMP). Applications in this domain model and simulate natural phenomena, and often direct visual components of motion pictures. We study a set of three workloads that exemplify the span and complexity of physical simulation applications used in a production environment: fluid dynamics, facial animation, and cloth simulation. They are computationally demanding, requiring from a few seconds to several minutes to simulate a single frame; therefore, they can benefit greatly from the acceleration possible with large scale CMPs.
 Starting with serial versions of these applications, we parallelize code accounting for at least 96% of the serial execution time, targeting a large number of threads.We then study the most expensive modules using a simulated 64-core CMP.
 For the code representing key modules, we achieve parallel scaling of 45x, 50x, and 30x for fluid, face, and cloth simulations, respectively. The modules have a spectrum of parallel task granularity and locking behavior, and all but one are dominated by loop-level parallelism. Many modules operate on streams of data. In some cases, modules iterate over their data, leading to significant temporal locality. This streaming behavior leads to very high on-die and main memory bandwidth requirements. Finally, most modules have little inter-thread communication since they are data-parallel, but a few require heavy communication between data-parallel operations.
16278268
ExScal: elements of an extreme scale wireless sensor network
Project ExScal (for extreme scale) fielded a 1000+ node wireless sensor network and a 200+ node peer-to-peer ad hoc network of 802.11 devices in a 13km by 300m remote area in Florida, USA during December 2004. In comparison with previous deployments, the ExScal application is relatively complex and its networks are the largest ones of either type fielded to date. In this paper, we overview the key requirements of ExScal, the corresponding design of the hardware/software platform and application, and some results of our experiments.
6244213
A Smorgasbord of Features for Statistical Machine Translation
We describe a methodology for rapid experimentation in statistical machine translation which we use to add a large number of features to a baseline system exploiting features from a wide range of levels of syntactic representation. Feature values were combined in a log-linear model to select the highest scoring candidate translation from an n-best list. Feature weights were optimized directly against the BLEU evaluation metric on held-out data. We present results for a small selection of features at each level of syntactic representation.
26327552
Increased watermark-to-host correlation of uniform random phase watermarks in audio signals
We analyze the watermark-to-host correlation (WHC) of random phase watermarks, in the context of additive embedding and blind correlation-detection of the watermark in audio signals. Interestingly, we find that WHC is higher when a legitimate watermark is present in the audio signal. Instead of trying to minimize the WHC, one could also attempt to harness this increased correlation in order to result in better detection performance. The analysis shows that uniformly distributed phase difference (between the host signal and the watermark) provides maximum advantage. It is also verified that, a uniformly distributed watermark phase provides the most advantage, experimented over a variety of audio signals.
9643537
Exploiting Inference for Approximate Parameter Learning in Discriminative Fields: An Empirical Study
Estimation of parameters of random field models from labeled training data is crucial for their good performance in many image analysis applications. In this paper, we present an approach for approximate maximum likelihood parameter learning in discriminative field models, which is based on approximating true expectations with simple piecewise constant functions constructed using inference techniques. Gradient ascent with these updates exhibits compelling limit cycle behavior which is tied closely to the number of errors made during inference. The performance of various approximations was evaluated with different inference techniques showing that the learned parameters lead to good classification performance so long as the method used for approximating the gradient is consistent with the inference mechanism. The proposed approach is general enough to be used for the training of, e.g., smoothing parameters of conventional Markov Random Fields (MRFs).