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Computational Biology and Bioinformatics, IEEE/ACM Transactions on

Issue 3 • Date July-Sept. 2004

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Displaying Results 1 - 11 of 11
  • [Front cover]

    Page(s): c1
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    Freely Available from IEEE
  • [Inside front cover]

    Page(s): c2
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  • Introduction of new Associate Editors

    Page(s): 97
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  • A stochastic downhill search algorithm for estimating the local false discovery rate

    Page(s): 98 - 108
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (952 KB) |  | HTML iconHTML  

    Screening for differential gene expression in microarray studies leads to difficult large-scale multiple testing problems. The local false discovery rate is a statistical concept for quantifying uncertainty in multiple testing. We introduce a novel estimator for the local false discovery rate that is based on an algorithm which splits all genes into two groups, representing induced and noninduced genes, respectively. Starting from the full set of genes, we successively exclude genes until the gene-wise p-values of the remaining genes look like a typical sample from a uniform distribution. In comparison to other methods, our algorithm performs compatibly in detecting the shape of the local false discovery rate and has a smaller bias with respect to estimating the overall percentage of noninduced genes. Our algorithm is implemented in the Bioconductor compatible R package TWILIGHT version 1.0.1, which is available from http://compdiag.molgen.mpg.de/software or from the Bioconductor project at http://www.bioconductor.org. View full abstract»

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  • Constructing splits graphs

    Page(s): 109 - 115
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (463 KB) |  | HTML iconHTML  

    Phylogenetic trees correspond one-to-one to compatible systems of splits and so splits play an important role in theoretical and computational aspects of phylogeny. Whereas any tree reconstruction method can be thought of as producing a compatible system of splits, an increasing number of phylogenetic algorithms are available that compute split systems that are not necessarily compatible and, thus, cannot always be represented by a tree. Such methods include the split decomposition, Neighbor-Net, consensus networks, and the Z-closure method. A more general split system of this kind can be represented graphically by a so-called splits graph, which generalizes the concept of a phylogenetic tree. This paper addresses the problem of computing a splits graph for a given set of splits. We have implemented all presented algorithms in a new program called SplitsTree4. View full abstract»

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  • Improved gapped alignment in BLAST

    Page(s): 116 - 129
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1137 KB) |  | HTML iconHTML  

    Homology search is a key tool for understanding the role, structure, and biochemical function of genomic sequences. The most popular technique for rapid homology search is BLAST, which has been in widespread use within universities, research centers, and commercial enterprises since the early 1990s. We propose a new step in the BLAST algorithm to reduce the computational cost of searching with negligible effect on accuracy. This new step - semigapped alignment - compromises between the efficiency of ungapped alignment and the accuracy of gapped alignment, allowing BLAST to accurately filter sequences with lower computational cost. In addition, we propose a heuristic - restricted insertion alignment - that avoids unlikely evolutionary paths with the aim of reducing gapped alignment cost with negligible effect on accuracy. Together, after including an optimization of the local alignment recursion, our two techniques more than double the speed of the gapped alignment stages in blast. We conclude that our techniques are an important improvement to the BLAST algorithm. Source code for the alignment algorithms is available for download at http://www.bsg.rmit.edu.au/iga/. View full abstract»

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  • Unidentifiable divergence times in rates-across-sites models

    Page(s): 130 - 134
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (178 KB) |  | HTML iconHTML  

    The rates-across-sites assumption in phylogenetic inference posits that the rate matrix governing the Markovian evolution of a character on an edge of the putative phylogenetic tree is the product of a character-specific scale factor and a rate matrix that is particular to that edge. Thus, evolution follows basically the same process for all characters, except that it occurs faster for some characters than others. To allow estimation of tree topologies and edge lengths for such models, it is commonly assumed that the scale factors are not arbitrary unknown constants, but rather unobserved, independent, identically distributed draws from a member of some parametric family of distributions. A popular choice is the gamma family. We consider an example of a clock-like tree with three taxa, one unknown edge length, a known root state, and a parametric family of scale factor distributions that contains the gamma family. This model has the property that, for a generic choice of unknown edge length and scale factor distribution, there is another edge length and scale factor distribution which generates data with exactly the same distribution, so that even with infinitely many data it will be typically impossible to make correct inferences about the unknown edge length. View full abstract»

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  • [Advertisement]

    Page(s): 135
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  • [Advertisement]

    Page(s): 136
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  • IEEE/ACM TCBB: Information for authors

    Page(s): c3
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  • [Back cover]

    Page(s): c4
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Aims & Scope

This bimonthly publishes archival research results related to the algorithmic, mathematical, statistical, and computational methods that are central in bioinformatics and computational biology.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Ying Xu
University of Georgia
xyn@bmb.uga.edu

Associate Editor-in-Chief
Dong Xu
University of Missouri
xudong@missouri.edu