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Computational Biology and Bioinformatics, IEEE/ACM Transactions on

Issue 2 • Date April-June 2004

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Displaying Results 1 - 10 of 10
  • [Front cover]

    Page(s): c1
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  • [Inside front cover]

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

    Page(s): 65
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  • Prediction of consensus RNA secondary structures including pseudoknots

    Page(s): 66 - 77
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1497 KB) |  | HTML iconHTML  

    Most functional RNA molecules have characteristic structures that are highly conserved in evolution. Many of them contain pseudoknots. Here, we present a method for computing the consensus structures including pseudoknots based on alignments of a few sequences. The algorithm combines thermodynamic and covariation information to assign scores to all possible base pairs, the base pairs are chosen with the help of the maximum weighted matching algorithm. We applied our algorithm to a number of different types of RNA known to contain pseudoknots. All pseudoknots were predicted correctly and more than 85 percent of the base pairs were identified. View full abstract»

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  • The number of recombination events in a sample history: conflict graph and lower bounds

    Page(s): 78 - 90
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (620 KB) |  | HTML iconHTML  

    We consider the following problem: Given a set of binary sequences, determine lower bounds on the minimum number of recombinations required to explain the history of the sample, under the infinite-sites model of mutation. The problem has implications for finding recombination hotspots and for the Ancestral Recombination Graph reconstruction problem. Hudson and Kaplan gave a lower bound based on the four-gamete test. In practice, their bound Rm often greatly underestimates the minimum number of recombinations. The problem was recently revisited by Myers and Griffiths, who introduced two new lower bounds Rh and Rs which are provably better, and also yield good bounds in practice. However, the worst-case complexities of their procedures for computing Rh and Rs are exponential and super-exponential, respectively. In this paper, we show that the number of nontrivial connected components, Rc, in the conflict graph for a given set of sequences, computable in time 0(nm2), is also a lower bound on the minimum number of recombination events. We show that in many cases, Rc is a better bound than Rh. The conflict graph was used by Gusfield et al. to obtain a polynomial time algorithm for the galled tree problem, which is a special case of the Ancestral Recombination Graph (ARG) reconstruction problem. Our results also offer some insight into the structural properties of this graph and are of interest for the general Ancestral Recombination Graph reconstruction problem. View full abstract»

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  • A uniform projection method for motif discovery in DNA sequences

    Page(s): 91 - 94
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (313 KB) |  | HTML iconHTML  

    Buhler and Tompa (2002) introduced the random projection algorithm for the motif discovery problem and demonstrated that this algorithm performs well on both simulated and biological samples. We describe a modification of the random projection algorithm, called the uniform projection algorithm, which utilizes a different choice of projections. We replace the random selection of projections by a greedy heuristic that approximately equalizes the coverage of the projections. We show that this change in selection of projections leads to improved performance on motif discovery problems. Furthermore, the uniform projection algorithm is directly applicable to other problems where the random projection algorithm has been used, including comparison of protein sequence databases. View full abstract»

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  • [Advertisement]

    Page(s): 95
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    Page(s): 96
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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.

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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