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Computational Biology and Bioinformatics, IEEE/ACM Transactions on

Issue 1 • Date Jan.-March 2005

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Displaying Results 1 - 15 of 15
  • [Front cover]

    Publication Year: 2005 , Page(s): c1
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  • [Inside front cover]

    Publication Year: 2005 , Page(s): c2
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  • Guest Editorial: WABI Special Section Part II

    Publication Year: 2005 , Page(s): 1 - 2
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  • A new distance for high level RNA secondary structure comparison

    Publication Year: 2005 , Page(s): 3 - 14
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1346 KB) |  | HTML iconHTML  

    We describe an algorithm for comparing two RNA secondary structures coded in the form of trees that introduces two new operations, called node fusion and edge fusion, besides the tree edit operations of deletion, insertion, and relabeling classically used in the literature. This allows us to address some serious limitations of the more traditional tree edit operations when the trees represent RNAs and what is searched for is a common structural core of two RNAs. Although the algorithm complexity has an exponential term, this term depends only on the number of successive fusions that may be applied to a same node, not on the total number of fusions. The algorithm remains therefore efficient in practice and is used for illustrative purposes on ribosomal as well as on other types of RNAs. View full abstract»

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  • Topological rearrangements and local search method for tandem duplication trees

    Publication Year: 2005 , Page(s): 15 - 28
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (949 KB) |  | HTML iconHTML  

    The problem of reconstructing the duplication history of a set of tandemly repeated sequences was first introduced by Fitch (1977). Many recent studies deal with this problem, showing the validity of the unequal recombination model proposed by Fitch, describing numerous inference algorithms, and exploring the combinatorial properties of these new mathematical objects, which are duplication trees. In this paper, we deal with the topological rearrangement of these trees. Classical rearrangements used in phylogeny (NNI, SPR, TBR, ...) cannot be applied directly on duplication trees. We show that restricting the neighborhood defined by the SPR (Subtree Pruning and Regrafting) rearrangement to valid duplication trees, allows exploring the whole duplication tree space. We use these restricted rearrangements in a local search method which improves an initial tree via successive rearrangements. This method is applied to the optimization of parsimony and minimum evolution criteria. We show through simulations that this method improves all existing programs for both reconstructing the topology of the true tree and recovering its duplication events. We apply this approach to tandemly repeated human Zinc finger genes and observe that a much better duplication tree is obtained by our method than using any other program. View full abstract»

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  • Optimizing multiple seeds for protein homology search

    Publication Year: 2005 , Page(s): 29 - 38
    Cited by:  Papers (2)  |  Patents (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (412 KB) |  | HTML iconHTML  

    We present a framework for improving local protein alignment algorithms. Specifically, we discuss how to extend local protein aligners to use a collection of vector seeds or ungapped alignment seeds to reduce noise hits. We model picking a set of seed models as an integer programming problem and give algorithms to choose such a set of seeds. While the problem is NP-hard, and Quasi-NP-hard to approximate to within a logarithmic factor, it can be solved easily in practice. A good set of seeds we have chosen allows four to five times fewer false positive hits, while preserving essentially identical sensitivity as BLASTP. View full abstract»

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  • State of the transaction [IEEE/ACM Transactions on Computational Biology and Bioinformatics]

    Publication Year: 2005
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    Reviews the first year of publication and outlines plans for the second year. View full abstract»

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  • Bases of motifs for generating repeated patterns with wild cards

    Publication Year: 2005 , Page(s): 40 - 50
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (252 KB) |  | HTML iconHTML  

    Motif inference represents one of the most important areas of research in computational biology, and one of its oldest ones. Despite this, the problem remains very much open in the sense that no existing definition is fully satisfying, either in formal terms, or in relation to the biological questions that involve finding such motifs. Two main types of motifs have been considered in the literature: matrices (of letter frequency per position in the motif) and patterns. There is no conclusive evidence in favor of either, and recent work has attempted to integrate the two types into a single model. In this paper, we address the formal issue in relation to motifs as patterns. This is essential to get at a better understanding of motifs in general. In particular, we consider a promising idea that was recently proposed, which attempted to avoid the combinatorial explosion in the number of motifs by means of a generator set for the motifs. Instead of exhibiting a complete list of motifs satisfying some input constraints, what is produced is a basis of such motifs from which all the other ones can be generated. We study the computational cost of determining such a basis of repeated motifs with wild cards in a sequence. We give new upper and lower bounds on such a cost, introducing a notion of basis that is provably contained in (and, thus, smaller) than previously defined ones. Our basis can be computed in less time and space, and is still able to generate the same set of motifs. We also prove that the number of motifs in all bases defined so far grows exponentially with the quorum, that is, with the minimal number of times a motif must appear in a sequence, something unnoticed in previous work. We show that there is no hope to efficiently compute such bases unless the quorum is fixed. View full abstract»

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  • Multiseed lossless filtration

    Publication Year: 2005 , Page(s): 51 - 61
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (420 KB) |  | HTML iconHTML  

    We study a method of seed-based lossless filtration for approximate string matching and related bioinformatics applications. The method is based on a simultaneous use of several spaced seeds rather than a single seed as studied by Burkhardt and Karkkainen. We present algorithms to compute several important parameters of seed families, study their combinatorial properties, and describe several techniques to construct efficient families. We also report a large-scale application of the proposed technique to the problem of oligonucleotide selection for an EST sequence database. View full abstract»

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  • Text Mining Biomedical Literature for Discovering Gene-to-Gene Relationships: A Comparative Study of Algorithms

    Publication Year: 2005 , Page(s): 62 - 76
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1768 KB) |  | HTML iconHTML  

    Partitioning closely related genes into clusters has become an important element of practically all statistical analyses of microarray data. A number of computer algorithms have been developed for this task. Although these algorithms have demonstrated their usefulness for gene clustering, some basic problems remain. This paper describes our work on extracting functional keywords from MEDLINE for a set of genes that are isolated for further study from microarray experiments based on their differential expression patterns. The sharing of functional keywords among genes is used as a basis for clustering in a new approach called BEA-PARTITION in this paper. Functional keywords associated with genes were extracted from MEDLINE abstracts. We modified the Bond Energy Algorithm (BEA), which is widely accepted in psychology and database design but is virtually unknown in bioinformatics, to cluster genes by functional keyword associations. The results showed that BEA-PARTITION and hierarchical clustering algorithm outperformed khbox{-}{rm{means}} clustering and self-organizing map by correctly assigning 25 of 26 genes in a test set of four known gene groups. To evaluate the effectiveness of BEA-PARTITION for clustering genes identified by microarray profiles, 44 yeast genes that are differentially expressed during the cell cycle and have been widely studied in the literature were used as a second test set. Using established measures of cluster quality, the results produced by BEA-PARTITION had higher purity, lower entropy, and higher mutual information than those produced by khbox{-}{rm{means}} and self-organizing map. Whereas BEA-PARTITION and the hierarchical clustering produced similar quality of clusters, BEA-PARTITION provides clear cluster boundaries compared to the hierarchical clustering. BEA-PARTITION is simple to implement and provides a powerful approach to clustering genes or to any clustering problem where starting matrices are available from experimental obs- - ervations. View full abstract»

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  • 2004 Reviewers list

    Publication Year: 2005 , Page(s): 77
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  • [Advertisement]

    Publication Year: 2005 , Page(s): 79
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  • [Advertisement]

    Publication Year: 2005 , Page(s): 80
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  • IEEE/ACM TCBB: Information for authors

    Publication Year: 2005 , Page(s): c3
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  • [Back cover]

    Publication Year: 2005 , 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