By Topic

Computational Biology and Bioinformatics, IEEE/ACM Transactions on

Issue 4 • Date Oct.-Dec. 2006

Filter Results

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

    Page(s): c1
    Save to Project icon | Request Permissions | PDF file iconPDF (124 KB)  
    Freely Available from IEEE
  • [Inside front cover]

    Page(s): c2
    Save to Project icon | Request Permissions | PDF file iconPDF (91 KB)  
    Freely Available from IEEE
  • Guest Editor's Introduction to the Special Issue on Computational Biology and Bioinformatics - Part 1

    Page(s): 321 - 322
    Save to Project icon | Request Permissions | PDF file iconPDF (54 KB)  
    Freely Available from IEEE
  • Using Max Cut to Enhance Rooted Trees Consistency

    Page(s): 323 - 333
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1844 KB) |  | HTML iconHTML  

    Supertree methods are used to construct a large tree over a large set of taxa from a set of small trees over overlapping subsets of the complete taxa set. Since accurate reconstruction methods are currently limited to a maximum of a few dozen taxa, the use of a supertree method in order to construct the tree of life is inevitable. Supertree methods are broadly divided according to the input trees: When the input trees are unrooted, the basic reconstruction unit is a quartet tree. In this case, the basic decision problem of whether there exists a tree that agrees with all quartets is NP-complete. On the other hand, when the input trees are rooted, the basic reconstruction unit is a rooted triplet and the above decision problem has a polynomial time algorithm. However, when there is no tree which agrees with all triplets, it would be desirable to find the tree that agrees with the maximum number of triplets. However, this optimization problem was shown to be NP-hard. Current heuristic approaches perform min cut on a graph representing the triplets inconsistency and return a tree that is guaranteed to satisfy some required properties. In this work, we present a different heuristic approach that guarantees the properties provided by the current methods and give experimental evidence that it significantly outperforms currently used methods. This method is based on a divide and conquer approach, where the min cut in the divide step is replaced by a max cut in a variant of the same graph. The latter is achieved by a lightweight semidefinite programming-like heuristic that leads to very fast running times View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Pattern Identification in Biogeography

    Page(s): 334 - 346
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (864 KB) |  | HTML iconHTML  

    Identifying common patterns among area cladograms that arise in historical biogeography is an important tool for biogeographical inference. We develop the first rigorous formalization of these pattern-identification problems. We develop metrics to compare area cladograms. We define the maximum agreement area cladogram (MAAC) and we develop efficient algorithms for finding the MAAC of two area cladograms, while showing that it is NP-hard to find the MAAC of several binary area cladograms. We also describe a linear-time algorithm to identify if two area cladograms are identical View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Efficient Detection of Network Motifs

    Page(s): 347 - 359
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2462 KB) |  | HTML iconHTML  

    Motifs in a given network are small connected subnetworks that occur in significantly higher frequencies than would be expected in random networks. They have recently gathered much attention as a concept to uncover structural design principles of complex networks. Kashtan et al. [Bioinformatics, 2004] proposed a sampling algorithm for performing the computationally challenging task of detecting network motifs. However, among other drawbacks, this algorithm suffers from a sampling bias and scales poorly with increasing subgraph size. Based on a detailed analysis of the previous algorithm, we present a new algorithm for network motif detection which overcomes these drawbacks. Furthermore, we present an efficient new approach for estimating the frequency of subgraphs in random networks that, in contrast to previous approaches, does not require the explicit generation of random networks. Experiments on a testbed of biological networks show our new algorithms to be orders of magnitude faster than previous approaches, allowing for the detection of larger motifs in bigger networks than previously possible and thus facilitating deeper insight into the field View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Motif Search in Graphs: Application to Metabolic Networks

    Page(s): 360 - 368
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (632 KB) |  | HTML iconHTML  

    The classic view of metabolism as a collection of metabolic pathways is being questioned with the currently available possibility of studying whole networks. Novel ways of decomposing the network into modules and motifs that could be considered as the building blocks of a network are being suggested. In this work, we introduce a new definition of motif in the context of metabolic networks. Unlike in previous works on (other) biochemical networks, this definition is not based only on topological features. We propose instead to use an alternative definition based on the functional nature of the components that form the motif, which we call a reaction motif. After introducing a formal framework motivated by biological considerations, we present complexity results on the problem of searching for all occurrences of a reaction motif in a network and introduce an algorithm that is fast in practice in most situations. We then show an initial application to the study of pathway evolution. Finally, we give some general features of the observed number of occurrences in order to highlight some structural features of metabolic networks View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A 1.375-Approximation Algorithm for Sorting by Transpositions

    Page(s): 369 - 379
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1542 KB) |  | HTML iconHTML  

    Sorting permutations by transpositions is an important problem in genome rearrangements. A transposition is a rearrangement operation in which a segment is cut out of the permutation and pasted in a different location. The complexity of this problem is still open and it has been a 10-year-old open problem to improve the best known 1.5-approximation algorithm. In this paper, we provide a 1.375-approximation algorithm for sorting by transpositions. The algorithm is based on a new upper bound on the diameter of 3-permutations. In addition, we present some new results regarding the transposition diameter: We improve the lower bound for the transposition diameter of the symmetric group and determine the exact transposition diameter of simple permutations View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • New Bounds and Tractable Instances for the Transposition Distance

    Page(s): 380 - 394
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2161 KB) |  | HTML iconHTML  

    The problem of sorting by transpositions asks for a sequence of adjacent interval exchanges that sorts a permutation and is of the shortest possible length. The distance of the permutation is defined as the length of such a sequence. Despite the apparently intuitive nature of this problem, introduced in 1995 by Bafna and Pevzner, the complexity of both finding an optimal sequence and computing the distance remains open today. In this paper, we establish connections between two different graph representations of permutations, which allows us to compute the distance of a few nontrivial classes of permutations in linear time and space, bypassing the use of any graph structure. By showing that every permutation can be obtained from one of these classes, we prove a new tight upper bound on the transposition distance. Finally, we give improved bounds on some other families of permutations and prove formulas for computing the exact distance of other classes of permutations, again in polynomial time View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Comparing Tandem Repeats with Duplications and Excisions of Variable Degree

    Page(s): 395 - 407
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1447 KB) |  | HTML iconHTML  

    Traditional sequence comparison by alignment employs a mutation model comprised of two events, substitutions and indels (insertions or deletions) of single positions. However, modern genetic analysis knows a variety of more complex mutation events (e.g., duplications, excisions, and rearrangements), especially regarding DNA. With ever more DNA sequence data becoming available, the need to accurately compare sequences which have clearly undergone more complicated types of mutational processes is becoming critical. Herein we introduce a new method for pairwise alignment and comparison of sequences with respect to the special evolution of tandem repeats: substitutions and indels of single positions and, additionally, duplications and excisions of variable degree (i.e., of one or more repeat copies simultaneously) are taken into account. To evaluate our method, we apply it to the spa VNTR (variable number of tandem repeats) cluster of Staphylococcus aureus, a bacterium of high medical importance View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Faster Algorithms for Optimal Multiple Sequence Alignment Based on Pairwise Comparisons

    Page(s): 408 - 422
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1878 KB) |  | HTML iconHTML  

    Multiple sequence alignment (MSA) is one of the most fundamental problems in computational molecular biology. The running time of the best known scheme for finding an optimal alignment, based on dynamic programming, increases exponentially with the number of input sequences. Hence, many heuristics were suggested for the problem. We consider a version of the MSA problem where the goal is to find an optimal alignment in which matches are restricted to positions in predefined matching segments. We present several techniques for making the dynamic programming algorithm more efficient, while still finding an optimal solution under these restrictions. We prove that it suffices to find an optimal alignment of the predefined sequence segments, rather than single letters, thereby reducing the input size and thus improving the running time. We also identify "shortcuts" that expedite the dynamic programming scheme. Empirical study shows that, taken together, these observations lead to an improved running time over the basic dynamic programming algorithm by 4 to 12 orders of magnitude, while still obtaining an optimal solution. Under the additional assumption that matches between segments are transitive, we further improve the running time for finding the optimal solution by restricting the search space of the dynamic programming algorithm View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Efficient Parameterized Algorithms for Biopolymer Structure-Sequence Alignment

    Page(s): 423 - 432
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (781 KB) |  | HTML iconHTML  

    Computational alignment of a biopolymer sequence (e.g., an RNA or a protein) to a structure is an effective approach to predict and search for the structure of new sequences. To identify the structure of remote homologs, the structure-sequence alignment has to consider not only sequence similarity, but also spatially conserved conformations caused by residue interactions and, consequently, is computationally intractable. It is difficult to cope with the inefficiency without compromising alignment accuracy, especially for structure search in genomes or large databases. This paper introduces a novel method and a parameterized algorithm for structure-sequence alignment. Both the structure and the sequence are represented as graphs, where, in general, the graph for a biopolymer structure has a naturally small tree width. The algorithm constructs an optimal alignment by finding in the sequence graph the maximum valued subgraph isomorphic to the structure graph. It has the computational time complexity O(k3N2) for the structure of N residues and its tree decomposition of width t. Parameter k, small in nature, is determined by a statistical cutoff for the correspondence between the structure and the sequence. This paper demonstrates a successful application of the algorithm to RNA structure search used for noncoding RNA identification. An application to protein threading is also discussed View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Annual index

    Page(s): Not in print
    Save to Project icon | Request Permissions | PDF file iconPDF (200 KB)  
    Freely Available from IEEE
  • IEEE/ACM TCBB: Information for authors

    Page(s): c3
    Save to Project icon | Request Permissions | PDF file iconPDF (90 KB)  
    Freely Available from IEEE
  • [Back cover]

    Page(s): c4
    Save to Project icon | Request Permissions | PDF file iconPDF (124 KB)  
    Freely Available from IEEE

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