University of Maryland LibrariesDigital Repository at the University of Maryland
    • Войти
    Просмотр элемента 
    •   Главная
    • College of Computer, Mathematical & Natural Sciences
    • Computer Science
    • Computer Science Research Works
    • Просмотр элемента
    •   Главная
    • College of Computer, Mathematical & Natural Sciences
    • Computer Science
    • Computer Science Research Works
    • Просмотр элемента
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Efficient decoding algorithms for generalized hidden Markov model gene finders

    Thumbnail
    Открыть
    Efficient.pdf (361.0Kb)
    No. of downloads: 295

    Дата
    2005-01-24
    Автор
    Majoros, William H.
    Pertea, Mihaela
    Delcher, Arthur L.
    Salzberg, Steven L.
    Citation
    Efficient decoding algorithms for generalized hidden Markov model gene finders. W.H. Majoros, M. Pertea, A.L. Delcher, and S.L. Salzberg. BMC Bioinformatics 6 (2005), 16.
    Metadata
    Показать полную информацию
    Аннотации
    Background: The Generalized Hidden Markov Model (GHMM) has proven a useful framework for the task of computational gene prediction in eukaryotic genomes, due to its flexibility and probabilistic underpinnings. As the focus of the gene finding community shifts toward the use of homology information to improve prediction accuracy, extensions to the basic GHMM model are being explored as possible ways to integrate this homology information into the prediction process. Particularly prominent among these extensions are those techniques which call for the simultaneous prediction of genes in two or more genomes at once, thereby increasing significantly the computational cost of prediction and highlighting the importance of speed and memory efficiency in the implementation of the underlying GHMM algorithms. Unfortunately, the task of implementing an efficient GHMM-based gene finder is already a nontrivial one, and it can be expected that this task will only grow more onerous as our models increase in complexity. Results: As a first step toward addressing the implementation challenges of these next-generation systems, we describe in detail two software architectures for GHMM-based gene finders, one comprising the common array-based approach, and the other a highly optimized algorithm which requires significantly less memory while achieving virtually identical speed. We then show how both of these architectures can be accelerated by a factor of two by optimizing their content sensors. We finish with a brief illustration of the impact these optimizations have had on the feasibility of our new homology-based gene finder, TWAIN. Conclusions: In describing a number of optimizations for GHMM-based gene finders and making available two complete open-source software systems embodying these methods, it is our hope that others will be more enabled to explore promising extensions to the GHMM framework, thereby improving the state-of-the-art in gene prediction techniques.
    URI
    http://hdl.handle.net/1903/8000
    Collections
    • Computer Science Research Works

    DRUM is brought to you by the University of Maryland Libraries
    University of Maryland, College Park, MD 20742-7011 (301)314-1328.
    Please send us your comments.
    Web Accessibility
     

     

    Просмотр

    Весь DSpaceСообщества и коллекцииДата публикацииАвторыНазванияТематикаЭта коллекцияДата публикацииАвторыНазванияТематика

    Моя учетная запись

    ВойтиРегистрация
    Pages
    About DRUMAbout Download Statistics

    DRUM is brought to you by the University of Maryland Libraries
    University of Maryland, College Park, MD 20742-7011 (301)314-1328.
    Please send us your comments.
    Web Accessibility