Site Map | Search | Terms & Conditions - PAIA
Company Profile | Products | Support | Contact Us | Community
...facilitating access to information
Legal | Electronic Publications | Online Reference | Collection Management | Library Systems | Cataloguing | Interlending | Client Access
HomeProductsElectronic Publications (Online Journals) Abstract Information
JournalsElectronic Publishing
Abstract Info
Latest issues added
More on this service
Subscription FAQ

Abstract Information

A two-phase strategy for detecting recombination in nucleotide sequences : reviewed article

  • Journal Title: South African Computer Journal
  • Volume: Volume 38
  • Publication Date: 2007
  • Pages: 20  - 27
  • Authors:  Cheong Xin Chan;  Robert G. Beiko;  Mark A. Ragan;
  • ISSN: 10157999
  • Abstract:  Genetic recombination can produce heterogeneous phylogenetic histories within a set of homologous genes. Delineating recombination events is important in the study of molecular evolution, as inference of such events provides a clearer picture of the phylogenetic relationships among different gene sequences or genomes. Nevertheless, detecting recombination events can be a daunting task, as the performance of different recombination-detecting approaches can vary, depending on evolutionary events that take place after recombination. We previously evaluated the effects of post-recombination events on the prediction accuracy of recombination-detecting approaches using simulated nucleotide sequence data. The main conclusion, supported by other studies, is that one should not depend on a single method when searching for recombination events. In this paper, we introduce a two-phase strategy, applying three statistical measures to detect the occurrence of recombination events, and a Bayesian phylogenetic approach to delineate breakpoints of such events in nucleotide sequences. We evaluate the performance of these approaches using simulated data, and demonstrate the applicability of this strategy to empirical data. The two-phase strategy proves to be time-efficient when applied to large datasets, and yields high-confidence results.
  • Read this article

For subscription and additional information please contact us on:

  • Tel: +27 12 643-9500
  • email:
  • alternatively you can complete this form and one of our portfolio managers will contact you!

Sabinet Online Ltd. is Proudly South AfricanCompany Profile | Products | Support | Contact Us | Community
P O Box 9785 Centurion 0046 | | 0800 11 85 95
Copyright © Sabinet Online Ltd 2008. All Rights Reserved. Terms & Conditions. PAIA.