Skip Navigation



Bioinformatics Advance Access published online on July 21, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn370
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow Supplementary Data
Right arrow All Versions of this Article:
24/18/1975    most recent
btn370v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Kalari, K. R
Right arrow Articles by Scheetz, T. E
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Kalari, K. R
Right arrow Articles by Scheetz, T. E
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

A KNOWLEDGE-BASED APPROACH TO PREDICT INTRAGENIC DELETIONS OR DUPLICATIONS

Krishna R Kalari 1,4, Thomas L Casavant 1,2,3,4 and Todd E Scheetz 1,3,4,*

Departments of 1Biomedical Engineering, 2Electrical and Computer Engineering, 3Ophthalmology and Visual Sciences, 4 Center for Bioinformatics and Computational Biology, University of Iowa, USA

*To whom correspondence should be addressed. Todd E Scheetz, E-mail: tscheetz{at}eng.uiowa.edu


  Abstract

Motivation: Despite recent improvements in high-throughput or classic molecular biology approaches it is still challenging to identify intermediate resolution genomic variations (50 bp - 50 kb). Al-though array-based technologies can be used to detect copy number variations in the human genome they are biased to detect only the largest such deletions or duplications. Several studies have identified deletions or duplications occurring within a gene that directly cause or predispose to disease. We have developed a novel computational system, SPeeDD (System to Prioritize Deletions or Duplications) that utilizes machine-learning techniques to predict likely candidate regions that delete or duplicate exon(s) within a gene.

Results: Data mining and machine learning methods were applied to identify sequence features that were predictive of homologous recom-bination events. The logistic model tree (LMT) method yielded the best results. Sensitivity varied from 20% to 71.6% depending on the specific machine learning model used, but specificity exceeded 90% for all methods evaluated. In addition, the SPeeDD system successfully predicted and prioritized a recently published novel BRCA1 mutation.

Conclusions: Results suggest that the SPeeDD system is effective at prioritizing candidate deletions and duplications within a gene. Use of SPeeDD enables more focused screening, which reduces the labor and associated costs of the molecular assays and may also lead to targeted design of new array-based screens to focus on candidate areas to accelerate the process of mutation discovery.

Contact: tscheetz{at}eng.uiowa.edu

Associate Editor: Prof. John Quackenbush

Received on December 19, 2007; revised on June 17, 2008; accepted on July 14, 2008

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.