Short Communication

Gene Therapy (2008) 15, 1294–1298; doi:10.1038/gt.2008.99; published online 26 June 2008

Automated analysis of viral integration sites in gene therapy research using the SeqMap web resource

B Peters1, S Dirscherl2, J Dantzer1, J Nowacki1, S Cross3, X Li1,4, K Cornetta3,5,6, M C Dinauer2,3,6,7 and S D Mooney1,3

  1. 1Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA
  2. 2Department of Pediatrics, Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA
  3. 3Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
  4. 4Division of Biostatistics, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
  5. 5Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
  6. 6Department of Microbiology and Immunology, Indiana University School of Medicine, Indianapolis, IN, USA
  7. 7Section of Pediatric Hematology/Oncology, Riley Hospital for Children, Indianapolis, IN, USA

Correspondence: Professor SD Mooney, Department of Medical and Molecular Genetics, Center for Computational Biology and Bioinformatics, 410 W 10th Street, Suite 5000, Indianapolis, IN 46202, USA. E-mail: sdmooney@iupui.edu

Received 4 September 2007; Revised 8 April 2008; Accepted 8 April 2008; Published online 26 June 2008.

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Abstract

Research in gene therapy involving genome-integrating vectors now often includes analysis of vector integration sites across the genome using methods such as ligation-mediated PCR (LM-PCR) or linear amplification-mediated PCR (LAM-PCR). To help researchers analyze these sites and the functions of nearby genes, we have developed SeqMap (http://seqmap.
compbio.iupui.edu/
) a secure, web-based comprehensive vector integration site management tool that automatically analyzes and annotates large numbers of vector integration sites derived from LM-PCR experiments in human and model organisms upon a common genome database. We believe the use of this resource will enable better reproducibility and understanding of this important data.

Keywords:

bioinformatics, LM-PCR, insertional mutagenesis, database