Novel molecular and computational methods improve the accuracy of insertion site analysis in Sleeping Beauty-induced tumors

Benjamin T Brett; Katherine E Berquam-Vrieze; Kishore Nannapaneni; Jian Huang; Todd E Scheetz; Adam J Dupuy

doi:10.1371/journal.pone.0024668

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Novel molecular and computational methods improve the accuracy of insertion site analysis in Sleeping Beauty-induced tumors

Journal article

Open access

Peer reviewed

Novel molecular and computational methods improve the accuracy of insertion site analysis in Sleeping Beauty-induced tumors

Benjamin T Brett, Katherine E Berquam-Vrieze, Kishore Nannapaneni, Jian Huang, Todd E Scheetz and Adam J Dupuy

PloS one, Vol.6(9), pp.e24668-e24668

2011

DOI: 10.1371/journal.pone.0024668

PMCID: PMC3172244

PMID: 21931803

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Abstract

The recent development of the Sleeping Beauty (SB) system has led to the development of novel mouse models of cancer. Unlike spontaneous models, SB causes cancer through the action of mutagenic transposons that are mobilized in the genomes of somatic cells to induce mutations in cancer genes. While previous methods have successfully identified many transposon-tagged mutations in SB-induced tumors, limitations in DNA sequencing technology have prevented a comprehensive analysis of large tumor cohorts. Here we describe a novel method for producing genetic profiles of SB-induced tumors using Illumina sequencing. This method has dramatically increased the number of transposon-induced mutations identified in each tumor sample to reveal a level of genetic complexity much greater than previously appreciated. In addition, Illumina sequencing has allowed us to more precisely determine the depth of sequencing required to obtain a reproducible signature of transposon-induced mutations within tumor samples. The use of Illumina sequencing to characterize SB-induced tumors should significantly reduce sampling error that undoubtedly occurs using previous sequencing methods. As a consequence, the improved accuracy and precision provided by this method will allow candidate cancer genes to be identified with greater confidence. Overall, this method will facilitate ongoing efforts to decipher the genetic complexity of the human cancer genome by providing more accurate comparative information from Sleeping Beauty models of cancer.

Neoplasms - metabolism

Computational Biology - methods

Animals

Neoplasms - genetics

Humans

DNA Transposable Elements - genetics

Mice

Details

Title: Subtitle: Novel molecular and computational methods improve the accuracy of insertion site analysis in Sleeping Beauty-induced tumors
Creators: Benjamin T Brett - Center for Bioinformatics and Computational Biology, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, United States of America
Katherine E Berquam-Vrieze
Kishore Nannapaneni
Jian Huang
Todd E Scheetz
Adam J Dupuy
Resource Type: Journal article
Publication Details: PloS one, Vol.6(9), pp.e24668-e24668
DOI: 10.1371/journal.pone.0024668
PMID: 21931803
PMCID: PMC3172244
NLM abbreviation: PLoS One
ISSN: 1932-6203
eISSN: 1932-6203
Publisher: United States
Grant note: T32 GM008629 / NIGMS NIH HHS R01 CA130867 / NCI NIH HHS 5R01CA130867-03 / NCI NIH HHS
Language: English
Date published: 2011
Academic Unit: Statistics and Actuarial Science; Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Anatomy and Cell Biology; Pathology; Ophthalmology and Visual Sciences
Record Identifier: 9983979948302771

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