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Comparing the landcapes of common retroviral insertion sites across tumor models
Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University. (MAM)
Uppsala University, Sweden.
Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics. (MAM)ORCID iD: 0000-0002-1624-5147
Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics. (MAM)ORCID iD: 0000-0003-4554-6528
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2017 (English)In: AIP Conference Proceedings, Volume 1798 / [ed] Seenith Sivasundaram, American Institute of Physics (AIP), 2017, Vol. 1798, 020173-1-020173-9 p., 020173Conference paper, Published paper (Refereed)
Abstract [en]

Retroviral tagging represents an important technique, which allows researchers to screen for candidate cancer genes. The technique is based on the integration of retroviral sequences into the genome of a host organism, which might then lead to the artificial inhibition or expression of proximal genetic elements. The identification of potential cancer genes in this framework involves the detection of genomic regions (common insertion sites; CIS) which contain a number of such viral integration sites that is greater than expected by chance. During the last two decades, a number of different methods have been discussed for the identification of such loci and the respective techniques have been applied to a variety of different retroviruses and/or tumor models. We have previously established a retrovirus driven brain tumor model and reported the CISs which were found based on a Monte Carlo statistics derived detection paradigm. In this study, we consider a recently proposed alternative graph theory based method for identifying CISs and compare the resulting CIS landscape in our brain tumor dataset to those obtained when using the Monte Carlo approach. Finally, we also employ the graph-based method to compare the CIS landscape in our brain tumor model with those of other published retroviral tumor models. 

Place, publisher, year, edition, pages
American Institute of Physics (AIP), 2017. Vol. 1798, 020173-1-020173-9 p., 020173
National Category
Mathematics Probability Theory and Statistics Bioinformatics and Systems Biology
Research subject
Mathematics/Applied Mathematics
Identifiers
URN: urn:nbn:se:mdh:diva-35006DOI: 10.1063/1.4972765ISI: 000399203000172Scopus ID: 2-s2.0-85013659805ISBN: 9780735414648 (print)OAI: oai:DiVA.org:mdh-35006DiVA: diva2:1080158
Conference
11th International Conference on Mathematical Problems in Engineering, Aerospace and Sciences, ICNPAA 2016, 4 July 2016 through 8 July 2016
Available from: 2017-03-09 Created: 2017-03-09 Last updated: 2017-10-06Bibliographically approved

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Publisher's full textScopushttp://aip.scitation.org/doi/abs/10.1063/1.4972765

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Engström, ChristopherSilvestrov, Sergei

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