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Determining common insertion sites based on retroviral insertion distribution across tumors

Paper ID Volume ID Publish Year Pages File Format Full-Text
15124 1379 2014 10 PDF Available
Title
Determining common insertion sites based on retroviral insertion distribution across tumors
Abstract

•We highlight the CISs and detect the new ones based on insertion distribution across tumor types.•The density based clustering can not only classify data correctly, but also filter noises effectively.•Taking insertion distribution into consideration improves to distinguish CISs from each other.•Novel CISs can be found by taking insertion quantity and insertion distribution into consideration.

A CIS (common insertion site) indicates a genome region that is hit more frequently by retroviral insertions than expected by chance. Such a region is strongly related to cancer gene loci, which leads to the detection of cancer genes. An algorithm for detecting CISs should satisfy the following: (1) it does not require any prior knowledge of underlying insertion distribution; (2) it can resolve the insertion biases caused by hotspots; (3) it can detect CISs of any biological width; (4) it can identify noises resulting from statistic mistakes and non-CIS insertions; and (5) it can identify the widths of CISs as accurately as possible. We develop a method to resolve these difficulties. We verify a region's significance from two perspectives: distribution width and distribution depth. The former indicates how many insertions in a region while the latter evaluates the insertion distribution across the tumors in a region. We compare our method with kernel density estimation and sliding window on the simulated data, showing that our method not only identifies cancer-related insertions effectively, but also filters noises correctly. The experiments on the real data show that taking insertion distribution into account can highlight significant CISs. We detect 53 novel CISs, some of which have been proven correct by the biological literature.

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Keywords
Common insertion site; Retroviral insertion; DBScan; Normal standard deviation
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Determining common insertion sites based on retroviral insertion distribution across tumors
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Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 51, August 2014, Pages 83–92
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us