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A High-Throughput Computational Framework for Identifying Significant Copy Number Aberrations from Array Comparative Genomic Hybridisation Data
Joint Authors
Scarpini, Cinzia G.
Karagavriilidou, Konstantina
Carter, Stephanie A.
Calleja, Mark
Barna, Jenny C. J.
Coleman, Nicholas
Roberts, Ian
Source
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-09-13
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Natural & Life Sciences (Multidisciplinary)
Biology
Abstract EN
Reliable identification of copy number aberrations (CNA) from comparative genomic hybridization data would be improved by the availability of a generalised method for processing large datasets.
To this end, we developed swatCGH, a data analysis framework and region detection heuristic for computational grids.
swatCGH analyses sequentially displaced (sliding) windows of neighbouring probes and applies adaptive thresholds of varying stringency to identify the 10% of each chromosome that contains the most frequently occurring CNAs.
We used the method to analyse a published dataset, comparing data preprocessed using four different DNA segmentation algorithms, and two methods for prioritising the detected CNAs.
The consolidated list of the most commonly detected aberrations confirmed the value of swatCGH as a simplified high-throughput method for identifying biologically significant CNA regions of interest.
American Psychological Association (APA)
Roberts, Ian& Carter, Stephanie A.& Scarpini, Cinzia G.& Karagavriilidou, Konstantina& Barna, Jenny C. J.& Calleja, Mark…[et al.]. 2012. A High-Throughput Computational Framework for Identifying Significant Copy Number Aberrations from Array Comparative Genomic Hybridisation Data. Advances in Bioinformatics،Vol. 2012, no. 2012, pp.1-12.
https://search.emarefa.net/detail/BIM-505533
Modern Language Association (MLA)
Roberts, Ian…[et al.]. A High-Throughput Computational Framework for Identifying Significant Copy Number Aberrations from Array Comparative Genomic Hybridisation Data. Advances in Bioinformatics No. 2012 (2012), pp.1-12.
https://search.emarefa.net/detail/BIM-505533
American Medical Association (AMA)
Roberts, Ian& Carter, Stephanie A.& Scarpini, Cinzia G.& Karagavriilidou, Konstantina& Barna, Jenny C. J.& Calleja, Mark…[et al.]. A High-Throughput Computational Framework for Identifying Significant Copy Number Aberrations from Array Comparative Genomic Hybridisation Data. Advances in Bioinformatics. 2012. Vol. 2012, no. 2012, pp.1-12.
https://search.emarefa.net/detail/BIM-505533
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-505533