A novel approach to detect copy number variation using segmentation and genetic algorithm

Chihyun Park, Youngmi Yoon, Jaegyoon Ahn, Myungjin Moon, Sanghyun Park

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Among many forms of genomic variations, copy-number variations (CNVs) can be defined as gains or losses of several kilobases to hundreds of kilobases of genomic DNA. Since many CNVs include genes that result in differential levels of gene expression, CNVs may account for a significant proportion of normal phenotypic variation. Some scientists demonstrated that a large portion of overlapping, currently known common human CNVs, were smaller in his dataset. However, previous experimental studies, performed primarily by a-CGH techniques, are limited to detection of CNVs of large-sized CNVs. Efficient algorithms for finding small-sized CNVs are essential. In our paper, we propose a novel approach to find small-sized CNVs on a-CGH data which is a sequential 2-dimensional clustering method. The algorithm we propose is robust to some level of noise. And regardless of the size of probes, our algorithm can find CNVs consisting of small number of probes.

Original languageEnglish
Title of host publication24th Annual ACM Symposium on Applied Computing, SAC 2009
Pages788-792
Number of pages5
DOIs
Publication statusPublished - 2009 Dec 1
Event24th Annual ACM Symposium on Applied Computing, SAC 2009 - Honolulu, HI, United States
Duration: 2009 Mar 82009 Mar 12

Publication series

NameProceedings of the ACM Symposium on Applied Computing

Other

Other24th Annual ACM Symposium on Applied Computing, SAC 2009
CountryUnited States
CityHonolulu, HI
Period09/3/809/3/12

All Science Journal Classification (ASJC) codes

  • Software

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  • Cite this

    Park, C., Yoon, Y., Ahn, J., Moon, M., & Park, S. (2009). A novel approach to detect copy number variation using segmentation and genetic algorithm. In 24th Annual ACM Symposium on Applied Computing, SAC 2009 (pp. 788-792). (Proceedings of the ACM Symposium on Applied Computing). https://doi.org/10.1145/1529282.1529447