Genetic variation detection using maximum likelihood estimator

Abdullah K. Alqallaf, Ahmed H. Tewfik, Scott Brian Selleck

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

Abstract

In recent years it has come to be appreciated that submicroscopic DNA copy number differences represent an important source of human genetic variation and contribute significantly to disease susceptibility. Array comparative genomic hybridization has emerged as a powerful tool for assessing copy number change and a number of algorithms have been developed to accurately assign copy number segments while minimizing errors from this inherently variable methodology. In this paper, we present an extended version of our previously proposed algorithm, maximum likelihood estimator, to clearly map and detect copy number variations. The extension accounts for both the unequal spacing distance between the contiguous probes and the regional evaluation of the detected segments based on biological information of the genomic positions. Using genomic DNA from well-characterized cell lines, we compare the performance of the proposed methods. Finally, the experimental results show that our proposed method outperforms other popular commercial programs and published algorithms.

Original languageEnglish (US)
Title of host publication2009 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2009
DOIs
StatePublished - Oct 2 2009
Event2009 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2009 - Minneapolis, MN, United States
Duration: May 17 2009May 21 2009

Other

Other2009 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2009
CountryUnited States
CityMinneapolis, MN
Period5/17/095/21/09

Fingerprint

Maximum likelihood
DNA
Comparative Genomic Hybridization
Disease Susceptibility
Medical Genetics
Cells
Cell Line

All Science Journal Classification (ASJC) codes

  • Molecular Biology
  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Biomedical Engineering

Cite this

Alqallaf, A. K., Tewfik, A. H., & Selleck, S. B. (2009). Genetic variation detection using maximum likelihood estimator. In 2009 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2009 [5174365] https://doi.org/10.1109/GENSIPS.2009.5174365
Alqallaf, Abdullah K. ; Tewfik, Ahmed H. ; Selleck, Scott Brian. / Genetic variation detection using maximum likelihood estimator. 2009 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2009. 2009.
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Alqallaf, AK, Tewfik, AH & Selleck, SB 2009, Genetic variation detection using maximum likelihood estimator. in 2009 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2009., 5174365, 2009 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2009, Minneapolis, MN, United States, 5/17/09. https://doi.org/10.1109/GENSIPS.2009.5174365

Genetic variation detection using maximum likelihood estimator. / Alqallaf, Abdullah K.; Tewfik, Ahmed H.; Selleck, Scott Brian.

2009 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2009. 2009. 5174365.

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

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AB - In recent years it has come to be appreciated that submicroscopic DNA copy number differences represent an important source of human genetic variation and contribute significantly to disease susceptibility. Array comparative genomic hybridization has emerged as a powerful tool for assessing copy number change and a number of algorithms have been developed to accurately assign copy number segments while minimizing errors from this inherently variable methodology. In this paper, we present an extended version of our previously proposed algorithm, maximum likelihood estimator, to clearly map and detect copy number variations. The extension accounts for both the unequal spacing distance between the contiguous probes and the regional evaluation of the detected segments based on biological information of the genomic positions. Using genomic DNA from well-characterized cell lines, we compare the performance of the proposed methods. Finally, the experimental results show that our proposed method outperforms other popular commercial programs and published algorithms.

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Alqallaf AK, Tewfik AH, Selleck SB. Genetic variation detection using maximum likelihood estimator. In 2009 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2009. 2009. 5174365 https://doi.org/10.1109/GENSIPS.2009.5174365