CNVeM: Copy Number Variation Detection Using Uncertainty of Read Mapping

  • Zhanyong Wang
  • Farhad Hormozdiari
  • Wen-Yun Yang
  • Eran Halperin
  • Eleazar Eskin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7262)

Abstract

Copy number variations (CNVs) are widely known to be an important mediator for diseases and traits. The development of high-throughput sequencing (HTS) technologies has provided great opportunities to identify CNV regions in mammalian genomes. In a typical experiment, millions of short reads obtained from a genome of interest are mapped to a reference genome. The mapping information can be used to identify CNV regions. One important challenge in analyzing the mapping information is the large fraction of reads that can be mapped to multiple positions. Most existing methods either only consider reads that can be uniquely mapped to the reference genome, or randomly place a read to one of its mapping positions. Therefore, these methods have low power to detect CNVs located within repeated sequences. In this study, we propose a probabilistic model, CNVeM, that utilizes the inherent uncertainty of read mapping. We use maximum likelihood to estimate locations and copy numbers of copied regions, and implement an expectation-maximization (EM) algorithm. One important contribution of our model is that we can distinguish between regions in the reference genome that differ from each other by as little as 0.1%. As our model aims to predict the copy number of each nucleotide, we can predict the CNV boundaries with high resolution. We apply our method to simulated datasets and achieve higher accuracy compared to CNVnator. Moreover, we apply our method to real data from which we detected known CNVs. To our knowledge, this is the first attempt to predict CNVs at nucleotide resolution, and to utilize uncertainty of read mapping.

Keywords

Reference Genome Read Depth Read Mapping Donor Genome Duplicate Segment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Abyzov, A., Urban, A.E., Snyder, M., Gerstein, M.: CNVnator: an approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing. Genome Research 21(6), 974–984 (2011)CrossRefGoogle Scholar
  2. 2.
    Alkan, C., Kidd, J.M., Marques-Bonet, T., Aksay, G., Antonacci, F., Hormozdiari, F., Kitzman, J.O., Baker, C., Malig, M., Mutlu, O., Cenk Sahinalp, S., Gibbs, R.A., Eichler, E.E.: Personalized copy number and segmental duplication maps using next-generation sequencing. Nature Genetics 41(10), 1061–1067 (2009)CrossRefGoogle Scholar
  3. 3.
    Cappuzzo, F., Hirsch, F.R., Rossi, E., Bartolini, S., Ceresoli, G.L., Bemis, L., Haney, J., Witta, S., Danenberg, K., Domenichini, I., Ludovini, V., Magrini, E., Gregorc, V., Doglioni, C., Sidoni, A., Tonato, M., Franklin, W.A., Crino, L., Bunn Jr., P.A., Varella-Garcia, M.: Epidermal growth factor receptor gene and protein and gefitinib sensitivity in non-small-cell lung cancer. Journal of National Cancer Institute 97(9), 643–655 (2005)CrossRefGoogle Scholar
  4. 4.
    Carter, N.P.: Methods and strategies for analyzing copy number variation using dna microarrays. Nature Genetics 39(suppl. 7), 16–21 (2007)CrossRefGoogle Scholar
  5. 5.
    Chen, P.-A., Liu, H.-F., Chao, K.-M.: CNVDetector: locating copy number variations using array CGH data. Bioinformatics 24(23), 2773–2775 (2008)CrossRefGoogle Scholar
  6. 6.
    Chiang, D.Y., Getz, G., Jaffe, D.B., O’Kelly, M.J.T., Zhao, X., Carter, S.L., Russ, C., Nusbaum, C., Meyerson, M., Lander, E.S.: High-resolution mapping of copy-number alterations with massively parallel sequencing. Nature Methods 6(1), 99–103 (2009)CrossRefGoogle Scholar
  7. 7.
    Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 603–619 (2002)CrossRefGoogle Scholar
  8. 8.
    1000 Genomes Project Consortium: A map of human genome variation from population-scale sequencing. Nature 467(7319), 1061–1073 (2010)CrossRefGoogle Scholar
  9. 9.
    Hach, F., Hormozdiari, F., Alkan, C., Hormozdiari, F., Birol, I., Eichler, E.E., Cenk Sahinalp, S.: mrsfast: a cache-oblivious algorithm for short-read mapping. Nature Methods 7(8), 576–577 (2010)CrossRefGoogle Scholar
  10. 10.
    Halperin, E., Hazan, E.: HAPLOFREQ-estimating haplotype frequencies efficiently. Journal of Computational Biology 13(2), 481–500 (2006)MathSciNetCrossRefGoogle Scholar
  11. 11.
    He, D., Furlotte, N., Eskin, E.: Detection and reconstruction of tandemly organized de novo copy number variations. BMC Bioinformatics 11(suppl. 11), S12 (2010)CrossRefGoogle Scholar
  12. 12.
    He, D., Hormozdiari, F., Furlotte, N., Eskin, E.: Efficient algorithms for tandem copy number variation reconstruction in repeat-rich regions. Bioinformatics 27(11), 1513–1520 (2011)CrossRefGoogle Scholar
  13. 13.
    Hormozdiari, F., Alkan, C., Eichler, E.E., Cenk Sahinalp, S.: Combinatorial algorithms for structural variation detection in high-throughput sequenced genomes. Genome Research 19(7), 1270–1278 (2009)CrossRefGoogle Scholar
  14. 14.
    John Iafrate, A., Feuk, L., Rivera, M.N., Listewnik, M.L., Donahoe, P.K., Qi, Y., Scherer, S.W., Lee, C.: Detection of large-scale variation in the human genome. Nature Genetics 36(9), 949–951 (2004)CrossRefGoogle Scholar
  15. 15.
    Langmead, B., Trapnell, C., Pop, M., Salzberg, S.L.: Ultrafast and memory-efficient alignment of short dna sequences to the human genome. Genome Biology 10(3), R25 (2009)CrossRefGoogle Scholar
  16. 16.
    Medvedev, P., Fiume, M., Dzamba, M., Smith, T., Brudno, M.: Detecting copy number variation with mated short reads. Genome Research 20(11), 1613–1622 (2010)CrossRefGoogle Scholar
  17. 17.
    Sebat, J., Lakshmi, B., Malhotra, D., Troge, J., Lese-Martin, C., Walsh, T., Yamrom, B., Yoon, S., Krasnitz, A., Kendall, J., Leotta, A., Pai, D., Zhang, R., Lee, Y.-H., Hicks, J., Spence, S.J., Lee, A.T., Puura, K., Lehtimki, T., Ledbetter, D., Gregersen, P.K., Bregman, J., Sutcliffe, J.S., Jobanputra, V., Chung, W., Warburton, D., King, M.-C., Skuse, D., Geschwind, D.H., Conrad Gilliam, T., Ye, K., Wigler, M.: Strong association of de novo copy number mutations with autism. Science 316(5823), 445–449 (2007)CrossRefGoogle Scholar
  18. 18.
    Simpson, J.T., McIntyre, R.E., Adams, D.J., Durbin, R.: Copy number variant detection in inbred strains from short read sequence data. Bioinformatics 26(4), 565–567 (2010)CrossRefGoogle Scholar
  19. 19.
    Sudbery, I., Stalker, J., Simpson, J.T., Keane, T., Rust, A.G., Hurles, M.E., Walter, K., Lynch, D., Teboul, L., Brown, S.D., Li, H., Ning, Z., Nadeau, J.H., Croniger, C.M., Durbin, R., Adams, D.J.: Deep short-read sequencing of chromosome 17 from the mouse strains A/J and CAST/Ei identifies significant germline variation and candidate genes that regulate liver triglyceride levels. Genome Biology (October 2009)Google Scholar
  20. 20.
    Sudmant, P.H., Kitzman, J.O., Antonacci, F., Alkan, C., Malig, M., Tsalenko, A., Sampas, N., Bruhn, L., Shendure, J., 1000 Genomes Project, Eichler, E.E.: Diversity of human copy number variation and multicopy genes. Science 330(6004), 641–646 (2010)CrossRefGoogle Scholar
  21. 21.
    Tuzun, E., Sharp, A.J., Bailey, J.A., Kaul, R., Anne Morrison, V., Pertz, L.M., Haugen, E., Hayden, H., Albertson, D., Pinkel, D., Olson, M.V., Eichler, E.E.: Fine-scale structural variation of the human genome. Nature Genetics 37(7), 727–732 (2005)CrossRefGoogle Scholar
  22. 22.
    Yoon, S., Xuan, Z., Makarov, V., Ye, K., Sebat, J.: Sensitive and accurate detection of copy number variants using read depth of coverage. Genome Research 19(9), 1586–1592 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Zhanyong Wang
    • 1
  • Farhad Hormozdiari
    • 1
  • Wen-Yun Yang
    • 1
  • Eran Halperin
    • 2
    • 3
  • Eleazar Eskin
    • 1
  1. 1.Computer Science DepartmentUniversity of CaliforniaLos AngelesUSA
  2. 2.Blavatnik School of Computer Science, The Department of Molecular Microbiology and BiotechnologyTel-Aviv UniversityIsrael
  3. 3.International Computer Science InstituteBerkeleyUSA

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