Advertisement

Matching Confidence Masks with Experts Annotations for Estimates of Chromosomal Copy Number Alterations

  • Jorge Muñoz-MinjaresEmail author
  • Yuriy S. Shmaliy
  • Tatiana Popova
  • R. J. Perez–Chimal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10814)

Abstract

Structural aberrations (SAs), gains or losses in large segments of genomes, are associated with several genetic disorders. The SAs are commonly called the copy number alterations (CNAs) and their identification/classification is required to identify diseases. Many methods have been proposed to estimate the breakpoints and segmental constants in the CNAs with highest precision using the most powerful technologies of hybridization. However, locations and lengths of CNAs estimated using well-elaborated methods are often contradictory due to extensive variability of measurements and performance of the algorithms. Still much less attention is given to the estimation accuracy and it is difficult to select the best estimator. In this work, we propose to modify the confidence masks replacing the skew Laplace distribution with the asymmetric exponential power distribution (AEP) to approximate the jitter distribution in CNAs. Next, the estimates obtained using different algorithms are matched with the annotations made by experts employing the improved masks. Finally, we specify the match confidence probability of each CNAs detector algorithm respect the experts estimates.

Keywords

Copy number alterations Modified confidence masks Asymmetric exponential power distribution Experts’ annotations 

References

  1. 1.
    Graham, N.A., Minasyan, A., Lomova, A., Cass, A., Balanis, N.G., et al.: Recurrent patterns of DNA copy number alterations in tumors reflect metabolic selection pressures. Mol. Syst. Biol. 13, 914 (2017)CrossRefGoogle Scholar
  2. 2.
    Weinberg, R.A.: The Biology of Cancer. Gallard Science Taylor & Francis Group, LLC., London (2007)Google Scholar
  3. 3.
    Forozan, F., Karhu, R., Kononen, J., Kallioniemi, A., Kallioniemi, O.P., et al.: Genome screening by comparative genomic hybridization. Trends Genet. 13, 405–409 (1997)CrossRefGoogle Scholar
  4. 4.
    Speicher, M.R., Carter, N.P.: The new cytogenetics: blurring the boundaries with molecular biology. Nat. Rev. Genet. 6, 782–792 (2005)CrossRefGoogle Scholar
  5. 5.
    Ng, P.C., Kirkness, E.F.: Whole genome sequencing. Methods Mol. Biol. 628, 215–226 (2010)CrossRefGoogle Scholar
  6. 6.
    Zare, F., Dow, M., Monteleone, N., Hosny, A., Nabavi, S., et al.: An evaluation of copy number variation detection tools for cancer using whole exome sequencing data. BMC Bioinform. 18, 286 (2017)CrossRefGoogle Scholar
  7. 7.
    Popova, T., Boeva, V., Manie, E., Rozenholc, Y., Barillot, E., et al.: Analysis of somatic alterations in cancer genome: from SNP arrays to next generation sequencing. In: Sequence and Genome Analysis I Humans, Animals and Plants. Ltd IP (edn.) iConcept Press Ltd. (2013)Google Scholar
  8. 8.
    Munoz, J.U., Shmaliy, Y.S.: Estimates of the breakpoints in genome copy number alteration profiles with masks. Biomed. Sig. Process Contr. 10, 238–248 (2017)Google Scholar
  9. 9.
    Pinkel, D., Segraves, R., Sudar, D., Clark, S., Poole, I., et al.: High resolution analysis of DNA copy number variation using comparative genomic hybridization to microarrays. Nat. Genet. 20, 207–211 (1998)CrossRefGoogle Scholar
  10. 10.
    Hocking, T.D., Schleiermacher, G., Janoueeix-Lerosey, I., Boeva, V., Cappo, J., et al.: Learning smoothing models of copy number profiles using breakpoint annotations. BMC Bioinform. 14, 164 (2013)CrossRefGoogle Scholar
  11. 11.
    Tibshirani, R., Wang, P.: Spatial smoothing and hotspot detection for CGH data using the fused lasso. Biostatistics 9, 18–29 (2007)CrossRefzbMATHGoogle Scholar
  12. 12.
    Munoz, J.U., Cabal, J., Shmaliy, Y.S.: Confidence masks for genome DNA copy number variations in applications to HR-CGH array measurements. Biomed. Sig. Process Control 13, 337–344 (2014)CrossRefGoogle Scholar
  13. 13.
    Minjares, J.M., Shmaliy, Y.S.: Matching confidence masks with experts annotations for estimates of chromosomal copy number alterations. J. Genet. Disord. 1(1), 9 (2017)Google Scholar
  14. 14.
    Munoz, J.U., Shmaliy, Y.S., Cabal, J.: Confidence limits for genome DNA copy number variations in HR-CGH array measurements. Biomed. Sig. Process. Control 10, 166–173 (2014)CrossRefGoogle Scholar
  15. 15.
    Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: LabelMe: a database and web-based tool for image annotation. Int. J. Comput. Vis. 77(1–3), 157–173 (2008)CrossRefGoogle Scholar
  16. 16.
    Jones, T.R., Carpenter, A.E., Lamprecht, M.R., Moffat, J., Silver, S.J., Grenier, J.K., Castoreno, A.B., Eggert, U.S., Root, D.E., Golland, P., Sabatini, D.M.: Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning. Proc. Natl. Acad. Sci. 106(6), 1826–1831 (2009)CrossRefGoogle Scholar
  17. 17.
    Munoz, J.U., Shmaliy, Y.S.: Improving estimates of genome CNVs with confidence masks using SNP array data. Biomed. Sig. Process. Control 31, 238–248 (2017)CrossRefGoogle Scholar
  18. 18.
    Munoz-Minjares, J.U., Cabal, J., Shmaliy, Y.S.: Effect of noise on estimate bounds for genome DNA structural changes. WSEAS Trans. Biol. Biomed. 11, 52–61 (2014)Google Scholar
  19. 19.
    Ayebo, A., Kozubowski, T.J.: An asymmetric generalization of Gaussian and Laplace laws. J. Prob. Statist. Sci. 1(2), 187–210 (2003)Google Scholar
  20. 20.
    Buldyrev, S.V., Growiec, J., Riccaboni, M., Stanley, H.E.: The growth of business firms: facts and theory. J. Eur. Econ. Assoc. 5(2–3), 574–584 (2007)CrossRefGoogle Scholar
  21. 21.
    Massey, F.J.: The Kolmogorov-Smirnov test for goodness of fit. J. Am. Stat. Assoc. 46(253), 68–78 (1951)CrossRefzbMATHGoogle Scholar
  22. 22.
    Munoz-Minjares, J., Shmaliy, Y.S., Olivera-Reyna, R., Olivera-Reyna, R., Perez-Chimal, R.J.: Jitter representation in SCNA breakpoints using asymmetric exponential power distribution. In: 14th International Conference on Electrical Engineering, Computing Science and Automatic Control (2017)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electronics EngineeringUniversidad de GuanajuatoSalamancaMexico
  2. 2.Centre de Recherche, Institut Curie, Department of UNITE 830 INSERM Unité de Génétique et Biologie des CancersParisFrance
  3. 3.Department of MechatronicsUniversidad Tecnologica de SalamancaSalamncaMexico

Personalised recommendations