Abstract
The study of rare diseases uses next-generation sequencing (NGS) technology to detect causative mutations in the human genome. NGS is a new approach for biomedical research, useful for the genetic diagnosis in extremely heterogeneous conditions. Nevertheless, only few publications address the problem when pooled experiments are considered, and existing tools are often inaccurate. In this work we focus on rare diseases and we describe how data are generated by NGS.
We present how data are organized in the pre-processing phase, how they are filtered and features constructed in the learning phase. We compare different computational procedures to identify and classify variants potentially related to rare diseases and we biologically validate the obtained results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
An Introduction to Next-Generation Sequencing Technology. www.illumina.com/NGS
Licastro, D., Mutarelli, M., Peluso, I., Neveling, K., Wieskamp, N., Rispoli, R., Vozzi, D., Athanasakis, E., D’Eustacchio, A., Pizzo, M., D’Amico, F., Ziviello, C., Simonelli, F., Fabretto, A., Scheffer, H., Gasparini, P., Banfi, S., Nigro, V.: Molecular diagnosis of Usher syndrome: application of two different next generation sequencing-based procedures. PLoS ONE 7, Article number 43799 (2012)
Cacciottolo, M., Numitone, G., Aurino, S., Caserta, I.R., Fanin, M., Politano, L., Minetti, C., Ricci, E., Piluso, G., Angelini, C., Nigro, V.: Muscular dystrophy with marked dysferlin deficiency is consistently caused by primary dysferlin gene mutations. Eur. J. Hum. Genet. 19, 974–980 (2011)
Nigro, V.: Improving the course of muscular dystrophy? (Editorial). Acta Myol. 31, 109 (2012)
Kaplan, J.C.: The 2012 version of the gene table of monogenic neuromuscular disorders. Neuromuscul. Disord. 21, 833–861 (2011)
Futschik, A., Schlotterer, C.: The next generation of molecular markers from massively parallel sequencing of pooled DNA samples. Genetics 186, 207–218 (2010)
Calvo, S., Tucker, E., Compton, A., Kirby, D., Crawford, G., Burtt, N., Rivas, M., Guiducci, C., Bruno, D., Goldberger, O., Redman, M., Wiltshire, E., Wilson, C., Altshuler, D., Gabriel, S., Daly, M., Thorburn, D., Mootha, V.: High-throughput, pooled sequencing identifies mutations in NUBPL and FOXRED1 in human complex I deficiency. Nat. Genet. 42(10), 851–860 (2011)
Wang, T., Pradhan, K., Ye, K., Wong, L.-J., Rohan, T.: Estimating allele frequency from next-generation sequencing of pooled mitochondrial DNA samples. Front. Genet. 2, 51 (2011)
Ding, J., Bashashati, A., Roth, A., Oloumi, A., Tse, K., Zeng, T., Haffari, G., Hirst, M., Marra, M., Condon, A., Aparicio, S., Shah, S.: Feature-based classifiers for somatic mutation detection in tumour-normal paired sequencing data. Bioinformatics 28, 167–175 (2012)
Next-Gen Sequencing: Advancing Sequencing for a Better World. Agilent Technologies Target Enrichment Solutions. www.agilent.com/genomics/ngs
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., G.P.D.P. Subgroup: The sequence alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009)
Mangasarian, O., Wild, E.: Multisurface proximal support vector machine classification via generalized eigenvalues. IEEE Trans. Pattern Anal. Mach. Intell. 28, 69–74 (2006)
Parlett, B.N.: The Symmetric Eigenvalue Problem, p. 357. SIAM, Philadelphia (1998)
Wilkinson, J.H.: The Algebraic Eigenvalue Problem. Clarendon Press, Oxford (1988)
Guarracino, M.R., Cifarelli, C., Seref, O., Pardalos, P.: A classification algorithm based on generalized eigenvalue problems. Optim. Method Softw. 22, 73–81 (2007)
Cifarelli, C., Guarracino, M., Seref, O., Cuciniello, S., Pardalos, P.: Incremental classification with generalized eigenvalues. J. Classif. 24, 205–219 (2007)
DePristo, M.A., Banks, E., Poplin, R.E., Garimella, K.V., Maguire, J.R., Hartl, C., Philippakis, A.A., del Angel, G., Rivas, M.A., Hanna, M., McKenna, A., Fennell, T.J., Kernytsky, A.M., Sivachenko, A.Y., Cibulskis, K., Gabriel, S.B., Altshuler, D., Daly, M.J.: A framework for variation discovery and genotyping using nextgeneration DNA sequencing data. Nat. Genet. 43, 491–498 (2011)
McKenna, A., Hanna, M., Banks, E., et al.: The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010)
Wei, Z., Wang, W., Hu, P., Lyon, G.J., Hakonarson, H.: SNVer: a statistical tool for variant calling in analysis of pooled or individual next-generation sequencing data. Nucleic Acids Res. 39, 1–13 (2011)
Garrison, E., Marth, G. (2012). Haplotype-based variant detection from short-read sequencing. arXiv:1207.3907
Bansal, V.: A statistical method for the detection of variants from next-generation resequencing of DNA pools. Bioinformatics 26, 318–324 (2010)
Platt, J.: Fast training of support vector machines using sequential minimal optimization. In: Schoelkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods - Support Vector Learning. MIT Press, Cambridge (1998)
Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29, 131–163 (1997)
Fix, E., Hodges, J.L.: Discriminatory analysis, non parametric discrimination: consistency properties. Technical report 4, USAF School of Aviation Medicine, Randolph Field, Texas (1951)
Broomhead, D.S., Lowe, D.: Multivariable functional interpolation and adaptive networks. Complex Syst. 2, 321–355 (1988)
Landwehr, N., Hall, M., Frank, E.: Logistic model trees. Mach. Learn. 95, 161–205 (2005)
Sumner, M., Frank, E., Hall, M.: Speeding up logistic model tree induction. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 675–683. Springer, Heidelberg (2005)
Rennie, J.D.M., Shih, L., Teevan, J., Karge, D.R.: Tackling the poor assumptions of naive bayes text classifiers. In: Proceedings of the Twentieth International Conference on Machine Learning, pp. 616–623 (2003)
Shalev-Shwartz, S., Singer, Y., Srebro, N.: Pegasos: primal estimated Sub-GrAdient SOlver for SVM. In: 24th International Conference on Machine Learning, pp. 807–814 (2007)
Ng, P.C., Henikoff, S.: SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res 31, 3812–3814 (2003)
Mitchell, T.: Machine Learning. McGraw Hill, Berkshire (1997)
Acknowledgment
Authors would like to thank V. Nigro, M. Savarese, G. Di Fruscio, T. Giugliano, M. Iacomino, A. Torella, A. Garofalo, C. Pisano, F. Del Vecchio Blanco and G. Piluso (Seconda Universitá di Napoli, Patologia Generale), M. Mutarelli, V. Singh Marwah and M. Dionisi (TIGEM), and Italian LGMD network. This work has been partially funded by Italian Flagship project Interomics and by \(\mathrm{{PON02}}\_\)00619 projects.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Ferraro, M.B., Guarracino, M.R. (2014). Prediction of Single-Nucleotide Polymorphisms Causative of Rare Diseases. In: Formenti, E., Tagliaferri, R., Wit, E. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2013. Lecture Notes in Computer Science(), vol 8452. Springer, Cham. https://doi.org/10.1007/978-3-319-09042-9_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-09042-9_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09041-2
Online ISBN: 978-3-319-09042-9
eBook Packages: Computer ScienceComputer Science (R0)