Abstract
The significance of any genetic or biological implication based on DNA sequencing depends on its accuracy. The statistical evaluation of accuracy requires a probabilistic model of measurement error. In this chapter, we describe two statistical models of sequence assembly from shotgun sequencing respectively for the cases of haploid and diploid target genome. The first model allows us to convert quality scores into probabilities. It combines quality scores of base-calling and the power of alignment to improve sequencing accuracy. Specifically, we start with assembled contigs and represent probabilistic errors by logistic models that takes quality scores and other genomic features as covariates. Since the true sequence is unknown, an EM algorithm is used to deal with missing data. The second model describes the case in which DNA reads are from one of diploid genome, and our aim is to reconstruct the two haplotypes including phase information. The statistical model consists of sequencing errors, compositional information and haplotype memberships of each DNA fragment. Consequently, optimal haplotype sequences can be inferred by maximizing the probability among all configurations conditional on the given assembly. In the meantime, this probability together with the coverage information provides an assessment of the confidence for the reconstruction.
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Acknowledgements
We thank Prof. Michael Waterman for initiating the works reported in this chapter. This work is supported by the NIH CEGS grant to University of Southern California and the NIH grant R01 GM75308;.
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Li, L.M. (2011). Accuracy Assessment of Consensus Sequence from Shotgun Sequencing. In: Lu, HS., Schölkopf, B., Zhao, H. (eds) Handbook of Statistical Bioinformatics. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16345-6_1
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DOI: https://doi.org/10.1007/978-3-642-16345-6_1
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