PROJECT TITLE :
Achieving Privacy-friendly Storage and Secure Statistics for Smart Meter Data on Outsourced Clouds - 2017
Haplotype phasing is crucial for identifying disease-inflicting variants with phase-dependent interactions and for the coalescent-based inference of demographic history. One among approaches for estimating haplotypes is to use section-informative reads, that span multiple heterozygous variant positions. Although the standard of estimated variants is crucial in haplotype phasing, accurate variant calling remains difficult thanks to errors on sequencing and scan mapping. Since a number of such errors can be corrected by considering haplotype phasing, simultaneous estimation of variants and haplotypes is vital. We tend to propose a statistical approach for variant calling and haplotype phasing named HapMonster2, where haplotype phasing info is employed for improving the accuracy of variant calling and also the improved variant calls are used for additional accurate haplotype phasing. Since parameter estimation falls into bad native optima from bad initial parameters, we have a tendency to propose new procedures for initialization of model parameters for accurate haplotype estimation. We have a tendency to also devise a brand new algorithm for filtering out unreliable haplotypes to avoid switching errors. From the comparison with existing ways on simulation and real sequencing knowledge, HapMonster2 is effective in each variant calling and haplotyping, while the results of HapMonster2 contain the least or second least switching errors in most of conditions.
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