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Haplotype information plays an important role in many genetic analyses. However, the identification of haplotypes based on sequencing methods is both expensive and time consuming. Current sequencing methods are only efficient to determine conflated data of haplotypes, that is, genotypes. This raises the need to develop computational methods to infer haplotypes from genotypes. Haplotype inference by...
Genotype data provide crucial information to understand effects of genetic variation to human health. Current microarray technologies are able to generate raw genotype data from thousands of samples across million of SNP sites. These raw data are processed by computational methods, called genotype caller, to obtain genotypes. Genotype calls of different callers might not be consistent due to noise...
Current microarray technologies are able to assay thousands of samples over million of SNPs simultaneously. Computational approaches have been developed to analyse a huge amount of data from microarray chips to understand sophisticated human genomes. The data from microarray chips might contain errors due to bad samples or bad SNPs. In this paper, we propose a method to detect bad SNPs from the probe...
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