TY  - JOUR
A1  - Li, Ruiqiang
A1  - Li, Yingrui
A1  - Fang, Xiaodong
A1  - Yang, Huanming
A1  - Wang, Jian
A1  - Kristiansen, Karsten
A1  - Wang, Jun
T1  - SNP detection for massively parallel whole-genome resequencing
Y1  - 2009/06/01 
JF  - Genome Research 
JO  - Genome Research 
SP  - 1124 
EP  - 1132 
DO  - 10.1101/gr.088013.108 
VL  - 19 
IS  - 6 
UR  - http://genome.cshlp.org/content/19/6/1124.abstract 
N2  - Next-generation massively parallel sequencing technologies provide ultrahigh throughput at two orders of magnitude lower unit cost than capillary Sanger sequencing technology. One of the key applications of next-generation sequencing is studying genetic variation between individuals using whole-genome or target region resequencing. Here, we have developed a consensus-calling and SNP-detection method for sequencing-by-synthesis Illumina Genome Analyzer technology. We designed this method by carefully considering the data quality, alignment, and experimental errors common to this technology. All of this information was integrated into a single quality score for each base under Bayesian theory to measure the accuracy of consensus calling. We tested this methodology using a large-scale human resequencing data set of 36× coverage and assembled a high-quality nonrepetitive consensus sequence for 92.25% of the diploid autosomes and 88.07% of the haploid X chromosome. Comparison of the consensus sequence with Illumina human 1M BeadChip genotyped alleles from the same DNA sample showed that 98.6% of the 37,933 genotyped alleles on the X chromosome and 98% of 999,981 genotyped alleles on autosomes were covered at 99.97% and 99.84% consistency, respectively. At a low sequencing depth, we used prior probability of dbSNP alleles and were able to improve coverage of the dbSNP sites significantly as compared to that obtained using a nonimputation model. Our analyses demonstrate that our method has a very low false call rate at any sequencing depth and excellent genome coverage at a high sequencing depth. 
ER  -