SNPdetector: A Software Tool for Sensitive and Accurate SNP Detection

Identification of single nucleotide polymorphisms (SNPs) and mutations is important for the discovery of genetic predisposition to complex diseases. PCR resequencing is the method of choice for de novo SNP discovery. However, manual curation of putative SNPs has been a major bottleneck in the application of this method to high-throughput screening. Therefore it is critical to develop a more sensitive and accurate computational method for automated SNP detection. We developed a software tool, SNPdetector, for automated identification of SNPs and mutations in fluorescence-based resequencing reads. SNPdetector was designed to model the process of human visual inspection and has a very low false positive and false negative rate. We demonstrate the superior performance of SNPdetector in SNP and mutation analysis by comparing its results with those derived by human inspection, PolyPhred (a popular SNP detection tool), and independent genotype assays in three large-scale investigations. The first study identified and validated inter- and intra-subspecies variations in 4,650 traces of 25 inbred mouse strains that belong to either the Mus musculus species or the M. spretus species. Unexpected heterozgyosity in CAST/Ei strain was observed in two out of 1,167 mouse SNPs. The second study identified 11,241 candidate SNPs in five ENCODE regions of the human genome covering 2.5 Mb of genomic sequence. Approximately 50% of the candidate SNPs were selected for experimental genotyping; the validation rate exceeded 95%. The third study detected ENU-induced mutations (at 0.04% allele frequency) in 64,896 traces of 1,236 zebra fish. Our analysis of three large and diverse test datasets demonstrated that SNPdetector is an effective tool for genome-scale research and for large-sample clinical studies. SNPdetector runs on Unix/Linux platform and is available publicly (http://lpg.nci.nih.gov).

单核苷酸多态性（Single Nucleotide Polymorphisms, SNPs）与突变的识别，对于揭示复杂疾病的遗传易感倾向具有重要价值。PCR重测序是从头SNP发现的首选技术手段。然而，对推定SNPs进行人工审核整理，是该方法应用于高通量筛选的主要瓶颈。因此，开发灵敏度与准确度更高的自动化SNP检测计算方法至关重要。
我们开发了一款名为SNPdetector的软件工具，用于基于荧光的重测序读段中SNPs与突变的自动化识别。SNPdetector的设计思路是模拟人类人工目视检查的流程，其假阳性率与假阴性率均极低。
我们通过三项大规模研究，将SNPdetector的分析结果与人工目视检查结果、PolyPhred（一款主流SNP检测工具）以及独立基因型检测结果进行对比，证实了其在SNP与突变分析中的优异性能。第一项研究对25个近交系小鼠品系（隶属于小家鼠Mus musculus或斯氏小家鼠M. spretus）的4650条测序峰图进行分析，鉴定并验证了种间与种内亚种间的变异。研究在1167个小鼠SNPs中，于CAST/Ei品系中发现了2处意外的杂合性现象。第二项研究在覆盖2.5Mb基因组序列的人类基因组5个ENCODE区域中，鉴定出11241个候选SNPs。其中约50%的候选SNPs被选用于实验基因型分型，验证成功率超过95%。第三项研究在1236条斑马鱼的64896条测序峰图中，检测到了ENU诱导的突变（等位基因频率为0.04%）。
我们对三个大规模且多样化的测试数据集的分析结果表明，SNPdetector是一款适用于基因组规模研究与大样本临床研究的高效工具。SNPdetector可在Unix/Linux平台上运行，且已公开获取（http://lpg.nci.nih.gov）。