背景:利用长读段进行单核苷酸多态性(SNP)定相已经变得很流行,为人类疾病研究和动植物遗传研究提供实质性支持。然而,由于SNP基因座之间的连锁关系和读取中的测序错误的复杂性,最近的方法仍然不能产生令人满意的结果。
结果:在这项研究中,我们提出了一种基于图的算法,GC阶段,它利用最小割算法来执行定相。首先,基于长读数和参考基因组之间的比对,GC阶段过滤掉不明确的SNP位点和无用的读取信息。第二,GCphase构建了一个图,其中顶点代表SNP基因座的等位基因,每个边代表读段支持的存在;此外,GCphase采用图最小割算法对SNP进行相位化。接下来,GCpahse使用两个纠错步骤来完善从上一步获得的相位结果,有效地降低了错误率。最后,GCphase获取相位块。将GC阶段与其他三种方法进行了比较,WhatsHap,HapCUT2和LongPhase,在纳米孔和PacBio长读数据集上。该代码可从https://github.com/baimawjy/GCphase获得。
结论:实验结果表明,与其他方法相比,在不同数据的不同测序深度下的GC相具有最少的切换误差和最高的准确性。
BACKGROUND: The utilization of long reads for single nucleotide polymorphism (SNP) phasing has become popular, providing substantial support for research on human diseases and genetic studies in animals and plants. However, due to the complexity of the linkage relationships between SNP loci and sequencing errors in the reads, the recent methods still cannot yield satisfactory results.
RESULTS: In this study, we present a graph-based algorithm, GCphase, which utilizes the minimum cut algorithm to perform phasing. First, based on alignment between long reads and the reference genome, GCphase filters out ambiguous SNP sites and useless read information. Second, GCphase constructs a graph in which a vertex represents alleles of an SNP locus and each edge represents the presence of read support; moreover, GCphase adopts a graph minimum-cut algorithm to phase the SNPs. Next, GCpahse uses two error correction steps to refine the phasing results obtained from the previous step, effectively reducing the error rate. Finally, GCphase obtains the phase block. GCphase was compared to three other methods, WhatsHap, HapCUT2, and LongPhase, on the Nanopore and PacBio long-read datasets. The code is available from https://github.com/baimawjy/GCphase .
CONCLUSIONS: Experimental results show that GCphase under different sequencing depths of different data has the least number of switch errors and the highest accuracy compared with other methods.