School of Informatics of Xiamen University, China.
Department of Computer Science, School of Informatics of Xiamen University, China.
Brief Bioinform. 2021 Sep 2;22(5). doi: 10.1093/bib/bbab064.
Metagenomics data provide rich information for the detection of foodborne pathogens from food and environmental samples that are mixed with complex background bacteria strains. While pathogen detection from metagenomic sequencing data has become an activity of increasing interest, shotgun sequencing of uncultured food samples typically produces data that contain reads from many different organisms, making accurate strain typing a challenging task. Particularly, as many pathogens may contain a common set of genes that are highly similar to those from normal bacteria in food samples, traditional strain-level abundance profiling approaches do not perform well at detecting pathogens of very low abundance levels. To overcome this limitation, we propose an abundance correction method based on species-specific genomic regions to achieve high sensitivity and high specificity in target pathogen detection at low abundance.
宏基因组数据为从食物和环境样本中检测与复杂背景细菌菌株混合的食源性病原体提供了丰富的信息。虽然从宏基因组测序数据中检测病原体已成为一项越来越受到关注的活动，但对未培养食物样本的鸟枪法测序通常会产生包含来自许多不同生物体的读取的数据，这使得准确的菌株分型成为一项具有挑战性的任务。特别是，由于许多病原体可能包含一组与食物样本中正常细菌高度相似的共同基因，因此传统的菌株丰度分析方法在检测非常低丰度水平的病原体时效果不佳。为了克服这一限制，我们提出了一种基于物种特异性基因组区域的丰度校正方法，以在低丰度水平下实现目标病原体检测的高灵敏度和高特异性。
