摘要宏基因组二代测序技术（mNGS）是下呼吸道感染病原诊断的重要方法。mNGS报告的解读需要参考标本类型、测序参数，详细结合患者具体情况，收集多种微生物学检测结果，根据治疗效果和疾病转归，进行仔细甄别和判断，并最终做出诊断。mNGS报告的解读也需要我们储备更多的微生物学、测序和生物信息学分析知识，需要我们更加注重团队的力量、在多学科协作诊疗中辨析真相。

abstractsMetagenome next generation sequencing (mNGS) is an important method for pathogen diagnosis of lower respiratory tract infection, but there are many difficulties in interpreting mNGS reports. The Expert Consensus on Clinical Interpretation Path of mNGS of Lower Respiratory Tract Infection, completed by Chinese Thoracic Society, provides a detailed path and guidance for report interpretation. The expert consensus covers clinical medicine, microbiology, molecular diagnosis and other aspects. On this basis, several important clinical issues need to be highlighted. First, the lower respiratory tract specimens used for mNGS must be obtained in a timely and qualified manner. Second, the correct interpretation of the mNGS report needs to be based on a full understanding of the patients and their conditions. Third, the quality of the report should be analyzed according to the main parameters in the mNGS report. Fourth, an understanding of basic microbiology knowledge is beneficial to identify valuable pathogens in the mNGS report. Fifth, other microbiological methods should be actively used during mNGS detection. Sixth, to seek the help of the team when necessary and organize multidisciplinary discussions are vital. Seventh, it is important to constantly adjust the diagnosis and treatment strategies according to the clinical treatment response to treatment and the evolution of the disease. In a word, the interpretation of mNGS results needs to refer to specimen types and sequencing parameters, to combine with the specific conditions of patients in detail, to collect a variety of microbiological test results, to carefully screen and make judgement according to the treatment effect and disease outcome, and finally to make the diagnosis. The interpretation of mNGS report also requires us to have more knowledge of microbiology, sequencing and bioinformatics analysis, and to pay more attention to the strength of the team to discriminate the truth in multidisciplinary collaboration.