Pathogen detection background microorganism judgment method and application

Abstract

The invention relates to a pathogen detection background microorganism judgment method and application, and belongs to the technical field of bioinformatics analysis. The judgment method comprises the following steps of: determining a core background microorganism list: taking a plurality of biological samples, comparing gene sequence data of each microorganism to a characteristic sequence region of a corresponding microorganism, performing correlation test on nucleic acid extraction concentration or library concentration to obtain a microorganism list with negative correlation, and obtaining a core background microorganism list; determining a core background microorganism correction index CBI: taking the sum of the specific comparison sequence numbers of all microorganisms in the core background microorganism list as the CBI; and carrying out background sequence judgment: dividing the specific comparison sequence number of the microorganisms in the sample to be detected by the CBI to obtain a judgment value. By adopting the method, the background related microorganisms can be corrected according to the background microorganism index, whether the pathogenic microorganisms exist in the sample or not is judged according to the corrected quantity, and a more accurate judgment result can be obtained.

Description

technical field [0001] The invention relates to the technical field of bioinformatics analysis, in particular to a method and application for determining background microorganisms in pathogen detection. Background technique [0002] Pathogenic metagenomics (mNGS) is a high-throughput sequencing technology that does not depend on culture, directly extracts nucleic acids from clinical specimens and detects pathogens. Compared with traditional clinical laboratory detection methods, pathogenic mNGS is based on the detection of sequences at the nucleic acid level, which can break through the limitations of different pathogen types, comprehensively cover thousands of pathogens without bias, and simultaneously identify bacteria, fungi, viruses and Various types of pathogenic microorganisms such as parasites, pathogenic mNGS has gradually become an important tool in the field of clinical microbial identification. [0003] However, the accuracy of mNGS is affected by contaminants—DN...

AI Technical Summary

Problems solved by technology

This type of method can only obtain a list of potential background contamination, and use a one-size-fits-all approach to directly remove these microorganisms

However, common clinical pathogens, such as Acinetobacter baumannii, Pseudomonas aeruginosa, Klebsiella pneumoniae, Stenotrophomonas maltophilia, Escherichia coli, Enterococcus faecalis, Serratia marcescens, etc., all exist at the same time Microorganisms in the background of nucleic acid extraction or reagents cannot determine whether the microorganisms in the sample are background contamination or originate from the sample itself.

[0007] The other method is to quantify by adding internal references to remove microorganisms whose relative abundance is below the threshold. This method has higher requirements for internal references and requires an internal reference set with accurate quantification and high complexity, and the operation is more complicated.

However, in actual operation, we found that different sample types require different pre-processing steps. When adding internal references will have different effects on the final quantitative results. Different sample types need to calculate the corresponding thresholds separately. In addition, this method is based on strict Assuming that the nucleic acid of background microorganisms is a stable constant, it is often found that the background contamination of different batches of experimental consumables will still fluctuate in practical operations, so it is easy to have batches of false positives or false negatives

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