The method and application of judging background introduced microbial sequence

Abstract

The invention relates to a method for judging background-introduced microbial sequences and an application thereof, belonging to the technical field of biological information analysis. The method includes the following steps: determining the list of background microorganisms: taking a number of negative samples of pathogenic microorganisms, and performing a correlation test on the quantitative value A of each microorganism abundance and the total amount of nucleic acid D, and if there is a negative correlation, it is determined as background pathogenic microorganisms; the judgment model is established : Take the negative control sample set and the positive sample set of the background pathogenic microorganism respectively, compare the gene sequence data of the background pathogenic microorganism to the characteristic sequence region of the microorganism, calculate the abundance, and establish a judgment model; quantitative analysis of the specific region of the microorganism: obtain the pending The gene sequence data of the background pathogenic microorganisms in the test sample shall be judged by the above-mentioned judgment model. The method can accurately determine whether the microbial sequence obtained by sequencing is background introduction, and can reduce the problem of batch false positive or false negative, and can be widely used in the data analysis of pathogenic microorganism detection.

Description

technical field [0001] The invention relates to the technical field of biological information analysis, in particular to a method for judging background-introduced microbial sequences and its application. 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 can be affected by contamina...

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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