Metagenome sequencing quality control prediction evaluation method and model

Abstract

The invention relates to a metagenome sequencing quality control prediction evaluation method and model, and belongs to the technical field of gene detection. The method comprises the following steps:a Q20 threshold system evaluation process: obtaining sequencing parameters, constructing a Q20 threshold model, and inputting the sequencing parameters into the Q20 threshold model for solving to obtain a relationship between Q20 proportion and correct rate; a sequencing data volume threshold system evaluation process: acquiring data parameters, constructing a sequencing data volume threshold model, and inputting the data parameters into the sequencing data volume threshold model for solving to obtain a relationship between the sequencing data volume and the detected strain unique region; anda sequencing fragment length threshold system evaluation process: acquiring strain mutation parameters, constructing a sequencing fragment length threshold model, and inputting the strain mutation parameters into the sequencing fragment length threshold model for solving to obtain a relationship between the sequencing fragment length and the strain reduction accuracy. The method can be applied tometagenome detection as preset quality control standard evaluation.

Description

technical field [0001] The invention relates to the technical field of gene detection, in particular to a metagenomic sequencing quality control prediction and evaluation method and model. Background technique [0002] The concept of metagenomics (Metagenomics) was first proposed in 1998. It refers to the use of genomics technology to perform indiscriminate and non-selective sequencing of nucleic acid molecules in the environment or biological samples. The sequencing results are consistent with known microbial sequences. Database comparison analysis is a technique for qualitative or quantitative analysis of microorganisms contained in samples. With the birth and development of next-generation sequencing technology (Next-Generation Sequencing, NGS), the cost of whole-genome sequencing has dropped by 10,000 times in ten years, and the coverage of pathogens by metagenomic NGS (mNGS) based on NGS It is extremely comprehensive, does not require microbial culture, does not need t...

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Problems solved by technology

[0003] However, in conventional metagenomic testing projects, setting key quality control indicators, such as: Q20, sequencing data volume, sequencing fragment length, etc., is usually set a relatively common value based on experience, and does not combine the actual situation of the project (sequencing platform, expected detection performance, etc.) to carry out systematic theoretical modeling evaluation, resulting in the lack of corresponding theoretical support for setting the threshold, and is usually not fully applicable

[0004] Moreover, when evaluating the detection performance of a metagenomic detection project, it is usually to accumulate a certain sample size, and then review the historical samples for statistics, resulting in no theoretical expectations for the detection performance at the beginning of the project, and it is impossible to base the threshold on the set threshold. To calculate the theoretical detection performance that can be achieved, there is no way to set the relevant threshold according to the desired detection performance

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