Method for detecting somatic mutation in plant genome

Abstract

The invention discloses a method for detecting somatic mutation in a plant genome. The method comprises the following steps: (1) evaluating re-sequencing data; (2) splicing the re-sequencing data; (3) identifying a mutation site; (4) evaluating a mutation site; (5) screening mutation sites; (6) classifying and tagging candidate mutation sites; and (7) manual inspection of mutation sites. The method provided by the invention can efficiently and comprehensively detect somatic mutation in various plant genomes, including single nucleotide variation (SNV) and insertion deletion (INDEL), and provides a powerful analysis tool for evaluating the plant somatic mutation rate, thereby providing a molecular basis for researching the influence of somatic mutation on plant development and evolution.

Description

technical field [0001] The present invention relates to the technical field of microorganisms. Specifically, the present invention relates to a method for detecting somatic mutations in plant genomes, in particular to the splicing and evaluation of plant original genome data and the identification and screening of somatic mutation sites. Background technique [0002] Plant genome variation, including mutation and recombination, is the basis for plant diversity and the internal driving force for plant adaptive evolution. How genomic variation is generated, retained, and inherited, and what factors are affected by it, have always been important scientific issues in the field of life sciences. Among them, mutation is one of the important scientific problems that classical genetics has been concerned about for a long time. With the development of resequencing technology, the research on mutations has made great progress in recent years. The current studies on mutations mainly ...

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Problems solved by technology

[0003] The current methods for detecting somatic mutations in plants are generally borrowed from research methods in humans and mice, but plant genomes have unique characteristics, and detection methods in animals cannot be fully applied to plant genomes

In order to ensure that reliable mutations are screened out, existing detection methods often first shield complex sequences in the genome, such as repetitive sequences, transposable elements, etc., which will cause a large amount of genomic information to be ignored

In addition, although existing somatic mutation detection methods can effectively remove false negative results, they cannot effectively evaluate false positive results, so the identified somatic mutations often have a high false positive rate

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