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Wilt disease prediction model based on high-throughput sequencing data and application

A technology for sequencing data and prediction models, applied in sequence analysis, instruments, biological systems, etc., can solve the problems of rising incidence and inability to predict diseases by pathogenic bacteria, and achieve the effect of simple application operation and reliable prediction results.

Pending Publication Date: 2020-06-05
NANJING AGRICULTURAL UNIVERSITY
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Problems solved by technology

Although Fusarium wilt is a fungal disease caused by Fusarium oxysporum, the increase in the number of pathogenic bacteria will inevitably lead to an increase in the incidence
However, it is still impossible to predict the occurrence of diseases by the number of pathogenic bacteria

Method used

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  • Wilt disease prediction model based on high-throughput sequencing data and application
  • Wilt disease prediction model based on high-throughput sequencing data and application
  • Wilt disease prediction model based on high-throughput sequencing data and application

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Embodiment 1

[0033] A Fusarium wilt incidence prediction model based on high-throughput sequencing data, comprising the following steps:

[0034] 1. Data collection

[0035] Based on the published research, use the keywords Fusarium wilt community (Fusarium wilt community) or Fusarium wilt structure (Fusarium wilt structure) or Fusarium wilt microbiome (Fusarium wilt microbiome) to search for research papers related to fungal wilt and microbial communities from Google Scholar, and get Sequencing sample accession numbers for Fusarium wilt-associated soil microbial communities. Most of the original data of related studies are stored in NCBI, and some are stored in DDBJ and European Nucleic Acid Database, from which sequencing data information can be obtained.

[0036] At present, NCBI stores a large amount of data that has not yet been published. The grouping of these data and their sampling information are often described very clearly. You can search for the keywords Fusarium wiltcommunity...

Embodiment 2

[0061] To verify the accuracy of the bacterial model, 13 independent bacterial datasets from the NCBI SRA database were analyzed next, including 26 fusarium wilt-onset soil samples and 321 healthy soil samples. For all collected samples, the average accuracy rate of the bacterial model was 94.46%, the accuracy rate of the diseased sample was 91.75%, and the accuracy rate of the healthy sample was 96.45% (see Table 3).

[0062] At the same time, 6 independent fungal data sets from the NCBI SRA database, including 21 soil samples with Fusarium wilt disease and 144 healthy soil samples, were used to validate the fungal model. The average accuracy of the fungal model for all collected samples was 93.05%, the accuracy for diseased samples was 91.67%, and the accuracy for healthy samples was 95.25% (see Table 4).

[0063] Table 3 Bacterial model prediction accuracy of NCBI dataset

[0064]

[0065] Table 4 Prediction accuracy of fungal model in NCBI dataset

[0066]

[0067]...

Embodiment 3

[0069] Soil sample collection: In late April 2019, field soil samples of 4 different crops of banana, cucumber, watermelon and lily in Hainan, Guangdong, Beijing and Jiangsu were collected. For the soil collection of diseases and insect pests, plots that had been continuously cropped and suffered from Fusarium wilt for at least 5 years were selected, while newly planted plots without Fusarium wilt were selected for healthy soil collection. In order to avoid the differences caused by geographical factors, the healthy ones are selected near the diseased areas. For each soil sample, 20 random soil cores (diameter 5cm×20cm) were taken from the 0-20cm soil layer, and each plot was sampled in S-shape to form a soil sample. Freshly collected soil was screened through a 2-mm sieve to remove plant debris and possible small animals, and then stored at -80°C prior to DNA extraction.

[0070] DNA extraction, gene amplification and sequencing: (1) Power Lyzer PowerSoil DNA Isolation Kit (...

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Abstract

The invention provides a wilt disease prediction model based on high-throughput sequencing data and application. The method comprises the following steps: integrating microorganism high-throughput sequencing data of wilt disease soil and healthy soil of different regions and different crops; analyzing the characteristics of bacterial communities and fungal communities in the diseased soil and thehealthy soil by using a machine learning method; obtaining relative abundance characteristics of classification operation units of 45 bacteria and 40 fungi and using the characteristics for distinguishing the healthy soil from wilt disease soil, and establishing a bacterium model and a fungus model respectively. The disease condition of soil wilt is accurately distinguished, and microflora characteristics of wilt disease soil are revealed.

Description

technical field [0001] The invention belongs to the technical field of agricultural plant protection, in particular to a fusarium wilt disease incidence prediction model based on high-throughput sequencing data and its application. Background technique [0002] Microorganisms are crucial to human activities. Soil microbial communities are closely related to our lives, especially our agricultural practices, and profoundly affect the quality and quantity of agricultural products. As a typical representative of soil-borne diseases, Fusarium wilt has greatly affected the production of crops worldwide. Hundreds of beneficial microorganisms have been isolated and tested against Fusarium wilt and Fusarium in the field. Although Fusarium wilt is a fungal disease caused by Fusarium oxysporum, the increase in the number of pathogenic bacteria will inevitably lead to an increase in the incidence rate. However, it is still impossible to predict the occurrence of diseases by the numbe...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16B40/00G16B30/00G16B5/00
CPCG16B40/00G16B30/00G16B5/00Y02A90/10
Inventor 袁军沈其荣文涛
Owner NANJING AGRICULTURAL UNIVERSITY
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