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Mutation Prediction of HA Sequence for AI Preparedness

원문정보

초록

영어

South Korea is experiencing intermittent avian flu outbreaks, and the situation is becoming worse because the majority of response efforts are concerned with post-emergence treatment instead of prevention. In this situation, the accuracy of the vaccine should be increased and a method of predicting the mutation of the virus is needed. The aim of this study was therefore to predict the mutation of H5N1 virus HA sequence among domestic poultry. Through analyzing the migratory routes of birds, it was found that the same birds that were infected with H5N1 in Korea were also present in China and Japan and shared breeding grounds. We then used BLAST to obtain molecular evidence that confirms that the AI in China and Japan spreads to Korea with mutations. The HA sequences from previous outbreaks were analyzed for their mutations, and this historical data was used to train a predictive model. Analysis revealed that if the Ka/Ks ratio was over 1, the mutation was preserved. If the Ka/Ks ratio was less than 1, the peaks of the Ka/Ks profiles showed diverse, various mutations. And if the Ka / Ks was significantly low, the mutation did not occur. As a result, based on the Ka/Ks ratios of the AI HA sequences from China and Japan, it was possible to predict which parts of the HA sequence in Korea will be mutated. This will help with vaccine development.

목차

ABSTRACT
1. INTRODUCTION
2. METHODS AND PROCESS
2.1. Exploration of geographic regions that share strains with Korean AI outbreaks and clade set up
2.2. Molecular Basis For The Set Ai Mutation Region
2.3. Analysis Of Evolutionary Patterns By Ka/Ks Ratio
2.4. Suggestion of Predicted Sequences
3. RESULTS
3.1. Korea, China, and Japan’s Phylogenetic Tree
3.2. Korea, China, and Japan outbreak year by clade histogram
3.3. Searching for AI most similar to Korean AI through BLAST
3.4. Year comparison of Korean Clade representative sequence and similar sequence
3.5. Comparison of the predicted sequence’s identity percentage with SimFlu
4. Conclusion
REFERENCES

저자정보

  • Ahn Sung-jin School address: Changwon Science High School, Pyeongsan-ro-159-gil 30, Uichang-gu, Changwon-si, Gyeongsangnam-do, South Korea
  • Heo Seo-jun School address: Changwon Science High School, Pyeongsan-ro-159-gil 30, Uichang-gu, Changwon-si, Gyeongsangnam-do, South Korea
  • Kim Yeong-seo School address: Changwon Science High School, Pyeongsan-ro-159-gil 30, Uichang-gu, Changwon-si, Gyeongsangnam-do, South Korea

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