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SIG Technical Reports(1) |
公開日 |
2018-03-02 |
タイトル |
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タイトル |
R-STEINER: Generation Method of 5’UTR for Increasing the Amount of Translated Proteins |
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言語 |
en |
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タイトル |
R-STEINER: Generation Method of 5’UTR for Increasing the Amount of Translated Proteins |
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言語 |
eng |
資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_18gh |
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資源タイプ |
technical report |
著者所属 |
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Nara Institute of Science and Technology |
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Nara Institute of Science and Technology |
著者所属 |
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Nara Institute of Science and Technology |
著者所属 |
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Nara Institute of Science and Technology |
著者所属 |
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Nara Institute of Science and Technology |
著者所属 |
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Nara Institute of Science and Technology |
著者所属(英) |
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en |
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Nara Institute of Science and Technology |
著者所属(英) |
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en |
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Nara Institute of Science and Technology |
著者所属(英) |
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en |
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Nara Institute of Science and Technology |
著者所属(英) |
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en |
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Nara Institute of Science and Technology |
著者所属(英) |
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en |
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Nara Institute of Science and Technology |
著者所属(英) |
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en |
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Nara Institute of Science and Technology |
著者名 |
Hiroaki, Tanaka
Yu, Suzuki
Shotaro, Yamasaki
Koichiro, Yoshino
Ko, Kato
Satoshi, Nakamura
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著者名(英) |
Hiroaki, Tanaka
Yu, Suzuki
Shotaro, Yamasaki
Koichiro, Yoshino
Ko, Kato
Satoshi, Nakamura
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論文抄録 |
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内容記述タイプ |
Other |
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内容記述 |
Protein production in plants is a hot topic because there are many benefits relative to bacteria, yeasts, and animals, but the amount of protein expression in plants is less. It is argued that editing 5'UTRs increases the amount of translated proteins. However, obtaining such 5'UTRs is difficult due to the cost, time and effort required in experiments. To solve this, we predict the amount of translated proteins by machine learning. In this paper, we propose a method, named “R-STEINER”, that generates 5'UTRs that increase the amount of proteins of a given gene. The proposed process involves building a model for predicting the amount of translated proteins, generating 5'UTRs, selecting them and increasing the proteins according to the model. This method enables us to obtain 5'UTRs that increase the amount of translated proteins without real synthesis experiments, resulting in reduced cost, time and effort. In our study, we built a prediction model for Oryza sativa and synthesized the 5'UTRs generated by R-STEINER. We confirmed that the model can predict the amount of translated proteins with a correlation coefficient of 0.89. |
論文抄録(英) |
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内容記述タイプ |
Other |
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内容記述 |
Protein production in plants is a hot topic because there are many benefits relative to bacteria, yeasts, and animals, but the amount of protein expression in plants is less. It is argued that editing 5'UTRs increases the amount of translated proteins. However, obtaining such 5'UTRs is difficult due to the cost, time and effort required in experiments. To solve this, we predict the amount of translated proteins by machine learning. In this paper, we propose a method, named “R-STEINER”, that generates 5'UTRs that increase the amount of proteins of a given gene. The proposed process involves building a model for predicting the amount of translated proteins, generating 5'UTRs, selecting them and increasing the proteins according to the model. This method enables us to obtain 5'UTRs that increase the amount of translated proteins without real synthesis experiments, resulting in reduced cost, time and effort. In our study, we built a prediction model for Oryza sativa and synthesized the 5'UTRs generated by R-STEINER. We confirmed that the model can predict the amount of translated proteins with a correlation coefficient of 0.89. |
書誌レコードID |
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収録物識別子タイプ |
NCID |
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収録物識別子 |
AA12055912 |
書誌情報 |
研究報告バイオ情報学(BIO)
巻 2018-BIO-53,
号 5,
p. 1-8,
発行日 2018-03-02
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ISSN |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
2188-8590 |
Notice |
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SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. |
出版者 |
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言語 |
ja |
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出版者 |
情報処理学会 |