2018 Fiscal Year Final Research Report
Development of guidelines for transcriptome data analysis with long-reads
| Project/Area Number |
15K06919
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
System genome science
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| Research Institution | The University of Tokyo |
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Principal Investigator |
Kadota Koji 東京大学, 大学院農学生命科学研究科(農学部), 准教授 (60392221)
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| Research Collaborator |
Terada Tomoko
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| Project Period (FY) |
2015-10-21 – 2019-03-31
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| Keywords | RNA-seq / 発現変動解析 |
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| Outline of Final Research Achievements |
In the life sciences field, researchers have investigated nucleotide sequences and expression levels of transcripts (called transcriptome) working in a sample that constitutes an organism. They include the measurement of expression similarities between samples and the identification of genes differentially expressed between conditions of interest. In this research, we proposed to use Silhouette scores to objectively estimate the degree of separation between groups of interest. We confirmed that silhouettes is useful for exploring data with predefined group labels. It would help provide both an objective evaluation of the sample clustering results and insights into the differential expression results with regard to the compared groups.
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| Free Research Field |
バイオインフォマティクス
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| Academic Significance and Societal Importance of the Research Achievements |
サンプル間の全体的な類似傾向を眺めるクラスタリングは、トランスクリプトームデータ解析において、必ずといっていいほどよく行われる作業である。しかしながら、得られる結果を都合よく主観的に評価することもできるため、任意のグルーピングにおける全体的な類似度を客観的に評価する必要性やその枠組みの提供は重要である。本研究で提案したシルエットスコアは、クラスタリング結果だけでなく、発現変動解析結果の解釈にも援用できるものであり意義深いものと考える。
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