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  SPLICE-q: aPython tool for genome-wide quantification of splicing efficiency

Melo Costa, V. R., Pfeuffer, J., Louloupi, A., Ørom, U. A. V., & Piro, R. M. (2021). SPLICE-q: aPython tool for genome-wide quantification of splicing efficiency. BMC Bioinformatics, 22(1):. doi:10.1186/s12859-021-04282-6.

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アイテムのパーマリンク: https://hdl.handle.net/21.11116/0000-0007-827E-9 版のパーマリンク: https://hdl.handle.net/21.11116/0000-000D-C0C8-8
資料種別: 学術論文

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de Melo Costa_2021.pdf (出版社版), 2MB
ファイルのパーマリンク:
https://hdl.handle.net/21.11116/0000-0009-0FED-D
ファイル名:
de Melo Costa_2021.pdf
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-
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Gold
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公開
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application/pdf / [MD5]
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-
著作権情報:
© The Author(s), 2021

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 作成者:
Melo Costa, Verônica R.1, 著者           
Pfeuffer, Julianus , 著者
Louloupi, Annita , 著者
Ørom, Ulf A. V. , 著者
Piro, Rosario M. , 著者
所属:
1Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433547              

内容説明

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キーワード: Splicing efficiency, RNA-seq, Co-transcriptional splicing, Splicing dynamics
 要旨: Background

Introns are generally removed from primary transcripts to form mature RNA molecules in a post-transcriptional process called splicing. An efficient splicing of primary transcripts is an essential step in gene expression and its misregulation is related to numerous human diseases. Thus, to better understand the dynamics of this process and the perturbations that might be caused by aberrant transcript processing it is important to quantify splicing efficiency.
Results

Here, we introduce SPLICE-q, a fast and user-friendly Python tool for genome-wide SPLICing Efficiency quantification. It supports studies focusing on the implications of splicing efficiency in transcript processing dynamics. SPLICE-q uses aligned reads from strand-specific RNA-seq to quantify splicing efficiency for each intron individually and allows the user to select different levels of restrictiveness concerning the introns’ overlap with other genomic elements such as exons of other genes. We applied SPLICE-q to globally assess the dynamics of intron excision in yeast and human nascent RNA-seq. We also show its application using total RNA-seq from a patient-matched prostate cancer sample.
Conclusions

Our analyses illustrate that SPLICE-q is suitable to detect a progressive increase of splicing efficiency throughout a time course of nascent RNA-seq and it might be useful when it comes to understanding cancer progression beyond mere gene expression levels. SPLICE-q is available at: https://github.com/vrmelo/SPLICE-q

資料詳細

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言語: eng - English
 日付: 2021-07-062021-07-15
 出版の状態: オンラインで出版済み
 ページ: -
 出版情報: -
 目次: -
 査読: -
 識別子(DOI, ISBNなど): DOI: 10.1186/s12859-021-04282-6
 学位: -

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出版物 1

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出版物名: BMC Bioinformatics
種別: 学術雑誌
 著者・編者:
所属:
出版社, 出版地: -
ページ: - 巻号: 22 (1) 通巻号: 368 開始・終了ページ: - 識別子(ISBN, ISSN, DOIなど): ISSN: 1471-2105
CoNE: https://pure.mpg.de/cone/journals/resource/111000136905000