isONform:来自牛津纳米孔数据的无参考转录组重建
Alexander J Petri1, Kristoffer Sahlin1
1Department of Mathematics, Science for Life Laboratory, Stockholm University, Stockholm 106 91, Sweden.
Bioinformatics (Oxford, England)
|June 30, 2023
概括
我们开发了isONform,这是一个新的算法,用于从牛津纳米孔技术 (ONT) cDNA测序数据中构建基因异型. 这种方法为转录异形预测提供了更高的灵敏度,特别是对于缺乏详细基因组注释的生物.
科学领域:
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
- 分子生物学分子生物学
背景情况:
- 长读转录组测序,特别是牛津纳米孔技术 (ONT),可以实现全面的转录特征.
- 然而,由于转录变异性和测序错误,ONT数据需要重要的生物信息处理来准确预测异型.
- 现有的基于参考的方法依赖于高质量的基因组和注释,而无参考的方法缺乏可比的灵敏度.
研究的目的:
- 本文介绍 isONform,一种高灵敏度算法,用于从 ONT cDNA 测序数据中构建基因异型.
- 解决现有的无参考和基于参考的转录预测方法的局限性.
- 提供一种工具,用于在具有有限基因组资源的生物体中构建异形,并用于验证现有的预测.
主要方法:
- isONform采用了一种代的泡式方法,用于从ONT读取中获得的模糊种子构建的基因图.
- 该算法使用模拟,合成和生物ONTcDNA数据集进行了评估.
- 性能与无参考方法RATTLE和基于注释的方法StringTie2.2进行了比较.
主要成果:
- 与RATTLE相比,isONform在各种数据集中预测转录异型的灵敏度要高得多.
- 虽然isONform的精度略有下降,但它对生物数据的预测与StringTie2.2的一致性明显更高.
- 该算法有效地处理长时间读取数据中固有的转录变异性和序列错误.
结论:
- isONform是一个敏感的算法,用于从ONTcDNA测序数据中构建转录异型.
- 它对于研究基因组注释不良的生物和对基于参考的方法进行交叉验证特别有价值.
- 该工具使用长读测序技术增强了转录组的表征.
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