Integrating massive RNA-seq data to elucidate transcriptome dynamics in Drosophila melanogaster

文献类型： 外文期刊

第一作者： Chen, Zhen-Xia

作者： Chen, Zhen-Xia;Qian, Sheng Hu;Shi, Meng-Wei;Wang, Dan-Yang;Chen, Lu;Tu, Yi-Xuan;Liu, Hong-Shan;Zhang, Yuan;Zhang, Shuai-Jie;Yu, Shan-Shan;Qian, Sheng Hu;Shi, Meng-Wei;Wang, Dan-Yang;Chen, Lu;Tu, Yi-Xuan;Liu, Hong-Shan;Zhang, Yuan;Zhang, Shuai-Jie;Yu, Shan-Shan;Fear, Justin M.;Oliver, Brian;Chen, Zhen-Xia;Chen, Zhen-Xia;Chen, Zhen-Xia

作者机构：

关键词： sequence read archive; gene expression dynamics; computational methods; data integration and quality control; massively parallel sequencing

期刊名称：BRIEFINGS IN BIOINFORMATICS （ 影响因子：9.5； 五年影响因子：10.6 ）

ISSN： 1467-5463

年卷期： 2023 年

页码：

收录情况： SCI

摘要： The volume of ribonucleic acid (RNA)-seq data has increased exponentially, providing numerous new insights into various biological processes. However, due to significant practical challenges, such as data heterogeneity, it is still difficult to ensure the quality of these data when integrated. Although some quality control methods have been developed, sample consistency is rarely considered and these methods are susceptible to artificial factors. Here, we developed MassiveQC, an unsupervised machine learning-based approach, to automatically download and filter large-scale high-throughput data. In addition to the read quality used in other tools, MassiveQC also uses the alignment and expression quality as model features. Meanwhile, it is user-friendly since the cutoff is generated from self-reporting and is applicable to multimodal data. To explore its value, we applied MassiveQC to Drosophila RNA-seq data and generated a comprehensive transcriptome atlas across 28 tissues from embryogenesis to adulthood. We systematically characterized fly gene expression dynamics and found that genes with high expression dynamics were likely to be evolutionarily young and expressed at late developmental stages, exhibiting high nonsynonymous substitution rates and low phenotypic severity, and they were involved in simple regulatory programs. We also discovered that human and Drosophila had strong positive correlations in gene expression in orthologous organs, revealing the great potential of the Drosophila system for studying human development and disease.

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