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

Abstract 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 integrat...

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Veröffentlicht in:	Briefings in bioinformatics 2023-07, Vol.24 (4)
Hauptverfasser:	Hu Qian, Sheng, Shi, Meng-Wei, Wang, Dan-Yang, Fear, Justin M, Chen, Lu, Tu, Yi-Xuan, Liu, Hong-Shan, Zhang, Yuan, Zhang, Shuai-Jie, Yu, Shan-Shan, Oliver, Brian, Chen, Zhen-Xia
Format:	Artikel
Sprache:	eng
Schlagworte:	Animals Biological activity Control methods Developmental stages Drosophila Drosophila melanogaster - genetics Drosophila melanogaster - metabolism Embryogenesis Embryonic growth stage Fruit flies Gene expression Gene Expression Profiling - methods Heterogeneity High-Throughput Nucleotide Sequencing - methods Humans Insects Machine learning Quality control Ribonucleic acid RNA RNA - genetics RNA-Seq Sequence Analysis, RNA Transcriptome Transcriptomes Unsupervised learning
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