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 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

SUBMITTER: Hu Qian S 

PROVIDER: S-EPMC10505420 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature