A Preliminary Study Of The Effect On Detection Of Expressed Genes By Varied RNA-sequencing Depth

With the rapid development of next-generation sequencing technologies, RNASeq has become an important method of transcriptome studies, and the detection of expressed genes is directly a?ected by the sequencing depth. This paper studied the e?ect of sequencing depth on the detection of expressed genes, and provided guides for the choice of appropriate sequencing depth, which serves di?erent purposes of experiments. In this study, we have analyzed RNA-Seq data from human brain, and established a standard work?ow of RNA-Seq data analysis. Firstly, we assessed the quality of sequencing data, which was formatted into FASTQ and?ltered those reads with strict quality control. Next, we used a random sampling method to generate di?erent number of reads from ?ltered reads and constructed libraries with di?erent sequencing depth. Finally, we analyzed all subsets and compared the changes of the number of expressed genes detected under di?erent sequencing depth. We found that the number of expressed genes increased with the increment of sequencing depth, but the incremental speed of expressed genes detected slowed down with the increase of sequencing depth. Their relationship was y = a · log2(x + b) + c, where x and y were the sequencing depth and the number of expressed genes detected respectively. a, b and c were constant. We also discussed the e?ect of sequencing depth on the detection of transcription factors and long non-coding RNA(lncRNA).