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Novel Method of Full-Length RNA-seq That Expands the Identification of Non-Polyadenylated RNAs Using Nanopore Sequencing.

Xiaohan LiKequan YuFuyu LiWenxiang LuYing WangWeizhong ZhangYun-Fei Bai
Published in: Analytical chemistry (2022)
The occurrence of diseases displayed transcriptome alteration, including both coding and non-coding transcripts. The third-generation sequencing (TGS) technologies allow for intensive and comprehensive research of the transcriptome. However, the present standard TGS RNA sequencing method is unable to detect many of the non-polyadenylated [non-poly(A)] RNAs. To obtain more complete transcriptome information, we presented a new comprehensive sequencing approach by performing conventional poly(A) RNA-sequencing combined with the sequencing of non-poly(A) RNA fraction which was tailed by poly(U) on HepG2 and HL-7702 cell lines, enabling the detection of multiple categories of non-poly(A) RNAs excluded by the existing standard approach. Moreover, the length distribution of the full-splice match transcripts was longer than that assembled by short-reads, which contributed to characterizing alternative splicing events and provided a comprehensive portrait of transcriptional complexity. Besides the detection of genes with differential expression patterns in the poly(A) library between HepG2 and HL-7702, we also found a cancer-related non-coding gene in the poly(U) data, that is, growth arrest special 5 (GAS5). Collectively, our results suggested that the novel method effectively captured both poly(A) and non-poly(A) transcripts in the tested cell lines and allowed a deeper exploration of the transcriptome.
Keyphrases
  • single cell
  • rna seq
  • genome wide
  • gene expression
  • machine learning
  • risk assessment
  • transcription factor
  • big data
  • cell proliferation
  • electronic health record
  • label free
  • social media