Baiting out a full length sequence from unmapped RNA-seq data

Dongwei Li, Qitong Huang, Lei Huang, Jikai Wen, Jing Luo, Qing Li, Yanling Peng, Yubo Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Background: As a powerful tool, RNA-Seq has been widely used in various studies. Usually, unmapped RNA-seq reads have been considered as useless and been trashed or ignored. Results: We develop a strategy to mining the full length sequence by unmapped reads combining with specific reverse transcription primers design and high throughput sequencing. In this study, we salvage 36 unmapped reads from standard RNA-Seq data and randomly select one 149 bp read as a model. Specific reverse transcription primers are designed to amplify its both ends, followed by next generation sequencing. Then we design a statistical model based on power law distribution to estimate its integrality and significance. Further, we validate it by Sanger sequencing. The result shows that the full length is 1556 bp, with insertion mutations in microsatellite structure. Conclusion: We believe this method would be a useful strategy to extract the sequences information from the unmapped RNA-seq data. Further, it is an alternative way to get the full length sequence of unknown cDNA.

Original languageEnglish
Article number857
JournalBMC Genomics
Volume22
DOIs
Publication statusPublished - 27 Dec 2021

Keywords

  • Full length sequence
  • RNA-seq
  • Statistical model
  • Unmapped reads

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