PAIso-seq

PAIso-seq quantifies transcriptome-wide poly(A) tail length and composition from full-length cDNA using PacBio HiFi sequencing to investigate post-transcriptional regulation of mRNA.


Key Features:

  • High Sensitivity: Detects poly(A) tails from subnanogram total RNA inputs, enabling analysis of single mammalian oocytes (~0.5 ng total RNA).
  • Comprehensive Analysis: Provides transcriptome-wide measurement of poly(A) tail length and identification of non-A residues while capturing full-length cDNA for isoform-level information.
  • PacBio Sequencing Integration: Leverages PacBio sequencing technology in HiFi mode to produce high-fidelity long reads for precise poly(A) tail characterization.
  • Detailed Protocol and Bioinformatics Pipeline: Includes a library preparation protocol for single or bulk oocyte samples and a bioinformatic pipeline for processing raw sequencing data through initial analysis.

Scientific Applications:

  • Low-input sample analysis: Enables poly(A) tail and transcriptome profiling from precious in vivo samples with limited RNA, including single oocytes.
  • Post-transcriptional regulation studies: Supports investigation of mRNA export, stability, and translation by providing tail length and composition data.
  • Isoform-resolved transcriptomics: Captures full-length cDNA to facilitate isoform-level analyses and transcriptome-wide studies of poly(A) tail dynamics.

Methodology:

Initial data analysis uses the provided bioinformatics pipeline to process raw PacBio HiFi data and can be completed within eight hours.

Topics

Details

License:
Not licensed
Cost:
Free of charge
Tool Type:
command-line tool
Operating Systems:
Mac, Linux, Windows
Programming Languages:
Python
Added:
9/30/2022
Last Updated:
11/24/2024

Operations

Publications

Liu Y, Zhang Y, Wang J, Lu F. Transcriptome-wide measurement of poly(A) tail length and composition at subnanogram total RNA sensitivity by PAIso-seq. Nature Protocols. 2022;17(9):1980-2007. doi:10.1038/s41596-022-00704-8. PMID:35831615.

PMID: 35831615
Funding: - Ministry of Science and Technology of the People’s Republic of China: 2018YFA0107001 - Chinese Academy of Sciences: XDA24020203 - National Natural Science Foundation of China: 31970588, 32170606, 81891001 - China Postdoctoral Science Foundation: 2020M670516, 2020T130687 - Natural Science Foundation of Heilongjiang Province: YQ2020C003