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