LABRAT
LABRAT (Lightweight Alignment-Based Reckoning of Alternative Three-prime ends) quantifies alternative polyadenylation (APA) and cleavage site usage from RNAseq data to determine relative APA site usage across experimental conditions.
Key Features:
- Quantification of APA isoforms: Determines the relative abundance of alternative polyadenylation isoforms and APA site usage across samples.
- Integration with transcriptome quantification techniques: Leverages transcriptome quantification methods to produce accurate measurements of APA and cleavage site usage from RNAseq data.
- Classification of APA types: Distinguishes between tandem UTRs and alternative last exons when assessing APA events.
Scientific Applications:
- Subcellular localization studies: Identifies relationships between gene-distal APA and RNA localization across cell types.
- Transcription dynamics: Enables analysis of how transcription speed influences APA site selection.
- Cancer research: Detects transcriptome-wide shifts in APA isoform abundance across hundreds of patient-derived tumor samples and links these shifts with patient prognosis.
- Gene expression correlation: Pinpoints individual genes where APA isoform changes strongly correlate with gene expression.
- RNA-binding protein regulation: Facilitates investigation of the roles of 191 RNA-binding proteins in promoting shifts in APA isoform abundance in vitro and in clinical samples.
Methodology:
Analyzes RNAseq data using transcriptome quantification techniques to quantify APA and cleavage site usage and distinguishes tandem UTRs from alternative last exons during quantification.
Topics
Details
- License:
- MIT
- Cost:
- Free of charge
- Tool Type:
- library
- Operating Systems:
- Mac, Linux, Windows
- Programming Languages:
- Python
- Added:
- 11/8/2021
- Last Updated:
- 11/8/2021
Operations
Publications
Goering R, Engel KL, Gillen AE, Fong N, Bentley DL, Taliaferro JM. LABRAT reveals association of alternative polyadenylation with transcript localization, RNA binding protein expression, transcription speed, and cancer survival. BMC Genomics. 2021;22(1). doi:10.1186/s12864-021-07781-1. PMID:34174817. PMCID:PMC8234626.